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|---|---|---|---|
architecture: CenterNet
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_pretrained.pdparams
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
CenterNet:
backbone: ResNet
neck: CenterNetDLAFPN
head: CenterNetHead
post_process: CenterNetPostProcess
ResNet:
depth: 50
variant: d
return_idx: [0, 1, 2, 3]
freeze_at: -1
norm_decay: 0.
dcn_v2_stages: [3]
CenterNetDLAFPN:
first_level: 0
last_level: 4
down_ratio: 4
dcn_v2: False
CenterNetHead:
head_planes: 256
regress_ltrb: False
CenterNetPostProcess:
max_per_img: 100
regress_ltrb: False
| PaddleDetection/configs/centernet/_base_/centernet_r50.yml/0 | {
"file_path": "PaddleDetection/configs/centernet/_base_/centernet_r50.yml",
"repo_id": "PaddleDetection",
"token_count": 258
} | 16 |
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'../ppyoloe/_base_/ppyoloe_crn.yml',
'../ppyoloe/_base_/ppyoloe_reader.yml',
]
depth_mult: 0.25
width_mult: 0.50
log_iter: 100
snapshot_epoch: 5
weights: output/ppyoloe_convnext_tiny_36e_coco/model_final
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/convnext_tiny_22k_224.pdparams
YOLOv3:
backbone: ConvNeXt
neck: CustomCSPPAN
yolo_head: PPYOLOEHead
post_process: ~
ConvNeXt:
arch: 'tiny'
drop_path_rate: 0.4
layer_scale_init_value: 1.0
return_idx: [1, 2, 3]
PPYOLOEHead:
static_assigner_epoch: 12
nms:
nms_top_k: 1000
keep_top_k: 300
score_threshold: 0.01
nms_threshold: 0.7
TrainReader:
batch_size: 16
epoch: 36
LearningRate:
base_lr: 0.0002
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [36]
use_warmup: false
OptimizerBuilder:
regularizer: false
optimizer:
type: AdamW
weight_decay: 0.0005
| PaddleDetection/configs/convnext/ppyoloe_convnext_tiny_36e_coco.yml/0 | {
"file_path": "PaddleDetection/configs/convnext/ppyoloe_convnext_tiny_36e_coco.yml",
"repo_id": "PaddleDetection",
"token_count": 454
} | 17 |
metric: WiderFace
num_classes: 1
TrainDataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face
anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images
data_fields: ['image', 'gt_bbox', 'gt_class']
EvalDataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face
anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images
data_fields: ['image']
TestDataset:
!ImageFolder
use_default_label: true
| PaddleDetection/configs/datasets/wider_face.yml/0 | {
"file_path": "PaddleDetection/configs/datasets/wider_face.yml",
"repo_id": "PaddleDetection",
"token_count": 212
} | 18 |
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'_base_/optimizer_1x.yml',
'_base_/faster_rcnn_r50_fpn.yml',
'_base_/faster_fpn_reader.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
weights: output/faster_rcnn_r50_vd_fpn_ssld_1x_coco/model_final
ResNet:
depth: 50
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
lr_mult_list: [0.05, 0.05, 0.1, 0.15]
epoch: 12
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [8, 11]
- !LinearWarmup
start_factor: 0.1
steps: 1000
| PaddleDetection/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_1x_coco.yml/0 | {
"file_path": "PaddleDetection/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_1x_coco.yml",
"repo_id": "PaddleDetection",
"token_count": 327
} | 19 |
architecture: DETR
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/vit_huge_mae_patch14_dec512d8b_pretrained.pdparams
hidden_dim: 256
use_focal_loss: True
DETR:
backbone: VisionTransformer2D
neck: SimpleFeaturePyramid
transformer: GroupDINOTransformer
detr_head: DINOHead
post_process: DETRPostProcess
VisionTransformer2D:
patch_size: 16
embed_dim: 1280
depth: 32
num_heads: 16
mlp_ratio: 4
attn_bias: True
drop_rate: 0.0
drop_path_rate: 0.1
lr_decay_rate: 0.7
global_attn_indexes: [7, 15, 23, 31]
use_abs_pos: False
use_rel_pos: True
rel_pos_zero_init: True
window_size: 14
out_indices: [ 31, ]
SimpleFeaturePyramid:
out_channels: 256
num_levels: 4
GroupDINOTransformer:
num_queries: 900
position_embed_type: sine
pe_temperature: 20
pe_offset: 0.0
num_levels: 4
nhead: 8
num_encoder_layers: 6
num_decoder_layers: 6
dim_feedforward: 2048
use_input_proj: False
dropout: 0.0
activation: relu
num_denoising: 100
label_noise_ratio: 0.5
box_noise_scale: 1.0
learnt_init_query: True
dual_queries: True
dual_groups: 10
DINOHead:
loss:
name: DINOLoss
loss_coeff: {class: 1, bbox: 5, giou: 2}
aux_loss: True
matcher:
name: HungarianMatcher
matcher_coeff: {class: 2, bbox: 5, giou: 2}
DETRPostProcess:
num_top_queries: 300
dual_queries: True
dual_groups: 10
| PaddleDetection/configs/group_detr/_base_/group_dino_vit_huge.yml/0 | {
"file_path": "PaddleDetection/configs/group_detr/_base_/group_dino_vit_huge.yml",
"repo_id": "PaddleDetection",
"token_count": 603
} | 20 |
English | [简体中文](README_cn.md)
# Detector for DeepSORT
## Introduction
[DeepSORT](https://arxiv.org/abs/1812.00442)(Deep Cosine Metric Learning SORT) is composed of a detector and a ReID model in series. The configs of several common detectors are provided here as a reference. Note that different training dataset, backbone, input size, training epochs and NMS threshold will lead to differences in model accuracy and performance. Please adapt according to your needs.
## Model Zoo
### Results on MOT17-half dataset
| Backbone | Model | input size | lr schedule | FPS | Box AP | download | config |
| :-------------- | :------------- | :--------: | :---------: | :-----------: | :-----: | :----------: | :-----: |
| DarkNet-53 | YOLOv3 | 608X608 | 40e | ---- | 42.7 | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort/yolov3_darknet53_40e_608x608_mot17half.pdparams) | [config](./yolov3_darknet53_40e_608x608_mot17half.yml) |
| ResNet50-vd | PPYOLOv2 | 640x640 | 365e | ---- | 46.8 | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams) | [config](./ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml) |
| CSPResNet | PPYOLOe | 640x640 | 36e | ---- | 52.9 | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyoloe_crn_l_36e_640x640_mot17half.pdparams) | [config](./ppyoloe_crn_l_36e_640x640_mot17half.yml) |
**Notes:**
- The above models are trained with **MOT17-half train** set, it can be downloaded from this [link](https://bj.bcebos.com/v1/paddledet/data/mot/MOT17.zip).
- **MOT17-half train** set is a dataset composed of pictures and labels of the first half frame of each video in MOT17 Train dataset (7 sequences in total). **MOT17-half val set** is used for evaluation, which is composed of the second half frame of each video. They can be downloaded from this [link](https://paddledet.bj.bcebos.com/data/mot/mot17half/annotations.zip). Download and unzip it in the `dataset/mot/MOT17/images/`folder.
- YOLOv3 is trained with the same pedestrian dataset as `configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml`, which is not open yet.
- For pedestrian tracking, please use pedestrian detector combined with pedestrian ReID model. For vehicle tracking, please use vehicle detector combined with vehicle ReID model.
- High quality detected boxes are required for DeepSORT tracking, so the post-processing settings such as NMS threshold of these models are different from those in pure detection tasks.
## Quick Start
Start the training and evaluation with the following command
```bash
job_name=ppyoloe_crn_l_36e_640x640_mot17half
config=configs/mot/deepsort/detector/${job_name}.yml
log_dir=log_dir/${job_name}
# 1. training
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp --fleet
# 2. evaluation
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/${job_name}.pdparams
```
| PaddleDetection/configs/mot/deepsort/detector/README.md/0 | {
"file_path": "PaddleDetection/configs/mot/deepsort/detector/README.md",
"repo_id": "PaddleDetection",
"token_count": 1178
} | 21 |
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker
CenterNet:
backbone: HRNet
head: CenterNetHead
post_process: CenterNetPostProcess
neck: CenterNetDLAFPN
HRNet:
width: 18
freeze_at: 0
return_idx: [0, 1, 2, 3]
upsample: False
CenterNetDLAFPN:
down_ratio: 4
last_level: 3
out_channel: 0
first_level: 0
dcn_v2: False
CenterNetPostProcess:
max_per_img: 500
JDETracker:
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
min_box_area: 200
vertical_ratio: 1.6 # for pedestrian
| PaddleDetection/configs/mot/fairmot/_base_/fairmot_hrnetv2_w18_dlafpn.yml/0 | {
"file_path": "PaddleDetection/configs/mot/fairmot/_base_/fairmot_hrnetv2_w18_dlafpn.yml",
"repo_id": "PaddleDetection",
"token_count": 291
} | 22 |
[English](README.md) | 简体中文
# 特色垂类跟踪模型
## 人头跟踪（Head Tracking)
现有行人跟踪器对高人群密度场景表现不佳，人头跟踪更适用于密集场景的跟踪。
[HT-21](https://motchallenge.net/data/Head_Tracking_21)是一个高人群密度拥挤场景的人头跟踪数据集，场景包括不同的光线和环境条件下的拥挤的室内和室外场景，所有序列的帧速率都是25fps。
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/205540742-820984c2-8920-467a-bdde-faea421018c5.gif" width='800'/>
</div>
## 模型库
### FairMOT 和 ByteTrack 在 HT-21 Training Set上的结果
| 模型 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: |
| FairMOT DLA-34 | 1088x608 | 64.7 | 69.0 | 8533 | 148817 | 234970 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) |
| ByteTrack-x | 1440x800 | 64.1 | 63.4 | 4191 | 185162 | 210240 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) |
### FairMOT 和 ByteTrack 在 HT-21 Test Set上的结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: |
| FairMOT DLA-34 | 1088x608 | 60.8 | 62.8 | 12781 | 118109 | 198896 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_headtracking21.yml) |
| ByteTrack-x | 1440x800 | 72.6 | 61.8 | 5163 | 71235 | 154139 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/bytetrack_yolox_ht21.pdparams) | [配置文件](../bytetrack/bytetrack_yolox_ht21.yml) |
**注意:**
- FairMOT DLA-34使用2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
- ByteTrack使用YOLOX-x做检测器，使用8个GPU进行训练，每个GPU上batch size为8，训练30个epoch，具体细节参照[bytetrack](../bytetrack/)。
- 此处提供PaddleDetection团队整理后的[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/HT21.zip)，下载后需解压放到`dataset/mot/`目录下，HT-21 Test集的结果需要交到[官网](https://motchallenge.net)评测。
## 快速开始
### 1. 训练
使用2个GPU通过如下命令一键式启动训练
```bash
python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_headtracking21/ --gpus 0,1 tools/train.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml
```
### 2. 评估
使用单张GPU通过如下命令一键式启动评估
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=output/fairmot_dla34_30e_1088x608_headtracking21/model_final.pdparams
```
### 3. 预测
使用单个GPU通过如下命令预测一个视频，并保存为视频
```bash
# 预测一个视频
CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams --video_file={your video name}.mp4 --save_videos
```
**注意:**
- 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
### 4. 导出预测模型
```bash
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
```
### 5. 用导出的模型基于Python去预测
```bash
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_headtracking21 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
**注意:**
- 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。
- 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。
## 引用
```
@article{zhang2020fair,
title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
journal={arXiv preprint arXiv:2004.01888},
year={2020}
}
@InProceedings{Sundararaman_2021_CVPR,
author = {Sundararaman, Ramana and De Almeida Braga, Cedric and Marchand, Eric and Pettre, Julien},
title = {Tracking Pedestrian Heads in Dense Crowd},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {3865-3875}
}
@article{zhang2021bytetrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
journal={arXiv preprint arXiv:2110.06864},
year={2021}
}
```
| PaddleDetection/configs/mot/headtracking21/README.md/0 | {
"file_path": "PaddleDetection/configs/mot/headtracking21/README.md",
"repo_id": "PaddleDetection",
"token_count": 3114
} | 23 |
_BASE_: [
'../fairmot/fairmot_dla34_30e_1088x608.yml',
'../../datasets/mcmot.yml'
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/fairmot_dla34_crowdhuman_pretrained.pdparams
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
weights: output/mcfairmot_dla34_30e_1088x608_visdrone/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL
| PaddleDetection/configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml/0 | {
"file_path": "PaddleDetection/configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml",
"repo_id": "PaddleDetection",
"token_count": 363
} | 24 |
_BASE_: [
'../fairmot/fairmot_dla34_30e_1088x608.yml'
]
weights: output/fairmot_dla34_30e_1088x608_pathtrack/model_final
# for MOT training
TrainDataset:
!MOTDataSet
dataset_dir: dataset/mot
image_lists: ['pathtrack.train']
data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
# for MOT evaluation
# If you want to change the MOT evaluation dataset, please modify 'data_root'
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: pathtrack/images/test
keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
# for MOT video inference
TestMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
keep_ori_im: True # set True if save visualization images or video
| PaddleDetection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml/0 | {
"file_path": "PaddleDetection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml",
"repo_id": "PaddleDetection",
"token_count": 278
} | 25 |
data_path=bdd100k
img_dir=${data_path}/images/track
label_dir=${data_path}/labels/box_track_20
save_path=${data_path}/bdd100kmot_vehicle
phasetrain=train
phaseval=val
classes=2,3,4,9,10
# gen mot dataset
python bdd100k2mot.py --data_path=${data_path} --phase=${phasetrain} --classes=${classes} --img_dir=${img_dir} --label_dir=${label_dir} --save_path=${save_path}
python bdd100k2mot.py --data_path=${data_path} --phase=${phaseval} --classes=${classes} --img_dir=${img_dir} --label_dir=${label_dir} --save_path=${save_path}
# gen new labels_with_ids
python gen_labels_MOT.py --mot_data=${data_path} --phase=${phasetrain}
python gen_labels_MOT.py --mot_data=${data_path} --phase=${phaseval}
| PaddleDetection/configs/mot/vehicle/tools/bdd100kmot/gen_bdd100kmot_vehicle.sh/0 | {
"file_path": "PaddleDetection/configs/mot/vehicle/tools/bdd100kmot/gen_bdd100kmot_vehicle.sh",
"repo_id": "PaddleDetection",
"token_count": 286
} | 26 |
worker_num: 6
eval_height: &eval_height 320
eval_width: &eval_width 320
eval_size: &eval_size [*eval_height, *eval_width]
TrainReader:
sample_transforms:
- Decode: {}
- RandomCrop: {}
- RandomFlip: {prob: 0.5}
- RandomDistort: {}
batch_transforms:
- BatchRandomResize: {target_size: [256, 288, 320, 352, 384], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_size: 128
shuffle: true
drop_last: true
collate_batch: false
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 8
shuffle: false
TestReader:
inputs_def:
image_shape: [1, 3, *eval_height, *eval_width]
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_size: 1
| PaddleDetection/configs/picodet/legacy_model/_base_/picodet_320_reader.yml/0 | {
"file_path": "PaddleDetection/configs/picodet/legacy_model/_base_/picodet_320_reader.yml",
"repo_id": "PaddleDetection",
"token_count": 497
} | 27 |
English | [简体中文](README_cn.md)
# PaddleDetection applied for specific scenarios
We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios.
| Task | Algorithm | Box AP | Download | Configs |
|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
| Pedestrian Detection | YOLOv3 | 51.8 | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](./pedestrian_yolov3_darknet.yml) |
## Pedestrian Detection
The main applications of pedetestrian detection include intelligent monitoring. In this scenary, photos of pedetestrians are taken by surveillance cameras in public areas, then pedestrian detection are conducted on these photos.
### 1. Network
The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53.
### 2. Configuration for training
PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
* num_classes: 1
* dataset_dir: dataset/pedestrian
### 3. Accuracy
The accuracy of the model trained and evaluted on our private data is shown as followed:
AP at IoU=.50:.05:.95 is 0.518.
AP at IoU=.50 is 0.792.
### 4. Inference
Users can employ the model to conduct the inference:
```
export CUDA_VISIBLE_DEVICES=0
python -u tools/infer.py -c configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml \
-o weights=https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams \
--infer_dir configs/pphuman/pedestrian_yolov3/demo \
--draw_threshold 0.3 \
--output_dir configs/pphuman/pedestrian_yolov3/demo/output
```
Some inference results are visualized below:
| PaddleDetection/configs/pphuman/pedestrian_yolov3/README.md/0 | {
"file_path": "PaddleDetection/configs/pphuman/pedestrian_yolov3/README.md",
"repo_id": "PaddleDetection",
"token_count": 876
} | 28 |
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'../ppyoloe/_base_/optimizer_300e.yml',
'../ppyoloe/_base_/ppyoloe_crn.yml',
'../ppyoloe/_base_/ppyoloe_reader.yml',
]
log_iter: 100
snapshot_epoch: 4
weights: output/mot_ppyoloe_l_36e_ppvehicle9cls/model_final
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
depth_mult: 1.0
width_mult: 1.0
num_classes: 9
TrainDataset:
!COCODataSet
image_dir: ""
anno_path: annotations/train_all_9cls.json
dataset_dir: dataset/ppvehicle
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
image_dir: ""
anno_path: annotations/val_all_9cls.json
dataset_dir: dataset/ppvehicle
TestDataset:
!ImageFolder
anno_path: annotations/val_all_9cls.json
dataset_dir: dataset/ppvehicle
TrainReader:
batch_size: 8
epoch: 36
LearningRate:
base_lr: 0.001
schedulers:
- !CosineDecay
max_epochs: 43
- !LinearWarmup
start_factor: 0.
epochs: 1
PPYOLOEHead:
static_assigner_epoch: -1
nms:
name: MultiClassNMS
nms_top_k: 1000
keep_top_k: 100
score_threshold: 0.01
nms_threshold: 0.6
| PaddleDetection/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle9cls.yml/0 | {
"file_path": "PaddleDetection/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle9cls.yml",
"repo_id": "PaddleDetection",
"token_count": 565
} | 29 |
简体中文 | [English](README.md)
# PP-YOLO 模型
## 内容
- [简介](#简介)
- [模型库与基线](#模型库与基线)
- [使用说明](#使用说明)
- [未来工作](#未来工作)
- [附录](#附录)
## 简介
[PP-YOLO](https://arxiv.org/abs/2007.12099)是PaddleDetection优化和改进的YOLOv3的模型，其精度(COCO数据集mAP)和推理速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)模型，要求使用PaddlePaddle 2.0.2(可使用pip安装) 或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-develop)。
PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45.9%，在单卡V100上FP32推理速度为72.9 FPS, V100上开启TensorRT下FP16推理速度为155.6 FPS。
<div align="center">
<img src="../../docs/images/ppyolo_map_fps.png" width=500 />
</div>
PP-YOLO和PP-YOLOv2从如下方面优化和提升YOLOv3模型的精度和速度：
- 更优的骨干网络: ResNet50vd-DCN
- 更大的训练batch size: 8 GPUs，每GPU batch_size=24，对应调整学习率和迭代轮数
- [Drop Block](https://arxiv.org/abs/1810.12890)
- [Exponential Moving Average](https://www.investopedia.com/terms/e/ema.asp)
- [IoU Loss](https://arxiv.org/pdf/1902.09630.pdf)
- [Grid Sensitive](https://arxiv.org/abs/2004.10934)
- [Matrix NMS](https://arxiv.org/pdf/2003.10152.pdf)
- [CoordConv](https://arxiv.org/abs/1807.03247)
- [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729)
- 更优的预训练模型
- [PAN](https://arxiv.org/abs/1803.01534)
- Iou aware Loss
- 更大的输入尺寸
## 模型库
### PP-YOLO模型
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 44.8 | 45.2 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 43.9 | 44.4 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.1 | 42.5 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 38.9 | 39.3 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 608 | 45.3 | 45.9 | 72.9 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 512 | 29.2 | 29.5 | 357.1 | 657.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLO | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r18vd_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet50vd | 640 | 49.1 | 49.5 | 68.9 | 106.5 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) |
| PP-YOLOv2 | 8 | 12 | ResNet101vd | 640 | 49.7 | 50.3 | 49.5 | 87.0 | [model](https://paddledet.bj.bcebos.com/models/ppyolov2_r101vd_dcn_365e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolov2_r101vd_dcn_365e_coco.yml) |
**注意:**
- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
- PP-YOLO模型训练过程中使用8 GPUs，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
- PP-YOLO模型FP32的推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
### PP-YOLO 轻量级模型
| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box AP<sup>val</sup> | Box AP50<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 | 配置文件 |
|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :------: |
| PP-YOLO_MobileNetV3_large | 4 | 32 | 28MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml) |
| PP-YOLO_MobileNetV3_small | 4 | 32 | 16MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_small_coco.yml) |
- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
### PP-YOLO tiny模型
| 模型 | GPU 个数 | 每GPU图片个数 | 模型体积 | 后量化模型体积 | 输入尺寸 | Box AP<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 | 配置文件 | 量化后模型 |
|:----------------------------:|:----------:|:-------------:| :--------: | :------------: | :----------:| :------------------: | :--------------------: | :------: | :------: | :--------: |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 320 | 20.6 | 92.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
| PP-YOLO tiny | 8 | 32 | 4.2MB | **1.3M** | 416 | 22.7 | 65.4 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
- PP-YOLO-tiny 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
- PP-YOLO-tiny 模型训练过程中使用8GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ/README.md)调整学习率和迭代次数。
- PP-YOLO-tiny 模型推理速度测试环境配置为麒麟990芯片4线程，arm8架构。
- 我们也提供的PP-YOLO-tiny的后量化压缩模型，将模型体积压缩到**1.3M**，对精度和预测速度基本无影响
### Pascal VOC数据集上的PP-YOLO
PP-YOLO在Pascal VOC数据集上训练模型如下:
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP50<sup>val</sup> | 模型下载 | 配置文件 |
|:------------------:|:-------:|:-------------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
| PP-YOLO | 8 | 12 | ResNet50vd | 608 | 84.9 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 416 | 84.3 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
| PP-YOLO | 8 | 12 | ResNet50vd | 320 | 82.2 | [model](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml) |
## 使用说明
### 1. 训练
使用8GPU通过如下命令一键式启动训练(以下命令均默认在PaddleDetection根目录运行), 通过`--eval`参数开启训练中交替评估。
```bash
python -m paddle.distributed.launch --log_dir=./ppyolo_dygraph/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml &>ppyolo_dygraph.log 2>&1 &
```
可选：在训练之前使用`tools/anchor_cluster.py`得到适用于你的数据集的anchor，并注意修改模型配置文件和Reader配置文件中的anchor设置，如`configs/ppyolo/_base_/ppyolo_tiny.yml`和`configs/ppyolo/_base_/ppyolo_tiny_reader.yml`中anchor设置
```bash
python tools/anchor_cluster.py -c configs/ppyolo/ppyolo_tiny_650e_coco.yml -n 9 -s 320 -m v2 -i 1000
```
### 2. 评估
使用单GPU通过如下命令一键式评估模型在COCO val2017数据集效果
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=output/ppyolo_r50vd_dcn_1x_coco/model_final
```
我们提供了`configs/ppyolo/ppyolo_test.yml`用于评估COCO test-dev2017数据集的效果，评估COCO test-dev2017数据集的效果须先从[COCO数据集下载页](https://cocodataset.org/#download)下载test-dev2017数据集，解压到`configs/ppyolo/ppyolo_test.yml`中`EvalReader.dataset`中配置的路径，并使用如下命令进行评估
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=output/ppyolo_r50vd_dcn_1x_coco/model_final
```
评估结果保存于`bbox.json`中，将其压缩为zip包后通过[COCO数据集评估页](https://competitions.codalab.org/competitions/20794#participate)提交评估。
**注意1:** `configs/ppyolo/ppyolo_test.yml`仅用于评估COCO test-dev数据集，不用于训练和评估COCO val2017数据集。
**注意2:** 由于动态图框架整体升级，以下几个PaddleDetection发布的权重模型评估时需要添加--bias字段, 例如
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --bias
```
主要有:
1.ppyolo_r50vd_dcn_1x_coco
2.ppyolo_r50vd_dcn_voc
3.ppyolo_r18vd_coco
4.ppyolo_mbv3_large_coco
5.ppyolo_mbv3_small_coco
6.ppyolo_tiny_650e_coco
### 3. 推理
使用单GPU通过如下命令一键式推理图像，通过`--infer_img`指定图像路径，或通过`--infer_dir`指定目录并推理目录下所有图像
```bash
# 推理单张图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
# 推理目录下所有图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_dir=demo
```
### 4. 推理部署
PP-YOLO模型部署及推理benchmark需要通过`tools/export_model.py`导出模型后使用Paddle预测库进行部署和推理，可通过如下命令一键式启动。
```bash
# 导出模型，默认存储于output/ppyolo目录
python tools/export_model.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
# 预测库推理
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=GPU
```
## 附录
PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
| 序号 | 模型 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | 参数量(M) | FLOPs(G) | V100 FP32 FPS |
| :--: | :--------------------------- | :------------------: | :-------------------: | :-------: | :------: | :-----------: |
| A | YOLOv3-DarkNet53 | 38.9 | - | 59.13 | 65.52 | 58.2 |
| B | YOLOv3-ResNet50vd-DCN | 39.1 | - | 43.89 | 44.71 | 79.2 |
| C | B + LB + EMA + DropBlock | 41.4 | - | 43.89 | 44.71 | 79.2 |
| D | C + IoU Loss | 41.9 | - | 43.89 | 44.71 | 79.2 |
| E | D + IoU Aware | 42.5 | - | 43.90 | 44.71 | 74.9 |
| F | E + Grid Sensitive | 42.8 | - | 43.90 | 44.71 | 74.8 |
| G | F + Matrix NMS | 43.5 | - | 43.90 | 44.71 | 74.8 |
| H | G + CoordConv | 44.0 | - | 43.93 | 44.76 | 74.1 |
| I | H + SPP | 44.3 | 45.2 | 44.93 | 45.12 | 72.9 |
| J | I + Better ImageNet Pretrain | 44.8 | 45.2 | 44.93 | 45.12 | 72.9 |
| K | J + 2x Scheduler | 45.3 | 45.9 | 44.93 | 45.12 | 72.9 |
**注意:**
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练，val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
- 推理速度为单卡V100上，batch size=1, 使用上述benchmark测试方法的测试结果，测试环境配置为CUDA 10.2，CUDNN 7.5.1
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/README.md)
## 引用
```
@article{huang2021pp,
title={PP-YOLOv2: A Practical Object Detector},
author={Huang, Xin and Wang, Xinxin and Lv, Wenyu and Bai, Xiaying and Long, Xiang and Deng, Kaipeng and Dang, Qingqing and Han, Shumin and Liu, Qiwen and Hu, Xiaoguang and others},
journal={arXiv preprint arXiv:2104.10419},
year={2021}
}
@misc{long2020ppyolo,
title={PP-YOLO: An Effective and Efficient Implementation of Object Detector},
author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen},
year={2020},
eprint={2007.12099},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
| PaddleDetection/configs/ppyolo/README_cn.md/0 | {
"file_path": "PaddleDetection/configs/ppyolo/README_cn.md",
"repo_id": "PaddleDetection",
"token_count": 12140
} | 30 |
_BASE_: [
'./_base_/sku110k.yml',
'../../runtime.yml'
]
log_iter: 10
snapshot_epoch: 20
weights: output/ppyoloe_plus_crn_s_80e_coco/model_final
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
depth_mult: 1.0
width_mult: 1.0
# arch
architecture: YOLOv3
norm_type: sync_bn
use_ema: true
ema_decay: 0.9998
custom_black_list: ['reduce_mean']
YOLOv3:
backbone: CSPResNet
neck: CustomCSPPAN
yolo_head: PPYOLOEHead
post_process: ~
CSPResNet:
layers: [3, 6, 6, 3]
channels: [64, 128, 256, 512, 1024]
return_idx: [1, 2, 3]
use_large_stem: True
use_alpha: True
CustomCSPPAN:
out_channels: [768, 384, 192]
stage_num: 1
block_num: 3
act: 'swish'
spp: true
use_alpha: True
PPYOLOEHead:
fpn_strides: [32, 16, 8]
grid_cell_scale: 5.0
grid_cell_offset: 0.5
static_assigner_epoch: -1
use_varifocal_loss: True
loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
static_assigner:
name: ATSSAssigner
topk: 9
assigner:
name: TaskAlignedAssigner
topk: 13
alpha: 1.0
beta: 6.0
nms:
name: MultiClassNMS
nms_top_k: 3000
keep_top_k: 1000
score_threshold: 0.01
nms_threshold: 0.7
# reader
worker_num: 8
eval_height: &eval_height 960
eval_width: &eval_width 960
eval_size: &eval_size [*eval_height, *eval_width]
TrainReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [3000, 1800], keep_ratio: True, interp: 2}
- RandomDistort: {}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
- PadGT: {}
batch_size: 4
shuffle: true
drop_last: true
use_shared_memory: true
collate_batch: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 2
TestReader:
inputs_def:
image_shape: [3, *eval_height, *eval_width]
sample_transforms:
- Decode: {}
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 1
# optimizer
epoch: 80
LearningRate:
base_lr: 0.002
schedulers:
- !CosineDecay
max_epochs: 96
- !LinearWarmup
start_factor: 0.
epochs: 5
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
| PaddleDetection/configs/ppyoloe/application/ppyoloe_plus_crn_l_80e_sku110k.yml/0 | {
"file_path": "PaddleDetection/configs/ppyoloe/application/ppyoloe_plus_crn_l_80e_sku110k.yml",
"repo_id": "PaddleDetection",
"token_count": 1282
} | 31 |
_BASE_: [
'../../datasets/objects365_detection.yml',
'../../runtime.yml',
'../_base_/optimizer_60e.yml',
'../_base_/ppyoloe_plus_crn.yml',
'../_base_/ppyoloe_plus_reader.yml',
]
log_iter: 100
snapshot_epoch: 5
weights: output/ppyoloe_plus_crn_m_60e_objects365/model_final
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_m_pretrained.pdparams
CSPResNet:
use_alpha: False
PPYOLOEHead:
static_assigner_epoch: 20
depth_mult: 0.67
width_mult: 0.75
| PaddleDetection/configs/ppyoloe/objects365/ppyoloe_plus_crn_m_60e_objects365.yml/0 | {
"file_path": "PaddleDetection/configs/ppyoloe/objects365/ppyoloe_plus_crn_m_60e_objects365.yml",
"repo_id": "PaddleDetection",
"token_count": 223
} | 32 |
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'./_base_/optimizer_80e.yml',
'./_base_/ppyoloe_plus_crn.yml',
'./_base_/ppyoloe_plus_reader.yml',
]
log_iter: 100
snapshot_epoch: 5
weights: output/ppyoloe_plus_crn_x_80e_coco/model_final
pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams
depth_mult: 1.33
width_mult: 1.25
| PaddleDetection/configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml/0 | {
"file_path": "PaddleDetection/configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml",
"repo_id": "PaddleDetection",
"token_count": 194
} | 33 |
# Res2Net
## Introduction
- Res2Net: A New Multi-scale Backbone Architecture: [https://arxiv.org/abs/1904.01169](https://arxiv.org/abs/1904.01169)
```
@article{DBLP:journals/corr/abs-1904-01169,
author = {Shanghua Gao and
Ming{-}Ming Cheng and
Kai Zhao and
Xinyu Zhang and
Ming{-}Hsuan Yang and
Philip H. S. Torr},
title = {Res2Net: {A} New Multi-scale Backbone Architecture},
journal = {CoRR},
volume = {abs/1904.01169},
year = {2019},
url = {http://arxiv.org/abs/1904.01169},
archivePrefix = {arXiv},
eprint = {1904.01169},
timestamp = {Thu, 25 Apr 2019 10:24:54 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1904-01169},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## Model Zoo
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| Res2Net50-FPN | Faster | 2 | 1x | - | 40.6 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml) |
| Res2Net50-FPN | Mask | 2 | 2x | - | 42.4 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml) |
| Res2Net50-vd-FPN | Mask | 2 | 2x | - | 42.6 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml) |
Note: all the above models are trained with 8 gpus.
| PaddleDetection/configs/res2net/README.md/0 | {
"file_path": "PaddleDetection/configs/res2net/README.md",
"repo_id": "PaddleDetection",
"token_count": 1146
} | 34 |
English | [简体中文](README.md)
# FCOSR
## Content
- [Introduction](#Introduction)
- [Model Zoo](#Model-Zoo)
- [Getting Start](#Getting-Start)
- [Deployment](#Deployment)
- [Citations](#Citations)
## Introduction
[FCOSR](https://arxiv.org/abs/2111.10780) is one stage anchor-free model based on [FCOS](https://arxiv.org/abs/1904.01355). FCOSR focuses on the label assignment strategy for oriented bounding boxes and proposes ellipse center sampling method and fuzzy sample assignment strategy. In terms of loss, FCOSR uses [ProbIoU](https://arxiv.org/abs/2106.06072) to avoid boundary discontinuity problem.
## Model Zoo
| Model | Backbone | mAP | Lr Scheduler | Angle | Aug | GPU Number | images/GPU | download | config |
|:---:|:--------:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
| FCOSR-M | ResNeXt-50 | 76.62 | 3x | oc | RR | 4 | 4 | [model](https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/fcosr/fcosr_x50_3x_dota.yml) |
**Notes:**
- if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
- Models in model zoo is trained and tested with single scale by default. If `MS` is indicated in the data augmentation column, it means that multi-scale training and multi-scale testing are used. If `RR` is indicated in the data augmentation column, it means that RandomRotate data augmentation is used for training.
## Getting Start
Refer to [Data-Preparation](../README_en.md#Data-Preparation) to prepare data.
### Training
Single GPU Training
``` bash
CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml
```
Multiple GPUs Training
``` bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml
```
### Inference
Run the follow command to infer single image, the result of inference will be saved in `output` directory by default.
``` bash
python tools/infer.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams --infer_img=demo/P0861__1.0__1154___824.png --draw_threshold=0.5
```
### Evaluation on DOTA Dataset
Refering to [DOTA Task](https://captain-whu.github.io/DOTA/tasks.html), You need to submit a zip file containing results for all test images for evaluation. The detection results of each category are stored in a txt file, each line of which is in the following format
`image_id score x1 y1 x2 y2 x3 y3 x4 y4`. To evaluate, you should submit the generated zip file to the Task1 of [DOTA Evaluation](https://captain-whu.github.io/DOTA/evaluation.html). You can run the following command to get the inference results of test dataset:
``` bash
python tools/infer.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output_fcosr --visualize=False --save_results=True
```
Process the prediction results into the format required for the official website evaluation:
``` bash
python configs/rotate/tools/generate_result.py --pred_txt_dir=output_fcosr/ --output_dir=submit/ --data_type=dota10
zip -r submit.zip submit
```
## Deployment
Please refer to the deployment tutorial[Deployment](../../../deploy/README_en.md)
## Citations
```
@article{li2021fcosr,
title={Fcosr: A simple anchor-free rotated detector for aerial object detection},
author={Li, Zhonghua and Hou, Biao and Wu, Zitong and Jiao, Licheng and Ren, Bo and Yang, Chen},
journal={arXiv preprint arXiv:2111.10780},
year={2021}
}
@inproceedings{tian2019fcos,
title={Fcos: Fully convolutional one-stage object detection},
author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={9627--9636},
year={2019}
}
@article{llerena2021gaussian,
title={Gaussian Bounding Boxes and Probabilistic Intersection-over-Union for Object Detection},
author={Llerena, Jeffri M and Zeni, Luis Felipe and Kristen, Lucas N and Jung, Claudio},
journal={arXiv preprint arXiv:2106.06072},
year={2021}
}
```
| PaddleDetection/configs/rotate/fcosr/README_en.md/0 | {
"file_path": "PaddleDetection/configs/rotate/fcosr/README_en.md",
"repo_id": "PaddleDetection",
"token_count": 1556
} | 35 |
_BASE_: [
'../../datasets/dota.yml',
'../../runtime.yml',
'_base_/optimizer_3x.yml',
'_base_/ppyoloe_r_reader.yml',
'_base_/ppyoloe_r_crn.yml'
]
log_iter: 50
snapshot_epoch: 1
weights: output/ppyoloe_r_crn_x_3x_dota/model_final
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_x_pretrained.pdparams
depth_mult: 1.33
width_mult: 1.25
| PaddleDetection/configs/rotate/ppyoloe_r/ppyoloe_r_crn_x_3x_dota.yml/0 | {
"file_path": "PaddleDetection/configs/rotate/ppyoloe_r/ppyoloe_r_crn_x_3x_dota.yml",
"repo_id": "PaddleDetection",
"token_count": 183
} | 36 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Reference: https://github.com/CAPTAIN-WHU/DOTA_devkit
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import copy
from numbers import Number
from multiprocessing import Pool
import cv2
import numpy as np
from tqdm import tqdm
import shapely.geometry as shgeo
def choose_best_pointorder_fit_another(poly1, poly2):
"""
To make the two polygons best fit with each point
"""
x1, y1, x2, y2, x3, y3, x4, y4 = poly1
combinate = [
np.array([x1, y1, x2, y2, x3, y3, x4, y4]),
np.array([x2, y2, x3, y3, x4, y4, x1, y1]),
np.array([x3, y3, x4, y4, x1, y1, x2, y2]),
np.array([x4, y4, x1, y1, x2, y2, x3, y3])
]
dst_coordinate = np.array(poly2)
distances = np.array(
[np.sum((coord - dst_coordinate)**2) for coord in combinate])
sorted = distances.argsort()
return combinate[sorted[0]]
def cal_line_length(point1, point2):
return math.sqrt(
math.pow(point1[0] - point2[0], 2) + math.pow(point1[1] - point2[1], 2))
class SliceBase(object):
def __init__(self,
gap=512,
subsize=1024,
thresh=0.7,
choosebestpoint=True,
ext='.png',
padding=True,
num_process=8,
image_only=False):
self.gap = gap
self.subsize = subsize
self.slide = subsize - gap
self.thresh = thresh
self.choosebestpoint = choosebestpoint
self.ext = ext
self.padding = padding
self.num_process = num_process
self.image_only = image_only
def get_windows(self, height, width):
windows = []
left, up = 0, 0
while (left < width):
if (left + self.subsize >= width):
left = max(width - self.subsize, 0)
up = 0
while (up < height):
if (up + self.subsize >= height):
up = max(height - self.subsize, 0)
right = min(left + self.subsize, width - 1)
down = min(up + self.subsize, height - 1)
windows.append((left, up, right, down))
if (up + self.subsize >= height):
break
else:
up = up + self.slide
if (left + self.subsize >= width):
break
else:
left = left + self.slide
return windows
def slice_image_single(self, image, windows, output_dir, output_name):
image_dir = os.path.join(output_dir, 'images')
for (left, up, right, down) in windows:
image_name = output_name + str(left) + '___' + str(up) + self.ext
subimg = copy.deepcopy(image[up:up + self.subsize, left:left +
self.subsize])
h, w, c = subimg.shape
if (self.padding):
outimg = np.zeros((self.subsize, self.subsize, 3))
outimg[0:h, 0:w, :] = subimg
cv2.imwrite(os.path.join(image_dir, image_name), outimg)
else:
cv2.imwrite(os.path.join(image_dir, image_name), subimg)
def iof(self, poly1, poly2):
inter_poly = poly1.intersection(poly2)
inter_area = inter_poly.area
poly1_area = poly1.area
half_iou = inter_area / poly1_area
return inter_poly, half_iou
def translate(self, poly, left, up):
n = len(poly)
out_poly = np.zeros(n)
for i in range(n // 2):
out_poly[i * 2] = int(poly[i * 2] - left)
out_poly[i * 2 + 1] = int(poly[i * 2 + 1] - up)
return out_poly
def get_poly4_from_poly5(self, poly):
distances = [
cal_line_length((poly[i * 2], poly[i * 2 + 1]),
(poly[(i + 1) * 2], poly[(i + 1) * 2 + 1]))
for i in range(int(len(poly) / 2 - 1))
]
distances.append(
cal_line_length((poly[0], poly[1]), (poly[8], poly[9])))
pos = np.array(distances).argsort()[0]
count = 0
out_poly = []
while count < 5:
if (count == pos):
out_poly.append(
(poly[count * 2] + poly[(count * 2 + 2) % 10]) / 2)
out_poly.append(
(poly[(count * 2 + 1) % 10] + poly[(count * 2 + 3) % 10]) /
2)
count = count + 1
elif (count == (pos + 1) % 5):
count = count + 1
continue
else:
out_poly.append(poly[count * 2])
out_poly.append(poly[count * 2 + 1])
count = count + 1
return out_poly
def slice_anno_single(self, annos, windows, output_dir, output_name):
anno_dir = os.path.join(output_dir, 'labelTxt')
for (left, up, right, down) in windows:
image_poly = shgeo.Polygon(
[(left, up), (right, up), (right, down), (left, down)])
anno_file = output_name + str(left) + '___' + str(up) + '.txt'
with open(os.path.join(anno_dir, anno_file), 'w') as f:
for anno in annos:
gt_poly = shgeo.Polygon(
[(anno['poly'][0], anno['poly'][1]),
(anno['poly'][2], anno['poly'][3]),
(anno['poly'][4], anno['poly'][5]),
(anno['poly'][6], anno['poly'][7])])
if gt_poly.area <= 0:
continue
inter_poly, iof = self.iof(gt_poly, image_poly)
if iof == 1:
final_poly = self.translate(anno['poly'], left, up)
elif iof > 0:
inter_poly = shgeo.polygon.orient(inter_poly, sign=1)
out_poly = list(inter_poly.exterior.coords)[0:-1]
if len(out_poly) < 4 or len(out_poly) > 5:
continue
final_poly = []
for p in out_poly:
final_poly.append(p[0])
final_poly.append(p[1])
if len(out_poly) == 5:
final_poly = self.get_poly4_from_poly5(final_poly)
if self.choosebestpoint:
final_poly = choose_best_pointorder_fit_another(
final_poly, anno['poly'])
final_poly = self.translate(final_poly, left, up)
final_poly = np.clip(final_poly, 1, self.subsize)
else:
continue
outline = ' '.join(list(map(str, final_poly)))
if iof >= self.thresh:
outline = outline + ' ' + anno['name'] + ' ' + str(anno[
'difficult'])
else:
outline = outline + ' ' + anno['name'] + ' ' + '2'
f.write(outline + '\n')
def slice_data_single(self, info, rate, output_dir):
file_name = info['image_file']
base_name = os.path.splitext(os.path.split(file_name)[-1])[0]
base_name = base_name + '__' + str(rate) + '__'
img = cv2.imread(file_name)
if img.shape == ():
return
if (rate != 1):
resize_img = cv2.resize(
img, None, fx=rate, fy=rate, interpolation=cv2.INTER_CUBIC)
else:
resize_img = img
height, width, _ = resize_img.shape
windows = self.get_windows(height, width)
self.slice_image_single(resize_img, windows, output_dir, base_name)
if not self.image_only:
annos = info['annotation']
for anno in annos:
anno['poly'] = list(map(lambda x: rate * x, anno['poly']))
self.slice_anno_single(annos, windows, output_dir, base_name)
def check_or_mkdirs(self, path):
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
def slice_data(self, infos, rates, output_dir):
"""
Args:
infos (list[dict]): data_infos
rates (float, list): scale rates
output_dir (str): output directory
"""
if isinstance(rates, Number):
rates = [rates, ]
self.check_or_mkdirs(output_dir)
self.check_or_mkdirs(os.path.join(output_dir, 'images'))
if not self.image_only:
self.check_or_mkdirs(os.path.join(output_dir, 'labelTxt'))
pbar = tqdm(total=len(rates) * len(infos), desc='slicing data')
if self.num_process <= 1:
for rate in rates:
for info in infos:
self.slice_data_single(info, rate, output_dir)
pbar.update()
else:
pool = Pool(self.num_process)
for rate in rates:
for info in infos:
pool.apply_async(
self.slice_data_single, (info, rate, output_dir),
callback=lambda x: pbar.update())
pool.close()
pool.join()
pbar.close()
| PaddleDetection/configs/rotate/tools/slicebase.py/0 | {
"file_path": "PaddleDetection/configs/rotate/tools/slicebase.py",
"repo_id": "PaddleDetection",
"token_count": 5357
} | 37 |
metric: COCO
num_classes: 80
# full labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
TrainDataset:
!SemiCOCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
# full unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
UnsupTrainDataset:
!SemiCOCODataSet
image_dir: unlabeled2017
anno_path: annotations/instances_unlabeled2017.json
dataset_dir: dataset/coco
data_fields: ['image']
supervised: False
EvalDataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
allow_empty: true
TestDataset:
!ImageFolder
anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
| PaddleDetection/configs/semi_det/_base_/coco_detection_full.yml/0 | {
"file_path": "PaddleDetection/configs/semi_det/_base_/coco_detection_full.yml",
"repo_id": "PaddleDetection",
"token_count": 380
} | 38 |
# Supervised Baseline 纯监督模型基线
## COCO数据集模型库
### [FCOS](../../fcos)
| 基础模型 | 监督数据比例 | Epochs (Iters) | mAP<sup>val<br>0.5:0.95 | 模型下载 | 配置文件 |
| :---------------: | :-------------: | :---------------: |:---------------------: |:--------: | :---------: |
| FCOS ResNet50-FPN | 5% | 24 (8712) | 21.3 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_2x_coco_sup005.pdparams) | [config](fcos_r50_fpn_2x_coco_sup005.yml) |
| FCOS ResNet50-FPN | 10% | 24 (17424) | 26.3 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_2x_coco_sup010.pdparams) | [config](fcos_r50_fpn_2x_coco_sup010.yml) |
| FCOS ResNet50-FPN | full | 24 (175896) | 42.6 | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_iou_multiscale_2x_coco.pdparams) | [config](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml) |
**注意:**
- 以上模型训练默认使用8 GPUs，总batch_size默认为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率。
### [PP-YOLOE+](../../ppyoloe)
| 基础模型 | 监督数据比例 | Epochs (Iters) | mAP<sup>val<br>0.5:0.95 | 模型下载 | 配置文件 |
| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
| PP-YOLOE+_s | 5% | 80 (7200) | 32.8 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco_sup005.pdparams) | [config](ppyoloe_plus_crn_s_80e_coco_sup005.yml) |
| PP-YOLOE+_s | 10% | 80 (14480) | 35.3 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco_sup010.pdparams) | [config](ppyoloe_plus_crn_s_80e_coco_sup010.yml) |
| PP-YOLOE+_s | full | 80 (146560) | 43.7 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) |
| PP-YOLOE+_l | 5% | 80 (7200) | 42.9 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco_sup005.pdparams) | [config](ppyoloe_plus_crn_l_80e_coco_sup005.yml) |
| PP-YOLOE+_l | 10% | 80 (14480) | 45.7 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco_sup010.pdparams) | [config](ppyoloe_plus_crn_l_80e_coco_sup010.yml) |
| PP-YOLOE+_l | full | 80 (146560) | 49.8 | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) | [config](../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) |
**注意:**
- 以上模型训练默认使用8 GPUs，总batch_size默认为64，默认初始学习率为0.001。如果改动了总batch_size，请按线性比例相应地调整学习率。
### [Faster R-CNN](../../faster_rcnn)
| 基础模型 | 监督数据比例 | Epochs (Iters) | mAP<sup>val<br>0.5:0.95 | 模型下载 | 配置文件 |
| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
| Faster R-CNN ResNet50-FPN | 5% | 24 (8712) | 20.7 | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco_sup005.pdparams) | [config](faster_rcnn_r50_fpn_2x_coco_sup005.yml) |
| Faster R-CNN ResNet50-FPN | 10% | 24 (17424) | 25.6 | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco_sup010.pdparams) | [config](faster_rcnn_r50_fpn_2x_coco_sup010.yml) |
| Faster R-CNN ResNet50-FPN | full | 24 (175896) | 40.0 | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco.pdparams) | [config](../../configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.yml) |
**注意:**
- 以上模型训练默认使用8 GPUs，总batch_size默认为16，默认初始学习率为0.02。如果改动了总batch_size，请按线性比例相应地调整学习率。
### [RetinaNet](../../retinanet)
| 基础模型 | 监督数据比例 | Epochs (Iters) | mAP<sup>val<br>0.5:0.95 | 模型下载 | 配置文件 |
| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
| RetinaNet ResNet50-FPN | 5% | 24 (8712) | 13.9 | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco_sup005.pdparams) | [config](retinanet_r50_fpn_2x_coco_sup005.yml) |
| RetinaNet ResNet50-FPN | 10% | 24 (17424) | 23.6 | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco_sup010.pdparams) | [config](retinanet_r50_fpn_2x_coco_sup010.yml) |
| RetinaNet ResNet50-FPN | full | 24 (175896) | 39.1 | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco.pdparams) | [config](../../configs/retinanet/retinanet_r50_fpn_2x_coco.yml) |
**注意:**
- 以上模型训练默认使用8 GPUs，总batch_size默认为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率。
### [RT-DETR](../../rtdetr)
| 基础模型 | 监督数据比例 | mAP<sup>val<br>0.5:0.95 | 模型下载 | 配置文件 |
| :---------------: | :-------------: | :---------------------: |:--------: | :---------: |
| RT-DETR ResNet5vd | 5% | 39.1 | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup005.pdparams) | [config](rtdetr_r50vd_6x_coco_sup005.yml) |
| RT-DETR ResNet5vd | 10% | 42.3 | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup010.pdparams) | [config](rtdetr_r50vd_6x_coco_sup010.yml) |
| RT-DETR ResNet5vd | VOC2007 | 62.7 | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_voc2007.pdparams) | [config](rtdetr_r50vd_6x_voc2007.yml) |
**注意:**
- RT-DETR模型训练默认使用4 GPUs，总batch_size默认为16，默认初始学习率为0.0001。如果改动了总batch_size，请按线性比例相应地调整学习率。
### 注意事项
- COCO部分监督数据集请参照 [数据集准备](../README.md) 去下载和准备，各个比例的训练集均为**从train2017中抽取部分百分比的子集**，默认使用`fold`号为1的划分子集，`sup010`表示抽取10%的监督数据训练，`sup005`表示抽取5%，`full`表示全部train2017，验证集均为val2017全量；
- 抽取部分百分比的监督数据的抽法不同，或使用的`fold`号不同，精度都会因此而有约0.5 mAP之多的差异；
- PP-YOLOE+ 使用Objects365预训练，其余模型均使用ImageNet预训练；
- 线型比例相应调整学习率，参照公式: **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**。
## 使用教程
将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行：
```bash
model_type=semi_det/baseline
job_name=ppyoloe_plus_crn_s_80e_coco_sup010 # 可修改，如 fcos_r50_fpn_2x_coco_sup010
config=configs/${model_type}/${job_name}.yml
log_dir=log_dir/${job_name}
weights=output/${job_name}/model_final.pdparams
# 1.training
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config}
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.eval
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights}
```
| PaddleDetection/configs/semi_det/baseline/README.md/0 | {
"file_path": "PaddleDetection/configs/semi_det/baseline/README.md",
"repo_id": "PaddleDetection",
"token_count": 4587
} | 39 |
_BASE_: [
'../../gfl/gfl_r101vd_fpn_mstrain_2x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/gfl_r101vd_fpn_mstrain_2x_coco.pdparams
slim: Distill
slim_method: CWD
distill_loss: CWDFeatureLoss
distill_loss_name: ['cls_f_4', 'cls_f_3', 'cls_f_2', 'cls_f_1', 'cls_f_0']
CWDFeatureLoss:
student_channels: 80
teacher_channels: 80
tau: 1.0
weight: 5.0
| PaddleDetection/configs/slim/distill/gfl_r101vd_fpn_coco_distill_cwd.yml/0 | {
"file_path": "PaddleDetection/configs/slim/distill/gfl_r101vd_fpn_coco_distill_cwd.yml",
"repo_id": "PaddleDetection",
"token_count": 201
} | 40 |
pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
slim: QAT
QAT:
quant_config: {
'activation_preprocess_type': 'PACT',
'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
'quantizable_layer_type': ['Conv2D', 'Linear']}
print_model: True
epoch: 50
snapshot_epoch: 8
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 30
- 45
- !LinearWarmup
start_factor: 0.
steps: 2000
TrainReader:
batch_size: 8
PPYOLOPAN:
drop_block: false
block_size: 3
keep_prob: 0.9
spp: true
| PaddleDetection/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml/0 | {
"file_path": "PaddleDetection/configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml",
"repo_id": "PaddleDetection",
"token_count": 334
} | 41 |
# VisDrone-DET 小目标检测模型
PaddleDetection团队提供了针对VisDrone-DET小目标数航拍场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。整理后的COCO格式VisDrone-DET数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone.zip)，检测其中的10类，包括 `pedestrian(1), people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10)`，原始数据集[下载链接](https://github.com/VisDrone/VisDrone-Dataset)。其他相关小目标数据集可参照 [DataDownload.md](../DataDownload.md)。
**注意:**
- VisDrone-DET数据集包括**train集6471张，val集548张，test_dev集1610张**，test-challenge集1580张(未开放检测框标注)，前三者均有开放检测框标注。
- 模型均**只使用train集训练**，在val集和test_dev集上分别验证精度，test_dev集图片数较多，精度参考性较高。
## 原图训练，原图评估：
| 模型 | COCOAPI mAP<sup>val<br>0.5:0.95 | COCOAPI mAP<sup>val<br>0.5 | COCOAPI mAP<sup>test_dev<br>0.5:0.95 | COCOAPI mAP<sup>test_dev<br>0.5 | MatlabAPI mAP<sup>test_dev<br>0.5:0.95 | MatlabAPI mAP<sup>test_dev<br>0.5 | 下载 | 配置文件 |
|:---------|:------:|:------:| :----: | :------:| :------: | :------:| :----: | :------:|
|PP-YOLOE-s| 23.5 | 39.9 | 19.4 | 33.6 | 23.68 | 40.66 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_visdrone.yml) |
|PP-YOLOE-P2-Alpha-s| 24.4 | 41.6 | 20.1 | 34.7 | 24.55 | 42.19 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_p2_alpha_80e_visdrone.yml) |
|**PP-YOLOE+_SOD-s**| **25.1** | **42.8** | **20.7** | **36.2** | **25.16** | **43.86** | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_s_80e_visdrone.yml) |
|PP-YOLOE-l| 29.2 | 47.3 | 23.5 | 39.1 | 28.00 | 46.20 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_80e_visdrone.yml) |
|PP-YOLOE-P2-Alpha-l| 30.1 | 48.9 | 24.3 | 40.8 | 28.47 | 48.16 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_80e_visdrone.yml) |
|**PP-YOLOE+_SOD-l**| **31.9** | **52.1** | **25.6** | **43.5** | **30.25** | **51.18** | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_l_80e_visdrone.yml) |
|PP-YOLOE-Alpha-largesize-l| 41.9 | 65.0 | 32.3 | 53.0 | 37.13 | 61.15 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml) |
|PP-YOLOE-P2-Alpha-largesize-l| 41.3 | 64.5 | 32.4 | 53.1 | 37.49 | 51.54 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml) |
|PP-YOLOE+_largesize-l | 43.3 | 66.7 | 33.5 | 54.7 | 38.24 | 62.76 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_crn_l_largesize_80e_visdrone.yml) |
|**PP-YOLOE+_SOD-largesize-l** | 42.7 | 65.9 | **33.6** | **55.1** | **38.4** | **63.07** | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml) |
**注意:**
- 上表中的模型均为**使用原图训练**，也**使用原图评估预测**，AP精度均为**原图验证集**上评估的结果。
- VisDrone-DET数据集**可使用原图训练，也可使用切图后训练**，通过数据集统计分布分析，推荐使用**原图训练**，推荐直接使用带**SOD**的模型配置文件去训练评估和预测部署，在显卡算力有限时也可使用切图后训练。
- 上表中的模型指标均是使用VisDrone-DET的train子集作为训练集，使用VisDrone-DET的val子集和test_dev子集作为验证集。
- **SOD**表示使用**基于向量的DFL算法**和针对小目标的**中心先验优化策略**，并**在模型的Neck结构中加入transformer**。
- **P2**表示增加P2层(1/4下采样层)的特征，共输出4个PPYOLOEHead。
- **Alpha**表示对CSPResNet骨干网络增加可一个学习权重参数Alpha参与训练。
- **largesize**表示使用**以1600尺度为基础的多尺度训练**和**1920尺度预测**，相应的训练batch_size也减小，以速度来换取高精度。
- MatlabAPI测试是使用官网评测工具[VisDrone2018-DET-toolkit](https://github.com/VisDrone/VisDrone2018-DET-toolkit)。
<details>
<summary> 快速开始 </summary>
```shell
# 训练
python -m paddle.distributed.launch --log_dir=logs/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml --amp --eval
# 评估
python tools/eval.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_80e_visdrone.pdparams
# 预测
python tools/infer.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_80e_visdrone.pdparams --infer_img=demo/visdrone_0000315_01601_d_0000509.jpg --draw_threshold=0.25
```
</details>
## 子图训练，原图评估和拼图评估：
| 模型 | 数据集 | SLICE_SIZE | OVERLAP_RATIO | 类别数 | mAP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | 下载链接 | 配置文件 |
|:---------|:---------------:|:---------------:|:---------------:|:------:|:-----------------------:|:-------------------:|:---------:| :-----: |
|PP-YOLOE-l(子图直接评估)| VisDrone-DET| 640 | 0.25 | 10 | 38.5(子图val) | 60.2 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) |
|PP-YOLOE-l(原图直接评估)| VisDrone-DET| 640 | 0.25 | 10 | 29.7(原图val) | 48.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) |
|PP-YOLOE-l (切图拼图评估)| VisDrone-DET| 640 | 0.25 | 10 | 37.3(原图val) | 59.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) |
**注意:**
- 上表中的模型均为使用**切图后的子图**训练，评估预测时分为两种，**直接使用原图**评估预测，和**使用子图自动拼成原图**评估预测，AP精度均为**原图验证集**上评估的结果。。
- **SLICE_SIZE**表示使用SAHI工具切图后子图的边长大小，**OVERLAP_RATIO**表示切图的子图之间的重叠率。
- VisDrone-DET的模型与[切图模型](../README.md#切图模型)表格中的VisDrone-DET是**同一个模型权重**，但此处AP精度是在**原图验证集**上评估的结果，需要提前修改`ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml`里的`EvalDataset`的默认的子图验证集路径为以下**原图验证集路径**：
```
EvalDataset:
!COCODataSet
image_dir: VisDrone2019-DET-val
anno_path: val.json
dataset_dir: dataset/visdrone
```
<details>
<summary> 快速开始 </summary>
```shell
# 训练
python -m paddle.distributed.launch --log_dir=logs/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml --amp --eval
# 子图直接评估
python tools/eval.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams
# 原图直接评估，注意需要提前修改此yml中的 `EvalDataset` 的默认的子图验证集路径 为 原图验证集路径：
python tools/eval.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams
# 切图拼图评估，加上 --slice_infer，注意是使用的带 _slice_infer 后缀的yml配置文件
python tools/eval.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025_slice_infer.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --slice_infer
# 切图拼图预测，加上 --slice_infer
python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_img=demo/visdrone_0000315_01601_d_0000509.jpg --draw_threshold=0.25 --slice_infer
```
</details>
## 注意事项：
- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
- 具体使用教程请参考[ppyoloe](../../ppyoloe#getting-start)。
- MatlabAPI测试是使用官网评测工具[VisDrone2018-DET-toolkit](https://github.com/VisDrone/VisDrone2018-DET-toolkit)。
## PP-YOLOE+_SOD 部署模型
| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| PP-YOLOE+_SOD-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_wo_nms.onnx) |
| PP-YOLOE+_SOD-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_wo_nms.onnx) |
| PP-YOLOE+_SOD-largesize-l | 1920 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_wo_nms.onnx) |
## 测速
1.参考[Paddle Inference文档](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python)，下载并安装与你的CUDA, CUDNN和TensorRT相应的wheel包。
测速需要设置`--run_benchmark=True`, 你需要安装以下依赖`pip install pynvml psutil GPUtil`。
导出ONNX，你需要安装以下依赖`pip install paddle2onnx`。
2.运行以下命令导出**带NMS的模型和ONNX**，并使用TensorRT FP16进行推理和测速
### 注意：
- 由于NMS参数设置对速度影响极大，部署测速时可调整`keep_top_k`和`nms_top_k`，在只低约0.1 mAP精度的情况下加快预测速度，导出模型的时候也可这样设置：
```
nms:
name: MultiClassNMS
nms_top_k: 1000 # 10000
keep_top_k: 100 # 500
score_threshold: 0.01
nms_threshold: 0.6
```
```bash
# 导出带NMS的模型
python tools/export_model.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams trt=True
# 导出带NMS的ONNX
paddle2onnx --model_dir output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx
# 推理单张图片
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_mode=trt_fp16
# 推理文件夹下的所有图片
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_dir=demo/ --device=gpu --run_mode=trt_fp16
# 单张图片普通测速
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True
# 单张图片TensorRT FP16测速
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16
```
3.运行以下命令导出**不带NMS的模型和ONNX**，并使用TensorRT FP16进行推理和测速，以及**ONNX下FP16测速**
```bash
# 导出带NMS的模型
python tools/export_model.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams trt=True exclude_nms=True
# 导出带NMS的ONNX
paddle2onnx --model_dir output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx
# 推理单张图片
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_mode=trt_fp16
# 推理文件夹下的所有图片
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_dir=demo/ --device=gpu --run_mode=trt_fp16
# 单张图片普通测速
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True
# 单张图片TensorRT FP16测速
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/visdrone_0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16
# 单张图片ONNX TensorRT FP16测速
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x1920x1920 --fp16
```
**注意：**
- TensorRT会根据网络的定义，执行针对当前硬件平台的优化，生成推理引擎并序列化为文件。该推理引擎只适用于当前软硬件平台。如果你的软硬件平台没有发生变化，你可以设置[enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/python/infer.py#L857)的参数`use_static=True`，这样生成的序列化文件将会保存在`output_inference`文件夹下，下次执行TensorRT时将加载保存的序列化文件。
- PaddleDetection release/2.4及其之后的版本将支持NMS调用TensorRT，需要依赖PaddlePaddle release/2.3及其之后的版本
# 引用
```
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}
}
```
| PaddleDetection/configs/smalldet/visdrone/README.md/0 | {
"file_path": "PaddleDetection/configs/smalldet/visdrone/README.md",
"repo_id": "PaddleDetection",
"token_count": 9217
} | 42 |
English | [简体中文](README_cn.md)
# SNIPER: Efficient Multi-Scale Training
## Model Zoo
| Sniper | GPU number | images/GPU | Model | Dataset | Schedulers | Box AP | Download | Config |
| :---------------- | :-------------------: | :------------------: | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| w/o | 4 | 1 | ResNet-r50-FPN | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) | 1x | 23.3 | [Download Link](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_visdrone.pdparams ) | [config](./faster_rcnn_r50_fpn_1x_visdrone.yml) |
| w/ | 4 | 1 | ResNet-r50-FPN | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) | 1x | 29.7 | [Download Link](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_sniper_visdrone.pdparams) | [config](./faster_rcnn_r50_fpn_1x_sniper_visdrone.yml) |
### Note
- Here, we use VisDrone dataset, and to detect 9 objects including `person, bicycles, car, van, truck, tricycle, awning-tricycle, bus, motor`.
- Do not support deploy by now because sniper dataset crop behavior.
## Getting Start
### 1. Training
a. optional: Run `tools/sniper_params_stats.py` to get image_target_sizes\valid_box_ratio_ranges\chip_target_size\chip_target_stride，and modify this params in configs/datasets/sniper_coco_detection.yml
```bash
python tools/sniper_params_stats.py FasterRCNN annotations/instances_train2017.json
```
b. optional: train detector to get negative proposals.
```bash
python -m paddle.distributed.launch --log_dir=./sniper/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml --save_proposals --proposals_path=./proposals.json &>sniper.log 2>&1 &
```
c. train models
```bash
python -m paddle.distributed.launch --log_dir=./sniper/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml --eval &>sniper.log 2>&1 &
```
### 2. Evaluation
Evaluating SNIPER on custom dataset in single GPU with following commands:
```bash
# use saved checkpoint in training
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final
```
### 3. Inference
Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.
```bash
# inference single image
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final --infer_img=demo/P0861__1.0__1154___824.png
# inference all images in the directory
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final --infer_dir=demo
```
## Citations
```
@misc{1805.09300,
Author = {Bharat Singh and Mahyar Najibi and Larry S. Davis},
Title = {SNIPER: Efficient Multi-Scale Training},
Year = {2018},
Eprint = {arXiv:1805.09300},
}
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}}
```
| PaddleDetection/configs/sniper/README.md/0 | {
"file_path": "PaddleDetection/configs/sniper/README.md",
"repo_id": "PaddleDetection",
"token_count": 1438
} | 43 |
_BASE_: [
'../datasets/coco_instance.yml',
'../runtime.yml',
'_base_/solov2_r50_fpn.yml',
'_base_/optimizer_1x.yml',
'_base_/solov2_reader.yml',
]
weights: output/solov2_r50_fpn_1x_coco/model_final
| PaddleDetection/configs/solov2/solov2_r50_fpn_1x_coco.yml/0 | {
"file_path": "PaddleDetection/configs/solov2/solov2_r50_fpn_1x_coco.yml",
"repo_id": "PaddleDetection",
"token_count": 110
} | 44 |
worker_num: 4
eval_height: &eval_height 640
eval_width: &eval_width 640
eval_size: &eval_size [*eval_height, *eval_width]
TrainReader:
sample_transforms:
- Decode: {}
- RandomDistort: {}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomCrop: {}
- RandomFlip: {}
batch_transforms:
- BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
- PadGT: {}
batch_size: 2
shuffle: true
drop_last: true
use_shared_memory: true
collate_batch: true
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 2
TestReader:
inputs_def:
image_shape: [3, *eval_height, *eval_width]
sample_transforms:
- Decode: {}
- Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
- Permute: {}
batch_size: 1
| PaddleDetection/configs/vitdet/_base_/ppyoloe_reader.yml/0 | {
"file_path": "PaddleDetection/configs/vitdet/_base_/ppyoloe_reader.yml",
"repo_id": "PaddleDetection",
"token_count": 529
} | 45 |
architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV1_pretrained.pdparams
norm_type: sync_bn
YOLOv3:
backbone: MobileNet
neck: YOLOv3FPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
MobileNet:
scale: 1
feature_maps: [4, 6, 13]
with_extra_blocks: false
extra_block_filters: []
# use default config
# YOLOv3FPN:
YOLOv3Head:
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
nms:
name: MultiClassNMS
keep_top_k: 100
score_threshold: 0.01
nms_threshold: 0.45
nms_top_k: 1000
| PaddleDetection/configs/yolov3/_base_/yolov3_mobilenet_v1.yml/0 | {
"file_path": "PaddleDetection/configs/yolov3/_base_/yolov3_mobilenet_v1.yml",
"repo_id": "PaddleDetection",
"token_count": 438
} | 46 |
# 自动化压缩
目录：
- [1.简介](#1简介)
- [2.Benchmark](#2Benchmark)
- [3.开始自动压缩](#自动压缩流程)
- [3.1 环境准备](#31-准备环境)
- [3.2 准备数据集](#32-准备数据集)
- [3.3 准备预测模型](#33-准备预测模型)
- [3.4 测试模型精度](#34-测试模型精度)
- [3.5 自动压缩并产出模型](#35-自动压缩并产出模型)
- [4.预测部署](#4预测部署)
## 1. 简介
本示例使用PaddleDetection中Inference部署模型进行自动化压缩，使用的自动化压缩策略为量化蒸馏。
## 2.Benchmark
### PP-YOLOE+
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PP-YOLOE+_s | 43.7 | - | 42.9 | - | - | - | [config](./configs/ppyoloe_plus_s_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_s_qat_dis.tar) |
| PP-YOLOE+_m | 49.8 | - | 49.3 | - | - | - | [config](./configs/ppyoloe_plus_m_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_m_qat_dis.tar) |
| PP-YOLOE+_l | 52.9 | - | 52.6 | - | - | - | [config](./configs/ppyoloe_plus_l_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_l_qat_dis.tar) |
| PP-YOLOE+_x | 54.7 | - | 54.4 | - | - | - | [config](./configs/ppyoloe_plus_x_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddledet/deploy/Inference/ppyoloe_plus_x_qat_dis.tar) |
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
### YOLOv8
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| YOLOv8-s | 44.9 | 43.9 | 44.3 | 9.27ms | 4.65ms | **3.78ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms_quant.tar) |
**注意：**
- 表格中YOLOv8模型均为带NMS的模型，可直接在TRT中部署，如果需要对齐测试标准，需要测试不带NMS的模型。
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
- 表格中的性能在Tesla T4的GPU环境下测试，并且开启TensorRT，batch_size=1。
### PP-YOLOE
| 模型 | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PP-YOLOE-l | 50.9 | - | 50.6 | 11.2ms | 7.7ms | **6.7ms** | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/deploy/auto_compression/configs/ppyoloe_l_qat_dis.yaml) | [Quant Model](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_crn_l_300e_coco_quant.tar) |
| PP-YOLOE-SOD | 38.5 | - | 37.6 | - | - | - | [config](./configs/ppyoloe_crn_l_80e_sliced_visdrone_640_025_qat.yml) | [Quant Model](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_sod_visdrone.tar) |
git
- PP-YOLOE-l mAP的指标在COCO val2017数据集中评测得到，IoU=0.5:0.95。
- PP-YOLOE-l模型在Tesla V100的GPU环境下测试，并且开启TensorRT，batch_size=1，包含NMS，测试脚本是[benchmark demo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy/python)。
- PP-YOLOE-SOD 的指标在VisDrone-DET数据集切图后的COCO格式[数据集](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone_sliced.zip)中评测得到，IoU=0.5:0.95。定义文件[ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml](../../configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml)
### PP-PicoDet
| 模型 | 策略 | mAP | FP32 | FP16 | INT8 | 配置文件 | 模型 |
| :-------- |:-------- |:--------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
| PicoDet-S-NPU | Baseline | 30.1 | - | - | - | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco_npu.yml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/picodet_s_416_coco_npu.tar) |
| PicoDet-S-NPU | 量化训练 | 29.7 | - | - | - | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/full_quantization/detection/configs/picodet_s_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/picodet_s_npu_quant.tar) |
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
### RT-DETR
| 模型 | Base mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :---------------- | :------- | :--------: | :------: | :------: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| RT-DETR-R50 | 53.1 | 53.0 | 32.05ms | 9.12ms | **6.96ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r50vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r50vd_6x_coco_quant.tar) |
| RT-DETR-R101 | 54.3 | 54.1 | 54.13ms | 12.68ms | **9.20ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r101vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r101vd_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-L | 53.0 | 52.9 | 26.16ms | 8.54ms | **6.65ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_l_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_l_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-X | 54.8 | 54.6 | 49.22ms | 12.50ms | **9.24ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_x_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_x_6x_coco_quant.tar) |
- 上表测试环境：Tesla T4，TensorRT 8.6.0，CUDA 11.7，batch_size=1。
| 模型 | Base mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 | 配置文件 | 量化模型 |
| :---------------- | :------- | :--------: | :------: | :------: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| RT-DETR-R50 | 53.1 | 53.0 | 9.64ms | 5.00ms | **3.99ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r50vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r50vd_6x_coco_quant.tar) |
| RT-DETR-R101 | 54.3 | 54.1 | 14.93ms | 7.15ms | **5.12ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_r101vd_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_r101vd_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-L | 53.0 | 52.9 | 8.17ms | 4.77ms | **4.00ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_l_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_l_6x_coco_quant.tar) |
| RT-DETR-HGNetv2-X | 54.8 | 54.6 | 12.81ms | 6.97ms | **5.32ms** | [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/rtdetr_hgnetv2_x_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/rtdetr_hgnetv2_x_6x_coco_quant.tar) |
- 上表测试环境：A10，TensorRT 8.6.0，CUDA 11.6，batch_size=1。
- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
## 3. 自动压缩流程
#### 3.1 准备环境
- PaddlePaddle >= 2.4 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
- PaddleSlim >= 2.4.1
- PaddleDet >= 2.5
- opencv-python
安装paddlepaddle：
```shell
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
安装paddleslim：
```shell
pip install paddleslim
```
安装paddledet：
```shell
pip install paddledet
```
**注意：** YOLOv8模型的自动化压缩需要依赖安装最新[Develop Paddle](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html)和[Develop PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim#%E5%AE%89%E8%A3%85)版本。
#### 3.2 准备数据集
本案例默认以COCO数据进行自动压缩实验，如果自定义COCO数据，或者其他格式数据，请参考[数据准备文档](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/docs/tutorials/data/PrepareDataSet.md) 来准备数据。
如果数据集为非COCO格式数据，请修改[configs](./configs)中reader配置文件中的Dataset字段。
以PP-YOLOE模型为例，如果已经准备好数据集，请直接修改[./configs/yolo_reader.yml]中`EvalDataset`的`dataset_dir`字段为自己数据集路径即可。
#### 3.3 准备预测模型
预测模型的格式为：`model.pdmodel` 和 `model.pdiparams`两个，带`pdmodel`的是模型文件，带`pdiparams`后缀的是权重文件。
根据[PaddleDetection文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED_cn.md#8-%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA) 导出Inference模型，具体可参考下方PP-YOLOE模型的导出示例：
- 下载代码
```
git clone https://github.com/PaddlePaddle/PaddleDetection.git
```
- 导出预测模型
PPYOLOE-l模型，包含NMS：如快速体验，可直接下载[PP-YOLOE-l导出模型](https://bj.bcebos.com/v1/paddle-slim-models/act/ppyoloe_crn_l_300e_coco.tar)
```shell
python tools/export_model.py \
-c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml \
-o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams \
trt=True \
```
YOLOv8-s模型，包含NMS，具体可参考[YOLOv8模型文档](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8), 然后执行：
```shell
python tools/export_model.py \
-c configs/yolov8/yolov8_s_500e_coco.yml \
-o weights=https://paddledet.bj.bcebos.com/models/yolov8_s_500e_coco.pdparams \
trt=True
```
如快速体验，可直接下载[YOLOv8-s导出模型](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms.tar)
#### 3.4 自动压缩并产出模型
蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
- 单卡训练：
```
export CUDA_VISIBLE_DEVICES=0
python run.py --config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
```
- 多卡训练：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --log_dir=log --gpus 0,1,2,3 run.py \
--config_path=./configs/ppyoloe_l_qat_dis.yaml --save_dir='./output/'
```
#### 3.5 测试模型精度
使用eval.py脚本得到模型的mAP：
```
export CUDA_VISIBLE_DEVICES=0
python eval.py --config_path=./configs/ppyoloe_l_qat_dis.yaml
```
使用paddle inference并使用trt int8得到模型的mAP:
```
export CUDA_VISIBLE_DEVICES=0
python paddle_inference_eval.py --model_path ./output/ --reader_config configs/ppyoloe_reader.yml --precision int8 --use_trt=True
```
**注意**：
- 要测试的模型路径可以在配置文件中`model_dir`字段下进行修改。
- --precision 默认为paddle，如果使用trt，需要设置--use_trt=True，同时--precision 可设置为fp32/fp16/int8
## 4.预测部署
- 可以参考[PaddleDetection部署教程](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy)，GPU上量化模型开启TensorRT并设置trt_int8模式进行部署。
| PaddleDetection/deploy/auto_compression/README.md/0 | {
"file_path": "PaddleDetection/deploy/auto_compression/README.md",
"repo_id": "PaddleDetection",
"token_count": 7016
} | 47 |
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <ctime>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
namespace PaddleDetection {
// Object Detection Result
struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect;
// Class id of detected object
int class_id;
// Confidence of detected object
float confidence;
// Mask of detected object
std::vector<int> mask;
};
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold);
} // namespace PaddleDetection
| PaddleDetection/deploy/cpp/include/utils.h/0 | {
"file_path": "PaddleDetection/deploy/cpp/include/utils.h",
"repo_id": "PaddleDetection",
"token_count": 344
} | 48 |
import sys
import requests
import cv2
import random
import time
import numpy as np
import tensorrt as trt
from cuda import cudart
from pathlib import Path
from collections import OrderedDict, namedtuple
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, r, (dw, dh)
w = Path(sys.argv[1])
assert w.exists() and w.suffix in ('.engine', '.plan'), 'Wrong engine path'
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
colors = {name: [random.randint(0, 255) for _ in range(3)] for i, name in enumerate(names)}
url = 'https://oneflow-static.oss-cn-beijing.aliyuncs.com/tripleMu/image1.jpg'
file = requests.get(url)
img = cv2.imdecode(np.frombuffer(file.content, np.uint8), 1)
_, stream = cudart.cudaStreamCreate()
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
# Infer TensorRT Engine
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
logger = trt.Logger(trt.Logger.ERROR)
trt.init_libnvinfer_plugins(logger, namespace="")
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
bindings = OrderedDict()
fp16 = False # default updated below
for index in range(model.num_bindings):
name = model.get_binding_name(index)
dtype = trt.nptype(model.get_binding_dtype(index))
shape = tuple(model.get_binding_shape(index))
data = np.empty(shape, dtype=np.dtype(dtype))
_, data_ptr = cudart.cudaMallocAsync(data.nbytes, stream)
bindings[name] = Binding(name, dtype, shape, data, data_ptr)
if model.binding_is_input(index) and dtype == np.float16:
fp16 = True
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
context = model.create_execution_context()
image = img.copy()
image, ratio, dwdh = letterbox(image, auto=False)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_copy = image.copy()
image = image.transpose((2, 0, 1))
image = np.expand_dims(image, 0)
image = np.ascontiguousarray(image)
im = image.astype(np.float32)
im /= 255
im -= mean
im /= std
_, image_ptr = cudart.cudaMallocAsync(im.nbytes, stream)
cudart.cudaMemcpyAsync(image_ptr, im.ctypes.data, im.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream)
# warmup for 10 times
for _ in range(10):
tmp = np.random.randn(1, 3, 640, 640).astype(np.float32)
_, tmp_ptr = cudart.cudaMallocAsync(tmp.nbytes, stream)
binding_addrs['image'] = tmp_ptr
context.execute_v2(list(binding_addrs.values()))
start = time.perf_counter()
binding_addrs['image'] = image_ptr
context.execute_v2(list(binding_addrs.values()))
print(f'Cost {(time.perf_counter() - start) * 1000}ms')
nums = bindings['num_dets'].data
boxes = bindings['det_boxes'].data
scores = bindings['det_scores'].data
classes = bindings['det_classes'].data
cudart.cudaMemcpyAsync(nums.ctypes.data,
bindings['num_dets'].ptr,
nums.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(boxes.ctypes.data,
bindings['det_boxes'].ptr,
boxes.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(scores.ctypes.data,
bindings['det_scores'].ptr,
scores.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(classes.ctypes.data,
bindings['det_classes'].ptr,
classes.data.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaStreamSynchronize(stream)
cudart.cudaStreamDestroy(stream)
for i in binding_addrs.values():
cudart.cudaFree(i)
num = int(nums[0][0])
box_img = boxes[0, :num].round().astype(np.int32)
score_img = scores[0, :num]
clss_img = classes[0, :num]
for i, (box, score, clss) in enumerate(zip(box_img, score_img, clss_img)):
name = names[int(clss)]
color = colors[name]
cv2.rectangle(image_copy, box[:2].tolist(), box[2:].tolist(), color, 2)
cv2.putText(image_copy, name, (int(box[0]), int(box[1]) - 2), cv2.FONT_HERSHEY_SIMPLEX,
0.75, [225, 255, 255], thickness=2)
cv2.imshow('Result', cv2.cvtColor(image_copy, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
| PaddleDetection/deploy/end2end_ppyoloe/cuda-python.py/0 | {
"file_path": "PaddleDetection/deploy/end2end_ppyoloe/cuda-python.py",
"repo_id": "PaddleDetection",
"token_count": 2925
} | 49 |
[English](README.md) | 简体中文
# PP-PicoDet + PP-TinyPose (Pipeline) 昆仑芯 XPU C++部署示例
本目录下提供`det_keypoint_unite_infer.cc`快速完成多人模型配置 PP-PicoDet + PP-TinyPose 在CPU/GPU，以及GPU上通过TensorRT加速部署的`单图多人关键点检测`示例。执行如下脚本即可完成。**注意**: PP-TinyPose单模型独立部署，请参考[PP-TinyPose 单模型](../README.md)
## 1. 部署环境准备
在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库。
## 2. 部署模型准备
在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../../README.md)或者[自行导出PaddleDetection部署模型](../../README.md)。
## 3. 运行部署示例
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
```bash
mkdir build
cd build
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# 下载部署示例代码
git clone https://github.com/PaddlePaddle/PaddleDetection.git
cd PaddleDetection/deploy/fastdeploy/kunlunxin/cpp/det_keypoint_unite
# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
# git checkout develop
# 下载PP-TinyPose和PP-PicoDet模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
tar -xvf PP_TinyPose_256x192_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
tar -xvf PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
# 运行部署示例
./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg
```
运行完成可视化结果如下图所示
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=359px, height=423px />
</div>
- 注意，以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考: [如何在Windows中使用FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows.md)
- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
## 4. PP-TinyPose 模型串联 C++ 接口
```c++
fastdeploy::pipeline::PPTinyPose(
fastdeploy::vision::detection::PicoDet* det_model,
fastdeploy::vision::keypointdetection::PPTinyPose* pptinypose_model)
```
PPTinyPose Pipeline模型加载和初始化。det_model表示初始化后的检测模型，pptinypose_model表示初始化后的关键点检测模型。
## 5. 更多指南
- [PaddleDetection C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1detection.html)
- [FastDeploy部署PaddleDetection模型概览](../../../)
- [Python部署](../../python/det_keypoint_unite/)
## 6. 常见问题
- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md) | PaddleDetection/deploy/fastdeploy/kunlunxin/cpp/det_keypoint_unite/README.md/0 | {
"file_path": "PaddleDetection/deploy/fastdeploy/kunlunxin/cpp/det_keypoint_unite/README.md",
"repo_id": "PaddleDetection",
"token_count": 2322
} | 50 |
[English](README.md) | 简体中文
# PaddleDetection 检测模型在瑞芯微NPU上的部署方案-FastDeploy
## 1. 说明
本示例基于RV1126来介绍如何使用FastDeploy部署PaddleDetection模型，支持如下芯片的部署：
- Rockchip RV1109
- Rockchip RV1126
- Rockchip RK1808
模型的量化和量化模型的下载请参考：[模型量化](../../quantize/README.md)
## 详细部署文档
在 RV1126 上只支持 C++ 的部署。
- [C++部署](cpp)
| PaddleDetection/deploy/fastdeploy/rockchip/rv1126/README.md/0 | {
"file_path": "PaddleDetection/deploy/fastdeploy/rockchip/rv1126/README.md",
"repo_id": "PaddleDetection",
"token_count": 300
} | 51 |
# Runtime Directory
This directory holds the model files.
Paddle models must be model.pdmodel and model.pdiparams files.
ONNX models must be model.onnx files.
| PaddleDetection/deploy/fastdeploy/serving/models/runtime/1/README.md/0 | {
"file_path": "PaddleDetection/deploy/fastdeploy/serving/models/runtime/1/README.md",
"repo_id": "PaddleDetection",
"token_count": 46
} | 52 |
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <fstream>
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include "json/json.h"
#ifdef _WIN32
#define OS_PATH_SEP "\\"
#else
#define OS_PATH_SEP "/"
#endif
namespace PaddleDetection {
void load_jsonf(std::string jsonfile, Json::Value& jsondata);
// Inference model configuration parser
class ConfigPaser {
public:
ConfigPaser() {}
~ConfigPaser() {}
bool load_config(const std::string& model_dir,
const std::string& cfg = "infer_cfg") {
Json::Value config;
load_jsonf(model_dir + OS_PATH_SEP + cfg + ".json", config);
// Get model arch : YOLO, SSD, RetinaNet, RCNN, Face, PicoDet, HRNet
if (config.isMember("arch")) {
arch_ = config["arch"].as<std::string>();
} else {
std::cerr
<< "Please set model arch,"
<< "support value : YOLO, SSD, RetinaNet, RCNN, Face, PicoDet, HRNet."
<< std::endl;
return false;
}
// Get draw_threshold for visualization
if (config.isMember("draw_threshold")) {
draw_threshold_ = config["draw_threshold"].as<float>();
} else {
std::cerr << "Please set draw_threshold." << std::endl;
return false;
}
// Get Preprocess for preprocessing
if (config.isMember("Preprocess")) {
preprocess_info_ = config["Preprocess"];
} else {
std::cerr << "Please set Preprocess." << std::endl;
return false;
}
// Get label_list for visualization
if (config.isMember("label_list")) {
label_list_.clear();
for (auto item : config["label_list"]) {
label_list_.emplace_back(item.as<std::string>());
}
} else {
std::cerr << "Please set label_list." << std::endl;
return false;
}
// Get NMS for postprocess
if (config.isMember("NMS")) {
nms_info_ = config["NMS"];
}
// Get fpn_stride in PicoDet
if (config.isMember("fpn_stride")) {
fpn_stride_.clear();
for (auto item : config["fpn_stride"]) {
fpn_stride_.emplace_back(item.as<int>());
}
}
return true;
}
float draw_threshold_;
std::string arch_;
Json::Value preprocess_info_;
Json::Value nms_info_;
std::vector<std::string> label_list_;
std::vector<int> fpn_stride_;
};
} // namespace PaddleDetection
| PaddleDetection/deploy/lite/include/config_parser.h/0 | {
"file_path": "PaddleDetection/deploy/lite/include/config_parser.h",
"repo_id": "PaddleDetection",
"token_count": 1139
} | 53 |
简体中文 | [English](README_en.md)
<img src="https://user-images.githubusercontent.com/48054808/185032511-0c97b21c-8bab-4ab1-89ee-16e5e81c22cc.png" title="" alt="" data-align="center">
**PaddleDetection深入探索核心行业的高频场景，提供了行人、车辆场景的开箱即用分析工具，支持图片/单镜头视频/多镜头视频/在线视频流多种输入方式，广泛应用于智慧交通、智慧城市、工业巡检等领域。支持服务器端部署及TensorRT加速，T4服务器上可达到实时。**
- 🚶♂️🚶♀️ **PP-Human支持四大产业级功能：五大异常行为识别、26种人体属性分析、实时人流计数、跨镜头（ReID）跟踪。**
- 🚗🚙 **PP-Vehicle囊括四大交通场景核心功能：车牌识别、属性识别、车流量统计、违章检测。**
## 📣 近期更新
- 🔥🔥🔥 2023.02.15: Jetson部署专用小模型PP-YOLOE-PLUS-Tiny发布，可在AGX平台实现4路视频流实时预测；PP-Vehicle发布违法分析功能车辆逆行和压车道线。
- **2022.8.20：PP-Vehicle首发，提供车牌识别、车辆属性分析（颜色、车型）、车流量统计以及违章检测四大功能，完善的文档教程支持高效完成二次开发与模型优化**
- **2022.7.13：PP-Human v2发布，新增打架、打电话、抽烟、闯入四大行为识别，底层算法性能升级，覆盖行人检测、跟踪、属性三类核心算法能力，提供保姆级全流程开发及模型优化策略**
- 2022.4.18：新增PP-Human全流程实战教程, 覆盖训练、部署、动作类型扩展等内容，AIStudio项目请见[链接](https://aistudio.baidu.com/aistudio/projectdetail/3842982)
- 2022.4.10：新增PP-Human范例，赋能社区智能精细化管理, AIStudio快速上手教程[链接](https://aistudio.baidu.com/aistudio/projectdetail/3679564)
- 2022.4.5：全新发布实时行人分析工具PP-Human，支持行人跟踪、人流量统计、人体属性识别与摔倒检测四大能力，基于真实场景数据特殊优化，精准识别各类摔倒姿势，适应不同环境背景、光线及摄像角度
## 🔮 功能介绍与效果展示
### PP-Human
| ⭐ 功能 | 💟 方案优势 | 💡示例图 |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| **跨镜跟踪（ReID）** | 超强性能：针对目标遮挡、完整度、模糊度等难点特殊优化，实现mAP 98.8、1.5ms/人 | <img title="" src="https://user-images.githubusercontent.com/48054808/173037607-0a5deadc-076e-4dcc-bd96-d54eea205f1f.png" alt="" width="191"> |
| **属性分析** | 兼容多种数据格式：支持图片、视频、在线视频流输入<br><br>高性能：融合开源数据集与企业真实数据进行训练，实现mAP 95.4、2ms/人<br><br>支持26种属性：性别、年龄、眼镜、上衣、鞋子、帽子、背包等26种高频属性 | <img title="" src="https://user-images.githubusercontent.com/48054808/173036043-68b90df7-e95e-4ada-96ae-20f52bc98d7c.png" alt="" width="191">|
| **行为识别（包含摔倒、打架、抽烟、打电话、人员闯入）** | 功能丰富：支持摔倒、打架、抽烟、打电话、人员闯入五种高频异常行为识别<br><br>鲁棒性强：对光照、视角、背景环境无限制<br><br>性能高：与视频识别技术相比，模型计算量大幅降低，支持本地化与服务化快速部署<br><br>训练速度快：仅需15分钟即可产出高精度行为识别模型 |<img title="" src="https://user-images.githubusercontent.com/48054808/173034825-623e4f78-22a5-4f14-9b83-dc47aa868478.gif" alt="" width="191"> |
| **人流量计数**<br>**轨迹记录** | 简洁易用：单个参数即可开启人流量计数与轨迹记录功能 | <img title="" src="https://user-images.githubusercontent.com/22989727/174736440-87cd5169-c939-48f8-90a1-0495a1fcb2b1.gif" alt="" width="191"> |
### PP-Vehicle
| ⭐ 功能 | 💟 方案优势 | 💡示例图 |
| ---------- | ------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- |
| **车牌识别** | 支持传统车牌和新能源绿色车牌 <br/><br/> 车牌识别采用长间隔采样识别与多次结果统计投票方式，算力消耗少，识别精度高，结果稳定性好。 检测模型 hmean: 0.979; 识别模型 acc: 0.773 | <img title="" src="https://user-images.githubusercontent.com/48054808/185027987-6144cafd-0286-4c32-8425-7ab9515d1ec3.png" alt="" width="191"> |
| **车辆属性分析** | 支持多种车型、颜色类别识别 <br/><br/> 使用更强力的Backbone模型PP-HGNet、PP-LCNet,精度高、速度快。识别精度: 90.81 | <img title="" src="https://user-images.githubusercontent.com/48054808/185044490-00edd930-1885-4e79-b3d4-3a39a77dea93.gif" alt="" width="207"> |
| **违章检测** | 简单易用：一行命令即可实现违停检测，自定义设置区域 <br/><br/> 检测、跟踪效果好，可实现违停车辆车牌识别 | <img title="" src="https://user-images.githubusercontent.com/48054808/185028419-58ae0af8-a035-42e7-9583-25f5e4ce0169.png" alt="" width="209"> |
| **车流量计数** | 简单易用：一行命令即可开启功能，自定义出入位置 <br/><br/> 可提供目标跟踪轨迹显示，统计准确度高 | <img title="" src="https://user-images.githubusercontent.com/48054808/185028798-9e07379f-7486-4266-9d27-3aec943593e0.gif" alt="" width="200"> |
| **违法分析-车辆逆行** | 简单易用：一行命令即可开启功能 <br/><br/> 车道线分割使用高精度模型PP-LIteSeg | <img title="" src="https://raw.githubusercontent.com/LokeZhou/PaddleDetection/develop/deploy/pipeline/docs/images/vehicle_retrograde.gif" alt="" width="200"> |
| **违法分析-压车道线** | 简单易用：一行命令即可开启功能 <br/><br/> 车道线分割使用高精度模型PP-LIteSeg | <img title="" src="https://raw.githubusercontent.com/LokeZhou/PaddleDetection/develop/deploy/pipeline/docs/images/vehicle_press.gif" alt="" width="200"> |
## 🗳 模型库
### PP-Human
<details>
<summary><b>端到端模型效果（点击展开）</b></summary>
| 任务 | 端到端速度（ms） | 模型方案 | 模型体积 |
|:---------:|:---------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------:|
| 行人检测（高精度） | 25.1ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| 行人检测（轻量级） | 16.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M |
| 行人检测（超轻量级） | 10ms(Jetson AGX) | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.tar.gz) | 17M |
| 行人跟踪（高精度） | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| 行人跟踪（轻量级） | 21.0ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M |
| 行人跟踪（超轻量级） | 13.2ms(Jetson AGX) | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.tar.gz) | 17M |
| 跨镜跟踪(REID) | 单人1.5ms | [REID](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) | REID：92M |
| 属性识别（高精度） | 单人8.5ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br> [属性识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | 目标检测：182M<br>属性识别：86M |
| 属性识别（轻量级） | 单人7.1ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br> [属性识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | 目标检测：182M<br>属性识别：86M |
| 摔倒识别 | 单人10ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) <br> [关键点检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) <br> [基于关键点行为识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | 多目标跟踪：182M<br>关键点检测：101M<br>基于关键点行为识别：21.8M |
| 闯入识别 | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M |
| 打架识别 | 19.7ms | [视频分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 90M |
| 抽烟识别 | 单人15.1ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br>[基于人体id的目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | 目标检测：182M<br>基于人体id的目标检测：27M |
| 打电话识别 | 单人ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)<br>[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M<br>基于人体id的图像分类：45M |
点击模型方案中的模型即可下载指定模型，下载后解压存放至`./output_inference`目录中
</details>
### PP-Vehicle
<details>
<summary><b>端到端模型效果（点击展开）</b></summary>
| 任务 | 端到端速度（ms）| 模型方案 | 模型体积 |
| :---------: | :-------: | :------: |:------: |
| 车辆检测（高精度） | 25.7ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M |
| 车辆检测（轻量级） | 13.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M |
| 车辆检测（超轻量级） | 10ms（Jetson AGX） | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.tar.gz) | 17M |
| 车辆跟踪（高精度） | 40ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M |
| 车辆跟踪（轻量级） | 25ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M |
| 车辆跟踪（超轻量级） | 13.2ms（Jetson AGX） | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.tar.gz) | 17M |
| 车牌识别 | 4.68ms | [车牌检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz) <br> [车牌字符识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | 车牌检测：3.9M <br> 车牌字符识别： 12M |
| 车辆属性 | 7.31ms | [车辆属性](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M |
| 车道线检测 | 47ms | [车道线模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/pp_lite_stdc2_bdd100k.zip) | 47M |
点击模型方案中的模型即可下载指定模型，下载后解压存放至`./output_inference`目录中
</details>
## 📚 详细文档
### 🚶♀️ 行人分析工具PP-Human
#### [快速开始](docs/tutorials/PPHuman_QUICK_STARTED.md)
#### 行为识别
- [快速开始](docs/tutorials/pphuman_action.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/action_recognotion/README.md)
#### 行人属性/特征识别
- [快速开始](docs/tutorials/pphuman_attribute.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/pphuman_attribute.md)
#### 跨镜跟踪/ReID
- [快速开始](docs/tutorials/pphuman_mtmct.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/pphuman_mtmct.md)
#### 行人跟踪、人流计数与轨迹记录
- [快速开始](docs/tutorials/pphuman_mot.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/pphuman_mot.md)
### 🚘 车辆分析工具PP-Vehicle
#### [快速开始](docs/tutorials/PPVehicle_QUICK_STARTED.md)
#### 车牌识别
- [快速开始](docs/tutorials/ppvehicle_plate.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/ppvehicle_plate.md)
#### 车辆属性分析
- [快速开始](docs/tutorials/ppvehicle_attribute.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/ppvehicle_attribute.md)
#### 违章检测
- [快速开始](docs/tutorials/ppvehicle_illegal_parking.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/pphuman_mot.md)
#### 车辆跟踪、车流计数与轨迹记录
- [快速开始](docs/tutorials/ppvehicle_mot.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/pphuman_mot.md)
#### 车辆违法压线
- [快速开始](docs/tutorials/ppvehicle_press.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/ppvehicle_violation.md)
#### 车辆逆行
- [快速开始](docs/tutorials/ppvehicle_retrograde.md)
- [二次开发教程](../../docs/advanced_tutorials/customization/ppvehicle_violation.md)
| PaddleDetection/deploy/pipeline/README.md/0 | {
"file_path": "PaddleDetection/deploy/pipeline/README.md",
"repo_id": "PaddleDetection",
"token_count": 10948
} | 54 |
crop_thresh: 0.5
kpt_thresh: 0.2
visual: True
warmup_frame: 50
MOT:
model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip
tracker_config: deploy/pipeline/config/tracker_config.yml
batch_size: 1
enable: True
KPT:
model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip
batch_size: 8
SKELETON_ACTION:
model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip
batch_size: 1
max_frames: 50
display_frames: 80
coord_size: [384, 512]
enable: True
| PaddleDetection/deploy/pipeline/config/examples/infer_cfg_fall_down.yml/0 | {
"file_path": "PaddleDetection/deploy/pipeline/config/examples/infer_cfg_fall_down.yml",
"repo_id": "PaddleDetection",
"token_count": 250
} | 55 |
# PP-Vehicle Illegal Parking Recognition Module
Illegal parking recognition in no-parking areas has a very wide range of applications in vehicle application scenarios. With the help of AI, human input can be reduced, and illegally parked vehicles can be accurately and quickly identified, and further behaviors such as broadcasting to expel the vehicles can be performed. Based on the vehicle tracking model, license plate detection model and license plate recognition model, the PP-Vehicle realizes the illegal parking recognition function. The specific model information is as follows:
| Task | Algorithm | Precision | Inference Speed(ms) |Inference Model Download Link |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
| Vehicle Tracking | PP-YOLOE-l | mAP: 63.9 | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) |
| Plate Detection | ch_PP-OCRv3_det | hmean: 0.979 | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz) |
| Plate Recognition | ch_PP-OCRv3_rec | acc: 0.773 | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) |
1. The tracking model uses the PPVehicle dataset (integrating BDD100K-MOT and UA-DETRAC), which combines car, truck, bus, van in BDD100K-MOT and car, bus, and van in UA-DETRAC into one class which named vehicle (1).
2. The license plate detection and recognition model is fine-tuned on the CCPD2019 and CCPD2020 using the PP-OCRv3 model.
## Instructions
1. Users can download the model from the link in the table above and unzip it to the ``PaddleDetection/output_inference``` path, and modify the model path in the configuration file, or download the model automatically by default. The model paths for the three models can be manually set in ``deploy/pipeline/config/examples/infer_cfg_illegal_parking.yml```.
Description of configuration items in `infer_cfg_illegal_parking.yml`:
```
MOT: # Tracking Module
model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip # Path of Tracking Model
tracker_config: deploy/pipeline/config/tracker_config.yml # Config Path of Tracking
batch_size: 1 # Tracking batch size
enable: True # Whether to Enable Tracking Function
VEHICLE_PLATE: # Plate Recognition Module
det_model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz # Path of Plate Detection Model
det_limit_side_len: 480 # Single Side Size of Detection Model
det_limit_type: "max" # Detection model Input Size Selection of Long and Short Sides, "max" Represents the Long Side
rec_model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz # Path of Plate Recognition Model
rec_image_shape: [3, 48, 320] # The Input Size of Plate Recognition Model
rec_batch_num: 6 # Plate Recognition batch size
word_dict_path: deploy/pipeline/ppvehicle/rec_word_dict.txt # OCR Model Look-up Table
enable: True # Whether to Enable Plate Recognition Function
```
2. Input video, the command is as follows:
```python
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_illegal_parking.yml \
--video_file=test_video.mp4 \
--device=gpu \
--draw_center_traj \
--illegal_parking_time=5 \
--region_type=custom \
--region_polygon 100 1000 1000 1000 900 1700 0 1700
The parameter description:
- config: config path;
- video_file: video path to be tested;
- device: device to infe;
- draw_center_traj: draw the trajectory of the center of the vehicle;
- illegal_parking_time: illegal parking time, in seconds;
- region_type: illegal parking region type, 'custom' means the region is customized;
- region_polygon: customized illegal parking region which includes three points at least.
3. Methods to modify the path of model:
- Method 1: Configure different model paths in ```./deploy/pipeline/config/examples/infer_cfg_illegal_parking.yml``` file;
- Method2: In the command line, add `-o VEHICLE_PLATE.det_model_dir=[YOUR_DETMODEL_PATH] VEHICLE_PLATE.rec_model_dir=[YOUR_RECMODEL_PATH]` after the --config configuration item to modify the model path.
Test Result:
<div width="600" align="center">
<img src="https://user-images.githubusercontent.com/22989727/205598624-bcf5165c-990c-4fe4-8cde-eb1d45298d8f.gif"/>
</div>
## Method Description
1. Target multi-target tracking obtains the vehicle detection frame in the picture/video input. The model scheme is PP-YOLOE. For detailed documentation, refer to [PP-YOLOE](../../../configs/ppyoloe/README_cn. md)
2. Obtain the trajectory of each vehicle based on the tracking algorithm. If the center of the vehicle is in the illegal parking area and does not move within the specified time, it is considered illegal parking;
3. Use the license plate recognition model to get the illegal parking license plate and visualize it.
## References
1. Detection Model in PaddeDetection:[PP-YOLOE](../../../../configs/ppyoloe).
2. Character Recognition Model Library in Paddle: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR).
| PaddleDetection/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking_en.md/0 | {
"file_path": "PaddleDetection/deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking_en.md",
"repo_id": "PaddleDetection",
"token_count": 2778
} | 56 |
import os
import glob
import random
import fnmatch
import re
import sys
class_id = {"nofight": 0, "fight": 1}
def get_list(path, key_func=lambda x: x[-11:], rgb_prefix='img_', level=1):
if level == 1:
frame_folders = glob.glob(os.path.join(path, '*'))
elif level == 2:
frame_folders = glob.glob(os.path.join(path, '*', '*'))
else:
raise ValueError('level can be only 1 or 2')
def count_files(directory):
lst = os.listdir(directory)
cnt = len(fnmatch.filter(lst, rgb_prefix + '*'))
return cnt
# check RGB
video_dict = {}
for f in frame_folders:
cnt = count_files(f)
k = key_func(f)
if level == 2:
k = k.split("/")[0]
video_dict[f] = str(cnt) + " " + str(class_id[k])
return video_dict
def fight_splits(video_dict, train_percent=0.8):
videos = list(video_dict.keys())
train_num = int(len(videos) * train_percent)
train_list = []
val_list = []
random.shuffle(videos)
for i in range(train_num):
train_list.append(videos[i] + " " + str(video_dict[videos[i]]))
for i in range(train_num, len(videos)):
val_list.append(videos[i] + " " + str(video_dict[videos[i]]))
print("train:", len(train_list), ",val:", len(val_list))
with open("fight_train_list.txt", "w") as f:
for item in train_list:
f.write(item + "\n")
with open("fight_val_list.txt", "w") as f:
for item in val_list:
f.write(item + "\n")
if __name__ == "__main__":
frame_dir = sys.argv[1] # "rawframes"
level = sys.argv[2] # 2
train_percent = sys.argv[3] # 0.8
if level == 2:
def key_func(x):
return '/'.join(x.split('/')[-2:])
else:
def key_func(x):
return x.split('/')[-1]
video_dict = get_list(frame_dir, key_func=key_func, level=level)
print("number:", len(video_dict))
fight_splits(video_dict, train_percent)
| PaddleDetection/deploy/pipeline/tools/split_fight_train_test_dataset.py/0 | {
"file_path": "PaddleDetection/deploy/pipeline/tools/split_fight_train_test_dataset.py",
"repo_id": "PaddleDetection",
"token_count": 927
} | 57 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/multitracker.py
"""
import numpy as np
from collections import defaultdict
from collections import deque, OrderedDict
from ..matching import jde_matching as matching
__all__ = [
'TrackState',
'BaseTrack',
'STrack',
'joint_stracks',
'sub_stracks',
'remove_duplicate_stracks',
]
class TrackState(object):
New = 0
Tracked = 1
Lost = 2
Removed = 3
class BaseTrack(object):
_count_dict = defaultdict(int) # support single class and multi classes
track_id = 0
is_activated = False
state = TrackState.New
history = OrderedDict()
features = []
curr_feat = None
score = 0
start_frame = 0
frame_id = 0
time_since_update = 0
# multi-camera
location = (np.inf, np.inf)
@property
def end_frame(self):
return self.frame_id
@staticmethod
def next_id(cls_id):
BaseTrack._count_dict[cls_id] += 1
return BaseTrack._count_dict[cls_id]
# @even: reset track id
@staticmethod
def init_count(num_classes):
"""
Initiate _count for all object classes
:param num_classes:
"""
for cls_id in range(num_classes):
BaseTrack._count_dict[cls_id] = 0
@staticmethod
def reset_track_count(cls_id):
BaseTrack._count_dict[cls_id] = 0
def activate(self, *args):
raise NotImplementedError
def predict(self):
raise NotImplementedError
def update(self, *args, **kwargs):
raise NotImplementedError
def mark_lost(self):
self.state = TrackState.Lost
def mark_removed(self):
self.state = TrackState.Removed
class STrack(BaseTrack):
def __init__(self, tlwh, score, cls_id, buff_size=30, temp_feat=None):
# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float32)
self.score = score
self.cls_id = cls_id
self.track_len = 0
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
self.use_reid = True if temp_feat is not None else False
if self.use_reid:
self.smooth_feat = None
self.update_features(temp_feat)
self.features = deque([], maxlen=buff_size)
self.alpha = 0.9
def update_features(self, feat):
# L2 normalizing, this function has no use for BYTETracker
feat /= np.linalg.norm(feat)
self.curr_feat = feat
if self.smooth_feat is None:
self.smooth_feat = feat
else:
self.smooth_feat = self.alpha * self.smooth_feat + (1.0 - self.alpha
) * feat
self.features.append(feat)
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
def predict(self):
mean_state = self.mean.copy()
if self.state != TrackState.Tracked:
mean_state[7] = 0
self.mean, self.covariance = self.kalman_filter.predict(mean_state,
self.covariance)
@staticmethod
def multi_predict(tracks, kalman_filter):
if len(tracks) > 0:
multi_mean = np.asarray([track.mean.copy() for track in tracks])
multi_covariance = np.asarray(
[track.covariance for track in tracks])
for i, st in enumerate(tracks):
if st.state != TrackState.Tracked:
multi_mean[i][7] = 0
multi_mean, multi_covariance = kalman_filter.multi_predict(
multi_mean, multi_covariance)
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
tracks[i].mean = mean
tracks[i].covariance = cov
@staticmethod
def multi_gmc(stracks, H=np.eye(2, 3)):
if len(stracks) > 0:
multi_mean = np.asarray([st.mean.copy() for st in stracks])
multi_covariance = np.asarray([st.covariance for st in stracks])
R = H[:2, :2]
R8x8 = np.kron(np.eye(4, dtype=float), R)
t = H[:2, 2]
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
mean = R8x8.dot(mean)
mean[:2] += t
cov = R8x8.dot(cov).dot(R8x8.transpose())
stracks[i].mean = mean
stracks[i].covariance = cov
def reset_track_id(self):
self.reset_track_count(self.cls_id)
def activate(self, kalman_filter, frame_id):
"""Start a new track"""
self.kalman_filter = kalman_filter
# update track id for the object class
self.track_id = self.next_id(self.cls_id)
self.mean, self.covariance = self.kalman_filter.initiate(
self.tlwh_to_xyah(self._tlwh))
self.track_len = 0
self.state = TrackState.Tracked # set flag 'tracked'
if frame_id == 1: # to record the first frame's detection result
self.is_activated = True
self.frame_id = frame_id
self.start_frame = frame_id
def re_activate(self, new_track, frame_id, new_id=False):
self.mean, self.covariance = self.kalman_filter.update(
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh))
if self.use_reid:
self.update_features(new_track.curr_feat)
self.track_len = 0
self.state = TrackState.Tracked
self.is_activated = True
self.frame_id = frame_id
if new_id: # update track id for the object class
self.track_id = self.next_id(self.cls_id)
def update(self, new_track, frame_id, update_feature=True):
self.frame_id = frame_id
self.track_len += 1
new_tlwh = new_track.tlwh
self.mean, self.covariance = self.kalman_filter.update(
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
self.state = TrackState.Tracked # set flag 'tracked'
self.is_activated = True # set flag 'activated'
self.score = new_track.score
if update_feature and self.use_reid:
self.update_features(new_track.curr_feat)
@property
def tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
"""
if self.mean is None:
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret
@property
def tlbr(self):
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret
@staticmethod
def tlwh_to_xyah(tlwh):
"""Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = np.asarray(tlwh).copy()
ret[:2] += ret[2:] / 2
ret[2] /= ret[3]
return ret
def to_xyah(self):
return self.tlwh_to_xyah(self.tlwh)
@staticmethod
def tlbr_to_tlwh(tlbr):
ret = np.asarray(tlbr).copy()
ret[2:] -= ret[:2]
return ret
@staticmethod
def tlwh_to_tlbr(tlwh):
ret = np.asarray(tlwh).copy()
ret[2:] += ret[:2]
return ret
def __repr__(self):
return 'OT_({}-{})_({}-{})'.format(self.cls_id, self.track_id,
self.start_frame, self.end_frame)
def joint_stracks(tlista, tlistb):
exists = {}
res = []
for t in tlista:
exists[t.track_id] = 1
res.append(t)
for t in tlistb:
tid = t.track_id
if not exists.get(tid, 0):
exists[tid] = 1
res.append(t)
return res
def sub_stracks(tlista, tlistb):
stracks = {}
for t in tlista:
stracks[t.track_id] = t
for t in tlistb:
tid = t.track_id
if stracks.get(tid, 0):
del stracks[tid]
return list(stracks.values())
def remove_duplicate_stracks(stracksa, stracksb):
pdist = matching.iou_distance(stracksa, stracksb)
pairs = np.where(pdist < 0.15)
dupa, dupb = list(), list()
for p, q in zip(*pairs):
timep = stracksa[p].frame_id - stracksa[p].start_frame
timeq = stracksb[q].frame_id - stracksb[q].start_frame
if timep > timeq:
dupb.append(q)
else:
dupa.append(p)
resa = [t for i, t in enumerate(stracksa) if not i in dupa]
resb = [t for i, t in enumerate(stracksb) if not i in dupb]
return resa, resb
| PaddleDetection/deploy/pptracking/python/mot/tracker/base_jde_tracker.py/0 | {
"file_path": "PaddleDetection/deploy/pptracking/python/mot/tracker/base_jde_tracker.py",
"repo_id": "PaddleDetection",
"token_count": 4490
} | 58 |
# Python端预测部署
在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 预测引擎使用了AnalysisPredictor，专门针对推理进行了优化，是基于[C++预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/native_infer.html)的Python接口，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。
Python端预测部署主要包含两个步骤：
- 导出预测模型
- 基于Python进行预测
## 1. 导出预测模型
PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](../EXPORT_MODEL.md)，例如
```bash
# 导出YOLOv3检测模型
python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
-o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
# 导出HigherHRNet(bottom-up)关键点检测模型
python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams
# 导出HRNet(top-down)关键点检测模型
python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_384x288.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams
# 导出FairMOT多目标跟踪模型
python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams
# 导出ByteTrack多目标跟踪模型(相当于只导出检测器)
python tools/export_model.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/ppyoloe_crn_l_36e_640x640_mot17half.pdparams
```
导出后目录下，包括`infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`四个文件。
## 2. 基于Python的预测
### 2.1 通用检测
在终端输入以下命令进行预测：
```bash
python deploy/python/infer.py --model_dir=./output_inference/yolov3_darknet53_270e_coco --image_file=./demo/000000014439.jpg --device=GPU
```
### 2.2 关键点检测
在终端输入以下命令进行预测：
```bash
# keypoint top-down(HRNet)/bottom-up(HigherHRNet)单独推理，该模式下top-down模型HRNet只支持单人截图预测
python deploy/python/keypoint_infer.py --model_dir=output_inference/hrnet_w32_384x288/ --image_file=./demo/hrnet_demo.jpg --device=GPU --threshold=0.5
python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=GPU --threshold=0.5
# detector 检测 + keypoint top-down模型联合部署（联合推理只支持top-down关键点模型）
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/yolov3_darknet53_270e_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file={your video name}.mp4 --device=GPU
```
**注意:**
- 关键点检测模型导出和预测具体可参照[keypoint](../../configs/keypoint/README.md)，可分别在各个模型的文档中查找具体用法；
- 此目录下的关键点检测部署为基础前向功能，更多关键点检测功能可使用PP-Human项目，参照[pipeline](../pipeline/README.md)；
### 2.3 多目标跟踪
在终端输入以下命令进行预测：
```bash
# FairMOT跟踪
python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU
# ByteTrack跟踪
python deploy/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=deploy/python/tracker_config.yml --video_file={your video name}.mp4 --device=GPU --scaled=True
# FairMOT多目标跟踪联合HRNet关键点检测（联合推理只支持top-down关键点模型）
python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file={your video name}.mp4 --device=GPU
```
**注意:**
- 多目标跟踪模型导出和预测具体可参照[mot]](../../configs/mot/README.md)，可分别在各个模型的文档中查找具体用法；
- 此目录下的跟踪部署为基础前向功能以及联合关键点部署，更多跟踪功能可使用PP-Human项目，参照[pipeline](../pipeline/README.md)，或PP-Tracking项目(绘制轨迹、出入口流量计数)，参照[pptracking](../pptracking/README.md)；
参数说明如下:
| 参数 | 是否必须| 含义 |
|-------|-------|---------------------------------------------------------------------------------------------|
| --model_dir | Yes| 上述导出的模型路径 |
| --image_file | Option | 需要预测的图片 |
| --image_dir | Option | 要预测的图片文件夹路径 |
| --video_file | Option | 需要预测的视频 |
| --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4 |
| --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU` |
| --run_mode | Option | 使用GPU时，默认为paddle, 可选（paddle/trt_fp32/trt_fp16/trt_int8） |
| --batch_size | Option | 预测时的batch size，在指定`image_dir`时有效，默认为1 |
| --threshold | Option| 预测得分的阈值，默认为0.5 |
| --output_dir | Option| 可视化结果保存的根目录，默认为output/ |
| --run_benchmark | Option| 是否运行benchmark，同时需指定`--image_file`或`--image_dir`，默认为False |
| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False |
| --cpu_threads | Option| 设置cpu线程数，默认为1 |
| --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
| --save_images | Option| 是否保存可视化结果 |
| --save_results | Option| 是否在文件夹下将图片的预测结果以JSON的形式保存 |
说明：
- 参数优先级顺序：`camera_id` > `video_file` > `image_dir` > `image_file`。
- run_mode：paddle代表使用AnalysisPredictor，精度float32来推理，其他参数指用AnalysisPredictor，TensorRT不同精度来推理。
- 如果安装的PaddlePaddle不支持基于TensorRT进行预测，需要自行编译，详细可参考[预测库编译教程](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html)。
- --run_benchmark如果设置为True，则需要安装依赖`pip install pynvml psutil GPUtil`。
- 如果需要使用导出模型在coco数据集上进行评估，请在推理时添加`--save_results`和`--use_coco_category`参数用以保存coco评估所需要的json文件
| PaddleDetection/deploy/python/README.md/0 | {
"file_path": "PaddleDetection/deploy/python/README.md",
"repo_id": "PaddleDetection",
"token_count": 5061
} | 59 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import logging
import paddle
import paddle.inference as paddle_infer
from pathlib import Path
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
LOG_PATH_ROOT = f"{CUR_DIR}/../../output"
class PaddleInferBenchmark(object):
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
"""
# PaddleInferBenchmark Log Version
self.log_version = "1.0.3"
# Paddle Version
self.paddle_version = paddle.__version__
self.paddle_commit = paddle.__git_commit__
paddle_infer_info = paddle_infer.get_version()
self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
# model info
self.model_info = model_info
# data info
self.data_info = data_info
# perf info
self.perf_info = perf_info
try:
# required value
self.model_name = model_info['model_name']
self.precision = model_info['precision']
self.batch_size = data_info['batch_size']
self.shape = data_info['shape']
self.data_num = data_info['data_num']
self.inference_time_s = round(perf_info['inference_time_s'], 4)
except:
self.print_help()
raise ValueError(
"Set argument wrong, please check input argument and its type")
self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
self.with_tracker = True if 'tracking_time_s' in perf_info else False
self.tracking_time_s = perf_info.get('tracking_time_s', 0)
self.total_time_s = perf_info.get('total_time_s', 0)
self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
self.succ_rate = perf_info.get("succ_rate", "")
self.qps = perf_info.get("qps", "")
# conf info
self.config_status = self.parse_config(config)
# mem info
if isinstance(resource_info, dict):
self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
else:
self.cpu_rss_mb = 0
self.cpu_vms_mb = 0
self.cpu_shared_mb = 0
self.cpu_dirty_mb = 0
self.cpu_util = 0
self.gpu_rss_mb = 0
self.gpu_util = 0
self.gpu_mem_util = 0
# init benchmark logger
self.benchmark_logger()
def benchmark_logger(self):
"""
benchmark logger
"""
# remove other logging handler
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
# Init logger
FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
log_output = f"{LOG_PATH_ROOT}/{self.model_name}.log"
Path(f"{LOG_PATH_ROOT}").mkdir(parents=True, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format=FORMAT,
handlers=[
logging.FileHandler(
filename=log_output, mode='w'),
logging.StreamHandler(),
])
self.logger = logging.getLogger(__name__)
self.logger.info(
f"Paddle Inference benchmark log will be saved to {log_output}")
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
config_status = {}
config_status['runtime_device'] = "gpu" if config.use_gpu(
) else "cpu"
config_status['ir_optim'] = config.ir_optim()
config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
config_status['precision'] = self.precision
config_status['enable_mkldnn'] = config.mkldnn_enabled()
config_status[
'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
)
elif isinstance(config, dict):
config_status['runtime_device'] = config.get('runtime_device', "")
config_status['ir_optim'] = config.get('ir_optim', "")
config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
config_status['precision'] = config.get('precision', "")
config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
config_status['cpu_math_library_num_threads'] = config.get(
'cpu_math_library_num_threads', "")
else:
self.print_help()
raise ValueError(
"Set argument config wrong, please check input argument and its type"
)
return config_status
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
"---------------------- Paddle info ----------------------")
self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
self.logger.info(f"{identifier} log_api_version: {self.log_version}")
self.logger.info(
"----------------------- Conf info -----------------------")
self.logger.info(
f"{identifier} runtime_device: {self.config_status['runtime_device']}"
)
self.logger.info(
f"{identifier} ir_optim: {self.config_status['ir_optim']}")
self.logger.info(f"{identifier} enable_memory_optim: {True}")
self.logger.info(
f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
)
self.logger.info(
f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
self.logger.info(
f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
)
self.logger.info(
"----------------------- Model info ----------------------")
self.logger.info(f"{identifier} model_name: {self.model_name}")
self.logger.info(f"{identifier} precision: {self.precision}")
self.logger.info(
"----------------------- Data info -----------------------")
self.logger.info(f"{identifier} batch_size: {self.batch_size}")
self.logger.info(f"{identifier} input_shape: {self.shape}")
self.logger.info(f"{identifier} data_num: {self.data_num}")
self.logger.info(
"----------------------- Perf info -----------------------")
self.logger.info(
f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
)
self.logger.info(
f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
)
self.logger.info(
f"{identifier} total time spent(s): {self.total_time_s}")
if self.with_tracker:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}, "
f"tracking_time(ms): {round(self.tracking_time_s*1000, 1)}")
else:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
)
if self.inference_time_s_90:
self.looger.info(
f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
)
if self.qps:
self.logger.info(f"{identifier} QPS: {self.qps}")
def print_help(self):
"""
print function help
"""
print("""Usage:
==== Print inference benchmark logs. ====
config = paddle.inference.Config()
model_info = {'model_name': 'resnet50'
'precision': 'fp32'}
data_info = {'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info = {'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info = {'cpu_rss_mb': 100
'gpu_rss_mb': 100
'gpu_util': 60}
log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
log('Test')
""")
def __call__(self, identifier=None):
"""
__call__
args:
identifier(string): identify log
"""
self.report(identifier)
| PaddleDetection/deploy/python/benchmark_utils.py/0 | {
"file_path": "PaddleDetection/deploy/python/benchmark_utils.py",
"repo_id": "PaddleDetection",
"token_count": 5743
} | 60 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import os
import ast
import argparse
import numpy as np
def argsparser():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--model_dir",
type=str,
default=None,
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
"'infer_cfg.yml', created by tools/export_model.py."),
required=True)
parser.add_argument(
"--image_file", type=str, default=None, help="Path of image file.")
parser.add_argument(
"--image_dir",
type=str,
default=None,
help="Dir of image file, `image_file` has a higher priority.")
parser.add_argument(
"--batch_size", type=int, default=1, help="batch_size for inference.")
parser.add_argument(
"--video_file",
type=str,
default=None,
help="Path of video file, `video_file` or `camera_id` has a highest priority."
)
parser.add_argument(
"--camera_id",
type=int,
default=-1,
help="device id of camera to predict.")
parser.add_argument(
"--threshold", type=float, default=0.5, help="Threshold of score.")
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory of output visualization files.")
parser.add_argument(
"--run_mode",
type=str,
default='paddle',
help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
parser.add_argument(
"--device",
type=str,
default='cpu',
help="Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU."
)
parser.add_argument(
"--use_gpu",
type=ast.literal_eval,
default=False,
help="Deprecated, please use `--device`.")
parser.add_argument(
"--run_benchmark",
type=ast.literal_eval,
default=False,
help="Whether to predict a image_file repeatedly for benchmark")
parser.add_argument(
"--enable_mkldnn",
type=ast.literal_eval,
default=False,
help="Whether use mkldnn with CPU.")
parser.add_argument(
"--enable_mkldnn_bfloat16",
type=ast.literal_eval,
default=False,
help="Whether use mkldnn bfloat16 inference with CPU.")
parser.add_argument(
"--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
parser.add_argument(
"--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
parser.add_argument(
"--trt_max_shape",
type=int,
default=1280,
help="max_shape for TensorRT.")
parser.add_argument(
"--trt_opt_shape",
type=int,
default=640,
help="opt_shape for TensorRT.")
parser.add_argument(
"--trt_calib_mode",
type=bool,
default=False,
help="If the model is produced by TRT offline quantitative "
"calibration, trt_calib_mode need to set True.")
parser.add_argument(
'--save_images',
type=ast.literal_eval,
default=True,
help='Save visualization image results.')
parser.add_argument(
'--save_mot_txts',
action='store_true',
help='Save tracking results (txt).')
parser.add_argument(
'--save_mot_txt_per_img',
action='store_true',
help='Save tracking results (txt) for each image.')
parser.add_argument(
'--scaled',
type=bool,
default=False,
help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
"True in general detector.")
parser.add_argument(
"--tracker_config", type=str, default=None, help=("tracker donfig"))
parser.add_argument(
"--reid_model_dir",
type=str,
default=None,
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
"'infer_cfg.yml', created by tools/export_model.py."))
parser.add_argument(
"--reid_batch_size",
type=int,
default=50,
help="max batch_size for reid model inference.")
parser.add_argument(
'--use_dark',
type=ast.literal_eval,
default=True,
help='whether to use darkpose to get better keypoint position predict ')
parser.add_argument(
"--action_file",
type=str,
default=None,
help="Path of input file for action recognition.")
parser.add_argument(
"--window_size",
type=int,
default=50,
help="Temporal size of skeleton feature for action recognition.")
parser.add_argument(
"--random_pad",
type=ast.literal_eval,
default=False,
help="Whether do random padding for action recognition.")
parser.add_argument(
"--save_results",
action='store_true',
default=False,
help="Whether save detection result to file using coco format")
parser.add_argument(
'--use_coco_category',
action='store_true',
default=False,
help='Whether to use the coco format dictionary `clsid2catid`')
parser.add_argument(
"--slice_infer",
action='store_true',
help="Whether to slice the image and merge the inference results for small object detection."
)
parser.add_argument(
'--slice_size',
nargs='+',
type=int,
default=[640, 640],
help="Height of the sliced image.")
parser.add_argument(
"--overlap_ratio",
nargs='+',
type=float,
default=[0.25, 0.25],
help="Overlap height ratio of the sliced image.")
parser.add_argument(
"--combine_method",
type=str,
default='nms',
help="Combine method of the sliced images' detection results, choose in ['nms', 'nmm', 'concat']."
)
parser.add_argument(
"--match_threshold",
type=float,
default=0.6,
help="Combine method matching threshold.")
parser.add_argument(
"--match_metric",
type=str,
default='ios',
help="Combine method matching metric, choose in ['iou', 'ios'].")
parser.add_argument(
"--collect_trt_shape_info",
action='store_true',
default=False,
help="Whether to collect dynamic shape before using tensorrt.")
parser.add_argument(
"--tuned_trt_shape_file",
type=str,
default="shape_range_info.pbtxt",
help="Path of a dynamic shape file for tensorrt.")
parser.add_argument("--use_fd_format", action="store_true")
return parser
class Times(object):
def __init__(self):
self.time = 0.
# start time
self.st = 0.
# end time
self.et = 0.
def start(self):
self.st = time.time()
def end(self, repeats=1, accumulative=True):
self.et = time.time()
if accumulative:
self.time += (self.et - self.st) / repeats
else:
self.time = (self.et - self.st) / repeats
def reset(self):
self.time = 0.
self.st = 0.
self.et = 0.
def value(self):
return round(self.time, 4)
class Timer(Times):
def __init__(self, with_tracker=False):
super(Timer, self).__init__()
self.with_tracker = with_tracker
self.preprocess_time_s = Times()
self.inference_time_s = Times()
self.postprocess_time_s = Times()
self.tracking_time_s = Times()
self.img_num = 0
def info(self, average=False):
pre_time = self.preprocess_time_s.value()
infer_time = self.inference_time_s.value()
post_time = self.postprocess_time_s.value()
track_time = self.tracking_time_s.value()
total_time = pre_time + infer_time + post_time
if self.with_tracker:
total_time = total_time + track_time
total_time = round(total_time, 4)
print("------------------ Inference Time Info ----------------------")
print("total_time(ms): {}, img_num: {}".format(total_time * 1000,
self.img_num))
preprocess_time = round(pre_time / max(1, self.img_num),
4) if average else pre_time
postprocess_time = round(post_time / max(1, self.img_num),
4) if average else post_time
inference_time = round(infer_time / max(1, self.img_num),
4) if average else infer_time
tracking_time = round(track_time / max(1, self.img_num),
4) if average else track_time
average_latency = total_time / max(1, self.img_num)
qps = 0
if total_time > 0:
qps = 1 / average_latency
print("average latency time(ms): {:.2f}, QPS: {:2f}".format(
average_latency * 1000, qps))
if self.with_tracker:
print(
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}".
format(preprocess_time * 1000, inference_time * 1000,
postprocess_time * 1000, tracking_time * 1000))
else:
print(
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".
format(preprocess_time * 1000, inference_time * 1000,
postprocess_time * 1000))
def report(self, average=False):
dic = {}
pre_time = self.preprocess_time_s.value()
infer_time = self.inference_time_s.value()
post_time = self.postprocess_time_s.value()
track_time = self.tracking_time_s.value()
dic['preprocess_time_s'] = round(pre_time / max(1, self.img_num),
4) if average else pre_time
dic['inference_time_s'] = round(infer_time / max(1, self.img_num),
4) if average else infer_time
dic['postprocess_time_s'] = round(post_time / max(1, self.img_num),
4) if average else post_time
dic['img_num'] = self.img_num
total_time = pre_time + infer_time + post_time
if self.with_tracker:
dic['tracking_time_s'] = round(track_time / max(1, self.img_num),
4) if average else track_time
total_time = total_time + track_time
dic['total_time_s'] = round(total_time, 4)
return dic
def get_current_memory_mb():
"""
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
"""
import pynvml
import psutil
import GPUtil
gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
pid = os.getpid()
p = psutil.Process(pid)
info = p.memory_full_info()
cpu_mem = info.uss / 1024. / 1024.
gpu_mem = 0
gpu_percent = 0
gpus = GPUtil.getGPUs()
if gpu_id is not None and len(gpus) > 0:
gpu_percent = gpus[gpu_id].load
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
gpu_mem = meminfo.used / 1024. / 1024.
return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)
def multiclass_nms(bboxs, num_classes, match_threshold=0.6, match_metric='iou'):
final_boxes = []
for c in range(num_classes):
idxs = bboxs[:, 0] == c
if np.count_nonzero(idxs) == 0: continue
r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1))
return final_boxes
def nms(dets, match_threshold=0.6, match_metric='iou'):
""" Apply NMS to avoid detecting too many overlapping bounding boxes.
Args:
dets: shape [N, 5], [score, x1, y1, x2, y2]
match_metric: 'iou' or 'ios'
match_threshold: overlap thresh for match metric.
"""
if dets.shape[0] == 0:
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int32)
for _i in range(ndets):
i = order[_i]
if suppressed[i] == 1:
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range(_i + 1, ndets):
j = order[_j]
if suppressed[j] == 1:
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, xx2 - xx1 + 1)
h = max(0.0, yy2 - yy1 + 1)
inter = w * h
if match_metric == 'iou':
union = iarea + areas[j] - inter
match_value = inter / union
elif match_metric == 'ios':
smaller = min(iarea, areas[j])
match_value = inter / smaller
else:
raise ValueError()
if match_value >= match_threshold:
suppressed[j] = 1
keep = np.where(suppressed == 0)[0]
dets = dets[keep, :]
return dets
coco_clsid2catid = {
0: 1,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 7,
7: 8,
8: 9,
9: 10,
10: 11,
11: 13,
12: 14,
13: 15,
14: 16,
15: 17,
16: 18,
17: 19,
18: 20,
19: 21,
20: 22,
21: 23,
22: 24,
23: 25,
24: 27,
25: 28,
26: 31,
27: 32,
28: 33,
29: 34,
30: 35,
31: 36,
32: 37,
33: 38,
34: 39,
35: 40,
36: 41,
37: 42,
38: 43,
39: 44,
40: 46,
41: 47,
42: 48,
43: 49,
44: 50,
45: 51,
46: 52,
47: 53,
48: 54,
49: 55,
50: 56,
51: 57,
52: 58,
53: 59,
54: 60,
55: 61,
56: 62,
57: 63,
58: 64,
59: 65,
60: 67,
61: 70,
62: 72,
63: 73,
64: 74,
65: 75,
66: 76,
67: 77,
68: 78,
69: 79,
70: 80,
71: 81,
72: 82,
73: 84,
74: 85,
75: 86,
76: 87,
77: 88,
78: 89,
79: 90
}
def gaussian_radius(bbox_size, min_overlap):
height, width = bbox_size
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = np.sqrt(b1**2 - 4 * a1 * c1)
radius1 = (b1 + sq1) / (2 * a1)
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = np.sqrt(b2**2 - 4 * a2 * c2)
radius2 = (b2 + sq2) / 2
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = np.sqrt(b3**2 - 4 * a3 * c3)
radius3 = (b3 + sq3) / 2
return min(radius1, radius2, radius3)
def gaussian2D(shape, sigma_x=1, sigma_y=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x / (2 * sigma_x * sigma_x) + y * y / (2 * sigma_y *
sigma_y)))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_umich_gaussian(heatmap, center, radius, k=1):
"""
draw_umich_gaussian, refer to https://github.com/xingyizhou/CenterNet/blob/master/src/lib/utils/image.py#L126
"""
diameter = 2 * radius + 1
gaussian = gaussian2D(
(diameter, diameter), sigma_x=diameter / 6, sigma_y=diameter / 6)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:
radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
return heatmap
| PaddleDetection/deploy/python/utils.py/0 | {
"file_path": "PaddleDetection/deploy/python/utils.py",
"repo_id": "PaddleDetection",
"token_count": 8217
} | 61 |
#!/bin/bash
# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved.
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
set -e
set -u
set -o pipefail
# Show usage
function show_usage() {
cat <<EOF
Usage: Set up running environment by installing the required prerequisites.
-h, --help
Display this help message.
EOF
}
if [ "$#" -eq 1 ] && [ "$1" == "--help" -o "$1" == "-h" ]; then
show_usage
exit 0
elif [ "$#" -ge 1 ]; then
show_usage
exit 1
fi
echo -e "\e[36mStart setting up running environment\e[0m"
# Install CMSIS
echo -e "\e[36mStart installing CMSIS\e[0m"
CMSIS_PATH="/opt/arm/ethosu/cmsis"
mkdir -p "${CMSIS_PATH}"
CMSIS_SHA="977abe9849781a2e788b02282986480ff4e25ea6"
CMSIS_SHASUM="86c88d9341439fbb78664f11f3f25bc9fda3cd7de89359324019a4d87d169939eea85b7fdbfa6ad03aa428c6b515ef2f8cd52299ce1959a5444d4ac305f934cc"
CMSIS_URL="http://github.com/ARM-software/CMSIS_5/archive/${CMSIS_SHA}.tar.gz"
DOWNLOAD_PATH="/tmp/${CMSIS_SHA}.tar.gz"
wget ${CMSIS_URL} -O "${DOWNLOAD_PATH}"
echo "$CMSIS_SHASUM" ${DOWNLOAD_PATH} | sha512sum -c
tar -xf "${DOWNLOAD_PATH}" -C "${CMSIS_PATH}" --strip-components=1
touch "${CMSIS_PATH}"/"${CMSIS_SHA}".sha
echo -e "\e[36mCMSIS Installation SUCCESS\e[0m"
# Install Arm(R) Ethos(TM)-U NPU driver stack
echo -e "\e[36mStart installing Arm(R) Ethos(TM)-U NPU driver stack\e[0m"
git clone "https://review.mlplatform.org/ml/ethos-u/ethos-u-core-platform" /opt/arm/ethosu/core_platform
cd /opt/arm/ethosu/core_platform
git checkout tags/"21.11"
echo -e "\e[36mArm(R) Ethos(TM)-U Core Platform Installation SUCCESS\e[0m"
# Install Arm(R) GNU Toolchain
echo -e "\e[36mStart installing Arm(R) GNU Toolchain\e[0m"
mkdir -p /opt/arm/gcc-arm-none-eabi
export gcc_arm_url='https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2?revision=ca0cbf9c-9de2-491c-ac48-898b5bbc0443&la=en&hash=68760A8AE66026BCF99F05AC017A6A50C6FD832A'
curl --retry 64 -sSL ${gcc_arm_url} | tar -C /opt/arm/gcc-arm-none-eabi --strip-components=1 -jx
export PATH=/opt/arm/gcc-arm-none-eabi/bin:$PATH
arm-none-eabi-gcc --version
arm-none-eabi-g++ --version
echo -e "\e[36mArm(R) Arm(R) GNU Toolchain Installation SUCCESS\e[0m"
# Install TVM from TLCPack
echo -e "\e[36mStart installing TVM\e[0m"
pip install tlcpack-nightly -f https://tlcpack.ai/wheels
echo -e "\e[36mTVM Installation SUCCESS\e[0m" | PaddleDetection/deploy/third_engine/demo_avh/configure_avh.sh/0 | {
"file_path": "PaddleDetection/deploy/third_engine/demo_avh/configure_avh.sh",
"repo_id": "PaddleDetection",
"token_count": 1252
} | 62 |
# TinyPose MNN Demo
This fold provides PicoDet+TinyPose inference code using
[Alibaba's MNN framework](https://github.com/alibaba/MNN). Most of the implements in
this fold are same as *demo_ncnn*.
## Install MNN
### Python library
Just run:
``` shell
pip install MNN
```
### C++ library
Please follow the [official document](https://www.yuque.com/mnn/en/build_linux) to build MNN engine.
- Create picodet_m_416_coco.onnx and tinypose256.onnx
example:
```shell
modelName=picodet_m_416_coco
# export model
python tools/export_model.py \
-c configs/picodet/${modelName}.yml \
-o weights=${modelName}.pdparams \
--output_dir=inference_model
# convert to onnx
paddle2onnx --model_dir inference_model/${modelName} \
--model_filename model.pdmodel \
--params_filename model.pdiparams \
--opset_version 11 \
--save_file ${modelName}.onnx
# onnxsim
python -m onnxsim ${modelName}.onnx ${modelName}_processed.onnx
```
- Convert model
example:
``` shell
python -m MNN.tools.mnnconvert -f ONNX --modelFile picodet-416.onnx --MNNModel picodet-416.mnn
```
Here are converted model
[picodet_m_416](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416.mnn).
[tinypose256](https://paddledet.bj.bcebos.com/deploy/third_engine/tinypose256.mnn)
## Build
For C++ code, replace `libMNN.so` under *./mnn/lib* with the one you just compiled, modify OpenCV path and MNN path at CMake file,
and run
``` shell
mkdir build && cd build
cmake ..
make
```
Note that a flag at `main.cpp` is used to control whether to show the detection result or save it into a fold.
``` c++
#define __SAVE_RESULT__ // if defined save drawed results to ../results, else show it in windows
```
#### ARM Build
Prepare OpenCV library [OpenCV_4_1](https://paddle-inference-dist.bj.bcebos.com/opencv4.1.0.tar.gz).
``` shell
mkdir third && cd third
wget https://paddle-inference-dist.bj.bcebos.com/opencv4.1.0.tar.gz
tar -zxvf opencv4.1.0.tar.gz
cd ..
mkdir build && cd build
cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI="arm64-v8a" -DANDROID_PLATFORM=android-21 -DANDROID_TOOLCHAIN=gcc ..
make
```
## Run
To detect images in a fold, run:
``` shell
./tinypose-mnn [mode] [image_file]
```
| param | detail |
| ---- | ---- |
| --mode | input mode，0:camera；1:image；2:video；3:benchmark |
| --image_file | input image path |
for example:
``` shell
./tinypose-mnn "1" "../imgs/test.jpg"
```
For speed benchmark:
``` shell
./tinypose-mnn "3" "0"
```
## Benchmark
Plateform: Kirin980
Model: [tinypose256](https://paddledet.bj.bcebos.com/deploy/third_engine/tinypose256.mnn)
| param | Min(s) | Max(s) | Avg(s) |
| -------- | ------ | ------ | ------ |
| Thread=4 | 0.018 | 0.021 | 0.019 |
| Thread=1 | 0.031 | 0.041 | 0.032 |
## Reference
[MNN](https://github.com/alibaba/MNN)
| PaddleDetection/deploy/third_engine/demo_mnn_kpts/README.md/0 | {
"file_path": "PaddleDetection/deploy/third_engine/demo_mnn_kpts/README.md",
"repo_id": "PaddleDetection",
"token_count": 1242
} | 63 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
from collections import OrderedDict
import os
import yaml
import json
import glob
import argparse
from preprocess import Compose
from preprocess import coco_clsid2catid
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml")
parser.add_argument(
"--trt_engine", required=True, type=str, help="trt engine path")
parser.add_argument("--image_dir", type=str)
parser.add_argument("--image_file", type=str)
parser.add_argument(
"--repeats",
type=int,
default=1,
help="Repeat the running test `repeats` times in benchmark")
parser.add_argument(
"--save_coco",
action='store_true',
default=False,
help="Whether to save coco results")
parser.add_argument(
"--coco_file", type=str, default="results.json", help="coco results path")
TRT_LOGGER = trt.Logger()
trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="")
# Global dictionary
SUPPORT_MODELS = {
'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet'
}
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
assert infer_dir is None or os.path.isdir(infer_dir), \
"{} is not a directory".format(infer_dir)
# infer_img has a higher priority
if infer_img and os.path.isfile(infer_img):
return [infer_img]
images = set()
infer_dir = os.path.abspath(infer_dir)
assert os.path.isdir(infer_dir), \
"infer_dir {} is not a directory".format(infer_dir)
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for ext in exts:
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
images = list(images)
assert len(images) > 0, "no image found in {}".format(infer_dir)
print("Found {} inference images in total.".format(len(images)))
return images
class PredictConfig(object):
"""set config of preprocess, postprocess and visualize
Args:
infer_config (str): path of infer_cfg.yml
"""
def __init__(self, infer_config):
# parsing Yaml config for Preprocess
with open(infer_config) as f:
yml_conf = yaml.safe_load(f)
self.check_model(yml_conf)
self.arch = yml_conf['arch']
self.preprocess_infos = yml_conf['Preprocess']
self.min_subgraph_size = yml_conf['min_subgraph_size']
self.label_list = yml_conf['label_list']
self.use_dynamic_shape = yml_conf['use_dynamic_shape']
self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
self.mask = yml_conf.get("mask", False)
self.tracker = yml_conf.get("tracker", None)
self.nms = yml_conf.get("NMS", None)
self.fpn_stride = yml_conf.get("fpn_stride", None)
if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
print(
'The RCNN export model is used for ONNX and it only supports batch_size = 1'
)
self.print_config()
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
'arch'], SUPPORT_MODELS))
def print_config(self):
print('----------- Model Configuration -----------')
print('%s: %s' % ('Model Arch', self.arch))
print('%s: ' % ('Transform Order'))
for op_info in self.preprocess_infos:
print('--%s: %s' % ('transform op', op_info['type']))
print('--------------------------------------------')
def load_trt_engine(engine_path):
assert os.path.exists(engine_path)
print("Reading engine from file {}".format(engine_path))
with open(engine_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
def predict_image(infer_config, engine, img_list, save_coco=False, repeats=1):
# load preprocess transforms
transforms = Compose(infer_config.preprocess_infos)
stream = cuda.Stream()
coco_results = []
num_data = len(img_list)
avg_time = []
with engine.create_execution_context() as context:
# Allocate host and device buffers
bindings = create_trt_bindings(engine, context)
# warmup
run_trt_context(context, bindings, stream, repeats=10)
# predict image
for i, img_path in enumerate(img_list):
inputs = transforms(img_path)
inputs_name = [k for k, v in bindings.items() if v['is_input']]
inputs = {
k: inputs[k][None, ]
for k in inputs.keys() if k in inputs_name
}
# run infer
for k, v in inputs.items():
bindings[k]['cpu_data'][...] = v
output = run_trt_context(context, bindings, stream, repeats=repeats)
print(f"{i + 1}/{num_data} infer time: {output['infer_time']} ms.")
avg_time.append(output['infer_time'])
# get output
for k, v in output.items():
if k in bindings.keys():
output[k] = np.reshape(v, bindings[k]['shape'])
if save_coco:
coco_results.extend(
format_coco_results(os.path.split(img_path)[-1], output))
avg_time = np.mean(avg_time)
print(
f"Run on {num_data} data, repeats {repeats} times, avg time: {avg_time} ms."
)
if save_coco:
with open(FLAGS.coco_file, 'w') as f:
json.dump(coco_results, f)
print(f"save coco json to {FLAGS.coco_file}")
def create_trt_bindings(engine, context):
bindings = OrderedDict()
for name in engine:
binding_idx = engine.get_binding_index(name)
size = trt.volume(context.get_binding_shape(binding_idx))
dtype = trt.nptype(engine.get_binding_dtype(name))
shape = list(engine.get_binding_shape(binding_idx))
if shape[0] == -1:
shape[0] = 1
bindings[name] = {
"idx": binding_idx,
"size": size,
"dtype": dtype,
"shape": shape,
"cpu_data": None,
"cuda_ptr": None,
"is_input": True if engine.binding_is_input(name) else False
}
if engine.binding_is_input(name):
bindings[name]['cpu_data'] = np.random.randn(*shape).astype(
np.float32)
bindings[name]['cuda_ptr'] = cuda.mem_alloc(bindings[name][
'cpu_data'].nbytes)
else:
bindings[name]['cpu_data'] = cuda.pagelocked_empty(size, dtype)
bindings[name]['cuda_ptr'] = cuda.mem_alloc(bindings[name][
'cpu_data'].nbytes)
return bindings
def run_trt_context(context, bindings, stream, repeats=1):
# Transfer input data to the GPU.
for k, v in bindings.items():
if v['is_input']:
cuda.memcpy_htod_async(v['cuda_ptr'], v['cpu_data'], stream)
in_bindings = [int(v['cuda_ptr']) for k, v in bindings.items()]
output_data = {}
avg_time = []
for _ in range(repeats):
# Run inference
t1 = time.time()
context.execute_async_v2(
bindings=in_bindings, stream_handle=stream.handle)
# Transfer prediction output from the GPU.
for k, v in bindings.items():
if not v['is_input']:
cuda.memcpy_dtoh_async(v['cpu_data'], v['cuda_ptr'], stream)
output_data[k] = v['cpu_data']
# Synchronize the stream
stream.synchronize()
t2 = time.time()
avg_time.append(t2 - t1)
output_data['infer_time'] = np.mean(avg_time) * 1000
return output_data
def format_coco_results(file_name, result):
try:
image_id = int(os.path.splitext(file_name)[0])
except:
image_id = file_name
num_dets = result['num_dets'].tolist()
det_classes = result['det_classes'].tolist()
det_scores = result['det_scores'].tolist()
det_boxes = result['det_boxes'].tolist()
per_result = [
{
'image_id': image_id,
'category_id': coco_clsid2catid[int(det_classes[0][idx])],
'file_name': file_name,
'bbox': [
det_boxes[0][idx][0], det_boxes[0][idx][1],
det_boxes[0][idx][2] - det_boxes[0][idx][0],
det_boxes[0][idx][3] - det_boxes[0][idx][1]
], # xyxy -> xywh
'score': det_scores[0][idx]
} for idx in range(num_dets[0][0])
]
return per_result
if __name__ == '__main__':
FLAGS = parser.parse_args()
# load image list
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
# load trt engine
engine = load_trt_engine(FLAGS.trt_engine)
# load infer config
infer_config = PredictConfig(FLAGS.infer_cfg)
predict_image(infer_config, engine, img_list, FLAGS.save_coco,
FLAGS.repeats)
print('Done!')
| PaddleDetection/deploy/third_engine/demo_onnx_trt/trt_infer.py/0 | {
"file_path": "PaddleDetection/deploy/third_engine/demo_onnx_trt/trt_infer.py",
"repo_id": "PaddleDetection",
"token_count": 4591
} | 64 |
# TinyPose OpenVINO Demo
This fold provides TinyPose inference code using
[Intel's OpenVINO Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html). Most of the implements in this fold are same as *demo_ncnn*.
**Recommand**
1. To use the xxx.tar.gz file to install instead of github method, [link](https://registrationcenter-download.intel.com/akdlm/irc_nas/18096/l_openvino_toolkit_p_2021.4.689.tgz).
2. Your can also deploy openvino with docker, the command is :
```
docker pull openvino/ubuntu18_dev:2021.4.1
```
## Install OpenVINO Toolkit
Go to [OpenVINO HomePage](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
Download a suitable version and install.
Follow the official Get Started Guides: https://docs.openvinotoolkit.org/latest/get_started_guides.html
## Set the Environment Variables
### Windows:
Run this command in cmd. (Every time before using OpenVINO)
```cmd
<INSTSLL_DIR>\openvino_2021\bin\setupvars.bat
```
Or set the system environment variables once for all:
Name |Value
:--------------------:|:--------:
INTEL_OPENVINO_DIR | <INSTSLL_DIR>\openvino_2021
INTEL_CVSDK_DIR | %INTEL_OPENVINO_DIR%
InferenceEngine_DIR | %INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\share
HDDL_INSTALL_DIR | %INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\external\hddl
ngraph_DIR | %INTEL_OPENVINO_DIR%\deployment_tools\ngraph\cmake
And add this to ```Path```
```
%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\bin\intel64\Debug;%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\bin\intel64\Release;%HDDL_INSTALL_DIR%\bin;%INTEL_OPENVINO_DIR%\deployment_tools\inference_engine\external\tbb\bin;%INTEL_OPENVINO_DIR%\deployment_tools\ngraph\lib
```
### Linux
Run this command in shell. (Every time before using OpenVINO)
```shell
source /opt/intel/openvino_2021/bin/setupvars.sh
```
Or edit .bashrc
```shell
vi ~/.bashrc
```
Add this line to the end of the file
```shell
source /opt/intel/openvino_2021/bin/setupvars.sh
```
## Convert model
**1. Conver to onnx**
Create picodet_m_416_coco.onnx and tinypose256.onnx
example:
```shell
modelName=picodet_m_416_coco
# export model
python tools/export_model.py \
-c configs/picodet/${modelName}.yml \
-o weights=${modelName}.pdparams \
--output_dir=inference_model
# convert to onnx
paddle2onnx --model_dir inference_model/${modelName} \
--model_filename model.pdmodel \
--params_filename model.pdiparams \
--opset_version 11 \
--save_file ${modelName}.onnx
# onnxsim
python -m onnxsim ${modelName}.onnx ${modelName}_sim.onnx
```
**2.Convert to OpenVINO**
``` shell
cd <INSTSLL_DIR>/openvino_2021/deployment_tools/model_optimizer
```
Install requirements for convert tool
```shell
cd ./install_prerequisites
sudo install_prerequisites_onnx.sh
```
Then convert model. Notice: mean_values and scale_values should be the same with your training settings in YAML config file.
```shell
mo_onnx.py --input_model <ONNX_MODEL> --mean_values [103.53,116.28,123.675] --scale_values [57.375,57.12,58.395] --input_shape [1,3,256,192]
```
**Note: The new version of openvino convert tools may cause error in Resize op. If you has problem with this, please try the version: openvino_2021.4.689**
## Build
### Windows
```cmd
<OPENVINO_INSTSLL_DIR>\openvino_2021\bin\setupvars.bat
mkdir -p build
cd build
cmake ..
msbuild tinypose_demo.vcxproj /p:configuration=release /p:platform=x64
```
### Linux
```shell
source /opt/intel/openvino_2021/bin/setupvars.sh
mkdir build
cd build
cmake ..
make
```
## Run demo
Download PicoDet openvino model [PicoDet openvino model download link](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_openvino.zip).
Download TinyPose openvino model [TinyPose openvino model download link](https://bj.bcebos.com/v1/paddledet/deploy/third_engine/demo_openvino_kpts.tar.gz), the origin paddlepaddle model is [Tinypose256](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_enhance/tinypose_256x192.pdparams).
move picodet and tinypose openvino model files to the demo's weight folder.
Note:
1. The model output node name may update by new version of paddle\paddle2onnx\onnxsim\openvino, please checkout your own model output node when the code can't find "conv2d_441.tmp_1"\"argmax_0.tmp_0".
2. If you happened with this error "Cannot find blob with name: transpose_1.tmp_0", it means your picodet model is oldversion. you can modify the below code to fix it.
```
#picodet_openvino.h line 50-54
std::vector<HeadInfo> heads_info_{
// cls_pred|dis_pred|stride
{"transpose_0.tmp_0", "transpose_1.tmp_0", 8},
{"transpose_2.tmp_0", "transpose_3.tmp_0", 16},
{"transpose_4.tmp_0", "transpose_5.tmp_0", 32},
{"transpose_6.tmp_0", "transpose_7.tmp_0", 64},
};
modify to:
std::vector<HeadInfo> heads_info_{
// cls_pred|dis_pred|stride
{"save_infer_model/scale_0.tmp_1", "save_infer_model/scale_4.tmp_1", 8},
{"save_infer_model/scale_1.tmp_1", "save_infer_model/scale_5.tmp_1", 16},
{"save_infer_model/scale_2.tmp_1", "save_infer_model/scale_6.tmp_1", 32},
{"save_infer_model/scale_3.tmp_1", "save_infer_model/scale_7.tmp_1", 64},
};
```
3. you can view your onnx model with [Netron](https://netron.app/).
### Edit file
```
step1:
main.cpp
#define image_size 416
...
cv::Mat image(256, 192, CV_8UC3, cv::Scalar(1, 1, 1));
std::vector<float> center = {128, 96};
std::vector<float> scale = {256, 192};
...
auto detector = PicoDet("../weight/picodet_m_416.xml");
auto kpts_detector = new KeyPointDetector("../weight/tinypose256.xml", -1, 256, 192);
...
step2:
picodet_openvino.h
#define image_size 416
```
### Run
Run command:
``` shell
./tinypose_demo [mode] [image_file]
```
| param | detail |
| ---- | ---- |
| --mode | input mode，0:camera；1:image；2:video；3:benchmark |
| --image_file | input image path |
#### Webcam
```shell
tinypose_demo 0 0
```
#### Inference images
```shell
tinypose_demo 1 IMAGE_FOLDER/*.jpg
```
#### Inference video
```shell
tinypose_demo 2 VIDEO_PATH
```
### Benchmark
```shell
tinypose_demo 3 0
```
Plateform: Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz x 24(核)
Model: [Tinypose256_Openvino](https://paddledet.bj.bcebos.com/deploy/third_engine/tinypose_256_openvino.zip)
| param | Min | Max | Avg |
| ------------- | ----- | ----- | ----- |
| infer time(s) | 0.018 | 0.062 | 0.028 |
| PaddleDetection/deploy/third_engine/demo_openvino_kpts/README.md/0 | {
"file_path": "PaddleDetection/deploy/third_engine/demo_openvino_kpts/README.md",
"repo_id": "PaddleDetection",
"token_count": 2675
} | 65 |
# 新增模型算法
为了让用户更好的使用PaddleDetection，本文档中，我们将介绍PaddleDetection的主要模型技术细节及应用
## 目录
- [1.简介](#1.简介)
- [2.新增模型](#2.新增模型)
- [2.1新增网络结构](#2.1新增网络结构)
- [2.1.1新增Backbone](#2.1.1新增Backbone)
- [2.1.2新增Neck](#2.1.2新增Neck)
- [2.1.3新增Head](#2.1.3新增Head)
- [2.1.4新增Loss](#2.1.4新增Loss)
- [2.1.5新增后处理模块](#2.1.5新增后处理模块)
- [2.1.6新增Architecture](#2.1.6新增Architecture)
- [2.2新增配置文件](#2.2新增配置文件)
- [2.2.1网络结构配置文件](#2.2.1网络结构配置文件)
- [2.2.2优化器配置文件](#2.2.2优化器配置文件)
- [2.2.3Reader配置文件](#2.2.3Reader配置文件)
### 1.简介
PaddleDetecion中的每一种模型对应一个文件夹，以yolov3为例，yolov3系列的模型对应于`configs/yolov3`文件夹，其中yolov3_darknet的总配置文件`configs/yolov3/yolov3_darknet53_270e_coco.yml`的内容如下：
```
_BASE_: [
'../datasets/coco_detection.yml', # 数据集配置文件，所有模型共用
'../runtime.yml', # 运行时相关配置
'_base_/optimizer_270e.yml', # 优化器相关配置
'_base_/yolov3_darknet53.yml', # yolov3网络结构配置文件
'_base_/yolov3_reader.yml', # yolov3 Reader模块配置
]
# 定义在此处的相关配置可以覆盖上述文件中的同名配置
snapshot_epoch: 5
weights: output/yolov3_darknet53_270e_coco/model_final
```
可以看到，配置文件中的模块进行了清晰的划分，除了公共的数据集配置以及运行时配置，其他配置被划分为优化器，网络结构以及Reader模块。PaddleDetection中支持丰富的优化器，学习率调整策略，预处理算子等，因此大多数情况下不需要编写优化器以及Reader相关的代码，而只需要在配置文件中配置即可。因此，新增一个模型的主要在于搭建网络结构。
PaddleDetection网络结构的代码在`ppdet/modeling/`中，所有网络结构以组件的形式进行定义与组合，网络结构的主要构成如下所示：
```
ppdet/modeling/
├── architectures
│ ├── faster_rcnn.py # Faster Rcnn模型
│ ├── ssd.py # SSD模型
│ ├── yolo.py # YOLOv3模型
│ │ ...
├── heads # 检测头模块
│ ├── xxx_head.py # 定义各类检测头
│ ├── roi_extractor.py #检测感兴趣区域提取
├── backbones # 基干网络模块
│ ├── resnet.py # ResNet网络
│ ├── mobilenet.py # MobileNet网络
│ │ ...
├── losses # 损失函数模块
│ ├── xxx_loss.py # 定义注册各类loss函数
├── necks # 特征融合模块
│ ├── xxx_fpn.py # 定义各种FPN模块
├── proposal_generator # anchor & proposal生成与匹配模块
│ ├── anchor_generator.py # anchor生成模块
│ ├── proposal_generator.py # proposal生成模块
│ ├── target.py # anchor & proposal的匹配函数
│ ├── target_layer.py # anchor & proposal的匹配模块
├── tests # 单元测试模块
│ ├── test_xxx.py # 对网络中的算子以及模块结构进行单元测试
├── ops.py # 封装各类PaddlePaddle物体检测相关公共检测组件/算子
├── layers.py # 封装及注册各类PaddlePaddle物体检测相关公共检测组件/算子
├── bbox_utils.py # 封装检测框相关的函数
├── post_process.py # 封装及注册后处理相关模块
├── shape_spec.py # 定义模块输出shape的类
```
### 2.新增模型
接下来，以单阶段检测器YOLOv3为例，对建立模型过程进行详细描述，按照此思路您可以快速搭建新的模型。
#### 2.1新增网络结构
##### 2.1.1新增Backbone
PaddleDetection中现有所有Backbone网络代码都放置在`ppdet/modeling/backbones`目录下，所以我们在其中新建`darknet.py`如下：
```python
import paddle.nn as nn
from ppdet.core.workspace import register, serializable
@register
@serializable
class DarkNet(nn.Layer):
__shared__ = ['norm_type']
def __init__(self,
depth=53,
return_idx=[2, 3, 4],
norm_type='bn',
norm_decay=0.):
super(DarkNet, self).__init__()
# 省略内容
def forward(self, inputs):
# 省略处理逻辑
pass
@property
def out_shape(self):
# 省略内容
pass
```
然后在`backbones/__init__.py`中加入引用：
```python
from . import darknet
from .darknet import *
```
**几点说明：**
- 为了在yaml配置文件中灵活配置网络，所有Backbone需要利用`ppdet.core.workspace`里的`register`进行注册，形式请参考如上示例。此外，可以使用`serializable`以使backbone支持序列化；
- 所有的Backbone需继承`paddle.nn.Layer`类，并实现forward函数。此外，还需实现out_shape属性定义输出的feature map的channel信息，具体可参见源码；
- `__shared__`为了实现一些参数的配置全局共享，这些参数可以被backbone, neck，head，loss等所有注册模块共享。
##### 2.1.2新增Neck
特征融合模块放置在`ppdet/modeling/necks`目录下，我们在其中新建`yolo_fpn.py`如下：
``` python
import paddle.nn as nn
from ppdet.core.workspace import register, serializable
@register
@serializable
class YOLOv3FPN(nn.Layer):
__shared__ = ['norm_type']
def __init__(self,
in_channels=[256, 512, 1024],
norm_type='bn'):
super(YOLOv3FPN, self).__init__()
# 省略内容
def forward(self, blocks):
# 省略内容
pass
@classmethod
def from_config(cls, cfg, input_shape):
# 省略内容
pass
@property
def out_shape(self):
# 省略内容
pass
```
然后在`necks/__init__.py`中加入引用：
```python
from . import yolo_fpn
from .yolo_fpn import *
```
**几点说明：**
- neck模块需要使用`register`进行注册，可以使用`serializable`进行序列化；
- neck模块需要继承`paddle.nn.Layer`类，并实现forward函数。除此之外，还需要实现`out_shape`属性，用于定义输出的feature map的channel信息，还需要实现类函数`from_config`用于在配置文件中推理出输入channel，并用于`YOLOv3FPN`的初始化；
- neck模块可以使用`__shared__`实现一些参数的配置全局共享。
##### 2.1.3新增Head
Head模块全部存放在`ppdet/modeling/heads`目录下，我们在其中新建`yolo_head.py`如下
``` python
import paddle.nn as nn
from ppdet.core.workspace import register
@register
class YOLOv3Head(nn.Layer):
__shared__ = ['num_classes']
__inject__ = ['loss']
def __init__(self,
anchors=[[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45],[59, 119],
[116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
num_classes=80,
loss='YOLOv3Loss',
iou_aware=False,
iou_aware_factor=0.4):
super(YOLOv3Head, self).__init__()
# 省略内容
def forward(self, feats, targets=None):
# 省略内容
pass
```
然后在`heads/__init__.py`中加入引用：
```python
from . import yolo_head
from .yolo_head import *
```
**几点说明：**
- Head模块需要使用`register`进行注册；
- Head模块需要继承`paddle.nn.Layer`类，并实现forward函数。
- `__inject__`表示引入全局字典中已经封装好的模块。如loss等。
##### 2.1.4新增Loss
Loss模块全部存放在`ppdet/modeling/losses`目录下，我们在其中新建`yolo_loss.py`下
```python
import paddle.nn as nn
from ppdet.core.workspace import register
@register
class YOLOv3Loss(nn.Layer):
__inject__ = ['iou_loss', 'iou_aware_loss']
__shared__ = ['num_classes']
def __init__(self,
num_classes=80,
ignore_thresh=0.7,
label_smooth=False,
downsample=[32, 16, 8],
scale_x_y=1.,
iou_loss=None,
iou_aware_loss=None):
super(YOLOv3Loss, self).__init__()
# 省略内容
def forward(self, inputs, targets, anchors):
# 省略内容
pass
```
然后在`losses/__init__.py`中加入引用：
```python
from . import yolo_loss
from .yolo_loss import *
```
**几点说明：**
- loss模块需要使用`register`进行注册；
- loss模块需要继承`paddle.nn.Layer`类，并实现forward函数。
- 可以使用`__inject__`表示引入全局字典中已经封装好的模块，使用`__shared__`可以实现一些参数的配置全局共享。
##### 2.1.5新增后处理模块
后处理模块定义在`ppdet/modeling/post_process.py`中，其中定义了`BBoxPostProcess`类来进行后处理操作，如下所示：
``` python
from ppdet.core.workspace import register
@register
class BBoxPostProcess(object):
__shared__ = ['num_classes']
__inject__ = ['decode', 'nms']
def __init__(self, num_classes=80, decode=None, nms=None):
# 省略内容
pass
def __call__(self, head_out, rois, im_shape, scale_factor):
# 省略内容
pass
```
**几点说明：**
- 后处理模块需要使用`register`进行注册
- `__inject__`注入了全局字典中封装好的模块，如decode和nms等。decode和nms定义在`ppdet/modeling/layers.py`中。
##### 2.1.6新增Architecture
所有architecture网络代码都放置在`ppdet/modeling/architectures`目录下，`meta_arch.py`中定义了`BaseArch`类，代码如下：
``` python
import paddle.nn as nn
from ppdet.core.workspace import register
@register
class BaseArch(nn.Layer):
def __init__(self):
super(BaseArch, self).__init__()
def forward(self, inputs):
self.inputs = inputs
self.model_arch()
if self.training:
out = self.get_loss()
else:
out = self.get_pred()
return out
def model_arch(self, ):
pass
def get_loss(self, ):
raise NotImplementedError("Should implement get_loss method!")
def get_pred(self, ):
raise NotImplementedError("Should implement get_pred method!")
```
所有的architecture需要继承`BaseArch`类，如`yolo.py`中的`YOLOv3`定义如下：
``` python
@register
class YOLOv3(BaseArch):
__category__ = 'architecture'
__inject__ = ['post_process']
def __init__(self,
backbone='DarkNet',
neck='YOLOv3FPN',
yolo_head='YOLOv3Head',
post_process='BBoxPostProcess'):
super(YOLOv3, self).__init__()
self.backbone = backbone
self.neck = neck
self.yolo_head = yolo_head
self.post_process = post_process
@classmethod
def from_config(cls, cfg, *args, **kwargs):
# 省略内容
pass
def get_loss(self):
# 省略内容
pass
def get_pred(self):
# 省略内容
pass
```
**几点说明：**
- 所有的architecture需要使用`register`进行注册
- 在组建一个完整的网络时必须要设定`__category__ = 'architecture'`来表示一个完整的物体检测模型；
- backbone, neck, yolo_head以及post_process等检测组件传入到architecture中组成最终的网络。像这样将检测模块化，提升了检测模型的复用性，可以通过组合不同的检测组件得到多个模型。
- from_config类函数实现了模块间组合时channel的自动配置。
#### 2.2新增配置文件
##### 2.2.1网络结构配置文件
上面详细地介绍了如何新增一个architecture，接下来演示如何配置一个模型，yolov3关于网络结构的配置在`configs/yolov3/_base_/`文件夹中定义，如`yolov3_darknet53.yml`定义了yolov3_darknet的网络结构，其定义如下：
```
architecture: YOLOv3
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/DarkNet53_pretrained.pdparams
norm_type: sync_bn
YOLOv3:
backbone: DarkNet
neck: YOLOv3FPN
yolo_head: YOLOv3Head
post_process: BBoxPostProcess
DarkNet:
depth: 53
return_idx: [2, 3, 4]
# use default config
# YOLOv3FPN:
YOLOv3Head:
anchors: [[10, 13], [16, 30], [33, 23],
[30, 61], [62, 45], [59, 119],
[116, 90], [156, 198], [373, 326]]
anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
loss: YOLOv3Loss
YOLOv3Loss:
ignore_thresh: 0.7
downsample: [32, 16, 8]
label_smooth: false
BBoxPostProcess:
decode:
name: YOLOBox
conf_thresh: 0.005
downsample_ratio: 32
clip_bbox: true
nms:
name: MultiClassNMS
keep_top_k: 100
score_threshold: 0.01
nms_threshold: 0.45
nms_top_k: 1000
```
可以看到在配置文件中，首先需要指定网络的architecture，pretrain_weights指定训练模型的url或者路径，norm_type等可以作为全局参数共享。模型的定义自上而下依次在文件中定义，与上节中的模型组件一一对应。对于一些模型组件，如果采用默认
的参数，可以不用配置，如上文中的`yolo_fpn`。通过改变相关配置，我们可以轻易地组合出另一个模型，比如`configs/yolov3/_base_/yolov3_mobilenet_v1.yml`将backbone从Darknet切换成MobileNet。
##### 2.2.2优化器配置文件
优化器配置文件定义模型使用的优化器以及学习率的调度策略，目前PaddleDetection中已经集成了多种多样的优化器和学习率策略，具体可参见代码`ppdet/optimizer.py`。比如，yolov3的优化器配置文件定义在`configs/yolov3/_base_/optimizer_270e.yml`，其定义如下：
```
epoch: 270
LearningRate:
base_lr: 0.001
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
# epoch数目
- 216
- 243
- !LinearWarmup
start_factor: 0.
steps: 4000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
```
**几点说明：**
- 可以通过OptimizerBuilder.optimizer指定优化器的类型及参数，目前支持的优化器可以参考[PaddlePaddle官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html)
- 可以设置LearningRate.schedulers设置不同学习率调整策略的组合，PaddlePaddle目前支持多种学习率调整策略，具体也可参考[PaddlePaddle官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html)。需要注意的是，你需要对于PaddlePaddle中的学习率调整策略进行简单的封装，具体可参考源码`ppdet/optimizer.py`。
##### 2.2.3Reader配置文件
关于Reader的配置可以参考[Reader配置文档](./READER.md#5.配置及运行)。
> 看过此文档，您应该对PaddleDetection中模型搭建与配置有了一定经验，结合源码会理解的更加透彻。关于模型技术，如您有其他问题或建议，请给我们提issue，我们非常欢迎您的反馈。
| PaddleDetection/docs/advanced_tutorials/MODEL_TECHNICAL.md/0 | {
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[简体中文](./keypoint_detection.md) | English
# Customized Keypoint Detection
When applying keypoint detection algorithms in real practice, inevitably, we may need customization as we may dissatisfy with the current pre-trained model results, or the current keypoint detection cannot meet the actual demand, or we may want to add or replace the definition of keypoints and train a new keypoint detection model. This document will introduce how to customize the keypoint detection algorithm in PaddleDetection.
## Data Preparation
### Basic Process Description
PaddleDetection currently supports `COCO` and `MPII` annotation data formats. For detailed descriptions of these two data formats, please refer to the document [Keypoint Data Preparation](./../tutorials/data/PrepareKeypointDataSet.md). In this step, by using annotation tools such as Labeme, the corresponding coordinates are annotated according to the feature point serial numbers and then converted into the corresponding trainable annotation format. And we recommend `COCO` format.
### Merging datasets
To extend the training data, we can merge several different datasets together. But different datasets often have different definitions of key points. Therefore, the first step in merging datasets is to unify the point definitions of different datasets, and determine the benchmark points, i.e., the types of feature points finally learned by the model, and then adjust them according to the relationship between the point definitions of each dataset and the benchmark point definitions.
- Points in the benchmark point location: adjust the point number to make it consistent with the benchmark point location
- Points that are not in the benchmark points: discard
- Points in the dataset that are missing from the benchmark: annotate the marked points as "unannotated".
In [Key point data preparation](... /... /tutorials/data/PrepareKeypointDataSet.md), we provide a case illustration of how to merge the `COCO` dataset and the `AI Challenger` dataset and unify them as a benchmark point definition with `COCO` for your reference.
## Model Optimization
### Detection and tracking model optimization
In PaddleDetection, the keypoint detection supports Top-Down and Bottom-Up solutions. Top-Down first detects the main body and then detects the local key points. It has higher accuracy but will take a longer time as the number of detected objects increases.The Bottom-Up plan first detects the keypoints and then combines them with the corresponding parts. It is fast and its speed is independent of the number of detected objects. Its disadvantage is that the accuracy is relatively low. For details of the two solutions and the corresponding models, please refer to [Keypoint Detection Series Models](../../../configs/keypoint/README.md)
When using the Top-Down solution, the model's effects depend on the previous detection or tracking effect. If the pedestrian position cannot be accurately detected in the actual practice, the performance of the keypoint detection will be limited. If you encounter the above problem in actual application, please refer to [Customized Object Detection](./detection_en.md) and [Customized Multi-target tracking](./pphuman_mot_en.md) for optimization of the detection and tracking model.
### Iterate with scenario-compatible data
The currently released keypoint detection algorithm models are mainly iterated on open source datasets such as `COCO`/ `AI Challenger`, which may lack surveillance scenarios (angles, lighting and other factors), sports scenarios (more unconventional poses) that are more similar to the actual task. Training with data that more closely matches the actual task scenario can help improve the model's results.
### Iteration via pre-trained models
The data annotation of the keypoint model is complex, and using the model directly to train on the business dataset from scratch is often difficult to meet the demand. When used in practical projects, it is recommended to load the pre-trained weights, which usually improve the model accuracy significantly. Let's take `HRNet` as an example with the following method:
```
python tools/train.py \
-c configs/keypoint/hrnet/hrnet_w32_256x192.yml \
-o pretrain_weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams
```
After loading the pre-trained model, the initial learning rate and the rounds of iterations can be reduced appropriately. It is recommended that the initial learning rate be 1/2 to 1/5 of the default configuration, and you can enable`--eval` to observe the change of AP values during the iterations.
## Data augmentation with occlusion
There are a lot of data in occlusion in keypoint tasks, including self-covered objects and occlusion between different objects.
1. Detection model optimization (only for Top-Down solutions)
Refer to [Target Detection Task Secondary Development](. /detection.md) to improve the detection model in complex scenarios.
2. Keypoint data augmentation
Augmentation of covered data in keypoint model training to improve model performance in such scenarios, please refer to [PP-TinyPose](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/keypoint/tiny_pose/)
### Smooth video prediction
The keypoint model is trained and predicted on the basis of image, and video input is also predicted by splitting the video into frames. Although the content is mostly similar between frames, small differences may still lead to large changes in the output of the model. As a result of that, although the predicted coordinates are roughly correct, there may be jitters in the visual effect.
By adding a smoothing filter process, the performance of the video output can be effectively improved by combining the predicted results of each frame and the historical results. For this part, please see [Filter Smoothing](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/python/det_keypoint_unite_infer.py#L206).
## Add or modify keypoint definition
### Data Preparation
Complete the data preparation according to the previous instructions and place it under `{root of PaddleDetection}/dataset`.
<details>
<summary><b> Examples of annotation file</b></summary>
```
self_dataset/
├── train_coco_joint.json # training set annotation file
├── val_coco_joint.json # Validation set annotation file
├── images/ # Store the image files
├── 0.jpg
├── 1.jpg
├── 2.jpg
```
Notable changes as follows:
```
{
"images": [
{
"file_name": "images/0.jpg",
"id": 0, # image id, id cannotdo not repeat
"height": 1080,
"width": 1920
},
{
"file_name": "images/1.jpg",
"id": 1,
"height": 1080,
"width": 1920
},
{
"file_name": "images/2.jpg",
"id": 2,
"height": 1080,
"width": 1920
},
...
"categories": [
{
"supercategory": "person",
"id": 1,
"name": "person",
"keypoints": [ # the name of the point serial number
"point1",
"point2",
"point3",
"point4",
"point5",
],
"skeleton": [ # Skeleton composed of points, not necessary for training
[
1,
2
],
[
1,
3
],
[
2,
4
],
[
3,
5
]
]
...
"annotations": [
{
{
"category_id": 1, # The category to which the instance belongs
"num_keypoints": 3, # the number of marked points of the instance
"bbox": [ # location of detection box,format is x, y, w, h
799,
575,
55,
185
],
# N*3 list of x, y, v.
"keypoints": [
807.5899658203125,
597.5455322265625,
2,
0,
0,
0, # unlabeled points noted as 0, 0, 0
805.8563232421875,
592.3446655273438,
2,
816.258056640625,
594.0783081054688,
2,
0,
0,
0
]
"id": 1, # the id of the instance, id cannot repeat
"image_id": 8, # The id of the image where the instance is located, repeatable. This represents the presence of multiple objects on a single image
"iscrowd": 0, # covered or not, when the value is 0, it will participate in training
"area": 10175 # the area occupied by the instance, can be simply taken as w * h. Note that when the value is 0, it will be skipped, and if it is too small, it will be ignored in eval
...
```
### Settings of configuration file
In the configuration file, refer to [config yaml configuration](... /... /tutorials/KeyPointConfigGuide_cn.md) for more details . Take [HRNet model configuration](... /... /... /configs/keypoint/hrnet/hrnet_w32_256x192.yml) as an example, we need to focus on following contents:
<details>
<summary><b> Example of configuration</b></summary>
```
use_gpu: true
log_iter: 5
save_dir: output
snapshot_epoch: 10
weights: output/hrnet_w32_256x192/model_final
epoch: 210
num_joints: &num_joints 5 # The number of predicted points matches the number of defined points
pixel_std: &pixel_std 200
Metric. keyPointTopDownCOCOEval
num_classes: 1
train_height: &train_height 256
train_width: &train_width 192
trainsize: &trainsize [*train_width, *train_height].
hmsize: &hmsize [48, 64].
flip_perm: &flip_perm [[1, 2], [3, 4]]. # Note that only points that are mirror-symmetric are recorded here.
...
# Ensure that dataset_dir + anno_path can correctly locate the annotation file
# Ensure that dataset_dir + image_dir + image path in annotation file can correctly locate the image.
TrainDataset:
!KeypointTopDownCocoDataset
image_dir: images
anno_path: train_coco_joint.json
dataset_dir: dataset/self_dataset
num_joints: *num_joints
trainsize. *trainsize
pixel_std: *pixel_std
use_gt_box: true
Evaluate the dataset.
!KeypointTopDownCocoDataset
image_dir: images
anno_path: val_coco_joint.json
dataset_dir: dataset/self_dataset
bbox_file: bbox.json
num_joints: *num_joints
trainsize. *trainsize
pixel_std: *pixel_std
use_gt_box: true
image_thre: 0.0
```
### Model Training and Evaluation
#### Model Training
Run the following command to start training:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml
```
#### Model Evaluation
After training the model, you can evaluate the model metrics by running the following commands:
```
python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml
```
### Model Export and Inference
#### Top-Down model deployment
```
#Export keypoint model
python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights={path_to_your_weights}
#detector detection + keypoint top-down model co-deployment（for top-down solutions only）
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_256x192/ --video_file=../video/xxx.mp4 --device=gpu
```
| PaddleDetection/docs/advanced_tutorials/customization/keypoint_detection_en.md/0 | {
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[English](DistributedTraining_en.md) | 简体中文
# 分布式训练
## 1. 简介
* 分布式训练指的是将训练任务按照一定方法拆分到多个计算节点进行计算，再按照一定的方法对拆分后计算得到的梯度等信息进行聚合与更新。飞桨分布式训练技术源自百度的业务实践，在自然语言处理、计算机视觉、搜索和推荐等领域经过超大规模业务检验。分布式训练的高性能，是飞桨的核心优势技术之一，PaddleDetection同时支持单机训练与多机训练。更多关于分布式训练的方法与文档可以参考：[分布式训练快速开始教程](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html)。
## 2. 使用方法
### 2.1 单机训练
* 以PP-YOLOE-s为例，本地准备好数据之后，使用`paddle.distributed.launch`或者`fleetrun`的接口启动训练任务即可。下面为运行脚本示例。
```bash
fleetrun \
--selected_gpu 0,1,2,3,4,5,6,7 \
tools/train.py -c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml \
--eval &>logs.txt 2>&1 &
```
### 2.2 多机训练
* 相比单机训练，多机训练时，只需要添加`--ips`的参数，该参数表示需要参与分布式训练的机器的ip列表，不同机器的ip用逗号隔开。下面为运行代码示例。
```shell
ip_list="10.127.6.17,10.127.5.142,10.127.45.13,10.127.44.151"
fleetrun \
--ips=${ip_list} \
--selected_gpu 0,1,2,3,4,5,6,7 \
tools/train.py -c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml \
--eval &>logs.txt 2>&1 &
```
**注：**
* 不同机器的ip信息需要用逗号隔开，可以通过`ifconfig`或者`ipconfig`查看。
* 不同机器之间需要做免密设置，且可以直接ping通，否则无法完成通信。
* 不同机器之间的代码、数据与运行命令或脚本需要保持一致，且所有的机器上都需要运行设置好的训练命令或者脚本。最终`ip_list`中的第一台机器的第一块设备是trainer0，以此类推。
* 不同机器的起始端口可能不同，建议在启动多机任务前，在不同的机器中设置相同的多机运行起始端口，命令为`export FLAGS_START_PORT=17000`，端口值建议在`10000~20000`之间。
## 3. 性能效果测试
* 在3机8卡V100的机器上进行模型训练，不同模型的精度、训练耗时、多机加速比情况如下所示。
| 模型 | 数据集 | 配置 | 单机8卡耗时/精度 | 3机8卡耗时/精度 | 加速比 |
|:---------:|:--------:|:--------:|:--------:|:--------:|:------:|
| PP-YOLOE-s | Objects365 | [ppyoloe_crn_s_300e_coco.yml](../../configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) | 301h/- | 162h/17.7% | **1.85** |
| PP-YOLOE-l | Objects365 | [ppyoloe_crn_l_300e_coco.yml](../../configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) | 401h/- | 178h/30.3% | **2.25** |
* 在4机8卡V100的机器上进行模型训练，不同模型的精度、训练耗时、多机加速比情况如下所示。
| 模型 | 数据集 | 配置 | 单机8卡耗时/精度 | 4机8卡耗时/精度 | 加速比 |
|:---------:|:--------:|:--------:|:--------:|:--------:|:------:|
| PP-YOLOE-s | COCO | [ppyoloe_crn_s_300e_coco.yml](../../configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) | 39h/42.7% | 13h/42.1% | **3.0** |
| PP-YOLOE-m | Objects365 | [ppyoloe_crn_m_300e_coco.yml](../../configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) | 337h/- | 112h/24.6% | **3.0** |
| PP-YOLOE-x | Objects365 | [ppyoloe_crn_x_300e_coco.yml](../../configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) | 464h/- | 125h/32.1% | **3.4** |
* **注意**
* 在训练的GPU卡数过多时，精度会稍微有所损失（1%左右），此时可以尝试通过添加warmup或者适当增加迭代轮数来弥补精度损失。
* 这里的配置文件均提供的是COCO数据集的配置文件，如果需要训练其他的数据集，需要修改数据集路径。
* 上面的`PP-YOLOE`系列模型在多机训练过程中，均设置单卡batch size为8，同时学习率相比于单机8卡保持不变。
| PaddleDetection/docs/tutorials/DistributedTraining_cn.md/0 | {
"file_path": "PaddleDetection/docs/tutorials/DistributedTraining_cn.md",
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# RCNN series model parameter configuration tutorial
Tag: Model parameter configuration
Take `faster_rcnn_r50_fpn_1x_coco.yml` as an example. The model consists of five sub-profiles:
- Data profile `coco_detection.yml`
```yaml
# Data evaluation type
metric: COCO
# The number of categories in the dataset
num_classes: 80
# TrainDataset
TrainDataset:
!COCODataSet
# Image data path, Relative path of dataset_dir, os.path.join(dataset_dir, image_dir)
image_dir: train2017
# Annotation file path, Relative path of dataset_dir, os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_train2017.json
# data file
dataset_dir: dataset/coco
# data_fields
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
# Image data path, Relative path of dataset_dir, os.path.join(dataset_dir, image_dir)
image_dir: val2017
# Annotation file path, Relative path of dataset_dir, os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_val2017.json
# data file file os.path.join(dataset_dir, anno_path)
dataset_dir: dataset/coco
TestDataset:
!ImageFolder
# Annotation file path, Relative path of dataset_dir, os.path.join(dataset_dir, anno_path)
anno_path: annotations/instances_val2017.json
```
- Optimizer configuration file `optimizer_1x.yml`
```yaml
# Total training epoches
epoch: 12
# learning rate setting
LearningRate:
# Default is 8 Gpus training learning rate
base_lr: 0.01
# Learning rate adjustment strategy
schedulers:
- !PiecewiseDecay
gamma: 0.1
# Position of change in learning rate (number of epoches)
milestones: [8, 11]
- !LinearWarmup
start_factor: 0.1
steps: 1000
# Optimizer
OptimizerBuilder:
# Optimizer
optimizer:
momentum: 0.9
type: Momentum
# Regularization
regularizer:
factor: 0.0001
type: L2
```
- Data reads configuration files `faster_fpn_reader.yml`
```yaml
# Number of PROCESSES per GPU Reader
worker_num: 2
# training data
TrainReader:
# Training data transforms
sample_transforms:
- Decode: {}
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
- RandomFlip: {prob: 0.5}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# Since the model has FPN structure, the input image needs a multiple of 32 padding
- PadBatch: {pad_to_stride: 32}
# Batch_size during training
batch_size: 1
# Read data is out of order
shuffle: true
# Whether to discard data that does not complete the batch
drop_last: true
# Set it to false. Then you have a sequence of values for GT: List [Tensor]
collate_batch: false
# Evaluate data
EvalReader:
# Evaluate data transforms
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# Since the model has FPN structure, the input image needs a multiple of 32 padding
- PadBatch: {pad_to_stride: 32}
# batch_size of evaluation
batch_size: 1
# Read data is out of order
shuffle: false
# Whether to discard data that does not complete the batch
drop_last: false
# test data
TestReader:
# test data transforms
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
# Since the model has FPN structure, the input image needs a multiple of 32 padding
- PadBatch: {pad_to_stride: 32}
# batch_size of test
batch_size: 1
# Read data is out of order
shuffle: false
# Whether to discard data that does not complete the batch
drop_last: false
```
- Model profile `faster_rcnn_r50_fpn.yml`
```yaml
# Model structure type
architecture: FasterRCNN
# Pretrain model address
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
# FasterRCNN
FasterRCNN:
# backbone
backbone: ResNet
# neck
neck: FPN
# rpn_head
rpn_head: RPNHead
# bbox_head
bbox_head: BBoxHead
# post process
bbox_post_process: BBoxPostProcess
# backbone
ResNet:
# index 0 stands for res2
depth: 50
# norm_type, Configurable parameter: bn or sync_bn
norm_type: bn
# freeze_at index, 0 represent res2
freeze_at: 0
# return_idx
return_idx: [0,1,2,3]
# num_stages
num_stages: 4
# FPN
FPN:
# channel of FPN
out_channel: 256
# RPNHead
RPNHead:
# anchor generator
anchor_generator:
aspect_ratios: [0.5, 1.0, 2.0]
anchor_sizes: [[32], [64], [128], [256], [512]]
strides: [4, 8, 16, 32, 64]
# rpn_target_assign
rpn_target_assign:
batch_size_per_im: 256
fg_fraction: 0.5
negative_overlap: 0.3
positive_overlap: 0.7
use_random: True
# The parameters of the proposal are generated during training
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 2000
post_nms_top_n: 1000
topk_after_collect: True
# The parameters of the proposal are generated during evaluation
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 1000
post_nms_top_n: 1000
# BBoxHead
BBoxHead:
# TwoFCHead as BBoxHead
head: TwoFCHead
# roi align
roi_extractor:
resolution: 7
sampling_ratio: 0
aligned: True
# bbox_assigner
bbox_assigner: BBoxAssigner
# BBoxAssigner
BBoxAssigner:
# batch_size_per_im
batch_size_per_im: 512
# Background the threshold
bg_thresh: 0.5
# Prospects for threshold
fg_thresh: 0.5
# Prospects of proportion
fg_fraction: 0.25
# Random sampling
use_random: True
# TwoFCHead
TwoFCHead:
# TwoFCHead feature dimension
out_channel: 1024
# BBoxPostProcess
BBoxPostProcess:
# decode
decode: RCNNBox
# nms
nms:
# use MultiClassNMS
name: MultiClassNMS
keep_top_k: 100
score_threshold: 0.05
nms_threshold: 0.5
```
- runtime configuration file `runtime.yml`
```yaml
# Whether to use gpu
use_gpu: true
# Log Printing interval
log_iter: 20
# save_dir
save_dir: output
# Model save interval
snapshot_epoch: 1
```
| PaddleDetection/docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md/0 | {
"file_path": "PaddleDetection/docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md",
"repo_id": "PaddleDetection",
"token_count": 2439
} | 69 |
# 数据准备
数据对于深度学习开发起到了至关重要的作用，数据采集和标注的质量是提升业务模型效果的重要因素。本文档主要介绍PaddleDetection中如何进行数据准备，包括采集高质量数据方法，覆盖多场景类型，提升模型泛化能力；以及各类任务数据标注工具和方法，并在PaddleDetection下使用
## 数据采集
在深度学习任务的实际落地中，数据采集往往决定了最终模型的效果，对于数据采集的几点建议如下：
### 确定方向
任务类型、数据的类别和目标场景这些因素决定了要收集什么数据，首先需要根据这些因素来确定整体数据收集的工作方向。
### 开源数据集
在实际场景中数据采集成本其实十分高昂，完全靠自己收集在时间和金钱上都有很高的成本，开源数据集是帮助增加训练数据量的重要手段，所以很多时候会考虑加入一些相似任务的开源数据。在使用中请遵守各个开源数据集的license规定的使用条件。
### 增加场景数据
开源数据一般不会覆盖实际使用的的目标场景，用户需要评估开源数据集中已包含的场景和目标场景间的差异，有针对性地补充目标场景数据，尽量让训练和部署数据的场景一致。
### 类别均衡
在采集阶段，也需要尽量保持类别均衡，帮助模型正确学习到目标特征。
## 数据标注及格式说明
| 任务类型 | 数据标注 | 数据格式说明 |
|:--------:| :--------:|:--------:|
| 目标检测 | [文档链接](DetAnnoTools.md) | [文档链接](PrepareDetDataSet.md) |
| 关键点检测 | [文档链接](KeyPointAnnoTools.md) | [文档链接](PrepareKeypointDataSet.md) |
| 多目标跟踪 | [文档链接](MOTAnnoTools.md) | [文档链接](PrepareMOTDataSet.md) |
| PaddleDetection/docs/tutorials/data/README.md/0 | {
"file_path": "PaddleDetection/docs/tutorials/data/README.md",
"repo_id": "PaddleDetection",
"token_count": 1342
} | 70 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os
import traceback
import six
import sys
if sys.version_info >= (3, 0):
pass
else:
pass
import numpy as np
import paddle
import paddle.nn.functional as F
from copy import deepcopy
from paddle.io import DataLoader, DistributedBatchSampler
from .utils import default_collate_fn
from ppdet.core.workspace import register
from . import transform
from .shm_utils import _get_shared_memory_size_in_M
from ppdet.utils.logger import setup_logger
logger = setup_logger('reader')
MAIN_PID = os.getpid()
class Compose(object):
def __init__(self, transforms, num_classes=80):
self.transforms = transforms
self.transforms_cls = []
for t in self.transforms:
for k, v in t.items():
op_cls = getattr(transform, k)
f = op_cls(**v)
if hasattr(f, 'num_classes'):
f.num_classes = num_classes
self.transforms_cls.append(f)
def __call__(self, data):
for f in self.transforms_cls:
try:
data = f(data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map sample transform [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
return data
class BatchCompose(Compose):
def __init__(self, transforms, num_classes=80, collate_batch=True):
super(BatchCompose, self).__init__(transforms, num_classes)
self.collate_batch = collate_batch
def __call__(self, data):
for f in self.transforms_cls:
try:
data = f(data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map batch transform [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
# remove keys which is not needed by model
extra_key = ['h', 'w', 'flipped']
for k in extra_key:
for sample in data:
if k in sample:
sample.pop(k)
# batch data, if user-define batch function needed
# use user-defined here
if self.collate_batch:
batch_data = default_collate_fn(data)
else:
batch_data = {}
for k in data[0].keys():
tmp_data = []
for i in range(len(data)):
tmp_data.append(data[i][k])
if not 'gt_' in k and not 'is_crowd' in k and not 'difficult' in k:
tmp_data = np.stack(tmp_data, axis=0)
batch_data[k] = tmp_data
return batch_data
class BaseDataLoader(object):
"""
Base DataLoader implementation for detection models
Args:
sample_transforms (list): a list of transforms to perform
on each sample
batch_transforms (list): a list of transforms to perform
on batch
batch_size (int): batch size for batch collating, default 1.
shuffle (bool): whether to shuffle samples
drop_last (bool): whether to drop the last incomplete,
default False
num_classes (int): class number of dataset, default 80
collate_batch (bool): whether to collate batch in dataloader.
If set to True, the samples will collate into batch according
to the batch size. Otherwise, the ground-truth will not collate,
which is used when the number of ground-truch is different in
samples.
use_shared_memory (bool): whether to use shared memory to
accelerate data loading, enable this only if you
are sure that the shared memory size of your OS
is larger than memory cost of input datas of model.
Note that shared memory will be automatically
disabled if the shared memory of OS is less than
1G, which is not enough for detection models.
Default False.
"""
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
num_classes=80,
collate_batch=True,
use_shared_memory=False,
**kwargs):
# sample transform
self._sample_transforms = Compose(
sample_transforms, num_classes=num_classes)
# batch transfrom
self._batch_transforms = BatchCompose(batch_transforms, num_classes,
collate_batch)
self.batch_size = batch_size
self.shuffle = shuffle
self.drop_last = drop_last
self.use_shared_memory = use_shared_memory
self.kwargs = kwargs
def __call__(self,
dataset,
worker_num,
batch_sampler=None,
return_list=False):
self.dataset = dataset
self.dataset.check_or_download_dataset()
self.dataset.parse_dataset()
# get data
self.dataset.set_transform(self._sample_transforms)
# set kwargs
self.dataset.set_kwargs(**self.kwargs)
# batch sampler
if batch_sampler is None:
self._batch_sampler = DistributedBatchSampler(
self.dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
drop_last=self.drop_last)
else:
self._batch_sampler = batch_sampler
# DataLoader do not start sub-process in Windows and Mac
# system, do not need to use shared memory
use_shared_memory = self.use_shared_memory and \
sys.platform not in ['win32', 'darwin']
# check whether shared memory size is bigger than 1G(1024M)
if use_shared_memory:
shm_size = _get_shared_memory_size_in_M()
if shm_size is not None and shm_size < 1024.:
logger.warning("Shared memory size is less than 1G, "
"disable shared_memory in DataLoader")
use_shared_memory = False
self.dataloader = DataLoader(
dataset=self.dataset,
batch_sampler=self._batch_sampler,
collate_fn=self._batch_transforms,
num_workers=worker_num,
return_list=return_list,
use_shared_memory=use_shared_memory)
self.loader = iter(self.dataloader)
return self
def __len__(self):
return len(self._batch_sampler)
def __iter__(self):
return self
def __next__(self):
try:
return next(self.loader)
except StopIteration:
self.loader = iter(self.dataloader)
six.reraise(*sys.exc_info())
def next(self):
# python2 compatibility
return self.__next__()
@register
class TrainReader(BaseDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=True,
drop_last=True,
num_classes=80,
collate_batch=True,
**kwargs):
super(TrainReader, self).__init__(sample_transforms, batch_transforms,
batch_size, shuffle, drop_last,
num_classes, collate_batch, **kwargs)
@register
class EvalReader(BaseDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
num_classes=80,
**kwargs):
super(EvalReader, self).__init__(sample_transforms, batch_transforms,
batch_size, shuffle, drop_last,
num_classes, **kwargs)
@register
class TestReader(BaseDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
num_classes=80,
**kwargs):
super(TestReader, self).__init__(sample_transforms, batch_transforms,
batch_size, shuffle, drop_last,
num_classes, **kwargs)
@register
class EvalMOTReader(BaseDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
num_classes=1,
**kwargs):
super(EvalMOTReader, self).__init__(sample_transforms, batch_transforms,
batch_size, shuffle, drop_last,
num_classes, **kwargs)
@register
class TestMOTReader(BaseDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
num_classes=1,
**kwargs):
super(TestMOTReader, self).__init__(sample_transforms, batch_transforms,
batch_size, shuffle, drop_last,
num_classes, **kwargs)
# For Semi-Supervised Object Detection (SSOD)
class Compose_SSOD(object):
def __init__(self, base_transforms, weak_aug, strong_aug, num_classes=80):
self.base_transforms = base_transforms
self.base_transforms_cls = []
for t in self.base_transforms:
for k, v in t.items():
op_cls = getattr(transform, k)
f = op_cls(**v)
if hasattr(f, 'num_classes'):
f.num_classes = num_classes
self.base_transforms_cls.append(f)
self.weak_augs = weak_aug
self.weak_augs_cls = []
for t in self.weak_augs:
for k, v in t.items():
op_cls = getattr(transform, k)
f = op_cls(**v)
if hasattr(f, 'num_classes'):
f.num_classes = num_classes
self.weak_augs_cls.append(f)
self.strong_augs = strong_aug
self.strong_augs_cls = []
for t in self.strong_augs:
for k, v in t.items():
op_cls = getattr(transform, k)
f = op_cls(**v)
if hasattr(f, 'num_classes'):
f.num_classes = num_classes
self.strong_augs_cls.append(f)
def __call__(self, data):
for f in self.base_transforms_cls:
try:
data = f(data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map sample transform [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
weak_data = deepcopy(data)
strong_data = deepcopy(data)
for f in self.weak_augs_cls:
try:
weak_data = f(weak_data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map weak aug [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
for f in self.strong_augs_cls:
try:
strong_data = f(strong_data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map strong aug [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
weak_data['strong_aug'] = strong_data
return weak_data
class BatchCompose_SSOD(Compose):
def __init__(self, transforms, num_classes=80, collate_batch=True):
super(BatchCompose_SSOD, self).__init__(transforms, num_classes)
self.collate_batch = collate_batch
def __call__(self, data):
# split strong_data from data(weak_data)
strong_data = []
for sample in data:
strong_data.append(sample['strong_aug'])
sample.pop('strong_aug')
for f in self.transforms_cls:
try:
data = f(data)
if 'BatchRandomResizeForSSOD' in f._id:
strong_data = f(strong_data, data[1])[0]
data = data[0]
else:
strong_data = f(strong_data)
except Exception as e:
stack_info = traceback.format_exc()
logger.warning("fail to map batch transform [{}] "
"with error: {} and stack:\n{}".format(
f, e, str(stack_info)))
raise e
# remove keys which is not needed by model
extra_key = ['h', 'w', 'flipped']
for k in extra_key:
for sample in data:
if k in sample:
sample.pop(k)
for sample in strong_data:
if k in sample:
sample.pop(k)
# batch data, if user-define batch function needed
# use user-defined here
if self.collate_batch:
batch_data = default_collate_fn(data)
strong_batch_data = default_collate_fn(strong_data)
return batch_data, strong_batch_data
else:
batch_data = {}
for k in data[0].keys():
tmp_data = []
for i in range(len(data)):
tmp_data.append(data[i][k])
if not 'gt_' in k and not 'is_crowd' in k and not 'difficult' in k:
tmp_data = np.stack(tmp_data, axis=0)
batch_data[k] = tmp_data
strong_batch_data = {}
for k in strong_data[0].keys():
tmp_data = []
for i in range(len(strong_data)):
tmp_data.append(strong_data[i][k])
if not 'gt_' in k and not 'is_crowd' in k and not 'difficult' in k:
tmp_data = np.stack(tmp_data, axis=0)
strong_batch_data[k] = tmp_data
return batch_data, strong_batch_data
class CombineSSODLoader(object):
def __init__(self, label_loader, unlabel_loader):
self.label_loader = label_loader
self.unlabel_loader = unlabel_loader
def __iter__(self):
while True:
try:
label_samples = next(self.label_loader_iter)
except:
self.label_loader_iter = iter(self.label_loader)
label_samples = next(self.label_loader_iter)
try:
unlabel_samples = next(self.unlabel_loader_iter)
except:
self.unlabel_loader_iter = iter(self.unlabel_loader)
unlabel_samples = next(self.unlabel_loader_iter)
yield (
label_samples[0], # sup weak
label_samples[1], # sup strong
unlabel_samples[0], # unsup weak
unlabel_samples[1] # unsup strong
)
def __call__(self):
return self.__iter__()
class BaseSemiDataLoader(object):
def __init__(self,
sample_transforms=[],
weak_aug=[],
strong_aug=[],
sup_batch_transforms=[],
unsup_batch_transforms=[],
sup_batch_size=1,
unsup_batch_size=1,
shuffle=True,
drop_last=True,
num_classes=80,
collate_batch=True,
use_shared_memory=False,
**kwargs):
# sup transforms
self._sample_transforms_label = Compose_SSOD(
sample_transforms, weak_aug, strong_aug, num_classes=num_classes)
self._batch_transforms_label = BatchCompose_SSOD(
sup_batch_transforms, num_classes, collate_batch)
self.batch_size_label = sup_batch_size
# unsup transforms
self._sample_transforms_unlabel = Compose_SSOD(
sample_transforms, weak_aug, strong_aug, num_classes=num_classes)
self._batch_transforms_unlabel = BatchCompose_SSOD(
unsup_batch_transforms, num_classes, collate_batch)
self.batch_size_unlabel = unsup_batch_size
# common
self.shuffle = shuffle
self.drop_last = drop_last
self.use_shared_memory = use_shared_memory
self.kwargs = kwargs
def __call__(self,
dataset_label,
dataset_unlabel,
worker_num,
batch_sampler_label=None,
batch_sampler_unlabel=None,
return_list=False):
# sup dataset
self.dataset_label = dataset_label
self.dataset_label.check_or_download_dataset()
self.dataset_label.parse_dataset()
self.dataset_label.set_transform(self._sample_transforms_label)
self.dataset_label.set_kwargs(**self.kwargs)
if batch_sampler_label is None:
self._batch_sampler_label = DistributedBatchSampler(
self.dataset_label,
batch_size=self.batch_size_label,
shuffle=self.shuffle,
drop_last=self.drop_last)
else:
self._batch_sampler_label = batch_sampler_label
# unsup dataset
self.dataset_unlabel = dataset_unlabel
self.dataset_unlabel.length = self.dataset_label.__len__()
self.dataset_unlabel.check_or_download_dataset()
self.dataset_unlabel.parse_dataset()
self.dataset_unlabel.set_transform(self._sample_transforms_unlabel)
self.dataset_unlabel.set_kwargs(**self.kwargs)
if batch_sampler_unlabel is None:
self._batch_sampler_unlabel = DistributedBatchSampler(
self.dataset_unlabel,
batch_size=self.batch_size_unlabel,
shuffle=self.shuffle,
drop_last=self.drop_last)
else:
self._batch_sampler_unlabel = batch_sampler_unlabel
# DataLoader do not start sub-process in Windows and Mac
# system, do not need to use shared memory
use_shared_memory = self.use_shared_memory and \
sys.platform not in ['win32', 'darwin']
# check whether shared memory size is bigger than 1G(1024M)
if use_shared_memory:
shm_size = _get_shared_memory_size_in_M()
if shm_size is not None and shm_size < 1024.:
logger.warning("Shared memory size is less than 1G, "
"disable shared_memory in DataLoader")
use_shared_memory = False
self.dataloader_label = DataLoader(
dataset=self.dataset_label,
batch_sampler=self._batch_sampler_label,
collate_fn=self._batch_transforms_label,
num_workers=worker_num,
return_list=return_list,
use_shared_memory=use_shared_memory)
self.dataloader_unlabel = DataLoader(
dataset=self.dataset_unlabel,
batch_sampler=self._batch_sampler_unlabel,
collate_fn=self._batch_transforms_unlabel,
num_workers=worker_num,
return_list=return_list,
use_shared_memory=use_shared_memory)
self.dataloader = CombineSSODLoader(self.dataloader_label,
self.dataloader_unlabel)
self.loader = iter(self.dataloader)
return self
def __len__(self):
return len(self._batch_sampler_label)
def __iter__(self):
return self
def __next__(self):
return next(self.loader)
def next(self):
# python2 compatibility
return self.__next__()
@register
class SemiTrainReader(BaseSemiDataLoader):
__shared__ = ['num_classes']
def __init__(self,
sample_transforms=[],
weak_aug=[],
strong_aug=[],
sup_batch_transforms=[],
unsup_batch_transforms=[],
sup_batch_size=1,
unsup_batch_size=1,
shuffle=True,
drop_last=True,
num_classes=80,
collate_batch=True,
**kwargs):
super(SemiTrainReader, self).__init__(
sample_transforms, weak_aug, strong_aug, sup_batch_transforms,
unsup_batch_transforms, sup_batch_size, unsup_batch_size, shuffle,
drop_last, num_classes, collate_batch, **kwargs)
| PaddleDetection/ppdet/data/reader.py/0 | {
"file_path": "PaddleDetection/ppdet/data/reader.py",
"repo_id": "PaddleDetection",
"token_count": 11548
} | 71 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import typing
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
import cv2
import copy
import math
import numpy as np
from .operators import register_op, BaseOperator, Resize
from .op_helper import jaccard_overlap, gaussian2D, gaussian_radius, draw_umich_gaussian
from .atss_assigner import ATSSAssigner
from scipy import ndimage
from ppdet.modeling import bbox_utils
from ppdet.utils.logger import setup_logger
from ppdet.modeling.keypoint_utils import get_affine_transform, affine_transform
logger = setup_logger(__name__)
__all__ = [
'PadBatch', 'BatchRandomResize', 'Gt2YoloTarget', 'Gt2FCOSTarget',
'Gt2TTFTarget', 'Gt2Solov2Target', 'Gt2SparseTarget', 'PadMaskBatch',
'Gt2GFLTarget', 'Gt2CenterNetTarget', 'Gt2CenterTrackTarget', 'PadGT',
'PadRGT', 'BatchRandomResizeForSSOD'
]
@register_op
class PadBatch(BaseOperator):
"""
Pad a batch of samples so they can be divisible by a stride.
The layout of each image should be 'CHW'.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
"""
def __init__(self, pad_to_stride=0):
super(PadBatch, self).__init__()
self.pad_to_stride = pad_to_stride
def __call__(self, samples, context=None):
"""
Args:
samples (list): a batch of sample, each is dict.
"""
coarsest_stride = self.pad_to_stride
# multi scale input is nested list
if isinstance(samples,
typing.Sequence) and len(samples) > 0 and isinstance(
samples[0], typing.Sequence):
inner_samples = samples[0]
else:
inner_samples = samples
max_shape = np.array(
[data['image'].shape for data in inner_samples]).max(axis=0)
if coarsest_stride > 0:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
for data in inner_samples:
im = data['image']
im_c, im_h, im_w = im.shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
data['image'] = padding_im
if 'semantic' in data and data['semantic'] is not None:
semantic = data['semantic']
padding_sem = np.zeros(
(1, max_shape[1], max_shape[2]), dtype=np.float32)
padding_sem[:, :im_h, :im_w] = semantic
data['semantic'] = padding_sem
if 'gt_segm' in data and data['gt_segm'] is not None:
gt_segm = data['gt_segm']
padding_segm = np.zeros(
(gt_segm.shape[0], max_shape[1], max_shape[2]),
dtype=np.uint8)
padding_segm[:, :im_h, :im_w] = gt_segm
data['gt_segm'] = padding_segm
return samples
@register_op
class BatchRandomResize(BaseOperator):
"""
Resize image to target size randomly. random target_size and interpolation method
Args:
target_size (int, list, tuple): image target size, if random size is True, must be list or tuple
keep_ratio (bool): whether keep_raio or not, default true
interp (int): the interpolation method
random_size (bool): whether random select target size of image
random_interp (bool): whether random select interpolation method
"""
def __init__(self,
target_size,
keep_ratio,
interp=cv2.INTER_NEAREST,
random_size=True,
random_interp=False):
super(BatchRandomResize, self).__init__()
self.keep_ratio = keep_ratio
self.interps = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
self.interp = interp
assert isinstance(target_size, (
int, Sequence)), "target_size must be int, list or tuple"
if random_size and not isinstance(target_size, list):
raise TypeError(
"Type of target_size is invalid when random_size is True. Must be List, now is {}".
format(type(target_size)))
self.target_size = target_size
self.random_size = random_size
self.random_interp = random_interp
def __call__(self, samples, context=None):
if self.random_size:
index = np.random.choice(len(self.target_size))
target_size = self.target_size[index]
else:
target_size = self.target_size
if self.random_interp:
interp = np.random.choice(self.interps)
else:
interp = self.interp
resizer = Resize(target_size, keep_ratio=self.keep_ratio, interp=interp)
return resizer(samples, context=context)
@register_op
class Gt2YoloTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Generate YOLOv3 targets by groud truth data, this operator is only used in
fine grained YOLOv3 loss mode
"""
def __init__(self,
anchors,
anchor_masks,
downsample_ratios,
num_classes=80,
iou_thresh=1.):
super(Gt2YoloTarget, self).__init__()
self.anchors = anchors
self.anchor_masks = anchor_masks
self.downsample_ratios = downsample_ratios
self.num_classes = num_classes
self.iou_thresh = iou_thresh
def __call__(self, samples, context=None):
assert len(self.anchor_masks) == len(self.downsample_ratios), \
"anchor_masks', and 'downsample_ratios' should have same length."
h, w = samples[0]['image'].shape[1:3]
an_hw = np.array(self.anchors) / np.array([[w, h]])
for sample in samples:
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
if 'gt_score' not in sample:
sample['gt_score'] = np.ones(
(gt_bbox.shape[0], 1), dtype=np.float32)
gt_score = sample['gt_score']
for i, (
mask, downsample_ratio
) in enumerate(zip(self.anchor_masks, self.downsample_ratios)):
grid_h = int(h / downsample_ratio)
grid_w = int(w / downsample_ratio)
target = np.zeros(
(len(mask), 6 + self.num_classes, grid_h, grid_w),
dtype=np.float32)
for b in range(gt_bbox.shape[0]):
gx, gy, gw, gh = gt_bbox[b, :]
cls = gt_class[b]
score = gt_score[b]
if gw <= 0. or gh <= 0. or score <= 0.:
continue
# find best match anchor index
best_iou = 0.
best_idx = -1
for an_idx in range(an_hw.shape[0]):
iou = jaccard_overlap(
[0., 0., gw, gh],
[0., 0., an_hw[an_idx, 0], an_hw[an_idx, 1]])
if iou > best_iou:
best_iou = iou
best_idx = an_idx
gi = int(gx * grid_w)
gj = int(gy * grid_h)
# gtbox should be regresed in this layes if best match
# anchor index in anchor mask of this layer
if best_idx in mask:
best_n = mask.index(best_idx)
# x, y, w, h, scale
target[best_n, 0, gj, gi] = gx * grid_w - gi
target[best_n, 1, gj, gi] = gy * grid_h - gj
target[best_n, 2, gj, gi] = np.log(
gw * w / self.anchors[best_idx][0])
target[best_n, 3, gj, gi] = np.log(
gh * h / self.anchors[best_idx][1])
target[best_n, 4, gj, gi] = 2.0 - gw * gh
# objectness record gt_score
target[best_n, 5, gj, gi] = score
# classification
target[best_n, 6 + cls, gj, gi] = 1.
# For non-matched anchors, calculate the target if the iou
# between anchor and gt is larger than iou_thresh
if self.iou_thresh < 1:
for idx, mask_i in enumerate(mask):
if mask_i == best_idx: continue
iou = jaccard_overlap(
[0., 0., gw, gh],
[0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
if iou > self.iou_thresh and target[idx, 5, gj,
gi] == 0.:
# x, y, w, h, scale
target[idx, 0, gj, gi] = gx * grid_w - gi
target[idx, 1, gj, gi] = gy * grid_h - gj
target[idx, 2, gj, gi] = np.log(
gw * w / self.anchors[mask_i][0])
target[idx, 3, gj, gi] = np.log(
gh * h / self.anchors[mask_i][1])
target[idx, 4, gj, gi] = 2.0 - gw * gh
# objectness record gt_score
target[idx, 5, gj, gi] = score
# classification
target[idx, 6 + cls, gj, gi] = 1.
sample['target{}'.format(i)] = target
# remove useless gt_class and gt_score after target calculated
sample.pop('gt_class')
sample.pop('gt_score')
return samples
@register_op
class Gt2FCOSTarget(BaseOperator):
"""
Generate FCOS targets by groud truth data
"""
def __init__(self,
object_sizes_boundary,
center_sampling_radius,
downsample_ratios,
num_shift=0.5,
multiply_strides_reg_targets=False,
norm_reg_targets=True):
super(Gt2FCOSTarget, self).__init__()
self.center_sampling_radius = center_sampling_radius
self.downsample_ratios = downsample_ratios
self.INF = np.inf
self.object_sizes_boundary = [-1] + object_sizes_boundary + [self.INF]
object_sizes_of_interest = []
for i in range(len(self.object_sizes_boundary) - 1):
object_sizes_of_interest.append([
self.object_sizes_boundary[i], self.object_sizes_boundary[i + 1]
])
self.object_sizes_of_interest = object_sizes_of_interest
self.num_shift = num_shift
self.multiply_strides_reg_targets = multiply_strides_reg_targets
self.norm_reg_targets = norm_reg_targets
def _compute_points(self, w, h):
"""
compute the corresponding points in each feature map
:param h: image height
:param w: image width
:return: points from all feature map
"""
locations = []
for stride in self.downsample_ratios:
shift_x = np.arange(0, w, stride).astype(np.float32)
shift_y = np.arange(0, h, stride).astype(np.float32)
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shift_x = shift_x.flatten()
shift_y = shift_y.flatten()
location = np.stack(
[shift_x, shift_y], axis=1) + stride * self.num_shift
locations.append(location)
num_points_each_level = [len(location) for location in locations]
locations = np.concatenate(locations, axis=0)
return locations, num_points_each_level
def _convert_xywh2xyxy(self, gt_bbox, w, h):
"""
convert the bounding box from style xywh to xyxy
:param gt_bbox: bounding boxes normalized into [0, 1]
:param w: image width
:param h: image height
:return: bounding boxes in xyxy style
"""
bboxes = gt_bbox.copy()
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] * w
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] * h
bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]
bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3]
return bboxes
def _check_inside_boxes_limited(self, gt_bbox, xs, ys,
num_points_each_level):
"""
check if points is within the clipped boxes
:param gt_bbox: bounding boxes
:param xs: horizontal coordinate of points
:param ys: vertical coordinate of points
:return: the mask of points is within gt_box or not
"""
bboxes = np.reshape(
gt_bbox, newshape=[1, gt_bbox.shape[0], gt_bbox.shape[1]])
bboxes = np.tile(bboxes, reps=[xs.shape[0], 1, 1])
ct_x = (bboxes[:, :, 0] + bboxes[:, :, 2]) / 2
ct_y = (bboxes[:, :, 1] + bboxes[:, :, 3]) / 2
beg = 0
clipped_box = bboxes.copy()
for lvl, stride in enumerate(self.downsample_ratios):
end = beg + num_points_each_level[lvl]
stride_exp = self.center_sampling_radius * stride
clipped_box[beg:end, :, 0] = np.maximum(
bboxes[beg:end, :, 0], ct_x[beg:end, :] - stride_exp)
clipped_box[beg:end, :, 1] = np.maximum(
bboxes[beg:end, :, 1], ct_y[beg:end, :] - stride_exp)
clipped_box[beg:end, :, 2] = np.minimum(
bboxes[beg:end, :, 2], ct_x[beg:end, :] + stride_exp)
clipped_box[beg:end, :, 3] = np.minimum(
bboxes[beg:end, :, 3], ct_y[beg:end, :] + stride_exp)
beg = end
l_res = xs - clipped_box[:, :, 0]
r_res = clipped_box[:, :, 2] - xs
t_res = ys - clipped_box[:, :, 1]
b_res = clipped_box[:, :, 3] - ys
clipped_box_reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
inside_gt_box = np.min(clipped_box_reg_targets, axis=2) > 0
return inside_gt_box
def __call__(self, samples, context=None):
assert len(self.object_sizes_of_interest) == len(self.downsample_ratios), \
"object_sizes_of_interest', and 'downsample_ratios' should have same length."
for sample in samples:
im = sample['image']
bboxes = sample['gt_bbox']
gt_class = sample['gt_class']
# calculate the locations
h, w = im.shape[1:3]
points, num_points_each_level = self._compute_points(w, h)
object_scale_exp = []
for i, num_pts in enumerate(num_points_each_level):
object_scale_exp.append(
np.tile(
np.array([self.object_sizes_of_interest[i]]),
reps=[num_pts, 1]))
object_scale_exp = np.concatenate(object_scale_exp, axis=0)
gt_area = (bboxes[:, 2] - bboxes[:, 0]) * (
bboxes[:, 3] - bboxes[:, 1])
xs, ys = points[:, 0], points[:, 1]
xs = np.reshape(xs, newshape=[xs.shape[0], 1])
xs = np.tile(xs, reps=[1, bboxes.shape[0]])
ys = np.reshape(ys, newshape=[ys.shape[0], 1])
ys = np.tile(ys, reps=[1, bboxes.shape[0]])
l_res = xs - bboxes[:, 0]
r_res = bboxes[:, 2] - xs
t_res = ys - bboxes[:, 1]
b_res = bboxes[:, 3] - ys
reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
if self.center_sampling_radius > 0:
is_inside_box = self._check_inside_boxes_limited(
bboxes, xs, ys, num_points_each_level)
else:
is_inside_box = np.min(reg_targets, axis=2) > 0
# check if the targets is inside the corresponding level
max_reg_targets = np.max(reg_targets, axis=2)
lower_bound = np.tile(
np.expand_dims(
object_scale_exp[:, 0], axis=1),
reps=[1, max_reg_targets.shape[1]])
high_bound = np.tile(
np.expand_dims(
object_scale_exp[:, 1], axis=1),
reps=[1, max_reg_targets.shape[1]])
is_match_current_level = \
(max_reg_targets > lower_bound) & \
(max_reg_targets < high_bound)
points2gtarea = np.tile(
np.expand_dims(
gt_area, axis=0), reps=[xs.shape[0], 1])
points2gtarea[is_inside_box == 0] = self.INF
points2gtarea[is_match_current_level == 0] = self.INF
points2min_area = points2gtarea.min(axis=1)
points2min_area_ind = points2gtarea.argmin(axis=1)
labels = gt_class[points2min_area_ind] + 1
labels[points2min_area == self.INF] = 0
reg_targets = reg_targets[range(xs.shape[0]), points2min_area_ind]
ctn_targets = np.sqrt((reg_targets[:, [0, 2]].min(axis=1) / \
reg_targets[:, [0, 2]].max(axis=1)) * \
(reg_targets[:, [1, 3]].min(axis=1) / \
reg_targets[:, [1, 3]].max(axis=1))).astype(np.float32)
ctn_targets = np.reshape(
ctn_targets, newshape=[ctn_targets.shape[0], 1])
ctn_targets[labels <= 0] = 0
pos_ind = np.nonzero(labels != 0)
reg_targets_pos = reg_targets[pos_ind[0], :]
split_sections = []
beg = 0
for lvl in range(len(num_points_each_level)):
end = beg + num_points_each_level[lvl]
split_sections.append(end)
beg = end
labels_by_level = np.split(labels, split_sections, axis=0)
reg_targets_by_level = np.split(reg_targets, split_sections, axis=0)
ctn_targets_by_level = np.split(ctn_targets, split_sections, axis=0)
for lvl in range(len(self.downsample_ratios)):
grid_w = int(np.ceil(w / self.downsample_ratios[lvl]))
grid_h = int(np.ceil(h / self.downsample_ratios[lvl]))
if self.norm_reg_targets:
if self.multiply_strides_reg_targets:
sample['reg_target{}'.format(lvl)] = np.reshape(
reg_targets_by_level[lvl],
newshape=[grid_h, grid_w, 4])
else:
sample['reg_target{}'.format(lvl)] = \
np.reshape(
reg_targets_by_level[lvl] / \
self.downsample_ratios[lvl],
newshape=[grid_h, grid_w, 4])
else:
sample['reg_target{}'.format(lvl)] = np.reshape(
reg_targets_by_level[lvl],
newshape=[grid_h, grid_w, 4])
sample['labels{}'.format(lvl)] = np.reshape(
labels_by_level[lvl], newshape=[grid_h, grid_w, 1])
sample['centerness{}'.format(lvl)] = np.reshape(
ctn_targets_by_level[lvl], newshape=[grid_h, grid_w, 1])
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
return samples
@register_op
class Gt2GFLTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Generate GFocal loss targets by groud truth data
"""
def __init__(self,
num_classes=80,
downsample_ratios=[8, 16, 32, 64, 128],
grid_cell_scale=4,
cell_offset=0,
compute_vlr_region=False):
super(Gt2GFLTarget, self).__init__()
self.num_classes = num_classes
self.downsample_ratios = downsample_ratios
self.grid_cell_scale = grid_cell_scale
self.cell_offset = cell_offset
self.compute_vlr_region = compute_vlr_region
self.assigner = ATSSAssigner()
def get_grid_cells(self, featmap_size, scale, stride, offset=0):
"""
Generate grid cells of a feature map for target assignment.
Args:
featmap_size: Size of a single level feature map.
scale: Grid cell scale.
stride: Down sample stride of the feature map.
offset: Offset of grid cells.
return:
Grid_cells xyxy position. Size should be [feat_w * feat_h, 4]
"""
cell_size = stride * scale
h, w = featmap_size
x_range = (np.arange(w, dtype=np.float32) + offset) * stride
y_range = (np.arange(h, dtype=np.float32) + offset) * stride
x, y = np.meshgrid(x_range, y_range)
y = y.flatten()
x = x.flatten()
grid_cells = np.stack(
[
x - 0.5 * cell_size, y - 0.5 * cell_size, x + 0.5 * cell_size,
y + 0.5 * cell_size
],
axis=-1)
return grid_cells
def get_sample(self, assign_gt_inds, gt_bboxes):
pos_inds = np.unique(np.nonzero(assign_gt_inds > 0)[0])
neg_inds = np.unique(np.nonzero(assign_gt_inds == 0)[0])
pos_assigned_gt_inds = assign_gt_inds[pos_inds] - 1
if gt_bboxes.size == 0:
# hack for index error case
assert pos_assigned_gt_inds.size == 0
pos_gt_bboxes = np.empty_like(gt_bboxes).reshape(-1, 4)
else:
if len(gt_bboxes.shape) < 2:
gt_bboxes = gt_bboxes.resize(-1, 4)
pos_gt_bboxes = gt_bboxes[pos_assigned_gt_inds, :]
return pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds
def __call__(self, samples, context=None):
assert len(samples) > 0
batch_size = len(samples)
# get grid cells of image
h, w = samples[0]['image'].shape[1:3]
multi_level_grid_cells = []
for stride in self.downsample_ratios:
featmap_size = (int(math.ceil(h / stride)),
int(math.ceil(w / stride)))
multi_level_grid_cells.append(
self.get_grid_cells(featmap_size, self.grid_cell_scale, stride,
self.cell_offset))
mlvl_grid_cells_list = [
multi_level_grid_cells for i in range(batch_size)
]
# pixel cell number of multi-level feature maps
num_level_cells = [
grid_cells.shape[0] for grid_cells in mlvl_grid_cells_list[0]
]
num_level_cells_list = [num_level_cells] * batch_size
# concat all level cells and to a single array
for i in range(batch_size):
mlvl_grid_cells_list[i] = np.concatenate(mlvl_grid_cells_list[i])
# target assign on all images
for sample, grid_cells, num_level_cells in zip(
samples, mlvl_grid_cells_list, num_level_cells_list):
gt_bboxes = sample['gt_bbox']
gt_labels = sample['gt_class'].squeeze()
if gt_labels.size == 1:
gt_labels = np.array([gt_labels]).astype(np.int32)
gt_bboxes_ignore = None
assign_gt_inds, _ = self.assigner(grid_cells, num_level_cells,
gt_bboxes, gt_bboxes_ignore,
gt_labels)
if self.compute_vlr_region:
vlr_region = self.assigner.get_vlr_region(
grid_cells, num_level_cells, gt_bboxes, gt_bboxes_ignore,
gt_labels)
sample['vlr_regions'] = vlr_region
pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds = self.get_sample(
assign_gt_inds, gt_bboxes)
num_cells = grid_cells.shape[0]
bbox_targets = np.zeros_like(grid_cells)
bbox_weights = np.zeros_like(grid_cells)
labels = np.ones([num_cells], dtype=np.int64) * self.num_classes
label_weights = np.zeros([num_cells], dtype=np.float32)
if len(pos_inds) > 0:
pos_bbox_targets = pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_weights[pos_inds, :] = 1.0
if not np.any(gt_labels):
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[pos_assigned_gt_inds]
label_weights[pos_inds] = 1.0
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
sample['grid_cells'] = grid_cells
sample['labels'] = labels
sample['label_weights'] = label_weights
sample['bbox_targets'] = bbox_targets
sample['pos_num'] = max(pos_inds.size, 1)
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
sample.pop('gt_score', None)
return samples
@register_op
class Gt2TTFTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Gt2TTFTarget
Generate TTFNet targets by ground truth data
Args:
num_classes(int): the number of classes.
down_ratio(int): the down ratio from images to heatmap, 4 by default.
alpha(float): the alpha parameter to generate gaussian target.
0.54 by default.
"""
def __init__(self, num_classes=80, down_ratio=4, alpha=0.54):
super(Gt2TTFTarget, self).__init__()
self.down_ratio = down_ratio
self.num_classes = num_classes
self.alpha = alpha
def __call__(self, samples, context=None):
output_size = samples[0]['image'].shape[1]
feat_size = output_size // self.down_ratio
for sample in samples:
heatmap = np.zeros(
(self.num_classes, feat_size, feat_size), dtype='float32')
box_target = np.ones(
(4, feat_size, feat_size), dtype='float32') * -1
reg_weight = np.zeros((1, feat_size, feat_size), dtype='float32')
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
bbox_w = gt_bbox[:, 2] - gt_bbox[:, 0] + 1
bbox_h = gt_bbox[:, 3] - gt_bbox[:, 1] + 1
area = bbox_w * bbox_h
boxes_areas_log = np.log(area)
boxes_ind = np.argsort(boxes_areas_log, axis=0)[::-1]
boxes_area_topk_log = boxes_areas_log[boxes_ind]
gt_bbox = gt_bbox[boxes_ind]
gt_class = gt_class[boxes_ind]
feat_gt_bbox = gt_bbox / self.down_ratio
feat_gt_bbox = np.clip(feat_gt_bbox, 0, feat_size - 1)
feat_hs, feat_ws = (feat_gt_bbox[:, 3] - feat_gt_bbox[:, 1],
feat_gt_bbox[:, 2] - feat_gt_bbox[:, 0])
ct_inds = np.stack(
[(gt_bbox[:, 0] + gt_bbox[:, 2]) / 2,
(gt_bbox[:, 1] + gt_bbox[:, 3]) / 2],
axis=1) / self.down_ratio
h_radiuses_alpha = (feat_hs / 2. * self.alpha).astype('int32')
w_radiuses_alpha = (feat_ws / 2. * self.alpha).astype('int32')
for k in range(len(gt_bbox)):
cls_id = gt_class[k]
fake_heatmap = np.zeros((feat_size, feat_size), dtype='float32')
self.draw_truncate_gaussian(fake_heatmap, ct_inds[k],
h_radiuses_alpha[k],
w_radiuses_alpha[k])
heatmap[cls_id] = np.maximum(heatmap[cls_id], fake_heatmap)
box_target_inds = fake_heatmap > 0
box_target[:, box_target_inds] = gt_bbox[k][:, None]
local_heatmap = fake_heatmap[box_target_inds]
ct_div = np.sum(local_heatmap)
local_heatmap *= boxes_area_topk_log[k]
reg_weight[0, box_target_inds] = local_heatmap / ct_div
sample['ttf_heatmap'] = heatmap
sample['ttf_box_target'] = box_target
sample['ttf_reg_weight'] = reg_weight
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
sample.pop('gt_score', None)
return samples
def draw_truncate_gaussian(self, heatmap, center, h_radius, w_radius):
h, w = 2 * h_radius + 1, 2 * w_radius + 1
sigma_x = w / 6
sigma_y = h / 6
gaussian = gaussian2D((h, w), sigma_x, sigma_y)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, w_radius), min(width - x, w_radius + 1)
top, bottom = min(y, h_radius), min(height - y, h_radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[h_radius - top:h_radius + bottom, w_radius -
left:w_radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
heatmap[y - top:y + bottom, x - left:x + right] = np.maximum(
masked_heatmap, masked_gaussian)
return heatmap
@register_op
class Gt2Solov2Target(BaseOperator):
"""Assign mask target and labels in SOLOv2 network.
The code of this function is based on:
https://github.com/WXinlong/SOLO/blob/master/mmdet/models/anchor_heads/solov2_head.py#L271
Args:
num_grids (list): The list of feature map grids size.
scale_ranges (list): The list of mask boundary range.
coord_sigma (float): The coefficient of coordinate area length.
sampling_ratio (float): The ratio of down sampling.
"""
def __init__(self,
num_grids=[40, 36, 24, 16, 12],
scale_ranges=[[1, 96], [48, 192], [96, 384], [192, 768],
[384, 2048]],
coord_sigma=0.2,
sampling_ratio=4.0):
super(Gt2Solov2Target, self).__init__()
self.num_grids = num_grids
self.scale_ranges = scale_ranges
self.coord_sigma = coord_sigma
self.sampling_ratio = sampling_ratio
def _scale_size(self, im, scale):
h, w = im.shape[:2]
new_size = (int(w * float(scale) + 0.5), int(h * float(scale) + 0.5))
resized_img = cv2.resize(
im, None, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
return resized_img
def __call__(self, samples, context=None):
sample_id = 0
max_ins_num = [0] * len(self.num_grids)
for sample in samples:
gt_bboxes_raw = sample['gt_bbox']
gt_labels_raw = sample['gt_class'] + 1
im_c, im_h, im_w = sample['image'].shape[:]
gt_masks_raw = sample['gt_segm'].astype(np.uint8)
mask_feat_size = [
int(im_h / self.sampling_ratio), int(im_w / self.sampling_ratio)
]
gt_areas = np.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
ins_ind_label_list = []
idx = 0
for (lower_bound, upper_bound), num_grid \
in zip(self.scale_ranges, self.num_grids):
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero()[0]
num_ins = len(hit_indices)
ins_label = []
grid_order = []
cate_label = np.zeros([num_grid, num_grid], dtype=np.int64)
ins_ind_label = np.zeros([num_grid**2], dtype=np.bool_)
if num_ins == 0:
ins_label = np.zeros(
[1, mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
[sample_id * num_grid * num_grid + 0], dtype=np.int32)
idx += 1
continue
gt_bboxes = gt_bboxes_raw[hit_indices]
gt_labels = gt_labels_raw[hit_indices]
gt_masks = gt_masks_raw[hit_indices, ...]
half_ws = 0.5 * (
gt_bboxes[:, 2] - gt_bboxes[:, 0]) * self.coord_sigma
half_hs = 0.5 * (
gt_bboxes[:, 3] - gt_bboxes[:, 1]) * self.coord_sigma
for seg_mask, gt_label, half_h, half_w in zip(
gt_masks, gt_labels, half_hs, half_ws):
if seg_mask.sum() == 0:
continue
# mass center
upsampled_size = (mask_feat_size[0] * 4,
mask_feat_size[1] * 4)
center_h, center_w = ndimage.measurements.center_of_mass(
seg_mask)
coord_w = int(
(center_w / upsampled_size[1]) // (1. / num_grid))
coord_h = int(
(center_h / upsampled_size[0]) // (1. / num_grid))
# left, top, right, down
top_box = max(0,
int(((center_h - half_h) / upsampled_size[0])
// (1. / num_grid)))
down_box = min(num_grid - 1,
int(((center_h + half_h) / upsampled_size[0])
// (1. / num_grid)))
left_box = max(0,
int(((center_w - half_w) / upsampled_size[1])
// (1. / num_grid)))
right_box = min(num_grid - 1,
int(((center_w + half_w) /
upsampled_size[1]) // (1. / num_grid)))
top = max(top_box, coord_h - 1)
down = min(down_box, coord_h + 1)
left = max(coord_w - 1, left_box)
right = min(right_box, coord_w + 1)
cate_label[top:(down + 1), left:(right + 1)] = gt_label
seg_mask = self._scale_size(
seg_mask, scale=1. / self.sampling_ratio)
for i in range(top, down + 1):
for j in range(left, right + 1):
label = int(i * num_grid + j)
cur_ins_label = np.zeros(
[mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
cur_ins_label[:seg_mask.shape[0], :seg_mask.shape[
1]] = seg_mask
ins_label.append(cur_ins_label)
ins_ind_label[label] = True
grid_order.append(sample_id * num_grid * num_grid +
label)
if ins_label == []:
ins_label = np.zeros(
[1, mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
[sample_id * num_grid * num_grid + 0], dtype=np.int32)
else:
ins_label = np.stack(ins_label, axis=0)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
grid_order, dtype=np.int32)
assert len(grid_order) > 0
max_ins_num[idx] = max(
max_ins_num[idx],
sample['ins_label{}'.format(idx)].shape[0])
idx += 1
ins_ind_labels = np.concatenate([
ins_ind_labels_level_img
for ins_ind_labels_level_img in ins_ind_label_list
])
fg_num = np.sum(ins_ind_labels)
sample['fg_num'] = fg_num
sample_id += 1
sample.pop('is_crowd')
sample.pop('gt_class')
sample.pop('gt_bbox')
sample.pop('gt_poly')
sample.pop('gt_segm')
# padding batch
for data in samples:
for idx in range(len(self.num_grids)):
gt_ins_data = np.zeros(
[
max_ins_num[idx],
data['ins_label{}'.format(idx)].shape[1],
data['ins_label{}'.format(idx)].shape[2]
],
dtype=np.uint8)
gt_ins_data[0:data['ins_label{}'.format(idx)].shape[
0], :, :] = data['ins_label{}'.format(idx)]
gt_grid_order = np.zeros([max_ins_num[idx]], dtype=np.int32)
gt_grid_order[0:data['grid_order{}'.format(idx)].shape[
0]] = data['grid_order{}'.format(idx)]
data['ins_label{}'.format(idx)] = gt_ins_data
data['grid_order{}'.format(idx)] = gt_grid_order
return samples
@register_op
class Gt2SparseTarget(BaseOperator):
def __init__(self, use_padding_shape=False):
super(Gt2SparseTarget, self).__init__()
self.use_padding_shape = use_padding_shape
def __call__(self, samples, context=None):
for sample in samples:
ori_h, ori_w = sample['h'], sample['w']
if self.use_padding_shape:
h, w = sample["image"].shape[1:3]
if "scale_factor" in sample:
sf_w, sf_h = sample["scale_factor"][1], sample[
"scale_factor"][0]
sample["scale_factor_whwh"] = np.array(
[sf_w, sf_h, sf_w, sf_h], dtype=np.float32)
else:
sample["scale_factor_whwh"] = np.array(
[1.0, 1.0, 1.0, 1.0], dtype=np.float32)
else:
h, w = round(sample['im_shape'][0]), round(sample['im_shape'][
1])
sample["scale_factor_whwh"] = np.array(
[w / ori_w, h / ori_h, w / ori_w, h / ori_h],
dtype=np.float32)
sample["img_whwh"] = np.array([w, h, w, h], dtype=np.float32)
sample["ori_shape"] = np.array([ori_h, ori_w], dtype=np.int32)
return samples
@register_op
class PadMaskBatch(BaseOperator):
"""
Pad a batch of samples so that they can be divisible by a stride.
The layout of each image should be 'CHW'.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
return_pad_mask (bool): If `return_pad_mask = True`, return
`pad_mask` for transformer.
"""
def __init__(self, pad_to_stride=0, return_pad_mask=True):
super(PadMaskBatch, self).__init__()
self.pad_to_stride = pad_to_stride
self.return_pad_mask = return_pad_mask
def __call__(self, samples, context=None):
"""
Args:
samples (list): a batch of sample, each is dict.
"""
coarsest_stride = self.pad_to_stride
max_shape = np.array([data['image'].shape for data in samples]).max(
axis=0)
if coarsest_stride > 0:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
for data in samples:
im = data['image']
im_c, im_h, im_w = im.shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im.astype(np.float32)
data['image'] = padding_im
if 'semantic' in data and data['semantic'] is not None:
semantic = data['semantic']
padding_sem = np.zeros(
(1, max_shape[1], max_shape[2]), dtype=np.float32)
padding_sem[:, :im_h, :im_w] = semantic
data['semantic'] = padding_sem
if 'gt_segm' in data and data['gt_segm'] is not None:
gt_segm = data['gt_segm']
padding_segm = np.zeros(
(gt_segm.shape[0], max_shape[1], max_shape[2]),
dtype=np.uint8)
padding_segm[:, :im_h, :im_w] = gt_segm
data['gt_segm'] = padding_segm
if self.return_pad_mask:
padding_mask = np.zeros(
(max_shape[1], max_shape[2]), dtype=np.float32)
padding_mask[:im_h, :im_w] = 1.
data['pad_mask'] = padding_mask
return samples
@register_op
class Gt2CenterNetTarget(BaseOperator):
__shared__ = ['num_classes']
"""Gt2CenterNetTarget
Genterate CenterNet targets by ground-truth
Args:
down_ratio (int): The down sample ratio between output feature and
input image.
num_classes (int): The number of classes, 80 by default.
max_objs (int): The maximum objects detected, 128 by default.
"""
def __init__(self, num_classes=80, down_ratio=4, max_objs=128):
super(Gt2CenterNetTarget, self).__init__()
self.nc = num_classes
self.down_ratio = down_ratio
self.max_objs = max_objs
def __call__(self, sample, context=None):
input_h, input_w = sample['image'].shape[1:]
output_h = input_h // self.down_ratio
output_w = input_w // self.down_ratio
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
hm = np.zeros((self.nc, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.int32)
cat_spec_wh = np.zeros((self.max_objs, self.nc * 2), dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, self.nc * 2), dtype=np.int32)
trans_output = get_affine_transform(
center=sample['center'],
input_size=[sample['scale'], sample['scale']],
rot=0,
output_size=[output_w, output_h])
gt_det = []
for i, (bbox, cls) in enumerate(zip(gt_bbox, gt_class)):
cls = int(cls)
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox_amodal = copy.deepcopy(bbox)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
# get hm,wh,reg,ind,ind_mask
draw_umich_gaussian(hm[cls], ct_int, radius)
wh[i] = 1. * w, 1. * h
reg[i] = ct - ct_int
ind[i] = ct_int[1] * output_w + ct_int[0]
reg_mask[i] = 1
cat_spec_wh[i, cls * 2:cls * 2 + 2] = wh[i]
cat_spec_mask[i, cls * 2:cls * 2 + 2] = 1
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls
])
sample.pop('gt_bbox', None)
sample.pop('gt_class', None)
sample.pop('center', None)
sample.pop('scale', None)
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample['index'] = ind
sample['index_mask'] = reg_mask
sample['heatmap'] = hm
sample['size'] = wh
sample['offset'] = reg
return sample
@register_op
class PadGT(BaseOperator):
"""
Pad 0 to `gt_class`, `gt_bbox`, `gt_score`...
The num_max_boxes is the largest for batch.
Args:
return_gt_mask (bool): If true, return `pad_gt_mask`,
1 means bbox, 0 means no bbox.
"""
def __init__(self, return_gt_mask=True, pad_img=False, minimum_gtnum=0):
super(PadGT, self).__init__()
self.return_gt_mask = return_gt_mask
self.pad_img = pad_img
self.minimum_gtnum = minimum_gtnum
def _impad(self,
img: np.ndarray,
*,
shape=None,
padding=None,
pad_val=0,
padding_mode='constant') -> np.ndarray:
"""Pad the given image to a certain shape or pad on all sides with
specified padding mode and padding value.
Args:
img (ndarray): Image to be padded.
shape (tuple[int]): Expected padding shape (h, w). Default: None.
padding (int or tuple[int]): Padding on each border. If a single int is
provided this is used to pad all borders. If tuple of length 2 is
provided this is the padding on left/right and top/bottom
respectively. If a tuple of length 4 is provided this is the
padding for the left, top, right and bottom borders respectively.
Default: None. Note that `shape` and `padding` can not be both
set.
pad_val (Number | Sequence[Number]): Values to be filled in padding
areas when padding_mode is 'constant'. Default: 0.
padding_mode (str): Type of padding. Should be: constant, edge,
reflect or symmetric. Default: constant.
- constant: pads with a constant value, this value is specified
with pad_val.
- edge: pads with the last value at the edge of the image.
- reflect: pads with reflection of image without repeating the last
value on the edge. For example, padding [1, 2, 3, 4] with 2
elements on both sides in reflect mode will result in
[3, 2, 1, 2, 3, 4, 3, 2].
- symmetric: pads with reflection of image repeating the last value
on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
both sides in symmetric mode will result in
[2, 1, 1, 2, 3, 4, 4, 3]
Returns:
ndarray: The padded image.
"""
assert (shape is not None) ^ (padding is not None)
if shape is not None:
width = max(shape[1] - img.shape[1], 0)
height = max(shape[0] - img.shape[0], 0)
padding = (0, 0, int(width), int(height))
# check pad_val
import numbers
if isinstance(pad_val, tuple):
assert len(pad_val) == img.shape[-1]
elif not isinstance(pad_val, numbers.Number):
raise TypeError('pad_val must be a int or a tuple. '
f'But received {type(pad_val)}')
# check padding
if isinstance(padding, tuple) and len(padding) in [2, 4]:
if len(padding) == 2:
padding = (padding[0], padding[1], padding[0], padding[1])
elif isinstance(padding, numbers.Number):
padding = (padding, padding, padding, padding)
else:
raise ValueError('Padding must be a int or a 2, or 4 element tuple.'
f'But received {padding}')
# check padding mode
assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
border_type = {
'constant': cv2.BORDER_CONSTANT,
'edge': cv2.BORDER_REPLICATE,
'reflect': cv2.BORDER_REFLECT_101,
'symmetric': cv2.BORDER_REFLECT
}
img = cv2.copyMakeBorder(
img,
padding[1],
padding[3],
padding[0],
padding[2],
border_type[padding_mode],
value=pad_val)
return img
def checkmaxshape(self, samples):
maxh, maxw = 0, 0
for sample in samples:
h, w = sample['im_shape']
if h > maxh:
maxh = h
if w > maxw:
maxw = w
return (maxh, maxw)
def __call__(self, samples, context=None):
num_max_boxes = max([len(s['gt_bbox']) for s in samples])
num_max_boxes = max(self.minimum_gtnum, num_max_boxes)
if self.pad_img:
maxshape = self.checkmaxshape(samples)
for sample in samples:
if self.pad_img:
img = sample['image']
padimg = self._impad(img, shape=maxshape)
sample['image'] = padimg
if self.return_gt_mask:
sample['pad_gt_mask'] = np.zeros(
(num_max_boxes, 1), dtype=np.float32)
if num_max_boxes == 0:
continue
num_gt = len(sample['gt_bbox'])
pad_gt_class = np.zeros((num_max_boxes, 1), dtype=np.int32)
pad_gt_bbox = np.zeros((num_max_boxes, 4), dtype=np.float32)
if num_gt > 0:
pad_gt_class[:num_gt] = sample['gt_class']
pad_gt_bbox[:num_gt] = sample['gt_bbox']
sample['gt_class'] = pad_gt_class
sample['gt_bbox'] = pad_gt_bbox
# pad_gt_mask
if 'pad_gt_mask' in sample:
sample['pad_gt_mask'][:num_gt] = 1
# gt_score
if 'gt_score' in sample:
pad_gt_score = np.zeros((num_max_boxes, 1), dtype=np.float32)
if num_gt > 0:
pad_gt_score[:num_gt] = sample['gt_score']
sample['gt_score'] = pad_gt_score
if 'is_crowd' in sample:
pad_is_crowd = np.zeros((num_max_boxes, 1), dtype=np.int32)
if num_gt > 0:
pad_is_crowd[:num_gt] = sample['is_crowd']
sample['is_crowd'] = pad_is_crowd
if 'difficult' in sample:
pad_diff = np.zeros((num_max_boxes, 1), dtype=np.int32)
if num_gt > 0:
pad_diff[:num_gt] = sample['difficult']
sample['difficult'] = pad_diff
if 'gt_joints' in sample:
num_joints = sample['gt_joints'].shape[1]
pad_gt_joints = np.zeros(
(num_max_boxes, num_joints, 3), dtype=np.float32)
if num_gt > 0:
pad_gt_joints[:num_gt] = sample['gt_joints']
sample['gt_joints'] = pad_gt_joints
if 'gt_areas' in sample:
pad_gt_areas = np.zeros((num_max_boxes, 1), dtype=np.float32)
if num_gt > 0:
pad_gt_areas[:num_gt, 0] = sample['gt_areas']
sample['gt_areas'] = pad_gt_areas
return samples
@register_op
class PadRGT(BaseOperator):
"""
Pad 0 to `gt_class`, `gt_bbox`, `gt_score`...
The num_max_boxes is the largest for batch.
Args:
return_gt_mask (bool): If true, return `pad_gt_mask`,
1 means bbox, 0 means no bbox.
"""
def __init__(self, return_gt_mask=True):
super(PadRGT, self).__init__()
self.return_gt_mask = return_gt_mask
def pad_field(self, sample, field, num_gt):
name, shape, dtype = field
if name in sample:
pad_v = np.zeros(shape, dtype=dtype)
if num_gt > 0:
pad_v[:num_gt] = sample[name]
sample[name] = pad_v
def __call__(self, samples, context=None):
num_max_boxes = max([len(s['gt_bbox']) for s in samples])
for sample in samples:
if self.return_gt_mask:
sample['pad_gt_mask'] = np.zeros(
(num_max_boxes, 1), dtype=np.float32)
if num_max_boxes == 0:
continue
num_gt = len(sample['gt_bbox'])
pad_gt_class = np.zeros((num_max_boxes, 1), dtype=np.int32)
pad_gt_bbox = np.zeros((num_max_boxes, 4), dtype=np.float32)
if num_gt > 0:
pad_gt_class[:num_gt] = sample['gt_class']
pad_gt_bbox[:num_gt] = sample['gt_bbox']
sample['gt_class'] = pad_gt_class
sample['gt_bbox'] = pad_gt_bbox
# pad_gt_mask
if 'pad_gt_mask' in sample:
sample['pad_gt_mask'][:num_gt] = 1
# gt_score
names = ['gt_score', 'is_crowd', 'difficult', 'gt_poly', 'gt_rbox']
dims = [1, 1, 1, 8, 5]
dtypes = [np.float32, np.int32, np.int32, np.float32, np.float32]
for name, dim, dtype in zip(names, dims, dtypes):
self.pad_field(sample, [name, (num_max_boxes, dim), dtype],
num_gt)
return samples
@register_op
class Gt2CenterTrackTarget(BaseOperator):
__shared__ = ['num_classes']
"""Gt2CenterTrackTarget
Genterate CenterTrack targets by ground-truth
Args:
num_classes (int): The number of classes, 1 by default.
down_ratio (int): The down sample ratio between output feature and
input image.
max_objs (int): The maximum objects detected, 256 by default.
"""
def __init__(self,
num_classes=1,
down_ratio=4,
max_objs=256,
hm_disturb=0.05,
lost_disturb=0.4,
fp_disturb=0.1,
pre_hm=True,
add_tracking=True,
add_ltrb_amodal=True):
super(Gt2CenterTrackTarget, self).__init__()
self.nc = num_classes
self.down_ratio = down_ratio
self.max_objs = max_objs
self.hm_disturb = hm_disturb
self.lost_disturb = lost_disturb
self.fp_disturb = fp_disturb
self.pre_hm = pre_hm
self.add_tracking = add_tracking
self.add_ltrb_amodal = add_ltrb_amodal
def _get_pre_dets(self, input_h, input_w, trans_input_pre, gt_bbox_pre,
gt_class_pre, gt_track_id_pre):
hm_h, hm_w = input_h, input_w
reutrn_hm = self.pre_hm
pre_hm = np.zeros(
(1, hm_h, hm_w), dtype=np.float32) if reutrn_hm else None
pre_cts, track_ids = [], []
for i, (
bbox, cls, track_id
) in enumerate(zip(gt_bbox_pre, gt_class_pre, gt_track_id_pre)):
cls = int(cls)
bbox[:2] = affine_transform(bbox[:2], trans_input_pre)
bbox[2:] = affine_transform(bbox[2:], trans_input_pre)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, hm_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, hm_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
max_rad = 1
if (h > 0 and w > 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
max_rad = max(max_rad, radius)
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct0 = ct.copy()
conf = 1
ct[0] = ct[0] + np.random.randn() * self.hm_disturb * w
ct[1] = ct[1] + np.random.randn() * self.hm_disturb * h
conf = 1 if np.random.rand() > self.lost_disturb else 0
ct_int = ct.astype(np.int32)
if conf == 0:
pre_cts.append(ct / self.down_ratio)
else:
pre_cts.append(ct0 / self.down_ratio)
track_ids.append(track_id)
if reutrn_hm:
draw_umich_gaussian(pre_hm[0], ct_int, radius, k=conf)
if np.random.rand() < self.fp_disturb and reutrn_hm:
ct2 = ct0.copy()
# Hard code heatmap disturb ratio, haven't tried other numbers.
ct2[0] = ct2[0] + np.random.randn() * 0.05 * w
ct2[1] = ct2[1] + np.random.randn() * 0.05 * h
ct2_int = ct2.astype(np.int32)
draw_umich_gaussian(pre_hm[0], ct2_int, radius, k=conf)
return pre_hm, pre_cts, track_ids
def __call__(self, sample, context=None):
input_h, input_w = sample['image'].shape[1:]
output_h = input_h // self.down_ratio
output_w = input_w // self.down_ratio
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
# init
hm = np.zeros((self.nc, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.int32)
if self.add_tracking:
tr = np.zeros((self.max_objs, 2), dtype=np.float32)
if self.add_ltrb_amodal:
ltrb_amodal = np.zeros((self.max_objs, 4), dtype=np.float32)
trans_output = get_affine_transform(
center=sample['center'],
input_size=[sample['scale'], sample['scale']],
rot=0,
output_size=[output_w, output_h])
pre_hm, pre_cts, track_ids = self._get_pre_dets(
input_h, input_w, sample['trans_input'], sample['pre_gt_bbox'],
sample['pre_gt_class'], sample['pre_gt_track_id'])
for i, (bbox, cls) in enumerate(zip(gt_bbox, gt_class)):
cls = int(cls)
rect = np.array(
[[bbox[0], bbox[1]], [bbox[0], bbox[3]], [bbox[2], bbox[3]],
[bbox[2], bbox[1]]],
dtype=np.float32)
for t in range(4):
rect[t] = affine_transform(rect[t], trans_output)
bbox[:2] = rect[:, 0].min(), rect[:, 1].min()
bbox[2:] = rect[:, 0].max(), rect[:, 1].max()
bbox_amodal = copy.deepcopy(bbox)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
# get hm,wh,reg,ind,ind_mask
draw_umich_gaussian(hm[cls], ct_int, radius)
wh[i] = 1. * w, 1. * h
reg[i] = ct - ct_int
ind[i] = ct_int[1] * output_w + ct_int[0]
reg_mask[i] = 1
if self.add_tracking:
if sample['gt_track_id'][i] in track_ids:
pre_ct = pre_cts[track_ids.index(sample['gt_track_id'][
i])]
tr[i] = pre_ct - ct_int
if self.add_ltrb_amodal:
ltrb_amodal[i] = \
bbox_amodal[0] - ct_int[0], bbox_amodal[1] - ct_int[1], \
bbox_amodal[2] - ct_int[0], bbox_amodal[3] - ct_int[1]
new_sample = {'image': sample['image']}
new_sample['index'] = ind
new_sample['index_mask'] = reg_mask
new_sample['heatmap'] = hm
new_sample['size'] = wh
new_sample['offset'] = reg
if self.add_tracking:
new_sample['tracking'] = tr
if self.add_ltrb_amodal:
new_sample['ltrb_amodal'] = ltrb_amodal
new_sample['pre_image'] = sample['pre_image']
new_sample['pre_hm'] = pre_hm
del sample
return new_sample
@register_op
class BatchRandomResizeForSSOD(BaseOperator):
"""
Resize image to target size randomly. random target_size and interpolation method
Args:
target_size (int, list, tuple): image target size, if random size is True, must be list or tuple
keep_ratio (bool): whether keep_raio or not, default true
interp (int): the interpolation method
random_size (bool): whether random select target size of image
random_interp (bool): whether random select interpolation method
"""
def __init__(self,
target_size,
keep_ratio,
interp=cv2.INTER_NEAREST,
random_size=True,
random_interp=False):
super(BatchRandomResizeForSSOD, self).__init__()
self.keep_ratio = keep_ratio
self.interps = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
self.interp = interp
assert isinstance(target_size, (
int, Sequence)), "target_size must be int, list or tuple"
if random_size and not isinstance(target_size, list):
raise TypeError(
"Type of target_size is invalid when random_size is True. Must be List, now is {}".
format(type(target_size)))
self.target_size = target_size
self.random_size = random_size
self.random_interp = random_interp
def __call__(self, samples, context=None):
if self.random_size:
index = np.random.choice(len(self.target_size))
target_size = self.target_size[index]
else:
target_size = self.target_size
if context is not None:
target_size = self.target_size[context]
if self.random_interp:
interp = np.random.choice(self.interps)
else:
interp = self.interp
resizer = Resize(target_size, keep_ratio=self.keep_ratio, interp=interp)
return [resizer(samples, context=context), index]
| PaddleDetection/ppdet/data/transform/batch_operators.py/0 | {
"file_path": "PaddleDetection/ppdet/data/transform/batch_operators.py",
"repo_id": "PaddleDetection",
"token_count": 35484
} | 72 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ppdet.core.workspace import create
from ppdet.utils.logger import setup_logger
logger = setup_logger('ppdet.engine')
from . import Trainer
__all__ = ['TrainerCot']
class TrainerCot(Trainer):
"""
Trainer for label-cotuning
calculate the relationship between base_classes and novel_classes
"""
def __init__(self, cfg, mode='train'):
super(TrainerCot, self).__init__(cfg, mode)
self.cotuning_init()
def cotuning_init(self):
num_classes_novel = self.cfg['num_classes']
self.load_weights(self.cfg.pretrain_weights)
self.model.eval()
relationship = self.model.relationship_learning(self.loader, num_classes_novel)
self.model.init_cot_head(relationship)
self.optimizer = create('OptimizerBuilder')(self.lr, self.model)
| PaddleDetection/ppdet/engine/trainer_cot.py/0 | {
"file_path": "PaddleDetection/ppdet/engine/trainer_cot.py",
"repo_id": "PaddleDetection",
"token_count": 483
} | 73 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import six
import numpy as np
def get_det_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
det_res = []
k = 0
for i in range(len(bbox_nums)):
cur_image_id = int(image_id[i][0])
det_nums = bbox_nums[i]
for j in range(det_nums):
dt = bboxes[k]
k = k + 1
num_id, score, xmin, ymin, xmax, ymax = dt.tolist()
if int(num_id) < 0:
continue
category_id = label_to_cat_id_map[int(num_id)]
w = xmax - xmin + bias
h = ymax - ymin + bias
bbox = [xmin, ymin, w, h]
dt_res = {
'image_id': cur_image_id,
'category_id': category_id,
'bbox': bbox,
'score': score
}
det_res.append(dt_res)
return det_res
def get_det_poly_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
det_res = []
k = 0
for i in range(len(bbox_nums)):
cur_image_id = int(image_id[i][0])
det_nums = bbox_nums[i]
for j in range(det_nums):
dt = bboxes[k]
k = k + 1
num_id, score, x1, y1, x2, y2, x3, y3, x4, y4 = dt.tolist()
if int(num_id) < 0:
continue
category_id = label_to_cat_id_map[int(num_id)]
rbox = [x1, y1, x2, y2, x3, y3, x4, y4]
dt_res = {
'image_id': cur_image_id,
'category_id': category_id,
'bbox': rbox,
'score': score
}
det_res.append(dt_res)
return det_res
def strip_mask(mask):
row = mask[0, 0, :]
col = mask[0, :, 0]
im_h = len(col) - np.count_nonzero(col == -1)
im_w = len(row) - np.count_nonzero(row == -1)
return mask[:, :im_h, :im_w]
def get_seg_res(masks, bboxes, mask_nums, image_id, label_to_cat_id_map):
import pycocotools.mask as mask_util
seg_res = []
k = 0
for i in range(len(mask_nums)):
cur_image_id = int(image_id[i][0])
det_nums = mask_nums[i]
mask_i = masks[k:k + det_nums]
mask_i = strip_mask(mask_i)
for j in range(det_nums):
mask = mask_i[j].astype(np.uint8)
score = float(bboxes[k][1])
label = int(bboxes[k][0])
k = k + 1
if label == -1:
continue
cat_id = label_to_cat_id_map[label]
rle = mask_util.encode(
np.array(
mask[:, :, None], order="F", dtype="uint8"))[0]
if six.PY3:
if 'counts' in rle:
rle['counts'] = rle['counts'].decode("utf8")
sg_res = {
'image_id': cur_image_id,
'category_id': cat_id,
'segmentation': rle,
'score': score
}
seg_res.append(sg_res)
return seg_res
def get_solov2_segm_res(results, image_id, num_id_to_cat_id_map):
import pycocotools.mask as mask_util
segm_res = []
# for each batch
segms = results['segm'].astype(np.uint8)
clsid_labels = results['cate_label']
clsid_scores = results['cate_score']
lengths = segms.shape[0]
im_id = int(image_id[0][0])
if lengths == 0 or segms is None:
return None
# for each sample
for i in range(lengths - 1):
clsid = int(clsid_labels[i])
catid = num_id_to_cat_id_map[clsid]
score = float(clsid_scores[i])
mask = segms[i]
segm = mask_util.encode(np.array(mask[:, :, np.newaxis], order='F'))[0]
segm['counts'] = segm['counts'].decode('utf8')
coco_res = {
'image_id': im_id,
'category_id': catid,
'segmentation': segm,
'score': score
}
segm_res.append(coco_res)
return segm_res
def get_keypoint_res(results, im_id):
anns = []
preds = results['keypoint']
for idx in range(im_id.shape[0]):
image_id = im_id[idx].item()
kpts, scores = preds[idx]
for kpt, score in zip(kpts, scores):
kpt = kpt.flatten()
ann = {
'image_id': image_id,
'category_id': 1, # XXX hard code
'keypoints': kpt.tolist(),
'score': float(score)
}
x = kpt[0::3]
y = kpt[1::3]
x0, x1, y0, y1 = np.min(x).item(), np.max(x).item(), np.min(y).item(
), np.max(y).item()
ann['area'] = (x1 - x0) * (y1 - y0)
ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]
anns.append(ann)
return anns
def get_pose3d_res(results, im_id):
anns = []
preds = results['pose3d']
for idx in range(im_id.shape[0]):
image_id = im_id[idx].item()
pose3d = preds[idx]
ann = {
'image_id': image_id,
'category_id': 1, # XXX hard code
'pose3d': pose3d.tolist(),
'score': float(1.)
}
anns.append(ann)
return anns
| PaddleDetection/ppdet/metrics/json_results.py/0 | {
"file_path": "PaddleDetection/ppdet/metrics/json_results.py",
"repo_id": "PaddleDetection",
"token_count": 3118
} | 74 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import meta_arch
from . import faster_rcnn
from . import mask_rcnn
from . import yolo
from . import ppyoloe
from . import cascade_rcnn
from . import ssd
from . import fcos
from . import solov2
from . import ttfnet
from . import s2anet
from . import keypoint_hrhrnet
from . import keypoint_hrnet
from . import keypoint_vitpose
from . import jde
from . import deepsort
from . import fairmot
from . import centernet
from . import gfl
from . import picodet
from . import detr
from . import sparse_rcnn
from . import tood
from . import retinanet
from . import bytetrack
from . import yolox
from . import yolof
from . import pose3d_metro
from . import centertrack
from . import queryinst
from . import detr_ssod
from . import multi_stream_detector
from . import clrnet
from .meta_arch import *
from .faster_rcnn import *
from .mask_rcnn import *
from .yolo import *
from .ppyoloe import *
from .cascade_rcnn import *
from .ssd import *
from .fcos import *
from .solov2 import *
from .ttfnet import *
from .s2anet import *
from .keypoint_hrhrnet import *
from .keypoint_hrnet import *
from .keypoint_vitpose import *
from .jde import *
from .deepsort import *
from .fairmot import *
from .centernet import *
from .blazeface import *
from .gfl import *
from .picodet import *
from .detr import *
from .sparse_rcnn import *
from .tood import *
from .retinanet import *
from .bytetrack import *
from .yolox import *
from .yolof import *
from .pose3d_metro import *
from .centertrack import *
from .queryinst import *
from .keypoint_petr import *
from .detr_ssod import *
from .multi_stream_detector import *
from .clrnet import *
| PaddleDetection/ppdet/modeling/architectures/__init__.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/architectures/__init__.py",
"repo_id": "PaddleDetection",
"token_count": 736
} | 75 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import numpy as np
import math
import cv2
from ppdet.core.workspace import register, create
from .meta_arch import BaseArch
from ..keypoint_utils import transform_preds
from .. import layers as L
from paddle.nn import functional as F
__all__ = ['TopDownHRNet', 'TinyPose3DHRNet', 'TinyPose3DHRHeatmapNet']
@register
class TopDownHRNet(BaseArch):
__category__ = 'architecture'
__inject__ = ['loss']
def __init__(self,
width,
num_joints,
backbone='HRNet',
loss='KeyPointMSELoss',
post_process='HRNetPostProcess',
flip_perm=None,
flip=True,
shift_heatmap=True,
use_dark=True):
"""
HRNet network, see https://arxiv.org/abs/1902.09212
Args:
backbone (nn.Layer): backbone instance
post_process (object): `HRNetPostProcess` instance
flip_perm (list): The left-right joints exchange order list
use_dark(bool): Whether to use DARK in post processing
"""
super(TopDownHRNet, self).__init__()
self.backbone = backbone
self.post_process = HRNetPostProcess(use_dark)
self.loss = loss
self.flip_perm = flip_perm
self.flip = flip
self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True)
self.shift_heatmap = shift_heatmap
self.deploy = False
@classmethod
def from_config(cls, cfg, *args, **kwargs):
# backbone
backbone = create(cfg['backbone'])
return {'backbone': backbone, }
def _forward(self):
feats = self.backbone(self.inputs)
hrnet_outputs = self.final_conv(feats[0])
if self.training:
return self.loss(hrnet_outputs, self.inputs)
elif self.deploy:
outshape = hrnet_outputs.shape
max_idx = paddle.argmax(
hrnet_outputs.reshape(
(outshape[0], outshape[1], outshape[2] * outshape[3])),
axis=-1)
return hrnet_outputs, max_idx
else:
if self.flip:
self.inputs['image'] = self.inputs['image'].flip([3])
feats = self.backbone(self.inputs)
output_flipped = self.final_conv(feats[0])
output_flipped = self.flip_back(output_flipped.numpy(),
self.flip_perm)
output_flipped = paddle.to_tensor(output_flipped.copy())
if self.shift_heatmap:
output_flipped[:, :, :, 1:] = output_flipped.clone(
)[:, :, :, 0:-1]
hrnet_outputs = (hrnet_outputs + output_flipped) * 0.5
imshape = (self.inputs['im_shape'].numpy()
)[:, ::-1] if 'im_shape' in self.inputs else None
center = self.inputs['center'].numpy(
) if 'center' in self.inputs else np.round(imshape / 2.)
scale = self.inputs['scale'].numpy(
) if 'scale' in self.inputs else imshape / 200.
outputs = self.post_process(hrnet_outputs, center, scale)
return outputs
def get_loss(self):
return self._forward()
def get_pred(self):
res_lst = self._forward()
outputs = {'keypoint': res_lst}
return outputs
def flip_back(self, output_flipped, matched_parts):
assert output_flipped.ndim == 4,\
'output_flipped should be [batch_size, num_joints, height, width]'
output_flipped = output_flipped[:, :, :, ::-1]
for pair in matched_parts:
tmp = output_flipped[:, pair[0], :, :].copy()
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
output_flipped[:, pair[1], :, :] = tmp
return output_flipped
class HRNetPostProcess(object):
def __init__(self, use_dark=True):
self.use_dark = use_dark
def get_max_preds(self, heatmaps):
'''get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
'''
assert isinstance(heatmaps,
np.ndarray), 'heatmaps should be numpy.ndarray'
assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
batch_size = heatmaps.shape[0]
num_joints = heatmaps.shape[1]
width = heatmaps.shape[3]
heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
idx = np.argmax(heatmaps_reshaped, 2)
maxvals = np.amax(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals
def gaussian_blur(self, heatmap, kernel):
border = (kernel - 1) // 2
batch_size = heatmap.shape[0]
num_joints = heatmap.shape[1]
height = heatmap.shape[2]
width = heatmap.shape[3]
for i in range(batch_size):
for j in range(num_joints):
origin_max = np.max(heatmap[i, j])
dr = np.zeros((height + 2 * border, width + 2 * border))
dr[border:-border, border:-border] = heatmap[i, j].copy()
dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
heatmap[i, j] = dr[border:-border, border:-border].copy()
heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
return heatmap
def dark_parse(self, hm, coord):
heatmap_height = hm.shape[0]
heatmap_width = hm.shape[1]
px = int(coord[0])
py = int(coord[1])
if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
+ hm[py-1][px-1])
dyy = 0.25 * (
hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
derivative = np.matrix([[dx], [dy]])
hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
if dxx * dyy - dxy**2 != 0:
hessianinv = hessian.I
offset = -hessianinv * derivative
offset = np.squeeze(np.array(offset.T), axis=0)
coord += offset
return coord
def dark_postprocess(self, hm, coords, kernelsize):
'''DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
Representation for Human Pose Estimation (CVPR 2020).
'''
hm = self.gaussian_blur(hm, kernelsize)
hm = np.maximum(hm, 1e-10)
hm = np.log(hm)
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
return coords
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
"""the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
"""
coords, maxvals = self.get_max_preds(heatmaps)
heatmap_height = heatmaps.shape[2]
heatmap_width = heatmaps.shape[3]
if self.use_dark:
coords = self.dark_postprocess(heatmaps, coords, kernelsize)
else:
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
hm = heatmaps[n][p]
px = int(math.floor(coords[n][p][0] + 0.5))
py = int(math.floor(coords[n][p][1] + 0.5))
if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
diff = np.array([
hm[py][px + 1] - hm[py][px - 1],
hm[py + 1][px] - hm[py - 1][px]
])
coords[n][p] += np.sign(diff) * .25
preds = coords.copy()
# Transform back
for i in range(coords.shape[0]):
preds[i] = transform_preds(coords[i], center[i], scale[i],
[heatmap_width, heatmap_height])
return preds, maxvals
def __call__(self, output, center, scale):
preds, maxvals = self.get_final_preds(output.numpy(), center, scale)
outputs = [[
np.concatenate(
(preds, maxvals), axis=-1), np.mean(
maxvals, axis=1)
]]
return outputs
class TinyPose3DPostProcess(object):
def __init__(self):
pass
def __call__(self, output, center, scale):
"""
Args:
output (numpy.ndarray): numpy.ndarray([batch_size, num_joints, 3]), keypoints coords
scale (numpy.ndarray): The scale factor
Returns:
preds: numpy.ndarray([batch_size, num_joints, 3]), keypoints coords
"""
preds = output.numpy().copy()
# Transform back
for i in range(output.shape[0]): # batch_size
preds[i][:, 0] = preds[i][:, 0] * scale[i][0]
preds[i][:, 1] = preds[i][:, 1] * scale[i][1]
return preds
def soft_argmax(heatmaps, joint_num):
dims = heatmaps.shape
depth_dim = (int)(dims[1] / joint_num)
heatmaps = heatmaps.reshape((-1, joint_num, depth_dim * dims[2] * dims[3]))
heatmaps = F.softmax(heatmaps, 2)
heatmaps = heatmaps.reshape((-1, joint_num, depth_dim, dims[2], dims[3]))
accu_x = heatmaps.sum(axis=(2, 3))
accu_y = heatmaps.sum(axis=(2, 4))
accu_z = heatmaps.sum(axis=(3, 4))
accu_x = accu_x * paddle.arange(1, 33)
accu_y = accu_y * paddle.arange(1, 33)
accu_z = accu_z * paddle.arange(1, 33)
accu_x = accu_x.sum(axis=2, keepdim=True) - 1
accu_y = accu_y.sum(axis=2, keepdim=True) - 1
accu_z = accu_z.sum(axis=2, keepdim=True) - 1
coord_out = paddle.concat(
(accu_x, accu_y, accu_z), axis=2) # [batch_size, joint_num, 3]
return coord_out
@register
class TinyPose3DHRHeatmapNet(BaseArch):
__category__ = 'architecture'
__inject__ = ['loss']
def __init__(
self,
width, # 40, backbone输出的channel数目
num_joints,
backbone='HRNet',
loss='KeyPointRegressionMSELoss',
post_process=TinyPose3DPostProcess):
"""
Args:
backbone (nn.Layer): backbone instance
post_process (object): post process instance
"""
super(TinyPose3DHRHeatmapNet, self).__init__()
self.backbone = backbone
self.post_process = TinyPose3DPostProcess()
self.loss = loss
self.deploy = False
self.num_joints = num_joints
self.final_conv = L.Conv2d(width, num_joints * 32, 1, 1, 0, bias=True)
@classmethod
def from_config(cls, cfg, *args, **kwargs):
# backbone
backbone = create(cfg['backbone'])
return {'backbone': backbone, }
def _forward(self):
feats = self.backbone(self.inputs) # feats:[[batch_size, 40, 32, 24]]
hrnet_outputs = self.final_conv(feats[0])
res = soft_argmax(hrnet_outputs, self.num_joints)
return res
def get_loss(self):
pose3d = self._forward()
loss = self.loss(pose3d, None, self.inputs)
outputs = {'loss': loss}
return outputs
def get_pred(self):
res_lst = self._forward()
outputs = {'pose3d': res_lst}
return outputs
def flip_back(self, output_flipped, matched_parts):
assert output_flipped.ndim == 4,\
'output_flipped should be [batch_size, num_joints, height, width]'
output_flipped = output_flipped[:, :, :, ::-1]
for pair in matched_parts:
tmp = output_flipped[:, pair[0], :, :].copy()
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
output_flipped[:, pair[1], :, :] = tmp
return output_flipped
@register
class TinyPose3DHRNet(BaseArch):
__category__ = 'architecture'
__inject__ = ['loss']
def __init__(self,
width,
num_joints,
fc_channel=768,
backbone='HRNet',
loss='KeyPointRegressionMSELoss',
post_process=TinyPose3DPostProcess):
"""
Args:
backbone (nn.Layer): backbone instance
post_process (object): post process instance
"""
super(TinyPose3DHRNet, self).__init__()
self.backbone = backbone
self.post_process = TinyPose3DPostProcess()
self.loss = loss
self.deploy = False
self.num_joints = num_joints
self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True)
self.flatten = paddle.nn.Flatten(start_axis=2, stop_axis=3)
self.fc1 = paddle.nn.Linear(fc_channel, 256)
self.act1 = paddle.nn.ReLU()
self.fc2 = paddle.nn.Linear(256, 64)
self.act2 = paddle.nn.ReLU()
self.fc3 = paddle.nn.Linear(64, 3)
@classmethod
def from_config(cls, cfg, *args, **kwargs):
# backbone
backbone = create(cfg['backbone'])
return {'backbone': backbone, }
def _forward(self):
'''
self.inputs is a dict
'''
feats = self.backbone(
self.inputs) # feats:[[batch_size, 40, width/4, height/4]]
hrnet_outputs = self.final_conv(
feats[0]) # hrnet_outputs: [batch_size, num_joints*32,32,32]
flatten_res = self.flatten(
hrnet_outputs) # [batch_size,num_joints*32,32*32]
res = self.fc1(flatten_res)
res = self.act1(res)
res = self.fc2(res)
res = self.act2(res)
res = self.fc3(res)
if self.training:
return self.loss(res, self.inputs)
else: # export model need
return res
def get_loss(self):
return self._forward()
def get_pred(self):
res_lst = self._forward()
outputs = {'pose3d': res_lst}
return outputs
def flip_back(self, output_flipped, matched_parts):
assert output_flipped.ndim == 4,\
'output_flipped should be [batch_size, num_joints, height, width]'
output_flipped = output_flipped[:, :, :, ::-1]
for pair in matched_parts:
tmp = output_flipped[:, pair[0], :, :].copy()
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
output_flipped[:, pair[1], :, :] = tmp
return output_flipped
| PaddleDetection/ppdet/modeling/architectures/keypoint_hrnet.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/architectures/keypoint_hrnet.py",
"repo_id": "PaddleDetection",
"token_count": 8120
} | 76 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from ppdet.core.workspace import register, create
from .meta_arch import BaseArch
__all__ = ['TTFNet']
@register
class TTFNet(BaseArch):
"""
TTFNet network, see https://arxiv.org/abs/1909.00700
Args:
backbone (object): backbone instance
neck (object): 'TTFFPN' instance
ttf_head (object): 'TTFHead' instance
post_process (object): 'BBoxPostProcess' instance
"""
__category__ = 'architecture'
__inject__ = ['post_process']
def __init__(self,
backbone='DarkNet',
neck='TTFFPN',
ttf_head='TTFHead',
post_process='BBoxPostProcess'):
super(TTFNet, self).__init__()
self.backbone = backbone
self.neck = neck
self.ttf_head = ttf_head
self.post_process = post_process
@classmethod
def from_config(cls, cfg, *args, **kwargs):
backbone = create(cfg['backbone'])
kwargs = {'input_shape': backbone.out_shape}
neck = create(cfg['neck'], **kwargs)
kwargs = {'input_shape': neck.out_shape}
ttf_head = create(cfg['ttf_head'], **kwargs)
return {
'backbone': backbone,
'neck': neck,
"ttf_head": ttf_head,
}
def _forward(self):
body_feats = self.backbone(self.inputs)
body_feats = self.neck(body_feats)
hm, wh = self.ttf_head(body_feats)
if self.training:
return hm, wh
else:
bbox, bbox_num = self.post_process(hm, wh, self.inputs['im_shape'],
self.inputs['scale_factor'])
return bbox, bbox_num
def get_loss(self, ):
loss = {}
heatmap = self.inputs['ttf_heatmap']
box_target = self.inputs['ttf_box_target']
reg_weight = self.inputs['ttf_reg_weight']
hm, wh = self._forward()
head_loss = self.ttf_head.get_loss(hm, wh, heatmap, box_target,
reg_weight)
loss.update(head_loss)
total_loss = paddle.add_n(list(loss.values()))
loss.update({'loss': total_loss})
return loss
def get_pred(self):
bbox_pred, bbox_num = self._forward()
output = {
"bbox": bbox_pred,
"bbox_num": bbox_num,
}
return output
| PaddleDetection/ppdet/modeling/architectures/ttfnet.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/architectures/ttfnet.py",
"repo_id": "PaddleDetection",
"token_count": 1426
} | 77 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn.functional as F
__all__ = [
'pad_gt', 'gather_topk_anchors', 'check_points_inside_bboxes',
'compute_max_iou_anchor', 'compute_max_iou_gt',
'generate_anchors_for_grid_cell'
]
def pad_gt(gt_labels, gt_bboxes, gt_scores=None):
r""" Pad 0 in gt_labels and gt_bboxes.
Args:
gt_labels (Tensor|List[Tensor], int64): Label of gt_bboxes,
shape is [B, n, 1] or [[n_1, 1], [n_2, 1], ...], here n = sum(n_i)
gt_bboxes (Tensor|List[Tensor], float32): Ground truth bboxes,
shape is [B, n, 4] or [[n_1, 4], [n_2, 4], ...], here n = sum(n_i)
gt_scores (Tensor|List[Tensor]|None, float32): Score of gt_bboxes,
shape is [B, n, 1] or [[n_1, 4], [n_2, 4], ...], here n = sum(n_i)
Returns:
pad_gt_labels (Tensor, int64): shape[B, n, 1]
pad_gt_bboxes (Tensor, float32): shape[B, n, 4]
pad_gt_scores (Tensor, float32): shape[B, n, 1]
pad_gt_mask (Tensor, float32): shape[B, n, 1], 1 means bbox, 0 means no bbox
"""
if isinstance(gt_labels, paddle.Tensor) and isinstance(gt_bboxes,
paddle.Tensor):
assert gt_labels.ndim == gt_bboxes.ndim and \
gt_bboxes.ndim == 3
pad_gt_mask = (
gt_bboxes.sum(axis=-1, keepdim=True) > 0).astype(gt_bboxes.dtype)
if gt_scores is None:
gt_scores = pad_gt_mask.clone()
assert gt_labels.ndim == gt_scores.ndim
return gt_labels, gt_bboxes, gt_scores, pad_gt_mask
elif isinstance(gt_labels, list) and isinstance(gt_bboxes, list):
assert len(gt_labels) == len(gt_bboxes), \
'The number of `gt_labels` and `gt_bboxes` is not equal. '
num_max_boxes = max([len(a) for a in gt_bboxes])
batch_size = len(gt_bboxes)
# pad label and bbox
pad_gt_labels = paddle.zeros(
[batch_size, num_max_boxes, 1], dtype=gt_labels[0].dtype)
pad_gt_bboxes = paddle.zeros(
[batch_size, num_max_boxes, 4], dtype=gt_bboxes[0].dtype)
pad_gt_scores = paddle.zeros(
[batch_size, num_max_boxes, 1], dtype=gt_bboxes[0].dtype)
pad_gt_mask = paddle.zeros(
[batch_size, num_max_boxes, 1], dtype=gt_bboxes[0].dtype)
for i, (label, bbox) in enumerate(zip(gt_labels, gt_bboxes)):
if len(label) > 0 and len(bbox) > 0:
pad_gt_labels[i, :len(label)] = label
pad_gt_bboxes[i, :len(bbox)] = bbox
pad_gt_mask[i, :len(bbox)] = 1.
if gt_scores is not None:
pad_gt_scores[i, :len(gt_scores[i])] = gt_scores[i]
if gt_scores is None:
pad_gt_scores = pad_gt_mask.clone()
return pad_gt_labels, pad_gt_bboxes, pad_gt_scores, pad_gt_mask
else:
raise ValueError('The input `gt_labels` or `gt_bboxes` is invalid! ')
def gather_topk_anchors(metrics, topk, largest=True, topk_mask=None, eps=1e-9):
r"""
Args:
metrics (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors
topk (int): The number of top elements to look for along the axis.
largest (bool) : largest is a flag, if set to true,
algorithm will sort by descending order, otherwise sort by
ascending order. Default: True
topk_mask (Tensor, float32): shape[B, n, 1], ignore bbox mask,
Default: None
eps (float): Default: 1e-9
Returns:
is_in_topk (Tensor, float32): shape[B, n, L], value=1. means selected
"""
num_anchors = metrics.shape[-1]
topk_metrics, topk_idxs = paddle.topk(
metrics, topk, axis=-1, largest=largest)
if topk_mask is None:
topk_mask = (
topk_metrics.max(axis=-1, keepdim=True) > eps).astype(metrics.dtype)
is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(
axis=-2).astype(metrics.dtype)
return is_in_topk * topk_mask
def check_points_inside_bboxes(points,
bboxes,
center_radius_tensor=None,
eps=1e-9,
sm_use=False):
r"""
Args:
points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors
bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format
center_radius_tensor (Tensor, float32): shape [L, 1]. Default: None.
eps (float): Default: 1e-9
Returns:
is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected
"""
points = points.unsqueeze([0, 1])
x, y = points.chunk(2, axis=-1)
xmin, ymin, xmax, ymax = bboxes.unsqueeze(2).chunk(4, axis=-1)
# check whether `points` is in `bboxes`
l = x - xmin
t = y - ymin
r = xmax - x
b = ymax - y
delta_ltrb = paddle.concat([l, t, r, b], axis=-1)
is_in_bboxes = (delta_ltrb.min(axis=-1) > eps)
if center_radius_tensor is not None:
# check whether `points` is in `center_radius`
center_radius_tensor = center_radius_tensor.unsqueeze([0, 1])
cx = (xmin + xmax) * 0.5
cy = (ymin + ymax) * 0.5
l = x - (cx - center_radius_tensor)
t = y - (cy - center_radius_tensor)
r = (cx + center_radius_tensor) - x
b = (cy + center_radius_tensor) - y
delta_ltrb_c = paddle.concat([l, t, r, b], axis=-1)
is_in_center = (delta_ltrb_c.min(axis=-1) > eps)
if sm_use:
return is_in_bboxes.astype(bboxes.dtype), is_in_center.astype(
bboxes.dtype)
else:
return (paddle.logical_and(is_in_bboxes, is_in_center),
paddle.logical_or(is_in_bboxes, is_in_center))
return is_in_bboxes.astype(bboxes.dtype)
def compute_max_iou_anchor(ious):
r"""
For each anchor, find the GT with the largest IOU.
Args:
ious (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors
Returns:
is_max_iou (Tensor, float32): shape[B, n, L], value=1. means selected
"""
num_max_boxes = ious.shape[-2]
max_iou_index = ious.argmax(axis=-2)
is_max_iou = F.one_hot(max_iou_index, num_max_boxes).transpose([0, 2, 1])
return is_max_iou.astype(ious.dtype)
def compute_max_iou_gt(ious):
r"""
For each GT, find the anchor with the largest IOU.
Args:
ious (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors
Returns:
is_max_iou (Tensor, float32): shape[B, n, L], value=1. means selected
"""
num_anchors = ious.shape[-1]
max_iou_index = ious.argmax(axis=-1)
is_max_iou = F.one_hot(max_iou_index, num_anchors)
return is_max_iou.astype(ious.dtype)
def generate_anchors_for_grid_cell(feats,
fpn_strides,
grid_cell_size=5.0,
grid_cell_offset=0.5,
dtype='float32'):
r"""
Like ATSS, generate anchors based on grid size.
Args:
feats (List[Tensor]): shape[s, (b, c, h, w)]
fpn_strides (tuple|list): shape[s], stride for each scale feature
grid_cell_size (float): anchor size
grid_cell_offset (float): The range is between 0 and 1.
Returns:
anchors (Tensor): shape[l, 4], "xmin, ymin, xmax, ymax" format.
anchor_points (Tensor): shape[l, 2], "x, y" format.
num_anchors_list (List[int]): shape[s], contains [s_1, s_2, ...].
stride_tensor (Tensor): shape[l, 1], contains the stride for each scale.
"""
assert len(feats) == len(fpn_strides)
anchors = []
anchor_points = []
num_anchors_list = []
stride_tensor = []
for feat, stride in zip(feats, fpn_strides):
_, _, h, w = feat.shape
cell_half_size = grid_cell_size * stride * 0.5
shift_x = (paddle.arange(end=w) + grid_cell_offset) * stride
shift_y = (paddle.arange(end=h) + grid_cell_offset) * stride
shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
anchor = paddle.stack(
[
shift_x - cell_half_size, shift_y - cell_half_size,
shift_x + cell_half_size, shift_y + cell_half_size
],
axis=-1).astype(dtype)
anchor_point = paddle.stack([shift_x, shift_y], axis=-1).astype(dtype)
anchors.append(anchor.reshape([-1, 4]))
anchor_points.append(anchor_point.reshape([-1, 2]))
num_anchors_list.append(len(anchors[-1]))
stride_tensor.append(
paddle.full(
[num_anchors_list[-1], 1], stride, dtype=dtype))
anchors = paddle.concat(anchors)
anchors.stop_gradient = True
anchor_points = paddle.concat(anchor_points)
anchor_points.stop_gradient = True
stride_tensor = paddle.concat(stride_tensor)
stride_tensor.stop_gradient = True
return anchors, anchor_points, num_anchors_list, stride_tensor
| PaddleDetection/ppdet/modeling/assigners/utils.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/assigners/utils.py",
"repo_id": "PaddleDetection",
"token_count": 4736
} | 78 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on
https://github.com/HRNet/Lite-HRNet/blob/hrnet/models/backbones/litehrnet.py
"""
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from numbers import Integral
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant
from ppdet.core.workspace import register
from ppdet.modeling.shape_spec import ShapeSpec
from ppdet.modeling.ops import channel_shuffle
from .. import layers as L
__all__ = ['LiteHRNet']
class ConvNormLayer(nn.Layer):
def __init__(self,
ch_in,
ch_out,
filter_size,
stride=1,
groups=1,
norm_type=None,
norm_groups=32,
norm_decay=0.,
freeze_norm=False,
act=None):
super(ConvNormLayer, self).__init__()
self.act = act
norm_lr = 0. if freeze_norm else 1.
if norm_type is not None:
assert norm_type in ['bn', 'sync_bn', 'gn'], \
"norm_type should be one of ['bn', 'sync_bn', 'gn'], but got {}".format(norm_type)
param_attr = ParamAttr(
initializer=Constant(1.0),
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay), )
bias_attr = ParamAttr(
learning_rate=norm_lr, regularizer=L2Decay(norm_decay))
global_stats = True if freeze_norm else None
if norm_type in ['bn', 'sync_bn']:
self.norm = nn.BatchNorm2D(
ch_out,
weight_attr=param_attr,
bias_attr=bias_attr,
use_global_stats=global_stats, )
elif norm_type == 'gn':
self.norm = nn.GroupNorm(
num_groups=norm_groups,
num_channels=ch_out,
weight_attr=param_attr,
bias_attr=bias_attr)
norm_params = self.norm.parameters()
if freeze_norm:
for param in norm_params:
param.stop_gradient = True
conv_bias_attr = False
else:
conv_bias_attr = True
self.norm = None
self.conv = nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.001)),
bias_attr=conv_bias_attr)
def forward(self, inputs):
out = self.conv(inputs)
if self.norm is not None:
out = self.norm(out)
if self.act == 'relu':
out = F.relu(out)
elif self.act == 'sigmoid':
out = F.sigmoid(out)
return out
class DepthWiseSeparableConvNormLayer(nn.Layer):
def __init__(self,
ch_in,
ch_out,
filter_size,
stride=1,
dw_norm_type=None,
pw_norm_type=None,
norm_decay=0.,
freeze_norm=False,
dw_act=None,
pw_act=None):
super(DepthWiseSeparableConvNormLayer, self).__init__()
self.depthwise_conv = ConvNormLayer(
ch_in=ch_in,
ch_out=ch_in,
filter_size=filter_size,
stride=stride,
groups=ch_in,
norm_type=dw_norm_type,
act=dw_act,
norm_decay=norm_decay,
freeze_norm=freeze_norm, )
self.pointwise_conv = ConvNormLayer(
ch_in=ch_in,
ch_out=ch_out,
filter_size=1,
stride=1,
norm_type=pw_norm_type,
act=pw_act,
norm_decay=norm_decay,
freeze_norm=freeze_norm, )
def forward(self, x):
x = self.depthwise_conv(x)
x = self.pointwise_conv(x)
return x
class CrossResolutionWeightingModule(nn.Layer):
def __init__(self,
channels,
ratio=16,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(CrossResolutionWeightingModule, self).__init__()
self.channels = channels
total_channel = sum(channels)
self.conv1 = ConvNormLayer(
ch_in=total_channel,
ch_out=total_channel // ratio,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.conv2 = ConvNormLayer(
ch_in=total_channel // ratio,
ch_out=total_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='sigmoid',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
def forward(self, x):
mini_size = x[-1].shape[-2:]
out = [F.adaptive_avg_pool2d(s, mini_size) for s in x[:-1]] + [x[-1]]
out = paddle.concat(out, 1)
out = self.conv1(out)
out = self.conv2(out)
out = paddle.split(out, self.channels, 1)
out = [
s * F.interpolate(
a, s.shape[-2:], mode='nearest') for s, a in zip(x, out)
]
return out
class SpatialWeightingModule(nn.Layer):
def __init__(self, in_channel, ratio=16, freeze_norm=False, norm_decay=0.):
super(SpatialWeightingModule, self).__init__()
self.global_avgpooling = nn.AdaptiveAvgPool2D(1)
self.conv1 = ConvNormLayer(
ch_in=in_channel,
ch_out=in_channel // ratio,
filter_size=1,
stride=1,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.conv2 = ConvNormLayer(
ch_in=in_channel // ratio,
ch_out=in_channel,
filter_size=1,
stride=1,
act='sigmoid',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
def forward(self, x):
out = self.global_avgpooling(x)
out = self.conv1(out)
out = self.conv2(out)
return x * out
class ConditionalChannelWeightingBlock(nn.Layer):
def __init__(self,
in_channels,
stride,
reduce_ratio,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(ConditionalChannelWeightingBlock, self).__init__()
assert stride in [1, 2]
branch_channels = [channel // 2 for channel in in_channels]
self.cross_resolution_weighting = CrossResolutionWeightingModule(
branch_channels,
ratio=reduce_ratio,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.depthwise_convs = nn.LayerList([
ConvNormLayer(
channel,
channel,
filter_size=3,
stride=stride,
groups=channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay) for channel in branch_channels
])
self.spatial_weighting = nn.LayerList([
SpatialWeightingModule(
channel,
ratio=4,
freeze_norm=freeze_norm,
norm_decay=norm_decay) for channel in branch_channels
])
def forward(self, x):
x = [s.chunk(2, axis=1) for s in x]
x1 = [s[0] for s in x]
x2 = [s[1] for s in x]
x2 = self.cross_resolution_weighting(x2)
x2 = [dw(s) for s, dw in zip(x2, self.depthwise_convs)]
x2 = [sw(s) for s, sw in zip(x2, self.spatial_weighting)]
out = [paddle.concat([s1, s2], axis=1) for s1, s2 in zip(x1, x2)]
out = [channel_shuffle(s, groups=2) for s in out]
return out
class ShuffleUnit(nn.Layer):
def __init__(self,
in_channel,
out_channel,
stride,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(ShuffleUnit, self).__init__()
branch_channel = out_channel // 2
self.stride = stride
if self.stride == 1:
assert in_channel == branch_channel * 2, \
"when stride=1, in_channel {} should equal to branch_channel*2 {}".format(in_channel, branch_channel * 2)
if stride > 1:
self.branch1 = nn.Sequential(
ConvNormLayer(
ch_in=in_channel,
ch_out=in_channel,
filter_size=3,
stride=self.stride,
groups=in_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay),
ConvNormLayer(
ch_in=in_channel,
ch_out=branch_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay), )
self.branch2 = nn.Sequential(
ConvNormLayer(
ch_in=branch_channel if stride == 1 else in_channel,
ch_out=branch_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay),
ConvNormLayer(
ch_in=branch_channel,
ch_out=branch_channel,
filter_size=3,
stride=self.stride,
groups=branch_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay),
ConvNormLayer(
ch_in=branch_channel,
ch_out=branch_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay), )
def forward(self, x):
if self.stride > 1:
x1 = self.branch1(x)
x2 = self.branch2(x)
else:
x1, x2 = x.chunk(2, axis=1)
x2 = self.branch2(x2)
out = paddle.concat([x1, x2], axis=1)
out = channel_shuffle(out, groups=2)
return out
class IterativeHead(nn.Layer):
def __init__(self,
in_channels,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(IterativeHead, self).__init__()
num_branches = len(in_channels)
self.in_channels = in_channels[::-1]
projects = []
for i in range(num_branches):
if i != num_branches - 1:
projects.append(
DepthWiseSeparableConvNormLayer(
ch_in=self.in_channels[i],
ch_out=self.in_channels[i + 1],
filter_size=3,
stride=1,
dw_act=None,
pw_act='relu',
dw_norm_type=norm_type,
pw_norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay))
else:
projects.append(
DepthWiseSeparableConvNormLayer(
ch_in=self.in_channels[i],
ch_out=self.in_channels[i],
filter_size=3,
stride=1,
dw_act=None,
pw_act='relu',
dw_norm_type=norm_type,
pw_norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay))
self.projects = nn.LayerList(projects)
def forward(self, x):
x = x[::-1]
y = []
last_x = None
for i, s in enumerate(x):
if last_x is not None:
last_x = F.interpolate(
last_x,
size=s.shape[-2:],
mode='bilinear',
align_corners=True)
s = s + last_x
s = self.projects[i](s)
y.append(s)
last_x = s
return y[::-1]
class Stem(nn.Layer):
def __init__(self,
in_channel,
stem_channel,
out_channel,
expand_ratio,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(Stem, self).__init__()
self.conv1 = ConvNormLayer(
in_channel,
stem_channel,
filter_size=3,
stride=2,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
mid_channel = int(round(stem_channel * expand_ratio))
branch_channel = stem_channel // 2
if stem_channel == out_channel:
inc_channel = out_channel - branch_channel
else:
inc_channel = out_channel - stem_channel
self.branch1 = nn.Sequential(
ConvNormLayer(
ch_in=branch_channel,
ch_out=branch_channel,
filter_size=3,
stride=2,
groups=branch_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay),
ConvNormLayer(
ch_in=branch_channel,
ch_out=inc_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay), )
self.expand_conv = ConvNormLayer(
ch_in=branch_channel,
ch_out=mid_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.depthwise_conv = ConvNormLayer(
ch_in=mid_channel,
ch_out=mid_channel,
filter_size=3,
stride=2,
groups=mid_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.linear_conv = ConvNormLayer(
ch_in=mid_channel,
ch_out=branch_channel
if stem_channel == out_channel else stem_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
def forward(self, x):
x = self.conv1(x)
x1, x2 = x.chunk(2, axis=1)
x1 = self.branch1(x1)
x2 = self.expand_conv(x2)
x2 = self.depthwise_conv(x2)
x2 = self.linear_conv(x2)
out = paddle.concat([x1, x2], axis=1)
out = channel_shuffle(out, groups=2)
return out
class LiteHRNetModule(nn.Layer):
def __init__(self,
num_branches,
num_blocks,
in_channels,
reduce_ratio,
module_type,
multiscale_output=False,
with_fuse=True,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(LiteHRNetModule, self).__init__()
assert num_branches == len(in_channels),\
"num_branches {} should equal to num_in_channels {}".format(num_branches, len(in_channels))
assert module_type in [
'LITE', 'NAIVE'
], "module_type should be one of ['LITE', 'NAIVE']"
self.num_branches = num_branches
self.in_channels = in_channels
self.multiscale_output = multiscale_output
self.with_fuse = with_fuse
self.norm_type = 'bn'
self.module_type = module_type
if self.module_type == 'LITE':
self.layers = self._make_weighting_blocks(
num_blocks,
reduce_ratio,
freeze_norm=freeze_norm,
norm_decay=norm_decay)
elif self.module_type == 'NAIVE':
self.layers = self._make_naive_branches(
num_branches,
num_blocks,
freeze_norm=freeze_norm,
norm_decay=norm_decay)
if self.with_fuse:
self.fuse_layers = self._make_fuse_layers(
freeze_norm=freeze_norm, norm_decay=norm_decay)
self.relu = nn.ReLU()
def _make_weighting_blocks(self,
num_blocks,
reduce_ratio,
stride=1,
freeze_norm=False,
norm_decay=0.):
layers = []
for i in range(num_blocks):
layers.append(
ConditionalChannelWeightingBlock(
self.in_channels,
stride=stride,
reduce_ratio=reduce_ratio,
norm_type=self.norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay))
return nn.Sequential(*layers)
def _make_naive_branches(self,
num_branches,
num_blocks,
freeze_norm=False,
norm_decay=0.):
branches = []
for branch_idx in range(num_branches):
layers = []
for i in range(num_blocks):
layers.append(
ShuffleUnit(
self.in_channels[branch_idx],
self.in_channels[branch_idx],
stride=1,
norm_type=self.norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay))
branches.append(nn.Sequential(*layers))
return nn.LayerList(branches)
def _make_fuse_layers(self, freeze_norm=False, norm_decay=0.):
if self.num_branches == 1:
return None
fuse_layers = []
num_out_branches = self.num_branches if self.multiscale_output else 1
for i in range(num_out_branches):
fuse_layer = []
for j in range(self.num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
L.Conv2d(
self.in_channels[j],
self.in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False, ),
nn.BatchNorm2D(self.in_channels[i]),
nn.Upsample(
scale_factor=2**(j - i), mode='nearest')))
elif j == i:
fuse_layer.append(None)
else:
conv_downsamples = []
for k in range(i - j):
if k == i - j - 1:
conv_downsamples.append(
nn.Sequential(
L.Conv2d(
self.in_channels[j],
self.in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=self.in_channels[j],
bias=False, ),
nn.BatchNorm2D(self.in_channels[j]),
L.Conv2d(
self.in_channels[j],
self.in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False, ),
nn.BatchNorm2D(self.in_channels[i])))
else:
conv_downsamples.append(
nn.Sequential(
L.Conv2d(
self.in_channels[j],
self.in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=self.in_channels[j],
bias=False, ),
nn.BatchNorm2D(self.in_channels[j]),
L.Conv2d(
self.in_channels[j],
self.in_channels[j],
kernel_size=1,
stride=1,
padding=0,
bias=False, ),
nn.BatchNorm2D(self.in_channels[j]),
nn.ReLU()))
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.LayerList(fuse_layer))
return nn.LayerList(fuse_layers)
def forward(self, x):
if self.num_branches == 1:
return [self.layers[0](x[0])]
if self.module_type == 'LITE':
out = self.layers(x)
elif self.module_type == 'NAIVE':
for i in range(self.num_branches):
x[i] = self.layers[i](x[i])
out = x
if self.with_fuse:
out_fuse = []
for i in range(len(self.fuse_layers)):
y = out[0] if i == 0 else self.fuse_layers[i][0](out[0])
for j in range(self.num_branches):
if j == 0:
y += y
elif i == j:
y += out[j]
else:
y += self.fuse_layers[i][j](out[j])
if i == 0:
out[i] = y
out_fuse.append(self.relu(y))
out = out_fuse
elif not self.multiscale_output:
out = [out[0]]
return out
@register
class LiteHRNet(nn.Layer):
"""
@inproceedings{Yulitehrnet21,
title={Lite-HRNet: A Lightweight High-Resolution Network},
author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
booktitle={CVPR},year={2021}
}
Args:
network_type (str): the network_type should be one of ["lite_18", "lite_30", "naive", "wider_naive"],
"naive": Simply combining the shuffle block in ShuffleNet and the highresolution design pattern in HRNet.
"wider_naive": Naive network with wider channels in each block.
"lite_18": Lite-HRNet-18, which replaces the pointwise convolution in a shuffle block by conditional channel weighting.
"lite_30": Lite-HRNet-30, with more blocks compared with Lite-HRNet-18.
freeze_at (int): the stage to freeze
freeze_norm (bool): whether to freeze norm in HRNet
norm_decay (float): weight decay for normalization layer weights
return_idx (List): the stage to return
"""
def __init__(self,
network_type,
freeze_at=0,
freeze_norm=True,
norm_decay=0.,
return_idx=[0, 1, 2, 3]):
super(LiteHRNet, self).__init__()
if isinstance(return_idx, Integral):
return_idx = [return_idx]
assert network_type in ["lite_18", "lite_30", "naive", "wider_naive"], \
"the network_type should be one of [lite_18, lite_30, naive, wider_naive]"
assert len(return_idx) > 0, "need one or more return index"
self.freeze_at = freeze_at
self.freeze_norm = freeze_norm
self.norm_decay = norm_decay
self.return_idx = return_idx
self.norm_type = 'bn'
self.module_configs = {
"lite_18": {
"num_modules": [2, 4, 2],
"num_branches": [2, 3, 4],
"num_blocks": [2, 2, 2],
"module_type": ["LITE", "LITE", "LITE"],
"reduce_ratios": [8, 8, 8],
"num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
},
"lite_30": {
"num_modules": [3, 8, 3],
"num_branches": [2, 3, 4],
"num_blocks": [2, 2, 2],
"module_type": ["LITE", "LITE", "LITE"],
"reduce_ratios": [8, 8, 8],
"num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
},
"naive": {
"num_modules": [2, 4, 2],
"num_branches": [2, 3, 4],
"num_blocks": [2, 2, 2],
"module_type": ["NAIVE", "NAIVE", "NAIVE"],
"reduce_ratios": [1, 1, 1],
"num_channels": [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
},
"wider_naive": {
"num_modules": [2, 4, 2],
"num_branches": [2, 3, 4],
"num_blocks": [2, 2, 2],
"module_type": ["NAIVE", "NAIVE", "NAIVE"],
"reduce_ratios": [1, 1, 1],
"num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
},
}
self.stages_config = self.module_configs[network_type]
self.stem = Stem(3, 32, 32, 1)
num_channels_pre_layer = [32]
for stage_idx in range(3):
num_channels = self.stages_config["num_channels"][stage_idx]
setattr(self, 'transition{}'.format(stage_idx),
self._make_transition_layer(num_channels_pre_layer,
num_channels, self.freeze_norm,
self.norm_decay))
stage, num_channels_pre_layer = self._make_stage(
self.stages_config, stage_idx, num_channels, True,
self.freeze_norm, self.norm_decay)
setattr(self, 'stage{}'.format(stage_idx), stage)
self.head_layer = IterativeHead(num_channels_pre_layer, 'bn',
self.freeze_norm, self.norm_decay)
def _make_transition_layer(self,
num_channels_pre_layer,
num_channels_cur_layer,
freeze_norm=False,
norm_decay=0.):
num_branches_pre = len(num_channels_pre_layer)
num_branches_cur = len(num_channels_cur_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(
nn.Sequential(
L.Conv2d(
num_channels_pre_layer[i],
num_channels_pre_layer[i],
kernel_size=3,
stride=1,
padding=1,
groups=num_channels_pre_layer[i],
bias=False),
nn.BatchNorm2D(num_channels_pre_layer[i]),
L.Conv2d(
num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=1,
stride=1,
padding=0,
bias=False, ),
nn.BatchNorm2D(num_channels_cur_layer[i]),
nn.ReLU()))
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
conv_downsamples.append(
nn.Sequential(
L.Conv2d(
num_channels_pre_layer[-1],
num_channels_pre_layer[-1],
groups=num_channels_pre_layer[-1],
kernel_size=3,
stride=2,
padding=1,
bias=False, ),
nn.BatchNorm2D(num_channels_pre_layer[-1]),
L.Conv2d(
num_channels_pre_layer[-1],
num_channels_cur_layer[i]
if j == i - num_branches_pre else
num_channels_pre_layer[-1],
kernel_size=1,
stride=1,
padding=0,
bias=False, ),
nn.BatchNorm2D(num_channels_cur_layer[i]
if j == i - num_branches_pre else
num_channels_pre_layer[-1]),
nn.ReLU()))
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.LayerList(transition_layers)
def _make_stage(self,
stages_config,
stage_idx,
in_channels,
multiscale_output,
freeze_norm=False,
norm_decay=0.):
num_modules = stages_config["num_modules"][stage_idx]
num_branches = stages_config["num_branches"][stage_idx]
num_blocks = stages_config["num_blocks"][stage_idx]
reduce_ratio = stages_config['reduce_ratios'][stage_idx]
module_type = stages_config['module_type'][stage_idx]
modules = []
for i in range(num_modules):
if not multiscale_output and i == num_modules - 1:
reset_multiscale_output = False
else:
reset_multiscale_output = True
modules.append(
LiteHRNetModule(
num_branches,
num_blocks,
in_channels,
reduce_ratio,
module_type,
multiscale_output=reset_multiscale_output,
with_fuse=True,
freeze_norm=freeze_norm,
norm_decay=norm_decay))
in_channels = modules[-1].in_channels
return nn.Sequential(*modules), in_channels
def forward(self, inputs):
x = inputs['image']
dims = x.shape
if len(dims) == 5:
x = paddle.reshape(x, (dims[0] * dims[1], dims[2], dims[3],
dims[4])) # [6, 3, 128, 96]
x = self.stem(x)
y_list = [x]
for stage_idx in range(3):
x_list = []
transition = getattr(self, 'transition{}'.format(stage_idx))
for j in range(self.stages_config["num_branches"][stage_idx]):
if transition[j] is not None:
if j >= len(y_list):
x_list.append(transition[j](y_list[-1]))
else:
x_list.append(transition[j](y_list[j]))
else:
x_list.append(y_list[j])
y_list = getattr(self, 'stage{}'.format(stage_idx))(x_list)
x = self.head_layer(y_list)
res = []
for i, layer in enumerate(x):
if i == self.freeze_at:
layer.stop_gradient = True
if i in self.return_idx:
res.append(layer)
return res
@property
def out_shape(self):
return [
ShapeSpec(
channels=self._out_channels[i], stride=self._out_strides[i])
for i in self.return_idx
]
| PaddleDetection/ppdet/modeling/backbones/lite_hrnet.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/backbones/lite_hrnet.py",
"repo_id": "PaddleDetection",
"token_count": 20500
} | 79 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle
import numpy as np
def bbox2delta(src_boxes, tgt_boxes, weights=[1.0, 1.0, 1.0, 1.0]):
"""Encode bboxes to deltas.
"""
src_w = src_boxes[:, 2] - src_boxes[:, 0]
src_h = src_boxes[:, 3] - src_boxes[:, 1]
src_ctr_x = src_boxes[:, 0] + 0.5 * src_w
src_ctr_y = src_boxes[:, 1] + 0.5 * src_h
tgt_w = tgt_boxes[:, 2] - tgt_boxes[:, 0]
tgt_h = tgt_boxes[:, 3] - tgt_boxes[:, 1]
tgt_ctr_x = tgt_boxes[:, 0] + 0.5 * tgt_w
tgt_ctr_y = tgt_boxes[:, 1] + 0.5 * tgt_h
wx, wy, ww, wh = weights
dx = wx * (tgt_ctr_x - src_ctr_x) / src_w
dy = wy * (tgt_ctr_y - src_ctr_y) / src_h
dw = ww * paddle.log(tgt_w / src_w)
dh = wh * paddle.log(tgt_h / src_h)
deltas = paddle.stack((dx, dy, dw, dh), axis=1)
return deltas
def delta2bbox(deltas, boxes, weights=[1.0, 1.0, 1.0, 1.0], max_shape=None):
"""Decode deltas to boxes. Used in RCNNBox,CascadeHead,RCNNHead,RetinaHead.
Note: return tensor shape [n,1,4]
If you want to add a reshape, please add after the calling code instead of here.
"""
clip_scale = math.log(1000.0 / 16)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into paddle.exp()
dw = paddle.clip(dw, max=clip_scale)
dh = paddle.clip(dh, max=clip_scale)
pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
pred_ctr_y = dy * heights.unsqueeze(1) + ctr_y.unsqueeze(1)
pred_w = paddle.exp(dw) * widths.unsqueeze(1)
pred_h = paddle.exp(dh) * heights.unsqueeze(1)
pred_boxes = []
pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
pred_boxes = paddle.stack(pred_boxes, axis=-1)
if max_shape is not None:
pred_boxes[..., 0::2] = pred_boxes[..., 0::2].clip(
min=0, max=max_shape[1])
pred_boxes[..., 1::2] = pred_boxes[..., 1::2].clip(
min=0, max=max_shape[0])
return pred_boxes
def bbox2delta_v2(src_boxes,
tgt_boxes,
delta_mean=[0.0, 0.0, 0.0, 0.0],
delta_std=[1.0, 1.0, 1.0, 1.0]):
"""Encode bboxes to deltas.
Modified from bbox2delta() which just use weight parameters to multiply deltas.
"""
src_w = src_boxes[:, 2] - src_boxes[:, 0]
src_h = src_boxes[:, 3] - src_boxes[:, 1]
src_ctr_x = src_boxes[:, 0] + 0.5 * src_w
src_ctr_y = src_boxes[:, 1] + 0.5 * src_h
tgt_w = tgt_boxes[:, 2] - tgt_boxes[:, 0]
tgt_h = tgt_boxes[:, 3] - tgt_boxes[:, 1]
tgt_ctr_x = tgt_boxes[:, 0] + 0.5 * tgt_w
tgt_ctr_y = tgt_boxes[:, 1] + 0.5 * tgt_h
dx = (tgt_ctr_x - src_ctr_x) / src_w
dy = (tgt_ctr_y - src_ctr_y) / src_h
dw = paddle.log(tgt_w / src_w)
dh = paddle.log(tgt_h / src_h)
deltas = paddle.stack((dx, dy, dw, dh), axis=1)
deltas = (
deltas - paddle.to_tensor(delta_mean)) / paddle.to_tensor(delta_std)
return deltas
def delta2bbox_v2(deltas,
boxes,
delta_mean=[0.0, 0.0, 0.0, 0.0],
delta_std=[1.0, 1.0, 1.0, 1.0],
max_shape=None,
ctr_clip=32.0):
"""Decode deltas to bboxes.
Modified from delta2bbox() which just use weight parameters to be divided by deltas.
Used in YOLOFHead.
Note: return tensor shape [n,1,4]
If you want to add a reshape, please add after the calling code instead of here.
"""
clip_scale = math.log(1000.0 / 16)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
deltas = deltas * paddle.to_tensor(delta_std) + paddle.to_tensor(delta_mean)
dx = deltas[:, 0::4]
dy = deltas[:, 1::4]
dw = deltas[:, 2::4]
dh = deltas[:, 3::4]
# Prevent sending too large values into paddle.exp()
dx = dx * widths.unsqueeze(1)
dy = dy * heights.unsqueeze(1)
if ctr_clip is not None:
dx = paddle.clip(dx, max=ctr_clip, min=-ctr_clip)
dy = paddle.clip(dy, max=ctr_clip, min=-ctr_clip)
dw = paddle.clip(dw, max=clip_scale)
dh = paddle.clip(dh, max=clip_scale)
else:
dw = dw.clip(min=-clip_scale, max=clip_scale)
dh = dh.clip(min=-clip_scale, max=clip_scale)
pred_ctr_x = dx + ctr_x.unsqueeze(1)
pred_ctr_y = dy + ctr_y.unsqueeze(1)
pred_w = paddle.exp(dw) * widths.unsqueeze(1)
pred_h = paddle.exp(dh) * heights.unsqueeze(1)
pred_boxes = []
pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
pred_boxes = paddle.stack(pred_boxes, axis=-1)
if max_shape is not None:
pred_boxes[..., 0::2] = pred_boxes[..., 0::2].clip(
min=0, max=max_shape[1])
pred_boxes[..., 1::2] = pred_boxes[..., 1::2].clip(
min=0, max=max_shape[0])
return pred_boxes
def expand_bbox(bboxes, scale):
w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5
h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5
x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5
y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5
w_half *= scale
h_half *= scale
bboxes_exp = np.zeros(bboxes.shape, dtype=np.float32)
bboxes_exp[:, 0] = x_c - w_half
bboxes_exp[:, 2] = x_c + w_half
bboxes_exp[:, 1] = y_c - h_half
bboxes_exp[:, 3] = y_c + h_half
return bboxes_exp
def clip_bbox(boxes, im_shape):
h, w = im_shape[0], im_shape[1]
x1 = boxes[:, 0].clip(0, w)
y1 = boxes[:, 1].clip(0, h)
x2 = boxes[:, 2].clip(0, w)
y2 = boxes[:, 3].clip(0, h)
return paddle.stack([x1, y1, x2, y2], axis=1)
def nonempty_bbox(boxes, min_size=0, return_mask=False):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
mask = paddle.logical_and(h > min_size, w > min_size)
if return_mask:
return mask
keep = paddle.nonzero(mask).flatten()
return keep
def bbox_area(boxes):
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
def bbox_overlaps(boxes1, boxes2):
"""
Calculate overlaps between boxes1 and boxes2
Args:
boxes1 (Tensor): boxes with shape [M, 4]
boxes2 (Tensor): boxes with shape [N, 4]
Return:
overlaps (Tensor): overlaps between boxes1 and boxes2 with shape [M, N]
"""
M = boxes1.shape[0]
N = boxes2.shape[0]
if M * N == 0:
return paddle.zeros([M, N], dtype='float32')
area1 = bbox_area(boxes1)
area2 = bbox_area(boxes2)
xy_max = paddle.minimum(
paddle.unsqueeze(boxes1, 1)[:, :, 2:], boxes2[:, 2:])
xy_min = paddle.maximum(
paddle.unsqueeze(boxes1, 1)[:, :, :2], boxes2[:, :2])
width_height = xy_max - xy_min
width_height = width_height.clip(min=0)
inter = width_height.prod(axis=2)
overlaps = paddle.where(inter > 0, inter /
(paddle.unsqueeze(area1, 1) + area2 - inter),
paddle.zeros_like(inter))
return overlaps
def batch_bbox_overlaps(bboxes1,
bboxes2,
mode='iou',
is_aligned=False,
eps=1e-6):
"""Calculate overlap between two set of bboxes.
If ``is_aligned `` is ``False``, then calculate the overlaps between each
bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
pair of bboxes1 and bboxes2.
Args:
bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
B indicates the batch dim, in shape (B1, B2, ..., Bn).
If ``is_aligned `` is ``True``, then m and n must be equal.
mode (str): "iou" (intersection over union) or "iof" (intersection over
foreground).
is_aligned (bool, optional): If True, then m and n must be equal.
Default False.
eps (float, optional): A value added to the denominator for numerical
stability. Default 1e-6.
Returns:
Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
"""
assert mode in ['iou', 'iof', 'giou'], 'Unsupported mode {}'.format(mode)
# Either the boxes are empty or the length of boxes's last dimenstion is 4
assert (bboxes1.shape[-1] == 4 or bboxes1.shape[0] == 0)
assert (bboxes2.shape[-1] == 4 or bboxes2.shape[0] == 0)
# Batch dim must be the same
# Batch dim: (B1, B2, ... Bn)
assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
batch_shape = bboxes1.shape[:-2]
rows = bboxes1.shape[-2] if bboxes1.shape[0] > 0 else 0
cols = bboxes2.shape[-2] if bboxes2.shape[0] > 0 else 0
if is_aligned:
assert rows == cols
if rows * cols == 0:
if is_aligned:
return paddle.full(batch_shape + (rows, ), 1)
else:
return paddle.full(batch_shape + (rows, cols), 1)
area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1])
area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1])
if is_aligned:
lt = paddle.maximum(bboxes1[:, :2], bboxes2[:, :2]) # [B, rows, 2]
rb = paddle.minimum(bboxes1[:, 2:], bboxes2[:, 2:]) # [B, rows, 2]
wh = (rb - lt).clip(min=0) # [B, rows, 2]
overlap = wh[:, 0] * wh[:, 1]
if mode in ['iou', 'giou']:
union = area1 + area2 - overlap
else:
union = area1
if mode == 'giou':
enclosed_lt = paddle.minimum(bboxes1[:, :2], bboxes2[:, :2])
enclosed_rb = paddle.maximum(bboxes1[:, 2:], bboxes2[:, 2:])
else:
lt = paddle.maximum(bboxes1[:, :2].reshape([rows, 1, 2]),
bboxes2[:, :2]) # [B, rows, cols, 2]
rb = paddle.minimum(bboxes1[:, 2:].reshape([rows, 1, 2]),
bboxes2[:, 2:]) # [B, rows, cols, 2]
wh = (rb - lt).clip(min=0) # [B, rows, cols, 2]
overlap = wh[:, :, 0] * wh[:, :, 1]
if mode in ['iou', 'giou']:
union = area1.reshape([rows,1]) \
+ area2.reshape([1,cols]) - overlap
else:
union = area1[:, None]
if mode == 'giou':
enclosed_lt = paddle.minimum(bboxes1[:, :2].reshape([rows, 1, 2]),
bboxes2[:, :2])
enclosed_rb = paddle.maximum(bboxes1[:, 2:].reshape([rows, 1, 2]),
bboxes2[:, 2:])
eps = paddle.to_tensor([eps])
union = paddle.maximum(union, eps)
ious = overlap / union
if mode in ['iou', 'iof']:
return ious
# calculate gious
enclose_wh = (enclosed_rb - enclosed_lt).clip(min=0)
enclose_area = enclose_wh[:, :, 0] * enclose_wh[:, :, 1]
enclose_area = paddle.maximum(enclose_area, eps)
gious = ious - (enclose_area - union) / enclose_area
return 1 - gious
def xywh2xyxy(box):
x, y, w, h = box
x1 = x - w * 0.5
y1 = y - h * 0.5
x2 = x + w * 0.5
y2 = y + h * 0.5
return [x1, y1, x2, y2]
def make_grid(h, w, dtype):
yv, xv = paddle.meshgrid([paddle.arange(h), paddle.arange(w)])
return paddle.stack((xv, yv), 2).cast(dtype=dtype)
def decode_yolo(box, anchor, downsample_ratio):
"""decode yolo box
Args:
box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
anchor (list): anchor with the shape [na, 2]
downsample_ratio (int): downsample ratio, default 32
scale (float): scale, default 1.
Return:
box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
"""
x, y, w, h = box
na, grid_h, grid_w = x.shape[1:4]
grid = make_grid(grid_h, grid_w, x.dtype).reshape((1, 1, grid_h, grid_w, 2))
x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
anchor = paddle.to_tensor(anchor, dtype=x.dtype)
anchor = anchor.reshape((1, na, 1, 1, 2))
w1 = paddle.exp(w) * anchor[:, :, :, :, 0:1] / (downsample_ratio * grid_w)
h1 = paddle.exp(h) * anchor[:, :, :, :, 1:2] / (downsample_ratio * grid_h)
return [x1, y1, w1, h1]
def batch_iou_similarity(box1, box2, eps=1e-9):
"""Calculate iou of box1 and box2 in batch
Args:
box1 (Tensor): box with the shape [N, M1, 4]
box2 (Tensor): box with the shape [N, M2, 4]
Return:
iou (Tensor): iou between box1 and box2 with the shape [N, M1, M2]
"""
box1 = box1.unsqueeze(2) # [N, M1, 4] -> [N, M1, 1, 4]
box2 = box2.unsqueeze(1) # [N, M2, 4] -> [N, 1, M2, 4]
px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
x1y1 = paddle.maximum(px1y1, gx1y1)
x2y2 = paddle.minimum(px2y2, gx2y2)
overlap = (x2y2 - x1y1).clip(0).prod(-1)
area1 = (px2y2 - px1y1).clip(0).prod(-1)
area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
union = area1 + area2 - overlap + eps
return overlap / union
def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
"""calculate the iou of box1 and box2
Args:
box1 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
box2 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
giou (bool): whether use giou or not, default False
diou (bool): whether use diou or not, default False
ciou (bool): whether use ciou or not, default False
eps (float): epsilon to avoid divide by zero
Return:
iou (Tensor): iou of box1 and box1, with the shape [b, na, h, w, 1]
"""
px1, py1, px2, py2 = box1
gx1, gy1, gx2, gy2 = box2
x1 = paddle.maximum(px1, gx1)
y1 = paddle.maximum(py1, gy1)
x2 = paddle.minimum(px2, gx2)
y2 = paddle.minimum(py2, gy2)
overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
area1 = (px2 - px1) * (py2 - py1)
area1 = area1.clip(0)
area2 = (gx2 - gx1) * (gy2 - gy1)
area2 = area2.clip(0)
union = area1 + area2 - overlap + eps
iou = overlap / union
if giou or ciou or diou:
# convex w, h
cw = paddle.maximum(px2, gx2) - paddle.minimum(px1, gx1)
ch = paddle.maximum(py2, gy2) - paddle.minimum(py1, gy1)
if giou:
c_area = cw * ch + eps
return iou - (c_area - union) / c_area
else:
# convex diagonal squared
c2 = cw**2 + ch**2 + eps
# center distance
rho2 = ((px1 + px2 - gx1 - gx2)**2 + (py1 + py2 - gy1 - gy2)**2) / 4
if diou:
return iou - rho2 / c2
else:
w1, h1 = px2 - px1, py2 - py1 + eps
w2, h2 = gx2 - gx1, gy2 - gy1 + eps
delta = paddle.atan(w1 / h1) - paddle.atan(w2 / h2)
v = (4 / math.pi**2) * paddle.pow(delta, 2)
alpha = v / (1 + eps - iou + v)
alpha.stop_gradient = True
return iou - (rho2 / c2 + v * alpha)
else:
return iou
def bbox_iou_np_expand(box1, box2, x1y1x2y2=True, eps=1e-16):
"""
Calculate the iou of box1 and box2 with numpy.
Args:
box1 (ndarray): [N, 4]
box2 (ndarray): [M, 4], usually N != M
x1y1x2y2 (bool): whether in x1y1x2y2 stype, default True
eps (float): epsilon to avoid divide by zero
Return:
iou (ndarray): iou of box1 and box2, [N, M]
"""
N, M = len(box1), len(box2) # usually N != M
if x1y1x2y2:
b1_x1, b1_y1 = box1[:, 0], box1[:, 1]
b1_x2, b1_y2 = box1[:, 2], box1[:, 3]
b2_x1, b2_y1 = box2[:, 0], box2[:, 1]
b2_x2, b2_y2 = box2[:, 2], box2[:, 3]
else:
# cxcywh style
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
# get the coordinates of the intersection rectangle
inter_rect_x1 = np.zeros((N, M), dtype=np.float32)
inter_rect_y1 = np.zeros((N, M), dtype=np.float32)
inter_rect_x2 = np.zeros((N, M), dtype=np.float32)
inter_rect_y2 = np.zeros((N, M), dtype=np.float32)
for i in range(len(box2)):
inter_rect_x1[:, i] = np.maximum(b1_x1, b2_x1[i])
inter_rect_y1[:, i] = np.maximum(b1_y1, b2_y1[i])
inter_rect_x2[:, i] = np.minimum(b1_x2, b2_x2[i])
inter_rect_y2[:, i] = np.minimum(b1_y2, b2_y2[i])
# Intersection area
inter_area = np.maximum(inter_rect_x2 - inter_rect_x1, 0) * np.maximum(
inter_rect_y2 - inter_rect_y1, 0)
# Union Area
b1_area = np.repeat(
((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).reshape(-1, 1), M, axis=-1)
b2_area = np.repeat(
((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).reshape(1, -1), N, axis=0)
ious = inter_area / (b1_area + b2_area - inter_area + eps)
return ious
def bbox2distance(points, bbox, max_dis=None, eps=0.1):
"""Decode bounding box based on distances.
Args:
points (Tensor): Shape (n, 2), [x, y].
bbox (Tensor): Shape (n, 4), "xyxy" format
max_dis (float): Upper bound of the distance.
eps (float): a small value to ensure target < max_dis, instead <=
Returns:
Tensor: Decoded distances.
"""
left = points[:, 0] - bbox[:, 0]
top = points[:, 1] - bbox[:, 1]
right = bbox[:, 2] - points[:, 0]
bottom = bbox[:, 3] - points[:, 1]
if max_dis is not None:
left = left.clip(min=0, max=max_dis - eps)
top = top.clip(min=0, max=max_dis - eps)
right = right.clip(min=0, max=max_dis - eps)
bottom = bottom.clip(min=0, max=max_dis - eps)
return paddle.stack([left, top, right, bottom], -1)
def distance2bbox(points, distance, max_shape=None):
"""Decode distance prediction to bounding box.
Args:
points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
boundaries (left, top, right, bottom).
max_shape (tuple): Shape of the image.
Returns:
Tensor: Decoded bboxes.
"""
x1 = points[:, 0] - distance[:, 0]
y1 = points[:, 1] - distance[:, 1]
x2 = points[:, 0] + distance[:, 2]
y2 = points[:, 1] + distance[:, 3]
if max_shape is not None:
x1 = x1.clip(min=0, max=max_shape[1])
y1 = y1.clip(min=0, max=max_shape[0])
x2 = x2.clip(min=0, max=max_shape[1])
y2 = y2.clip(min=0, max=max_shape[0])
return paddle.stack([x1, y1, x2, y2], -1)
def bbox_center(boxes):
"""Get bbox centers from boxes.
Args:
boxes (Tensor): boxes with shape (..., 4), "xmin, ymin, xmax, ymax" format.
Returns:
Tensor: boxes centers with shape (..., 2), "cx, cy" format.
"""
boxes_cx = (boxes[..., 0] + boxes[..., 2]) / 2
boxes_cy = (boxes[..., 1] + boxes[..., 3]) / 2
return paddle.stack([boxes_cx, boxes_cy], axis=-1)
def batch_distance2bbox(points, distance, max_shapes=None):
"""Decode distance prediction to bounding box for batch.
Args:
points (Tensor): [B, ..., 2], "xy" format
distance (Tensor): [B, ..., 4], "ltrb" format
max_shapes (Tensor): [B, 2], "h,w" format, Shape of the image.
Returns:
Tensor: Decoded bboxes, "x1y1x2y2" format.
"""
lt, rb = paddle.split(distance, 2, -1)
# while tensor add parameters, parameters should be better placed on the second place
x1y1 = -lt + points
x2y2 = rb + points
out_bbox = paddle.concat([x1y1, x2y2], -1)
if max_shapes is not None:
max_shapes = max_shapes.flip(-1).tile([1, 2])
delta_dim = out_bbox.ndim - max_shapes.ndim
for _ in range(delta_dim):
max_shapes.unsqueeze_(1)
out_bbox = paddle.where(out_bbox < max_shapes, out_bbox, max_shapes)
out_bbox = paddle.where(out_bbox > 0, out_bbox,
paddle.zeros_like(out_bbox))
return out_bbox
def iou_similarity(box1, box2, eps=1e-10):
"""Calculate iou of box1 and box2
Args:
box1 (Tensor): box with the shape [M1, 4]
box2 (Tensor): box with the shape [M2, 4]
Return:
iou (Tensor): iou between box1 and box2 with the shape [M1, M2]
"""
box1 = box1.unsqueeze(1) # [M1, 4] -> [M1, 1, 4]
box2 = box2.unsqueeze(0) # [M2, 4] -> [1, M2, 4]
px1y1, px2y2 = box1[:, :, 0:2], box1[:, :, 2:4]
gx1y1, gx2y2 = box2[:, :, 0:2], box2[:, :, 2:4]
x1y1 = paddle.maximum(px1y1, gx1y1)
x2y2 = paddle.minimum(px2y2, gx2y2)
overlap = (x2y2 - x1y1).clip(0).prod(-1)
area1 = (px2y2 - px1y1).clip(0).prod(-1)
area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
union = area1 + area2 - overlap + eps
return overlap / union
| PaddleDetection/ppdet/modeling/bbox_utils.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/bbox_utils.py",
"repo_id": "PaddleDetection",
"token_count": 11035
} | 80 |
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
this code is base on https://github.com/hikvision-research/opera/blob/main/opera/models/dense_heads/petr_head.py
"""
import copy
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
import paddle.distributed as dist
from ..transformers.petr_transformer import inverse_sigmoid, masked_fill
from ..initializer import constant_, normal_
__all__ = ["PETRHead"]
from functools import partial
def bias_init_with_prob(prior_prob: float) -> float:
"""initialize conv/fc bias value according to a given probability value."""
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
return bias_init
def multi_apply(func, *args, **kwargs):
"""Apply function to a list of arguments.
Note:
This function applies the ``func`` to multiple inputs and
map the multiple outputs of the ``func`` into different
list. Each list contains the same type of outputs corresponding
to different inputs.
Args:
func (Function): A function that will be applied to a list of
arguments
Returns:
tuple(list): A tuple containing multiple list, each list contains \
a kind of returned results by the function
"""
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
res = tuple(map(list, zip(*map_results)))
return res
def reduce_mean(tensor):
""""Obtain the mean of tensor on different GPUs."""
if not (dist.get_world_size() and dist.is_initialized()):
return tensor
tensor = tensor.clone()
dist.all_reduce(
tensor.divide(
paddle.to_tensor(
dist.get_world_size(), dtype='float32')),
op=dist.ReduceOp.SUM)
return tensor
def gaussian_radius(det_size, min_overlap=0.7):
"""calculate gaussian radius according to object size.
"""
height, width = det_size
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = paddle.sqrt(b1**2 - 4 * a1 * c1)
r1 = (b1 + sq1) / 2
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = paddle.sqrt(b2**2 - 4 * a2 * c2)
r2 = (b2 + sq2) / 2
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = paddle.sqrt(b3**2 - 4 * a3 * c3)
r3 = (b3 + sq3) / 2
return min(r1, r2, r3)
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y = paddle.arange(-m, m + 1, dtype="float32")[:, None]
x = paddle.arange(-n, n + 1, dtype="float32")[None, :]
# y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = paddle.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(np.float32).eps * h.max()] = 0
return h
def draw_umich_gaussian(heatmap, center, radius, k=1):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
gaussian = paddle.to_tensor(gaussian, dtype=heatmap.dtype)
x, y = int(center[0]), int(center[1])
radius = int(radius)
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:
radius + right]
# assert masked_gaussian.equal(1).float().sum() == 1
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
heatmap[y - top:y + bottom, x - left:x + right] = paddle.maximum(
masked_heatmap, masked_gaussian * k)
return heatmap
@register
class PETRHead(nn.Layer):
"""Head of `End-to-End Multi-Person Pose Estimation with Transformers`.
Args:
num_classes (int): Number of categories excluding the background.
in_channels (int): Number of channels in the input feature map.
num_query (int): Number of query in Transformer.
num_kpt_fcs (int, optional): Number of fully-connected layers used in
`FFN`, which is then used for the keypoint regression head.
Default 2.
transformer (obj:`mmcv.ConfigDict`|dict): ConfigDict is used for
building the Encoder and Decoder. Default: None.
sync_cls_avg_factor (bool): Whether to sync the avg_factor of
all ranks. Default to False.
positional_encoding (obj:`mmcv.ConfigDict`|dict):
Config for position encoding.
loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the
classification loss. Default `CrossEntropyLoss`.
loss_kpt (obj:`mmcv.ConfigDict`|dict): Config of the
regression loss. Default `L1Loss`.
loss_oks (obj:`mmcv.ConfigDict`|dict): Config of the
regression oks loss. Default `OKSLoss`.
loss_hm (obj:`mmcv.ConfigDict`|dict): Config of the
regression heatmap loss. Default `NegLoss`.
as_two_stage (bool) : Whether to generate the proposal from
the outputs of encoder.
with_kpt_refine (bool): Whether to refine the reference points
in the decoder. Defaults to True.
test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of
transformer head.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
__inject__ = [
"transformer", "positional_encoding", "assigner", "sampler", "loss_cls",
"loss_kpt", "loss_oks", "loss_hm", "loss_kpt_rpn", "loss_kpt_refine",
"loss_oks_refine"
]
def __init__(self,
num_classes,
in_channels,
num_query=100,
num_kpt_fcs=2,
num_keypoints=17,
transformer=None,
sync_cls_avg_factor=True,
positional_encoding='SinePositionalEncoding',
loss_cls='FocalLoss',
loss_kpt='L1Loss',
loss_oks='OKSLoss',
loss_hm='CenterFocalLoss',
with_kpt_refine=True,
assigner='PoseHungarianAssigner',
sampler='PseudoSampler',
loss_kpt_rpn='L1Loss',
loss_kpt_refine='L1Loss',
loss_oks_refine='opera.OKSLoss',
test_cfg=dict(max_per_img=100),
init_cfg=None,
**kwargs):
# NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
# since it brings inconvenience when the initialization of
# `AnchorFreeHead` is called.
super().__init__()
self.bg_cls_weight = 0
self.sync_cls_avg_factor = sync_cls_avg_factor
self.assigner = assigner
self.sampler = sampler
self.num_query = num_query
self.num_classes = num_classes
self.in_channels = in_channels
self.num_kpt_fcs = num_kpt_fcs
self.test_cfg = test_cfg
self.fp16_enabled = False
self.as_two_stage = transformer.as_two_stage
self.with_kpt_refine = with_kpt_refine
self.num_keypoints = num_keypoints
self.loss_cls = loss_cls
self.loss_kpt = loss_kpt
self.loss_kpt_rpn = loss_kpt_rpn
self.loss_kpt_refine = loss_kpt_refine
self.loss_oks = loss_oks
self.loss_oks_refine = loss_oks_refine
self.loss_hm = loss_hm
if self.loss_cls.use_sigmoid:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
self.positional_encoding = positional_encoding
self.transformer = transformer
self.embed_dims = self.transformer.embed_dims
# assert 'num_feats' in positional_encoding
num_feats = positional_encoding.num_pos_feats
assert num_feats * 2 == self.embed_dims, 'embed_dims should' \
f' be exactly 2 times of num_feats. Found {self.embed_dims}' \
f' and {num_feats}.'
self._init_layers()
self.init_weights()
def _init_layers(self):
"""Initialize classification branch and keypoint branch of head."""
fc_cls = nn.Linear(self.embed_dims, self.cls_out_channels)
kpt_branch = []
kpt_branch.append(nn.Linear(self.embed_dims, 512))
kpt_branch.append(nn.ReLU())
for _ in range(self.num_kpt_fcs):
kpt_branch.append(nn.Linear(512, 512))
kpt_branch.append(nn.ReLU())
kpt_branch.append(nn.Linear(512, 2 * self.num_keypoints))
kpt_branch = nn.Sequential(*kpt_branch)
def _get_clones(module, N):
return nn.LayerList([copy.deepcopy(module) for i in range(N)])
# last kpt_branch is used to generate proposal from
# encode feature map when as_two_stage is True.
num_pred = (self.transformer.decoder.num_layers + 1) if \
self.as_two_stage else self.transformer.decoder.num_layers
if self.with_kpt_refine:
self.cls_branches = _get_clones(fc_cls, num_pred)
self.kpt_branches = _get_clones(kpt_branch, num_pred)
else:
self.cls_branches = nn.LayerList([fc_cls for _ in range(num_pred)])
self.kpt_branches = nn.LayerList(
[kpt_branch for _ in range(num_pred)])
self.query_embedding = nn.Embedding(self.num_query, self.embed_dims * 2)
refine_kpt_branch = []
for _ in range(self.num_kpt_fcs):
refine_kpt_branch.append(
nn.Linear(self.embed_dims, self.embed_dims))
refine_kpt_branch.append(nn.ReLU())
refine_kpt_branch.append(nn.Linear(self.embed_dims, 2))
refine_kpt_branch = nn.Sequential(*refine_kpt_branch)
if self.with_kpt_refine:
num_pred = self.transformer.refine_decoder.num_layers
self.refine_kpt_branches = _get_clones(refine_kpt_branch, num_pred)
self.fc_hm = nn.Linear(self.embed_dims, self.num_keypoints)
def init_weights(self):
"""Initialize weights of the PETR head."""
self.transformer.init_weights()
if self.loss_cls.use_sigmoid:
bias_init = bias_init_with_prob(0.01)
for m in self.cls_branches:
constant_(m.bias, bias_init)
for m in self.kpt_branches:
constant_(m[-1].bias, 0)
# initialization of keypoint refinement branch
if self.with_kpt_refine:
for m in self.refine_kpt_branches:
constant_(m[-1].bias, 0)
# initialize bias for heatmap prediction
bias_init = bias_init_with_prob(0.1)
normal_(self.fc_hm.weight, std=0.01)
constant_(self.fc_hm.bias, bias_init)
def forward(self, mlvl_feats, img_metas):
"""Forward function.
Args:
mlvl_feats (tuple[Tensor]): Features from the upstream
network, each is a 4D-tensor with shape
(N, C, H, W).
img_metas (list[dict]): List of image information.
Returns:
outputs_classes (Tensor): Outputs from the classification head,
shape [nb_dec, bs, num_query, cls_out_channels]. Note
cls_out_channels should include background.
outputs_kpts (Tensor): Sigmoid outputs from the regression
head with normalized coordinate format (cx, cy, w, h).
Shape [nb_dec, bs, num_query, K*2].
enc_outputs_class (Tensor): The score of each point on encode
feature map, has shape (N, h*w, num_class). Only when
as_two_stage is Ture it would be returned, otherwise
`None` would be returned.
enc_outputs_kpt (Tensor): The proposal generate from the
encode feature map, has shape (N, h*w, K*2). Only when
as_two_stage is Ture it would be returned, otherwise
`None` would be returned.
"""
batch_size = mlvl_feats[0].shape[0]
input_img_h, input_img_w = img_metas[0]['batch_input_shape']
img_masks = paddle.zeros(
(batch_size, input_img_h, input_img_w), dtype=mlvl_feats[0].dtype)
for img_id in range(batch_size):
img_h, img_w, _ = img_metas[img_id]['img_shape']
img_masks[img_id, :img_h, :img_w] = 1
mlvl_masks = []
mlvl_positional_encodings = []
for feat in mlvl_feats:
mlvl_masks.append(
F.interpolate(
img_masks[None], size=feat.shape[-2:]).squeeze(0))
mlvl_positional_encodings.append(
self.positional_encoding(mlvl_masks[-1]).transpose(
[0, 3, 1, 2]))
query_embeds = self.query_embedding.weight
hs, init_reference, inter_references, \
enc_outputs_class, enc_outputs_kpt, hm_proto, memory = \
self.transformer(
mlvl_feats,
mlvl_masks,
query_embeds,
mlvl_positional_encodings,
kpt_branches=self.kpt_branches \
if self.with_kpt_refine else None, # noqa:E501
cls_branches=self.cls_branches \
if self.as_two_stage else None # noqa:E501
)
outputs_classes = []
outputs_kpts = []
for lvl in range(hs.shape[0]):
if lvl == 0:
reference = init_reference
else:
reference = inter_references[lvl - 1]
reference = inverse_sigmoid(reference)
outputs_class = self.cls_branches[lvl](hs[lvl])
tmp_kpt = self.kpt_branches[lvl](hs[lvl])
assert reference.shape[-1] == self.num_keypoints * 2
tmp_kpt += reference
outputs_kpt = F.sigmoid(tmp_kpt)
outputs_classes.append(outputs_class)
outputs_kpts.append(outputs_kpt)
outputs_classes = paddle.stack(outputs_classes)
outputs_kpts = paddle.stack(outputs_kpts)
if hm_proto is not None:
# get heatmap prediction (training phase)
hm_memory, hm_mask = hm_proto
hm_pred = self.fc_hm(hm_memory)
hm_proto = (hm_pred.transpose((0, 3, 1, 2)), hm_mask)
if self.as_two_stage:
return outputs_classes, outputs_kpts, \
enc_outputs_class, F.sigmoid(enc_outputs_kpt), \
hm_proto, memory, mlvl_masks
else:
raise RuntimeError('only "as_two_stage=True" is supported.')
def forward_refine(self, memory, mlvl_masks, refine_targets, losses,
img_metas):
"""Forward function.
Args:
mlvl_masks (tuple[Tensor]): The key_padding_mask from
different level used for encoder and decoder,
each is a 3D-tensor with shape (bs, H, W).
losses (dict[str, Tensor]): A dictionary of loss components.
img_metas (list[dict]): List of image information.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
kpt_preds, kpt_targets, area_targets, kpt_weights = refine_targets
pos_inds = kpt_weights.sum(-1) > 0
if not pos_inds.any():
pos_kpt_preds = paddle.zeros_like(kpt_preds[:1])
pos_img_inds = paddle.zeros([1], dtype="int64")
else:
pos_kpt_preds = kpt_preds[pos_inds]
pos_img_inds = (pos_inds.nonzero() /
self.num_query).squeeze(1).astype("int64")
hs, init_reference, inter_references = self.transformer.forward_refine(
mlvl_masks,
memory,
pos_kpt_preds.detach(),
pos_img_inds,
kpt_branches=self.refine_kpt_branches
if self.with_kpt_refine else None, # noqa:E501
)
outputs_kpts = []
for lvl in range(hs.shape[0]):
if lvl == 0:
reference = init_reference
else:
reference = inter_references[lvl - 1]
reference = inverse_sigmoid(reference)
tmp_kpt = self.refine_kpt_branches[lvl](hs[lvl])
assert reference.shape[-1] == 2
tmp_kpt += reference
outputs_kpt = F.sigmoid(tmp_kpt)
outputs_kpts.append(outputs_kpt)
outputs_kpts = paddle.stack(outputs_kpts)
if not self.training:
return outputs_kpts
num_valid_kpt = paddle.clip(
reduce_mean(kpt_weights.sum()), min=1).item()
num_total_pos = paddle.to_tensor(
[outputs_kpts.shape[1]], dtype=kpt_weights.dtype)
num_total_pos = paddle.clip(reduce_mean(num_total_pos), min=1).item()
if not pos_inds.any():
for i, kpt_refine_preds in enumerate(outputs_kpts):
loss_kpt = loss_oks = kpt_refine_preds.sum() * 0
losses[f'd{i}.loss_kpt_refine'] = loss_kpt
losses[f'd{i}.loss_oks_refine'] = loss_oks
continue
return losses
batch_size = mlvl_masks[0].shape[0]
factors = []
for img_id in range(batch_size):
img_h, img_w, _ = img_metas[img_id]['img_shape']
factor = paddle.to_tensor(
[img_w, img_h, img_w, img_h],
dtype="float32").squeeze(-1).unsqueeze(0).tile(
(self.num_query, 1))
factors.append(factor)
factors = paddle.concat(factors, 0)
factors = factors[pos_inds][:, :2].tile((1, kpt_preds.shape[-1] // 2))
pos_kpt_weights = kpt_weights[pos_inds]
pos_kpt_targets = kpt_targets[pos_inds]
pos_kpt_targets_scaled = pos_kpt_targets * factors
pos_areas = area_targets[pos_inds]
pos_valid = kpt_weights[pos_inds][:, 0::2]
for i, kpt_refine_preds in enumerate(outputs_kpts):
if not pos_inds.any():
print("refine kpt and oks skip")
loss_kpt = loss_oks = kpt_refine_preds.sum() * 0
losses[f'd{i}.loss_kpt_refine'] = loss_kpt
losses[f'd{i}.loss_oks_refine'] = loss_oks
continue
# kpt L1 Loss
pos_refine_preds = kpt_refine_preds.reshape(
(kpt_refine_preds.shape[0], -1))
loss_kpt = self.loss_kpt_refine(
pos_refine_preds,
pos_kpt_targets,
pos_kpt_weights,
avg_factor=num_valid_kpt)
losses[f'd{i}.loss_kpt_refine'] = loss_kpt
# kpt oks loss
pos_refine_preds_scaled = pos_refine_preds * factors
assert (pos_areas > 0).all()
loss_oks = self.loss_oks_refine(
pos_refine_preds_scaled,
pos_kpt_targets_scaled,
pos_valid,
pos_areas,
avg_factor=num_total_pos)
losses[f'd{i}.loss_oks_refine'] = loss_oks
return losses
# over-write because img_metas are needed as inputs for bbox_head.
def forward_train(self,
x,
img_metas,
gt_bboxes,
gt_labels=None,
gt_keypoints=None,
gt_areas=None,
gt_bboxes_ignore=None,
proposal_cfg=None,
**kwargs):
"""Forward function for training mode.
Args:
x (list[Tensor]): Features from backbone.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes (list[Tensor]): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_labels (list[Tensor]): Ground truth labels of each box,
shape (num_gts,).
gt_keypoints (list[Tensor]): Ground truth keypoints of the image,
shape (num_gts, K*3).
gt_areas (list[Tensor]): Ground truth mask areas of each box,
shape (num_gts,).
gt_bboxes_ignore (list[Tensor]): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert proposal_cfg is None, '"proposal_cfg" must be None'
outs = self(x, img_metas)
memory, mlvl_masks = outs[-2:]
outs = outs[:-2]
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, gt_keypoints, gt_areas, img_metas)
else:
loss_inputs = outs + (gt_bboxes, gt_labels, gt_keypoints, gt_areas,
img_metas)
losses_and_targets = self.loss(
*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
# losses = losses_and_targets
losses, refine_targets = losses_and_targets
# get pose refinement loss
losses = self.forward_refine(memory, mlvl_masks, refine_targets, losses,
img_metas)
return losses
def loss(self,
all_cls_scores,
all_kpt_preds,
enc_cls_scores,
enc_kpt_preds,
enc_hm_proto,
gt_bboxes_list,
gt_labels_list,
gt_keypoints_list,
gt_areas_list,
img_metas,
gt_bboxes_ignore=None):
"""Loss function.
Args:
all_cls_scores (Tensor): Classification score of all
decoder layers, has shape
[nb_dec, bs, num_query, cls_out_channels].
all_kpt_preds (Tensor): Sigmoid regression
outputs of all decode layers. Each is a 4D-tensor with
normalized coordinate format (x_{i}, y_{i}) and shape
[nb_dec, bs, num_query, K*2].
enc_cls_scores (Tensor): Classification scores of
points on encode feature map, has shape
(N, h*w, num_classes). Only be passed when as_two_stage is
True, otherwise is None.
enc_kpt_preds (Tensor): Regression results of each points
on the encode feature map, has shape (N, h*w, K*2). Only be
passed when as_two_stage is True, otherwise is None.
gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels_list (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
gt_keypoints_list (list[Tensor]): Ground truth keypoints for each
image with shape (num_gts, K*3) in [p^{1}_x, p^{1}_y, p^{1}_v,
..., p^{K}_x, p^{K}_y, p^{K}_v] format.
gt_areas_list (list[Tensor]): Ground truth mask areas for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
which can be ignored for each image. Default None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert gt_bboxes_ignore is None, \
f'{self.__class__.__name__} only supports ' \
f'for gt_bboxes_ignore setting to None.'
num_dec_layers = len(all_cls_scores)
all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
all_gt_keypoints_list = [
gt_keypoints_list for _ in range(num_dec_layers)
]
all_gt_areas_list = [gt_areas_list for _ in range(num_dec_layers)]
img_metas_list = [img_metas for _ in range(num_dec_layers)]
losses_cls, losses_kpt, losses_oks, kpt_preds_list, kpt_targets_list, \
area_targets_list, kpt_weights_list = multi_apply(
self.loss_single, all_cls_scores, all_kpt_preds,
all_gt_labels_list, all_gt_keypoints_list,
all_gt_areas_list, img_metas_list)
loss_dict = dict()
# loss of proposal generated from encode feature map.
if enc_cls_scores is not None:
binary_labels_list = [
paddle.zeros_like(gt_labels_list[i])
for i in range(len(img_metas))
]
enc_loss_cls, enc_losses_kpt = \
self.loss_single_rpn(
enc_cls_scores, enc_kpt_preds, binary_labels_list,
gt_keypoints_list, gt_areas_list, img_metas)
loss_dict['enc_loss_cls'] = enc_loss_cls
loss_dict['enc_loss_kpt'] = enc_losses_kpt
# loss from the last decoder layer
loss_dict['loss_cls'] = losses_cls[-1]
loss_dict['loss_kpt'] = losses_kpt[-1]
loss_dict['loss_oks'] = losses_oks[-1]
# loss from other decoder layers
num_dec_layer = 0
for loss_cls_i, loss_kpt_i, loss_oks_i in zip(
losses_cls[:-1], losses_kpt[:-1], losses_oks[:-1]):
loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
loss_dict[f'd{num_dec_layer}.loss_kpt'] = loss_kpt_i
loss_dict[f'd{num_dec_layer}.loss_oks'] = loss_oks_i
num_dec_layer += 1
# losses of heatmap generated from P3 feature map
hm_pred, hm_mask = enc_hm_proto
loss_hm = self.loss_heatmap(hm_pred, hm_mask, gt_keypoints_list,
gt_labels_list, gt_bboxes_list)
loss_dict['loss_hm'] = loss_hm
return loss_dict, (kpt_preds_list[-1], kpt_targets_list[-1],
area_targets_list[-1], kpt_weights_list[-1])
def loss_heatmap(self, hm_pred, hm_mask, gt_keypoints, gt_labels,
gt_bboxes):
assert hm_pred.shape[-2:] == hm_mask.shape[-2:]
num_img, _, h, w = hm_pred.shape
# placeholder of heatmap target (Gaussian distribution)
hm_target = paddle.zeros(hm_pred.shape, hm_pred.dtype)
for i, (gt_label, gt_bbox, gt_keypoint
) in enumerate(zip(gt_labels, gt_bboxes, gt_keypoints)):
if gt_label.shape[0] == 0:
continue
gt_keypoint = gt_keypoint.reshape((gt_keypoint.shape[0], -1,
3)).clone()
gt_keypoint[..., :2] /= 8
assert gt_keypoint[..., 0].max() <= w + 0.5 # new coordinate system
assert gt_keypoint[..., 1].max() <= h + 0.5 # new coordinate system
gt_bbox /= 8
gt_w = gt_bbox[:, 2] - gt_bbox[:, 0]
gt_h = gt_bbox[:, 3] - gt_bbox[:, 1]
for j in range(gt_label.shape[0]):
# get heatmap radius
kp_radius = paddle.clip(
paddle.floor(
gaussian_radius(
(gt_h[j], gt_w[j]), min_overlap=0.9)),
min=0,
max=3)
for k in range(self.num_keypoints):
if gt_keypoint[j, k, 2] > 0:
gt_kp = gt_keypoint[j, k, :2]
gt_kp_int = paddle.floor(gt_kp)
hm_target[i, k] = draw_umich_gaussian(
hm_target[i, k], gt_kp_int, kp_radius)
# compute heatmap loss
hm_pred = paddle.clip(
F.sigmoid(hm_pred), min=1e-4, max=1 - 1e-4) # refer to CenterNet
loss_hm = self.loss_hm(
hm_pred,
hm_target.detach(),
mask=~hm_mask.astype("bool").unsqueeze(1))
return loss_hm
def loss_single(self, cls_scores, kpt_preds, gt_labels_list,
gt_keypoints_list, gt_areas_list, img_metas):
"""Loss function for outputs from a single decoder layer of a single
feature level.
Args:
cls_scores (Tensor): Box score logits from a single decoder layer
for all images. Shape [bs, num_query, cls_out_channels].
kpt_preds (Tensor): Sigmoid outputs from a single decoder layer
for all images, with normalized coordinate (x_{i}, y_{i}) and
shape [bs, num_query, K*2].
gt_labels_list (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
gt_keypoints_list (list[Tensor]): Ground truth keypoints for each
image with shape (num_gts, K*3) in [p^{1}_x, p^{1}_y, p^{1}_v,
..., p^{K}_x, p^{K}_y, p^{K}_v] format.
gt_areas_list (list[Tensor]): Ground truth mask areas for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
Returns:
dict[str, Tensor]: A dictionary of loss components for outputs from
a single decoder layer.
"""
num_imgs = cls_scores.shape[0]
cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
kpt_preds_list = [kpt_preds[i] for i in range(num_imgs)]
cls_reg_targets = self.get_targets(cls_scores_list, kpt_preds_list,
gt_labels_list, gt_keypoints_list,
gt_areas_list, img_metas)
(labels_list, label_weights_list, kpt_targets_list, kpt_weights_list,
area_targets_list, num_total_pos, num_total_neg) = cls_reg_targets
labels = paddle.concat(labels_list, 0)
label_weights = paddle.concat(label_weights_list, 0)
kpt_targets = paddle.concat(kpt_targets_list, 0)
kpt_weights = paddle.concat(kpt_weights_list, 0)
area_targets = paddle.concat(area_targets_list, 0)
# classification loss
cls_scores = cls_scores.reshape((-1, self.cls_out_channels))
# construct weighted avg_factor to match with the official DETR repo
cls_avg_factor = num_total_pos * 1.0 + \
num_total_neg * self.bg_cls_weight
if self.sync_cls_avg_factor:
cls_avg_factor = reduce_mean(
paddle.to_tensor(
[cls_avg_factor], dtype=cls_scores.dtype))
cls_avg_factor = max(cls_avg_factor, 1)
loss_cls = self.loss_cls(
cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
# Compute the average number of gt keypoints accross all gpus, for
# normalization purposes
num_total_pos = paddle.to_tensor([num_total_pos], dtype=loss_cls.dtype)
num_total_pos = paddle.clip(reduce_mean(num_total_pos), min=1).item()
# construct factors used for rescale keypoints
factors = []
for img_meta, kpt_pred in zip(img_metas, kpt_preds):
img_h, img_w, _ = img_meta['img_shape']
factor = paddle.to_tensor(
[img_w, img_h, img_w, img_h],
dtype=kpt_pred.dtype).squeeze().unsqueeze(0).tile(
(kpt_pred.shape[0], 1))
factors.append(factor)
factors = paddle.concat(factors, 0)
# keypoint regression loss
kpt_preds = kpt_preds.reshape((-1, kpt_preds.shape[-1]))
num_valid_kpt = paddle.clip(
reduce_mean(kpt_weights.sum()), min=1).item()
# assert num_valid_kpt == (kpt_targets>0).sum().item()
loss_kpt = self.loss_kpt(
kpt_preds,
kpt_targets.detach(),
kpt_weights.detach(),
avg_factor=num_valid_kpt)
# keypoint oks loss
pos_inds = kpt_weights.sum(-1) > 0
if not pos_inds.any():
loss_oks = kpt_preds.sum() * 0
else:
factors = factors[pos_inds][:, :2].tile((
(1, kpt_preds.shape[-1] // 2)))
pos_kpt_preds = kpt_preds[pos_inds] * factors
pos_kpt_targets = kpt_targets[pos_inds] * factors
pos_areas = area_targets[pos_inds]
pos_valid = kpt_weights[pos_inds][..., 0::2]
assert (pos_areas > 0).all()
loss_oks = self.loss_oks(
pos_kpt_preds,
pos_kpt_targets,
pos_valid,
pos_areas,
avg_factor=num_total_pos)
return loss_cls, loss_kpt, loss_oks, kpt_preds, kpt_targets, \
area_targets, kpt_weights
def get_targets(self, cls_scores_list, kpt_preds_list, gt_labels_list,
gt_keypoints_list, gt_areas_list, img_metas):
"""Compute regression and classification targets for a batch image.
Outputs from a single decoder layer of a single feature level are used.
Args:
cls_scores_list (list[Tensor]): Box score logits from a single
decoder layer for each image with shape [num_query,
cls_out_channels].
kpt_preds_list (list[Tensor]): Sigmoid outputs from a single
decoder layer for each image, with normalized coordinate
(x_{i}, y_{i}) and shape [num_query, K*2].
gt_labels_list (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
gt_keypoints_list (list[Tensor]): Ground truth keypoints for each
image with shape (num_gts, K*3).
gt_areas_list (list[Tensor]): Ground truth mask areas for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
Returns:
tuple: a tuple containing the following targets.
- labels_list (list[Tensor]): Labels for all images.
- label_weights_list (list[Tensor]): Label weights for all
images.
- kpt_targets_list (list[Tensor]): Keypoint targets for all
images.
- kpt_weights_list (list[Tensor]): Keypoint weights for all
images.
- area_targets_list (list[Tensor]): area targets for all
images.
- num_total_pos (int): Number of positive samples in all
images.
- num_total_neg (int): Number of negative samples in all
images.
"""
(labels_list, label_weights_list, kpt_targets_list, kpt_weights_list,
area_targets_list, pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single, cls_scores_list, kpt_preds_list,
gt_labels_list, gt_keypoints_list, gt_areas_list, img_metas)
num_total_pos = sum((inds.numel() for inds in pos_inds_list))
num_total_neg = sum((inds.numel() for inds in neg_inds_list))
return (labels_list, label_weights_list, kpt_targets_list,
kpt_weights_list, area_targets_list, num_total_pos,
num_total_neg)
def _get_target_single(self, cls_score, kpt_pred, gt_labels, gt_keypoints,
gt_areas, img_meta):
"""Compute regression and classification targets for one image.
Outputs from a single decoder layer of a single feature level are used.
Args:
cls_score (Tensor): Box score logits from a single decoder layer
for one image. Shape [num_query, cls_out_channels].
kpt_pred (Tensor): Sigmoid outputs from a single decoder layer
for one image, with normalized coordinate (x_{i}, y_{i}) and
shape [num_query, K*2].
gt_labels (Tensor): Ground truth class indices for one image
with shape (num_gts, ).
gt_keypoints (Tensor): Ground truth keypoints for one image with
shape (num_gts, K*3) in [p^{1}_x, p^{1}_y, p^{1}_v, ..., \
p^{K}_x, p^{K}_y, p^{K}_v] format.
gt_areas (Tensor): Ground truth mask areas for one image
with shape (num_gts, ).
img_meta (dict): Meta information for one image.
Returns:
tuple[Tensor]: a tuple containing the following for one image.
- labels (Tensor): Labels of each image.
- label_weights (Tensor): Label weights of each image.
- kpt_targets (Tensor): Keypoint targets of each image.
- kpt_weights (Tensor): Keypoint weights of each image.
- area_targets (Tensor): Area targets of each image.
- pos_inds (Tensor): Sampled positive indices for each image.
- neg_inds (Tensor): Sampled negative indices for each image.
"""
num_bboxes = kpt_pred.shape[0]
# assigner and sampler
assign_result = self.assigner.assign(cls_score, kpt_pred, gt_labels,
gt_keypoints, gt_areas, img_meta)
sampling_result = self.sampler.sample(assign_result, kpt_pred,
gt_keypoints)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
# label targets
labels = paddle.full((num_bboxes, ), self.num_classes, dtype="int64")
label_weights = paddle.ones((num_bboxes, ), dtype=gt_labels.dtype)
kpt_targets = paddle.zeros_like(kpt_pred)
kpt_weights = paddle.zeros_like(kpt_pred)
area_targets = paddle.zeros((kpt_pred.shape[0], ), dtype=kpt_pred.dtype)
if pos_inds.size == 0:
return (labels, label_weights, kpt_targets, kpt_weights,
area_targets, pos_inds, neg_inds)
labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds][
..., 0].astype("int64")
img_h, img_w, _ = img_meta['img_shape']
# keypoint targets
pos_gt_kpts = gt_keypoints[sampling_result.pos_assigned_gt_inds]
pos_gt_kpts = pos_gt_kpts.reshape(
(len(sampling_result.pos_assigned_gt_inds), -1, 3))
valid_idx = pos_gt_kpts[:, :, 2] > 0
pos_kpt_weights = kpt_weights[pos_inds].reshape(
(pos_gt_kpts.shape[0], kpt_weights.shape[-1] // 2, 2))
# pos_kpt_weights[valid_idx][...] = 1.0
pos_kpt_weights = masked_fill(pos_kpt_weights,
valid_idx.unsqueeze(-1), 1.0)
kpt_weights[pos_inds] = pos_kpt_weights.reshape(
(pos_kpt_weights.shape[0], kpt_pred.shape[-1]))
factor = paddle.to_tensor(
[img_w, img_h], dtype=kpt_pred.dtype).squeeze().unsqueeze(0)
pos_gt_kpts_normalized = pos_gt_kpts[..., :2]
pos_gt_kpts_normalized[..., 0] = pos_gt_kpts_normalized[..., 0] / \
factor[:, 0:1]
pos_gt_kpts_normalized[..., 1] = pos_gt_kpts_normalized[..., 1] / \
factor[:, 1:2]
kpt_targets[pos_inds] = pos_gt_kpts_normalized.reshape(
(pos_gt_kpts.shape[0], kpt_pred.shape[-1]))
pos_gt_areas = gt_areas[sampling_result.pos_assigned_gt_inds][..., 0]
area_targets[pos_inds] = pos_gt_areas
return (labels, label_weights, kpt_targets, kpt_weights, area_targets,
pos_inds, neg_inds)
def loss_single_rpn(self, cls_scores, kpt_preds, gt_labels_list,
gt_keypoints_list, gt_areas_list, img_metas):
"""Loss function for outputs from a single decoder layer of a single
feature level.
Args:
cls_scores (Tensor): Box score logits from a single decoder layer
for all images. Shape [bs, num_query, cls_out_channels].
kpt_preds (Tensor): Sigmoid outputs from a single decoder layer
for all images, with normalized coordinate (x_{i}, y_{i}) and
shape [bs, num_query, K*2].
gt_labels_list (list[Tensor]): Ground truth class indices for each
image with shape (num_gts, ).
gt_keypoints_list (list[Tensor]): Ground truth keypoints for each
image with shape (num_gts, K*3) in [p^{1}_x, p^{1}_y, p^{1}_v,
..., p^{K}_x, p^{K}_y, p^{K}_v] format.
gt_areas_list (list[Tensor]): Ground truth mask areas for each
image with shape (num_gts, ).
img_metas (list[dict]): List of image meta information.
Returns:
dict[str, Tensor]: A dictionary of loss components for outputs from
a single decoder layer.
"""
num_imgs = cls_scores.shape[0]
cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
kpt_preds_list = [kpt_preds[i] for i in range(num_imgs)]
cls_reg_targets = self.get_targets(cls_scores_list, kpt_preds_list,
gt_labels_list, gt_keypoints_list,
gt_areas_list, img_metas)
(labels_list, label_weights_list, kpt_targets_list, kpt_weights_list,
area_targets_list, num_total_pos, num_total_neg) = cls_reg_targets
labels = paddle.concat(labels_list, 0)
label_weights = paddle.concat(label_weights_list, 0)
kpt_targets = paddle.concat(kpt_targets_list, 0)
kpt_weights = paddle.concat(kpt_weights_list, 0)
# classification loss
cls_scores = cls_scores.reshape((-1, self.cls_out_channels))
# construct weighted avg_factor to match with the official DETR repo
cls_avg_factor = num_total_pos * 1.0 + \
num_total_neg * self.bg_cls_weight
if self.sync_cls_avg_factor:
cls_avg_factor = reduce_mean(
paddle.to_tensor(
[cls_avg_factor], dtype=cls_scores.dtype))
cls_avg_factor = max(cls_avg_factor, 1)
cls_avg_factor = max(cls_avg_factor, 1)
loss_cls = self.loss_cls(
cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
# Compute the average number of gt keypoints accross all gpus, for
# normalization purposes
# num_total_pos = loss_cls.to_tensor([num_total_pos])
# num_total_pos = paddle.clip(reduce_mean(num_total_pos), min=1).item()
# keypoint regression loss
kpt_preds = kpt_preds.reshape((-1, kpt_preds.shape[-1]))
num_valid_kpt = paddle.clip(
reduce_mean(kpt_weights.sum()), min=1).item()
# assert num_valid_kpt == (kpt_targets>0).sum().item()
loss_kpt = self.loss_kpt_rpn(
kpt_preds, kpt_targets, kpt_weights, avg_factor=num_valid_kpt)
return loss_cls, loss_kpt
def get_bboxes(self,
all_cls_scores,
all_kpt_preds,
enc_cls_scores,
enc_kpt_preds,
hm_proto,
memory,
mlvl_masks,
img_metas,
rescale=False):
"""Transform network outputs for a batch into bbox predictions.
Args:
all_cls_scores (Tensor): Classification score of all
decoder layers, has shape
[nb_dec, bs, num_query, cls_out_channels].
all_kpt_preds (Tensor): Sigmoid regression
outputs of all decode layers. Each is a 4D-tensor with
normalized coordinate format (x_{i}, y_{i}) and shape
[nb_dec, bs, num_query, K*2].
enc_cls_scores (Tensor): Classification scores of points on
encode feature map, has shape (N, h*w, num_classes).
Only be passed when as_two_stage is True, otherwise is None.
enc_kpt_preds (Tensor): Regression results of each points
on the encode feature map, has shape (N, h*w, K*2). Only be
passed when as_two_stage is True, otherwise is None.
img_metas (list[dict]): Meta information of each image.
rescale (bool, optional): If True, return boxes in original
image space. Defalut False.
Returns:
list[list[Tensor, Tensor]]: Each item in result_list is 3-tuple.
The first item is an (n, 5) tensor, where the first 4 columns
are bounding box positions (tl_x, tl_y, br_x, br_y) and the
5-th column is a score between 0 and 1. The second item is a
(n,) tensor where each item is the predicted class label of
the corresponding box. The third item is an (n, K, 3) tensor
with [p^{1}_x, p^{1}_y, p^{1}_v, ..., p^{K}_x, p^{K}_y,
p^{K}_v] format.
"""
cls_scores = all_cls_scores[-1]
kpt_preds = all_kpt_preds[-1]
result_list = []
for img_id in range(len(img_metas)):
cls_score = cls_scores[img_id]
kpt_pred = kpt_preds[img_id]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
# TODO: only support single image test
# memory_i = memory[:, img_id, :]
# mlvl_mask = mlvl_masks[img_id]
proposals = self._get_bboxes_single(cls_score, kpt_pred, img_shape,
scale_factor, memory,
mlvl_masks, rescale)
result_list.append(proposals)
return result_list
def _get_bboxes_single(self,
cls_score,
kpt_pred,
img_shape,
scale_factor,
memory,
mlvl_masks,
rescale=False):
"""Transform outputs from the last decoder layer into bbox predictions
for each image.
Args:
cls_score (Tensor): Box score logits from the last decoder layer
for each image. Shape [num_query, cls_out_channels].
kpt_pred (Tensor): Sigmoid outputs from the last decoder layer
for each image, with coordinate format (x_{i}, y_{i}) and
shape [num_query, K*2].
img_shape (tuple[int]): Shape of input image, (height, width, 3).
scale_factor (ndarray, optional): Scale factor of the image arange
as (w_scale, h_scale, w_scale, h_scale).
rescale (bool, optional): If True, return boxes in original image
space. Default False.
Returns:
tuple[Tensor]: Results of detected bboxes and labels.
- det_bboxes: Predicted bboxes with shape [num_query, 5],
where the first 4 columns are bounding box positions
(tl_x, tl_y, br_x, br_y) and the 5-th column are scores
between 0 and 1.
- det_labels: Predicted labels of the corresponding box with
shape [num_query].
- det_kpts: Predicted keypoints with shape [num_query, K, 3].
"""
assert len(cls_score) == len(kpt_pred)
max_per_img = self.test_cfg.get('max_per_img', self.num_query)
# exclude background
if self.loss_cls.use_sigmoid:
cls_score = F.sigmoid(cls_score)
scores, indexs = cls_score.reshape([-1]).topk(max_per_img)
det_labels = indexs % self.num_classes
bbox_index = indexs // self.num_classes
kpt_pred = kpt_pred[bbox_index]
else:
scores, det_labels = F.softmax(cls_score, axis=-1)[..., :-1].max(-1)
scores, bbox_index = scores.topk(max_per_img)
kpt_pred = kpt_pred[bbox_index]
det_labels = det_labels[bbox_index]
# ----- results after pose decoder -----
# det_kpts = kpt_pred.reshape((kpt_pred.shape[0], -1, 2))
# ----- results after joint decoder (default) -----
# import time
# start = time.time()
refine_targets = (kpt_pred, None, None, paddle.ones_like(kpt_pred))
refine_outputs = self.forward_refine(memory, mlvl_masks, refine_targets,
None, None)
# end = time.time()
# print(f'refine time: {end - start:.6f}')
det_kpts = refine_outputs[-1]
det_kpts[..., 0] = det_kpts[..., 0] * img_shape[1]
det_kpts[..., 1] = det_kpts[..., 1] * img_shape[0]
det_kpts[..., 0].clip_(min=0, max=img_shape[1])
det_kpts[..., 1].clip_(min=0, max=img_shape[0])
if rescale:
det_kpts /= paddle.to_tensor(
scale_factor[:2],
dtype=det_kpts.dtype).unsqueeze(0).unsqueeze(0)
# use circumscribed rectangle box of keypoints as det bboxes
x1 = det_kpts[..., 0].min(axis=1, keepdim=True)
y1 = det_kpts[..., 1].min(axis=1, keepdim=True)
x2 = det_kpts[..., 0].max(axis=1, keepdim=True)
y2 = det_kpts[..., 1].max(axis=1, keepdim=True)
det_bboxes = paddle.concat([x1, y1, x2, y2], axis=1)
det_bboxes = paddle.concat((det_bboxes, scores.unsqueeze(1)), -1)
det_kpts = paddle.concat(
(det_kpts, paddle.ones(
det_kpts[..., :1].shape, dtype=det_kpts.dtype)),
axis=2)
return det_bboxes, det_labels, det_kpts
def simple_test(self, feats, img_metas, rescale=False):
"""Test det bboxes without test-time augmentation.
Args:
feats (tuple[paddle.Tensor]): Multi-level features from the
upstream network, each is a 4D-tensor.
img_metas (list[dict]): List of image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
3-tuple. The first item is ``bboxes`` with shape (n, 5),
where 5 represent (tl_x, tl_y, br_x, br_y, score).
The shape of the second tensor in the tuple is ``labels``
with shape (n,). The third item is ``kpts`` with shape
(n, K, 3), in [p^{1}_x, p^{1}_y, p^{1}_v, p^{K}_x, p^{K}_y,
p^{K}_v] format.
"""
# forward of this head requires img_metas
outs = self.forward(feats, img_metas)
results_list = self.get_bboxes(*outs, img_metas, rescale=rescale)
return results_list
def get_loss(self, boxes, scores, gt_bbox, gt_class, prior_boxes):
return self.loss(boxes, scores, gt_bbox, gt_class, prior_boxes)
| PaddleDetection/ppdet/modeling/heads/petr_head.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/heads/petr_head.py",
"repo_id": "PaddleDetection",
"token_count": 26434
} | 81 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from ppdet.core.workspace import register
import math
import numpy as np
from ..initializer import bias_init_with_prob, constant_
from ..backbones.csp_darknet import BaseConv, DWConv
from ..losses import IouLoss
from ppdet.modeling.assigners.simota_assigner import SimOTAAssigner
from ppdet.modeling.bbox_utils import bbox_overlaps
from ppdet.modeling.layers import MultiClassNMS
__all__ = ['YOLOv3Head', 'YOLOXHead']
def _de_sigmoid(x, eps=1e-7):
x = paddle.clip(x, eps, 1. / eps)
x = paddle.clip(1. / x - 1., eps, 1. / eps)
x = -paddle.log(x)
return x
@register
class YOLOv3Head(nn.Layer):
__shared__ = ['num_classes', 'data_format']
__inject__ = ['loss']
def __init__(self,
in_channels=[1024, 512, 256],
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
num_classes=80,
loss='YOLOv3Loss',
iou_aware=False,
iou_aware_factor=0.4,
data_format='NCHW'):
"""
Head for YOLOv3 network
Args:
num_classes (int): number of foreground classes
anchors (list): anchors
anchor_masks (list): anchor masks
loss (object): YOLOv3Loss instance
iou_aware (bool): whether to use iou_aware
iou_aware_factor (float): iou aware factor
data_format (str): data format, NCHW or NHWC
"""
super(YOLOv3Head, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
self.num_classes = num_classes
self.loss = loss
self.iou_aware = iou_aware
self.iou_aware_factor = iou_aware_factor
self.parse_anchor(anchors, anchor_masks)
self.num_outputs = len(self.anchors)
self.data_format = data_format
self.yolo_outputs = []
for i in range(len(self.anchors)):
if self.iou_aware:
num_filters = len(self.anchors[i]) * (self.num_classes + 6)
else:
num_filters = len(self.anchors[i]) * (self.num_classes + 5)
name = 'yolo_output.{}'.format(i)
conv = nn.Conv2D(
in_channels=self.in_channels[i],
out_channels=num_filters,
kernel_size=1,
stride=1,
padding=0,
data_format=data_format,
bias_attr=ParamAttr(regularizer=L2Decay(0.)))
conv.skip_quant = True
yolo_output = self.add_sublayer(name, conv)
self.yolo_outputs.append(yolo_output)
def parse_anchor(self, anchors, anchor_masks):
self.anchors = [[anchors[i] for i in mask] for mask in anchor_masks]
self.mask_anchors = []
anchor_num = len(anchors)
for masks in anchor_masks:
self.mask_anchors.append([])
for mask in masks:
assert mask < anchor_num, "anchor mask index overflow"
self.mask_anchors[-1].extend(anchors[mask])
def forward(self, feats, targets=None):
assert len(feats) == len(self.anchors)
yolo_outputs = []
for i, feat in enumerate(feats):
yolo_output = self.yolo_outputs[i](feat)
if self.data_format == 'NHWC':
yolo_output = paddle.transpose(yolo_output, [0, 3, 1, 2])
yolo_outputs.append(yolo_output)
if self.training:
return self.loss(yolo_outputs, targets, self.anchors)
else:
if self.iou_aware:
y = []
for i, out in enumerate(yolo_outputs):
na = len(self.anchors[i])
ioup, x = out[:, 0:na, :, :], out[:, na:, :, :]
b, c, h, w = x.shape
no = c // na
x = x.reshape((b, na, no, h * w))
ioup = ioup.reshape((b, na, 1, h * w))
obj = x[:, :, 4:5, :]
ioup = F.sigmoid(ioup)
obj = F.sigmoid(obj)
obj_t = (obj**(1 - self.iou_aware_factor)) * (
ioup**self.iou_aware_factor)
obj_t = _de_sigmoid(obj_t)
loc_t = x[:, :, :4, :]
cls_t = x[:, :, 5:, :]
y_t = paddle.concat([loc_t, obj_t, cls_t], axis=2)
y_t = y_t.reshape((b, c, h, w))
y.append(y_t)
return y
else:
return yolo_outputs
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
@register
class YOLOXHead(nn.Layer):
__shared__ = ['num_classes', 'width_mult', 'act', 'trt', 'exclude_nms']
__inject__ = ['assigner', 'nms']
def __init__(self,
num_classes=80,
width_mult=1.0,
depthwise=False,
in_channels=[256, 512, 1024],
feat_channels=256,
fpn_strides=(8, 16, 32),
l1_epoch=285,
act='silu',
assigner=SimOTAAssigner(use_vfl=False),
nms='MultiClassNMS',
loss_weight={
'cls': 1.0,
'obj': 1.0,
'iou': 5.0,
'l1': 1.0,
},
trt=False,
exclude_nms=False):
super(YOLOXHead, self).__init__()
self._dtype = paddle.framework.get_default_dtype()
self.num_classes = num_classes
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
feat_channels = int(feat_channels * width_mult)
self.fpn_strides = fpn_strides
self.l1_epoch = l1_epoch
self.assigner = assigner
self.nms = nms
if isinstance(self.nms, MultiClassNMS) and trt:
self.nms.trt = trt
self.exclude_nms = exclude_nms
self.loss_weight = loss_weight
self.iou_loss = IouLoss(loss_weight=1.0) # default loss_weight 2.5
ConvBlock = DWConv if depthwise else BaseConv
self.stem_conv = nn.LayerList()
self.conv_cls = nn.LayerList()
self.conv_reg = nn.LayerList() # reg [x,y,w,h] + obj
for in_c in self.in_channels:
self.stem_conv.append(BaseConv(in_c, feat_channels, 1, 1, act=act))
self.conv_cls.append(
nn.Sequential(* [
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act), ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
nn.Conv2D(
feat_channels,
self.num_classes,
1,
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
]))
self.conv_reg.append(
nn.Sequential(* [
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
nn.Conv2D(
feat_channels,
4 + 1, # reg [x,y,w,h] + obj
1,
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
]))
self._init_weights()
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
def _init_weights(self):
bias_cls = bias_init_with_prob(0.01)
bias_reg = paddle.full([5], math.log(5.), dtype=self._dtype)
bias_reg[:2] = 0.
bias_reg[-1] = bias_cls
for cls_, reg_ in zip(self.conv_cls, self.conv_reg):
constant_(cls_[-1].weight)
constant_(cls_[-1].bias, bias_cls)
constant_(reg_[-1].weight)
reg_[-1].bias.set_value(bias_reg)
def _generate_anchor_point(self, feat_sizes, strides, offset=0.):
anchor_points, stride_tensor = [], []
num_anchors_list = []
for feat_size, stride in zip(feat_sizes, strides):
h, w = feat_size
x = (paddle.arange(w) + offset) * stride
y = (paddle.arange(h) + offset) * stride
y, x = paddle.meshgrid(y, x)
anchor_points.append(paddle.stack([x, y], axis=-1).reshape([-1, 2]))
stride_tensor.append(
paddle.full(
[len(anchor_points[-1]), 1], stride, dtype=self._dtype))
num_anchors_list.append(len(anchor_points[-1]))
anchor_points = paddle.concat(anchor_points).astype(self._dtype)
anchor_points.stop_gradient = True
stride_tensor = paddle.concat(stride_tensor)
stride_tensor.stop_gradient = True
return anchor_points, stride_tensor, num_anchors_list
def forward(self, feats, targets=None):
assert len(feats) == len(self.fpn_strides), \
"The size of feats is not equal to size of fpn_strides"
feat_sizes = [[f.shape[-2], f.shape[-1]] for f in feats]
cls_score_list, reg_pred_list = [], []
obj_score_list = []
for i, feat in enumerate(feats):
feat = self.stem_conv[i](feat)
cls_logit = self.conv_cls[i](feat)
reg_pred = self.conv_reg[i](feat)
# cls prediction
cls_score = F.sigmoid(cls_logit)
cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1]))
# reg prediction
reg_xywh, obj_logit = paddle.split(reg_pred, [4, 1], axis=1)
reg_xywh = reg_xywh.flatten(2).transpose([0, 2, 1])
reg_pred_list.append(reg_xywh)
# obj prediction
obj_score = F.sigmoid(obj_logit)
obj_score_list.append(obj_score.flatten(2).transpose([0, 2, 1]))
cls_score_list = paddle.concat(cls_score_list, axis=1)
reg_pred_list = paddle.concat(reg_pred_list, axis=1)
obj_score_list = paddle.concat(obj_score_list, axis=1)
# bbox decode
anchor_points, stride_tensor, _ =\
self._generate_anchor_point(feat_sizes, self.fpn_strides)
reg_xy, reg_wh = paddle.split(reg_pred_list, 2, axis=-1)
reg_xy += (anchor_points / stride_tensor)
reg_wh = paddle.exp(reg_wh) * 0.5
bbox_pred_list = paddle.concat(
[reg_xy - reg_wh, reg_xy + reg_wh], axis=-1)
if self.training:
anchor_points, stride_tensor, num_anchors_list =\
self._generate_anchor_point(feat_sizes, self.fpn_strides, 0.5)
yolox_losses = self.get_loss([
cls_score_list, bbox_pred_list, obj_score_list, anchor_points,
stride_tensor, num_anchors_list
], targets)
return yolox_losses
else:
pred_scores = (cls_score_list * obj_score_list).sqrt()
return pred_scores, bbox_pred_list, stride_tensor
def get_loss(self, head_outs, targets):
pred_cls, pred_bboxes, pred_obj,\
anchor_points, stride_tensor, num_anchors_list = head_outs
gt_labels = targets['gt_class']
gt_bboxes = targets['gt_bbox']
pred_scores = (pred_cls * pred_obj).sqrt()
# label assignment
center_and_strides = paddle.concat(
[anchor_points, stride_tensor, stride_tensor], axis=-1)
pos_num_list, label_list, bbox_target_list = [], [], []
for pred_score, pred_bbox, gt_box, gt_label in zip(
pred_scores.detach(),
pred_bboxes.detach() * stride_tensor, gt_bboxes, gt_labels):
pos_num, label, _, bbox_target = self.assigner(
pred_score, center_and_strides, pred_bbox, gt_box, gt_label)
pos_num_list.append(pos_num)
label_list.append(label)
bbox_target_list.append(bbox_target)
labels = paddle.to_tensor(np.stack(label_list, axis=0))
bbox_targets = paddle.to_tensor(np.stack(bbox_target_list, axis=0))
bbox_targets /= stride_tensor # rescale bbox
# 1. obj score loss
mask_positive = (labels != self.num_classes)
loss_obj = F.binary_cross_entropy(
pred_obj,
mask_positive.astype(pred_obj.dtype).unsqueeze(-1),
reduction='sum')
num_pos = sum(pos_num_list)
if num_pos > 0:
num_pos = paddle.to_tensor(num_pos, dtype=self._dtype).clip(min=1)
loss_obj /= num_pos
# 2. iou loss
bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
pred_bboxes_pos = paddle.masked_select(pred_bboxes,
bbox_mask).reshape([-1, 4])
assigned_bboxes_pos = paddle.masked_select(
bbox_targets, bbox_mask).reshape([-1, 4])
bbox_iou = bbox_overlaps(pred_bboxes_pos, assigned_bboxes_pos)
bbox_iou = paddle.diag(bbox_iou)
loss_iou = self.iou_loss(
pred_bboxes_pos.split(
4, axis=-1),
assigned_bboxes_pos.split(
4, axis=-1))
loss_iou = loss_iou.sum() / num_pos
# 3. cls loss
cls_mask = mask_positive.unsqueeze(-1).tile(
[1, 1, self.num_classes])
pred_cls_pos = paddle.masked_select(
pred_cls, cls_mask).reshape([-1, self.num_classes])
assigned_cls_pos = paddle.masked_select(labels, mask_positive)
assigned_cls_pos = F.one_hot(assigned_cls_pos,
self.num_classes + 1)[..., :-1]
assigned_cls_pos *= bbox_iou.unsqueeze(-1)
loss_cls = F.binary_cross_entropy(
pred_cls_pos, assigned_cls_pos, reduction='sum')
loss_cls /= num_pos
# 4. l1 loss
if targets['epoch_id'] >= self.l1_epoch:
loss_l1 = F.l1_loss(
pred_bboxes_pos, assigned_bboxes_pos, reduction='sum')
loss_l1 /= num_pos
else:
loss_l1 = paddle.zeros([1])
loss_l1.stop_gradient = False
else:
loss_cls = paddle.zeros([1])
loss_iou = paddle.zeros([1])
loss_l1 = paddle.zeros([1])
loss_cls.stop_gradient = False
loss_iou.stop_gradient = False
loss_l1.stop_gradient = False
loss = self.loss_weight['obj'] * loss_obj + \
self.loss_weight['cls'] * loss_cls + \
self.loss_weight['iou'] * loss_iou
if targets['epoch_id'] >= self.l1_epoch:
loss += (self.loss_weight['l1'] * loss_l1)
yolox_losses = {
'loss': loss,
'loss_cls': loss_cls,
'loss_obj': loss_obj,
'loss_iou': loss_iou,
'loss_l1': loss_l1,
}
return yolox_losses
def post_process(self, head_outs, img_shape, scale_factor):
pred_scores, pred_bboxes, stride_tensor = head_outs
pred_scores = pred_scores.transpose([0, 2, 1])
pred_bboxes *= stride_tensor
# scale bbox to origin image
scale_factor = scale_factor.flip(-1).tile([1, 2]).unsqueeze(1)
pred_bboxes /= scale_factor
if self.exclude_nms:
# `exclude_nms=True` just use in benchmark
return pred_bboxes.sum(), pred_scores.sum()
else:
bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
return bbox_pred, bbox_num
| PaddleDetection/ppdet/modeling/heads/yolo_head.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/heads/yolo_head.py",
"repo_id": "PaddleDetection",
"token_count": 8959
} | 82 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.nn.functional as F
from ppdet.core.workspace import register, serializable
from .iou_loss import IouLoss
from ..bbox_utils import bbox_iou
@register
@serializable
class IouAwareLoss(IouLoss):
"""
iou aware loss, see https://arxiv.org/abs/1912.05992
Args:
loss_weight (float): iou aware loss weight, default is 1.0
max_height (int): max height of input to support random shape input
max_width (int): max width of input to support random shape input
"""
def __init__(self, loss_weight=1.0, giou=False, diou=False, ciou=False):
super(IouAwareLoss, self).__init__(
loss_weight=loss_weight, giou=giou, diou=diou, ciou=ciou)
def __call__(self, ioup, pbox, gbox):
iou = bbox_iou(
pbox, gbox, giou=self.giou, diou=self.diou, ciou=self.ciou)
iou.stop_gradient = True
loss_iou_aware = F.binary_cross_entropy_with_logits(
ioup, iou, reduction='none')
loss_iou_aware = loss_iou_aware * self.loss_weight
return loss_iou_aware
| PaddleDetection/ppdet/modeling/losses/iou_aware_loss.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/losses/iou_aware_loss.py",
"repo_id": "PaddleDetection",
"token_count": 658
} | 83 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import numpy as np
def get_color(idx):
idx = idx * 3
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
return color
def plot_tracking(image,
tlwhs,
obj_ids,
scores=None,
frame_id=0,
fps=0.,
ids2names=[]):
im = np.ascontiguousarray(np.copy(image))
im_h, im_w = im.shape[:2]
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
text_scale = max(1, image.shape[1] / 1600.)
text_thickness = 2
line_thickness = max(1, int(image.shape[1] / 500.))
radius = max(5, int(im_w / 140.))
cv2.putText(
im,
'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
(0, int(15 * text_scale)),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=2)
for i, tlwh in enumerate(tlwhs):
x1, y1, w, h = tlwh
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
obj_id = int(obj_ids[i])
id_text = '{}'.format(int(obj_id))
if ids2names != []:
assert len(
ids2names) == 1, "plot_tracking only supports single classes."
id_text = '{}_'.format(ids2names[0]) + id_text
_line_thickness = 1 if obj_id <= 0 else line_thickness
color = get_color(abs(obj_id))
cv2.rectangle(
im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
cv2.putText(
im,
id_text, (intbox[0], intbox[1] - 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=text_thickness)
if scores is not None:
text = '{:.2f}'.format(float(scores[i]))
cv2.putText(
im,
text, (intbox[0], intbox[1] + 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 255, 255),
thickness=text_thickness)
return im
def plot_tracking_dict(image,
num_classes,
tlwhs_dict,
obj_ids_dict,
scores_dict,
frame_id=0,
fps=0.,
ids2names=[]):
im = np.ascontiguousarray(np.copy(image))
im_h, im_w = im.shape[:2]
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
text_scale = max(1, image.shape[1] / 1600.)
text_thickness = 2
line_thickness = max(1, int(image.shape[1] / 500.))
radius = max(5, int(im_w / 140.))
for cls_id in range(num_classes):
tlwhs = tlwhs_dict[cls_id]
obj_ids = obj_ids_dict[cls_id]
scores = scores_dict[cls_id]
cv2.putText(
im,
'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
(0, int(15 * text_scale)),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=2)
for i, tlwh in enumerate(tlwhs):
x1, y1, w, h = tlwh
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
obj_id = int(obj_ids[i])
id_text = '{}'.format(int(obj_id))
if ids2names != []:
id_text = '{}_{}'.format(ids2names[cls_id], id_text)
else:
id_text = 'class{}_{}'.format(cls_id, id_text)
_line_thickness = 1 if obj_id <= 0 else line_thickness
color = get_color(abs(obj_id))
cv2.rectangle(
im,
intbox[0:2],
intbox[2:4],
color=color,
thickness=line_thickness)
cv2.putText(
im,
id_text, (intbox[0], intbox[1] - 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=text_thickness)
if scores is not None:
text = '{:.2f}'.format(float(scores[i]))
cv2.putText(
im,
text, (intbox[0], intbox[1] + 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 255, 255),
thickness=text_thickness)
return im
| PaddleDetection/ppdet/modeling/mot/visualization.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/mot/visualization.py",
"repo_id": "PaddleDetection",
"token_count": 2729
} | 84 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn.functional as F
import paddle.nn as nn
from paddle import ParamAttr
from paddle.regularizer import L2Decay
try:
import paddle._legacy_C_ops as C_ops
except:
import paddle._C_ops as C_ops
from paddle import in_dynamic_mode
from paddle.common_ops_import import Variable, LayerHelper, check_variable_and_dtype, check_type, check_dtype
__all__ = [
'prior_box', 'generate_proposals', 'box_coder', 'multiclass_nms',
'distribute_fpn_proposals', 'matrix_nms', 'batch_norm', 'mish', 'silu',
'swish', 'identity', 'anchor_generator'
]
def identity(x):
return x
def mish(x):
return F.mish(x) if hasattr(F, mish) else x * F.tanh(F.softplus(x))
def silu(x):
return F.silu(x)
def swish(x):
return x * F.sigmoid(x)
TRT_ACT_SPEC = {'swish': swish, 'silu': swish}
ACT_SPEC = {'mish': mish, 'silu': silu}
def get_act_fn(act=None, trt=False):
assert act is None or isinstance(act, (
str, dict)), 'name of activation should be str, dict or None'
if not act:
return identity
if isinstance(act, dict):
name = act['name']
act.pop('name')
kwargs = act
else:
name = act
kwargs = dict()
if trt and name in TRT_ACT_SPEC:
fn = TRT_ACT_SPEC[name]
elif name in ACT_SPEC:
fn = ACT_SPEC[name]
else:
fn = getattr(F, name)
return lambda x: fn(x, **kwargs)
def batch_norm(ch,
norm_type='bn',
norm_decay=0.,
freeze_norm=False,
initializer=None,
data_format='NCHW'):
norm_lr = 0. if freeze_norm else 1.
weight_attr = ParamAttr(
initializer=initializer,
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay),
trainable=False if freeze_norm else True)
bias_attr = ParamAttr(
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay),
trainable=False if freeze_norm else True)
if norm_type in ['sync_bn', 'bn']:
norm_layer = nn.BatchNorm2D(
ch,
weight_attr=weight_attr,
bias_attr=bias_attr,
data_format=data_format)
norm_params = norm_layer.parameters()
if freeze_norm:
for param in norm_params:
param.stop_gradient = True
return norm_layer
@paddle.jit.not_to_static
def anchor_generator(input,
anchor_sizes=None,
aspect_ratios=None,
variance=[0.1, 0.1, 0.2, 0.2],
stride=None,
offset=0.5):
"""
**Anchor generator operator**
Generate anchors for Faster RCNN algorithm.
Each position of the input produce N anchors, N =
size(anchor_sizes) * size(aspect_ratios). The order of generated anchors
is firstly aspect_ratios loop then anchor_sizes loop.
Args:
input(Variable): 4-D Tensor with shape [N,C,H,W]. The input feature map.
anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated
anchors, given in absolute pixels e.g. [64., 128., 256., 512.].
For instance, the anchor size of 64 means the area of this anchor
equals to 64**2. None by default.
aspect_ratios(float32|list|tuple, optional): The height / width ratios
of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.
variance(list|tuple, optional): The variances to be used in box
regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by
default.
stride(list|tuple, optional): The anchors stride across width and height.
The data type is float32. e.g. [16.0, 16.0]. None by default.
offset(float32, optional): Prior boxes center offset. 0.5 by default.
Returns:
Tuple:
Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4].
H is the height of input, W is the width of input,
num_anchors is the box count of each position.
Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
Variances(Variable): The expanded variances of anchors
with a layout of [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_anchors is the box count of each position.
Each variance is in (xcenter, ycenter, w, h) format.
Examples:
.. code-block:: python
import paddle.fluid as fluid
conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
anchor, var = fluid.layers.anchor_generator(
input=conv1,
anchor_sizes=[64, 128, 256, 512],
aspect_ratios=[0.5, 1.0, 2.0],
variance=[0.1, 0.1, 0.2, 0.2],
stride=[16.0, 16.0],
offset=0.5)
"""
def _is_list_or_tuple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
if not _is_list_or_tuple_(anchor_sizes):
anchor_sizes = [anchor_sizes]
if not _is_list_or_tuple_(aspect_ratios):
aspect_ratios = [aspect_ratios]
if not (_is_list_or_tuple_(stride) and len(stride) == 2):
raise ValueError('stride should be a list or tuple ',
'with length 2, (stride_width, stride_height).')
anchor_sizes = list(map(float, anchor_sizes))
aspect_ratios = list(map(float, aspect_ratios))
stride = list(map(float, stride))
if in_dynamic_mode():
attrs = ('anchor_sizes', anchor_sizes, 'aspect_ratios', aspect_ratios,
'variances', variance, 'stride', stride, 'offset', offset)
anchor, var = C_ops.anchor_generator(input, *attrs)
return anchor, var
helper = LayerHelper("anchor_generator", **locals())
dtype = helper.input_dtype()
attrs = {
'anchor_sizes': anchor_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'stride': stride,
'offset': offset
}
anchor = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="anchor_generator",
inputs={"Input": input},
outputs={"Anchors": anchor,
"Variances": var},
attrs=attrs, )
anchor.stop_gradient = True
var.stop_gradient = True
return anchor, var
@paddle.jit.not_to_static
def distribute_fpn_proposals(fpn_rois,
min_level,
max_level,
refer_level,
refer_scale,
pixel_offset=False,
rois_num=None,
name=None):
r"""
**This op only takes LoDTensor as input.** In Feature Pyramid Networks
(FPN) models, it is needed to distribute all proposals into different FPN
level, with respect to scale of the proposals, the referring scale and the
referring level. Besides, to restore the order of proposals, we return an
array which indicates the original index of rois in current proposals.
To compute FPN level for each roi, the formula is given as follows:
.. math::
roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
where BBoxArea is a function to compute the area of each roi.
Args:
fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is
float32 or float64. The input fpn_rois.
min_level(int32): The lowest level of FPN layer where the proposals come
from.
max_level(int32): The highest level of FPN layer where the proposals
come from.
refer_level(int32): The referring level of FPN layer with specified scale.
refer_scale(int32): The referring scale of FPN layer with specified level.
rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image.
The shape is [B] and data type is int32. B is the number of images.
If it is not None then return a list of 1-D Tensor. Each element
is the output RoIs' number of each image on the corresponding level
and the shape is [B]. None by default.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Tuple:
multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4]
and data type of float32 and float64. The length is
max_level-min_level+1. The proposals in each FPN level.
restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is
the number of total rois. The data type is int32. It is
used to restore the order of fpn_rois.
rois_num_per_level(List): A list of 1-D Tensor and each Tensor is
the RoIs' number in each image on the corresponding level. The shape
is [B] and data type of int32. B is the number of images
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
paddle.enable_static()
fpn_rois = paddle.static.data(
name='data', shape=[None, 4], dtype='float32', lod_level=1)
multi_rois, restore_ind = ops.distribute_fpn_proposals(
fpn_rois=fpn_rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224)
"""
num_lvl = max_level - min_level + 1
if in_dynamic_mode():
assert rois_num is not None, "rois_num should not be None in dygraph mode."
attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
refer_level, 'refer_scale', refer_scale, 'pixel_offset',
pixel_offset)
multi_rois, restore_ind, rois_num_per_level = C_ops.distribute_fpn_proposals(
fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
return multi_rois, restore_ind, rois_num_per_level
else:
check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
'distribute_fpn_proposals')
helper = LayerHelper('distribute_fpn_proposals', **locals())
dtype = helper.input_dtype('fpn_rois')
multi_rois = [
helper.create_variable_for_type_inference(dtype)
for i in range(num_lvl)
]
restore_ind = helper.create_variable_for_type_inference(dtype='int32')
inputs = {'FpnRois': fpn_rois}
outputs = {
'MultiFpnRois': multi_rois,
'RestoreIndex': restore_ind,
}
if rois_num is not None:
inputs['RoisNum'] = rois_num
rois_num_per_level = [
helper.create_variable_for_type_inference(dtype='int32')
for i in range(num_lvl)
]
outputs['MultiLevelRoIsNum'] = rois_num_per_level
else:
rois_num_per_level = None
helper.append_op(
type='distribute_fpn_proposals',
inputs=inputs,
outputs=outputs,
attrs={
'min_level': min_level,
'max_level': max_level,
'refer_level': refer_level,
'refer_scale': refer_scale,
'pixel_offset': pixel_offset
})
return multi_rois, restore_ind, rois_num_per_level
@paddle.jit.not_to_static
def prior_box(input,
image,
min_sizes,
max_sizes=None,
aspect_ratios=[1.],
variance=[0.1, 0.1, 0.2, 0.2],
flip=False,
clip=False,
steps=[0.0, 0.0],
offset=0.5,
min_max_aspect_ratios_order=False,
name=None):
"""
This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Parameters:
input(Tensor): 4-D tensor(NCHW), the data type should be float32 or float64.
image(Tensor): 4-D tensor(NCHW), the input image data of PriorBoxOp,
the data type should be float32 or float64.
min_sizes(list|tuple|float): the min sizes of generated prior boxes.
max_sizes(list|tuple|None): the max sizes of generated prior boxes.
Default: None.
aspect_ratios(list|tuple|float): the aspect ratios of generated
prior boxes. Default: [1.].
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
flip(bool): Whether to flip aspect ratios. Default:False.
clip(bool): Whether to clip out-of-boundary boxes. Default: False.
step(list|tuple): Prior boxes step across width and height, If
step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
height or weight of the input will be automatically calculated.
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
min_max_aspect_ratios_order(bool): If set True, the output prior box is
in order of [min, max, aspect_ratios], which is consistent with
Caffe. Please note, this order affects the weights order of
convolution layer followed by and does not affect the final
detection results. Default: False.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Tuple: A tuple with two Variable (boxes, variances)
boxes(Tensor): the output prior boxes of PriorBox.
4-D tensor, the layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total box count of each position of input.
variances(Tensor): the expanded variances of PriorBox.
4-D tensor, the layput is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total box count of each position of input
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
paddle.enable_static()
input = paddle.static.data(name="input", shape=[None,3,6,9])
image = paddle.static.data(name="image", shape=[None,3,9,12])
box, var = ops.prior_box(
input=input,
image=image,
min_sizes=[100.],
clip=True,
flip=True)
"""
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
check_variable_and_dtype(
input, 'input', ['uint8', 'int8', 'float32', 'float64'], 'prior_box')
def _is_list_or_tuple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
if not _is_list_or_tuple_(min_sizes):
min_sizes = [min_sizes]
if not _is_list_or_tuple_(aspect_ratios):
aspect_ratios = [aspect_ratios]
if not (_is_list_or_tuple_(steps) and len(steps) == 2):
raise ValueError('steps should be a list or tuple ',
'with length 2, (step_width, step_height).')
min_sizes = list(map(float, min_sizes))
aspect_ratios = list(map(float, aspect_ratios))
steps = list(map(float, steps))
cur_max_sizes = None
if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
if not _is_list_or_tuple_(max_sizes):
max_sizes = [max_sizes]
cur_max_sizes = max_sizes
if in_dynamic_mode():
attrs = ('min_sizes', min_sizes, 'aspect_ratios', aspect_ratios,
'variances', variance, 'flip', flip, 'clip', clip, 'step_w',
steps[0], 'step_h', steps[1], 'offset', offset,
'min_max_aspect_ratios_order', min_max_aspect_ratios_order)
if cur_max_sizes is not None:
attrs += ('max_sizes', cur_max_sizes)
box, var = C_ops.prior_box(input, image, *attrs)
return box, var
else:
attrs = {
'min_sizes': min_sizes,
'aspect_ratios': aspect_ratios,
'variances': variance,
'flip': flip,
'clip': clip,
'step_w': steps[0],
'step_h': steps[1],
'offset': offset,
'min_max_aspect_ratios_order': min_max_aspect_ratios_order
}
if cur_max_sizes is not None:
attrs['max_sizes'] = cur_max_sizes
box = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
"Image": image},
outputs={"Boxes": box,
"Variances": var},
attrs=attrs, )
box.stop_gradient = True
var.stop_gradient = True
return box, var
@paddle.jit.not_to_static
def multiclass_nms(bboxes,
scores,
score_threshold,
nms_top_k,
keep_top_k,
nms_threshold=0.3,
normalized=True,
nms_eta=1.,
background_label=-1,
return_index=False,
return_rois_num=True,
rois_num=None,
name=None):
"""
This operator is to do multi-class non maximum suppression (NMS) on
boxes and scores.
In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
Args:
bboxes (Tensor): Two types of bboxes are supported:
1. (Tensor) A 3-D Tensor with shape
[N, M, 4 or 8 16 24 32] represents the
predicted locations of M bounding bboxes,
N is the batch size. Each bounding box has four
coordinate values and the layout is
[xmin, ymin, xmax, ymax], when box size equals to 4.
2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
M is the number of bounding boxes, C is the
class number
scores (Tensor): Two types of scores are supported:
1. (Tensor) A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions.
N is the batch size, C is the class number, M is
number of bounding boxes. For each category there
are total M scores which corresponding M bounding
boxes. Please note, M is equal to the 2nd dimension
of BBoxes.
2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
M is the number of bbox, C is the class number.
In this case, input BBoxes should be the second
case with shape [M, C, 4].
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0
score_threshold (float): Threshold to filter out bounding boxes with
low confidence score. If not provided,
consider all boxes.
nms_top_k (int): Maximum number of detections to be kept according to
the confidences after the filtering detections based
on score_threshold.
nms_threshold (float): The threshold to be used in NMS. Default: 0.3
nms_eta (float): The threshold to be used in NMS. Default: 1.0
keep_top_k (int): Number of total bboxes to be kept per image after NMS
step. -1 means keeping all bboxes after NMS step.
normalized (bool): Whether detections are normalized. Default: True
return_index(bool): Whether return selected index. Default: False
rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image.
The shape is [B] and data type is int32. B is the number of images.
If it is not None then return a list of 1-D Tensor. Each element
is the output RoIs' number of each image on the corresponding level
and the shape is [B]. None by default.
name(str): Name of the multiclass nms op. Default: None.
Returns:
A tuple with two Variables: (Out, Index) if return_index is True,
otherwise, a tuple with one Variable(Out) is returned.
Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
or A 2-D LoDTensor with shape [No, 10] represents the detections.
Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3,
x4, y4]. No is the total number of detections.
If all images have not detected results, all elements in LoD will be
0, and output tensor is empty (None).
Index: Only return when return_index is True. A 2-D LoDTensor with
shape [No, 1] represents the selected index which type is Integer.
The index is the absolute value cross batches. No is the same number
as Out. If the index is used to gather other attribute such as age,
one needs to reshape the input(N, M, 1) to (N * M, 1) as first, where
N is the batch size and M is the number of boxes.
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
boxes = paddle.static.data(name='bboxes', shape=[81, 4],
dtype='float32', lod_level=1)
scores = paddle.static.data(name='scores', shape=[81],
dtype='float32', lod_level=1)
out, index = ops.multiclass_nms(bboxes=boxes,
scores=scores,
background_label=0,
score_threshold=0.5,
nms_top_k=400,
nms_threshold=0.3,
keep_top_k=200,
normalized=False,
return_index=True)
"""
helper = LayerHelper('multiclass_nms3', **locals())
if in_dynamic_mode():
attrs = ('background_label', background_label, 'score_threshold',
score_threshold, 'nms_top_k', nms_top_k, 'nms_threshold',
nms_threshold, 'keep_top_k', keep_top_k, 'nms_eta', nms_eta,
'normalized', normalized)
output, index, nms_rois_num = C_ops.multiclass_nms3(bboxes, scores,
rois_num, *attrs)
if not return_index:
index = None
return output, nms_rois_num, index
else:
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int32')
inputs = {'BBoxes': bboxes, 'Scores': scores}
outputs = {'Out': output, 'Index': index}
if rois_num is not None:
inputs['RoisNum'] = rois_num
if return_rois_num:
nms_rois_num = helper.create_variable_for_type_inference(
dtype='int32')
outputs['NmsRoisNum'] = nms_rois_num
helper.append_op(
type="multiclass_nms3",
inputs=inputs,
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'nms_top_k': nms_top_k,
'nms_threshold': nms_threshold,
'keep_top_k': keep_top_k,
'nms_eta': nms_eta,
'normalized': normalized
},
outputs=outputs)
output.stop_gradient = True
index.stop_gradient = True
if not return_index:
index = None
if not return_rois_num:
nms_rois_num = None
return output, nms_rois_num, index
@paddle.jit.not_to_static
def matrix_nms(bboxes,
scores,
score_threshold,
post_threshold,
nms_top_k,
keep_top_k,
use_gaussian=False,
gaussian_sigma=2.,
background_label=0,
normalized=True,
return_index=False,
return_rois_num=True,
name=None):
"""
**Matrix NMS**
This operator does matrix non maximum suppression (NMS).
First selects a subset of candidate bounding boxes that have higher scores
than score_threshold (if provided), then the top k candidate is selected if
nms_top_k is larger than -1. Score of the remaining candidate are then
decayed according to the Matrix NMS scheme.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
Args:
bboxes (Tensor): A 3-D Tensor with shape [N, M, 4] represents the
predicted locations of M bounding bboxes,
N is the batch size. Each bounding box has four
coordinate values and the layout is
[xmin, ymin, xmax, ymax], when box size equals to 4.
The data type is float32 or float64.
scores (Tensor): A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions.
N is the batch size, C is the class number, M is
number of bounding boxes. For each category there
are total M scores which corresponding M bounding
boxes. Please note, M is equal to the 2nd dimension
of BBoxes. The data type is float32 or float64.
score_threshold (float): Threshold to filter out bounding boxes with
low confidence score.
post_threshold (float): Threshold to filter out bounding boxes with
low confidence score AFTER decaying.
nms_top_k (int): Maximum number of detections to be kept according to
the confidences after the filtering detections based
on score_threshold.
keep_top_k (int): Number of total bboxes to be kept per image after NMS
step. -1 means keeping all bboxes after NMS step.
use_gaussian (bool): Use Gaussian as the decay function. Default: False
gaussian_sigma (float): Sigma for Gaussian decay function. Default: 2.0
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0
normalized (bool): Whether detections are normalized. Default: True
return_index(bool): Whether return selected index. Default: False
return_rois_num(bool): whether return rois_num. Default: True
name(str): Name of the matrix nms op. Default: None.
Returns:
A tuple with three Tensor: (Out, Index, RoisNum) if return_index is True,
otherwise, a tuple with two Tensor (Out, RoisNum) is returned.
Out (Tensor): A 2-D Tensor with shape [No, 6] containing the
detection results.
Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
(After version 1.3, when no boxes detected, the lod is changed
from {0} to {1})
Index (Tensor): A 2-D Tensor with shape [No, 1] containing the
selected indices, which are absolute values cross batches.
rois_num (Tensor): A 1-D Tensor with shape [N] containing
the number of detected boxes in each image.
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
boxes = paddle.static.data(name='bboxes', shape=[None,81, 4],
dtype='float32', lod_level=1)
scores = paddle.static.data(name='scores', shape=[None,81],
dtype='float32', lod_level=1)
out = ops.matrix_nms(bboxes=boxes, scores=scores, background_label=0,
score_threshold=0.5, post_threshold=0.1,
nms_top_k=400, keep_top_k=200, normalized=False)
"""
check_variable_and_dtype(bboxes, 'BBoxes', ['float32', 'float64'],
'matrix_nms')
check_variable_and_dtype(scores, 'Scores', ['float32', 'float64'],
'matrix_nms')
check_type(score_threshold, 'score_threshold', float, 'matrix_nms')
check_type(post_threshold, 'post_threshold', float, 'matrix_nms')
check_type(nms_top_k, 'nums_top_k', int, 'matrix_nms')
check_type(keep_top_k, 'keep_top_k', int, 'matrix_nms')
check_type(normalized, 'normalized', bool, 'matrix_nms')
check_type(use_gaussian, 'use_gaussian', bool, 'matrix_nms')
check_type(gaussian_sigma, 'gaussian_sigma', float, 'matrix_nms')
check_type(background_label, 'background_label', int, 'matrix_nms')
if in_dynamic_mode():
attrs = ('background_label', background_label, 'score_threshold',
score_threshold, 'post_threshold', post_threshold, 'nms_top_k',
nms_top_k, 'gaussian_sigma', gaussian_sigma, 'use_gaussian',
use_gaussian, 'keep_top_k', keep_top_k, 'normalized',
normalized)
out, index, rois_num = C_ops.matrix_nms(bboxes, scores, *attrs)
if not return_index:
index = None
if not return_rois_num:
rois_num = None
return out, rois_num, index
else:
helper = LayerHelper('matrix_nms', **locals())
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int32')
outputs = {'Out': output, 'Index': index}
if return_rois_num:
rois_num = helper.create_variable_for_type_inference(dtype='int32')
outputs['RoisNum'] = rois_num
helper.append_op(
type="matrix_nms",
inputs={'BBoxes': bboxes,
'Scores': scores},
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'post_threshold': post_threshold,
'nms_top_k': nms_top_k,
'gaussian_sigma': gaussian_sigma,
'use_gaussian': use_gaussian,
'keep_top_k': keep_top_k,
'normalized': normalized
},
outputs=outputs)
output.stop_gradient = True
if not return_index:
index = None
if not return_rois_num:
rois_num = None
return output, rois_num, index
@paddle.jit.not_to_static
def box_coder(prior_box,
prior_box_var,
target_box,
code_type="encode_center_size",
box_normalized=True,
axis=0,
name=None):
r"""
**Box Coder Layer**
Encode/Decode the target bounding box with the priorbox information.
The Encoding schema described below:
.. math::
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = \log(\abs(tw / pw)) / pwv
oh = \log(\abs(th / ph)) / phv
The Decoding schema described below:
.. math::
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = \exp(pwv * tw) * pw + tw / 2
oh = \exp(phv * th) * ph + th / 2
where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates,
width and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote
the priorbox's (anchor) center coordinates, width and height. `pxv`,
`pyv`, `pwv`, `phv` denote the variance of the priorbox and `ox`, `oy`,
`ow`, `oh` denote the encoded/decoded coordinates, width and height.
During Box Decoding, two modes for broadcast are supported. Say target
box has shape [N, M, 4], and the shape of prior box can be [N, 4] or
[M, 4]. Then prior box will broadcast to target box along the
assigned axis.
Args:
prior_box(Tensor): Box list prior_box is a 2-D Tensor with shape
[M, 4] holds M boxes and data type is float32 or float64. Each box
is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the
left top coordinate of the anchor box, if the input is image feature
map, they are close to the origin of the coordinate system.
[xmax, ymax] is the right bottom coordinate of the anchor box.
prior_box_var(List|Tensor|None): prior_box_var supports three types
of input. One is Tensor with shape [M, 4] which holds M group and
data type is float32 or float64. The second is list consist of
4 elements shared by all boxes and data type is float32 or float64.
Other is None and not involved in calculation.
target_box(Tensor): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'. This input also can
be a 3-D Tensor with shape [N, M, 4] when code_type is
'decode_center_size'. Each box is represented as
[xmin, ymin, xmax, ymax]. The data type is float32 or float64.
code_type(str): The code type used with the target box. It can be
`encode_center_size` or `decode_center_size`. `encode_center_size`
by default.
box_normalized(bool): Whether treat the priorbox as a normalized box.
Set true by default.
axis(int): Which axis in PriorBox to broadcast for box decode,
for example, if axis is 0 and TargetBox has shape [N, M, 4] and
PriorBox has shape [M, 4], then PriorBox will broadcast to [N, M, 4]
for decoding. It is only valid when code type is
`decode_center_size`. Set 0 by default.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Tensor:
output_box(Tensor): When code_type is 'encode_center_size', the
output tensor of box_coder_op with shape [N, M, 4] representing the
result of N target boxes encoded with M Prior boxes and variances.
When code_type is 'decode_center_size', N represents the batch size
and M represents the number of decoded boxes.
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
paddle.enable_static()
# For encode
prior_box_encode = paddle.static.data(name='prior_box_encode',
shape=[512, 4],
dtype='float32')
target_box_encode = paddle.static.data(name='target_box_encode',
shape=[81, 4],
dtype='float32')
output_encode = ops.box_coder(prior_box=prior_box_encode,
prior_box_var=[0.1,0.1,0.2,0.2],
target_box=target_box_encode,
code_type="encode_center_size")
# For decode
prior_box_decode = paddle.static.data(name='prior_box_decode',
shape=[512, 4],
dtype='float32')
target_box_decode = paddle.static.data(name='target_box_decode',
shape=[512, 81, 4],
dtype='float32')
output_decode = ops.box_coder(prior_box=prior_box_decode,
prior_box_var=[0.1,0.1,0.2,0.2],
target_box=target_box_decode,
code_type="decode_center_size",
box_normalized=False,
axis=1)
"""
check_variable_and_dtype(prior_box, 'prior_box', ['float32', 'float64'],
'box_coder')
check_variable_and_dtype(target_box, 'target_box', ['float32', 'float64'],
'box_coder')
if in_dynamic_mode():
if isinstance(prior_box_var, Variable):
output_box = C_ops.box_coder(
prior_box, prior_box_var, target_box, "code_type", code_type,
"box_normalized", box_normalized, "axis", axis)
elif isinstance(prior_box_var, list):
output_box = C_ops.box_coder(
prior_box, None, target_box, "code_type", code_type,
"box_normalized", box_normalized, "axis", axis, "variance",
prior_box_var)
else:
raise TypeError(
"Input variance of box_coder must be Variable or list")
return output_box
else:
helper = LayerHelper("box_coder", **locals())
output_box = helper.create_variable_for_type_inference(
dtype=prior_box.dtype)
inputs = {"PriorBox": prior_box, "TargetBox": target_box}
attrs = {
"code_type": code_type,
"box_normalized": box_normalized,
"axis": axis
}
if isinstance(prior_box_var, Variable):
inputs['PriorBoxVar'] = prior_box_var
elif isinstance(prior_box_var, list):
attrs['variance'] = prior_box_var
else:
raise TypeError(
"Input variance of box_coder must be Variable or list")
helper.append_op(
type="box_coder",
inputs=inputs,
attrs=attrs,
outputs={"OutputBox": output_box})
return output_box
@paddle.jit.not_to_static
def generate_proposals(scores,
bbox_deltas,
im_shape,
anchors,
variances,
pre_nms_top_n=6000,
post_nms_top_n=1000,
nms_thresh=0.5,
min_size=0.1,
eta=1.0,
pixel_offset=False,
return_rois_num=False,
name=None):
"""
**Generate proposal Faster-RCNN**
This operation proposes RoIs according to each box with their
probability to be a foreground object and
the box can be calculated by anchors. Bbox_deltais and scores
to be an object are the output of RPN. Final proposals
could be used to train detection net.
For generating proposals, this operation performs following steps:
1. Transposes and resizes scores and bbox_deltas in size of
(H*W*A, 1) and (H*W*A, 4)
2. Calculate box locations as proposals candidates.
3. Clip boxes to image
4. Remove predicted boxes with small area.
5. Apply NMS to get final proposals as output.
Args:
scores(Tensor): A 4-D Tensor with shape [N, A, H, W] represents
the probability for each box to be an object.
N is batch size, A is number of anchors, H and W are height and
width of the feature map. The data type must be float32.
bbox_deltas(Tensor): A 4-D Tensor with shape [N, 4*A, H, W]
represents the difference between predicted box location and
anchor location. The data type must be float32.
im_shape(Tensor): A 2-D Tensor with shape [N, 2] represents H, W, the
origin image size or input size. The data type can be float32 or
float64.
anchors(Tensor): A 4-D Tensor represents the anchors with a layout
of [H, W, A, 4]. H and W are height and width of the feature map,
num_anchors is the box count of each position. Each anchor is
in (xmin, ymin, xmax, ymax) format an unnormalized. The data type must be float32.
variances(Tensor): A 4-D Tensor. The expanded variances of anchors with a layout of
[H, W, num_priors, 4]. Each variance is in
(xcenter, ycenter, w, h) format. The data type must be float32.
pre_nms_top_n(float): Number of total bboxes to be kept per
image before NMS. The data type must be float32. `6000` by default.
post_nms_top_n(float): Number of total bboxes to be kept per
image after NMS. The data type must be float32. `1000` by default.
nms_thresh(float): Threshold in NMS. The data type must be float32. `0.5` by default.
min_size(float): Remove predicted boxes with either height or
width < min_size. The data type must be float32. `0.1` by default.
eta(float): Apply in adaptive NMS, if adaptive `threshold > 0.5`,
`adaptive_threshold = adaptive_threshold * eta` in each iteration.
return_rois_num(bool): When setting True, it will return a 1D Tensor with shape [N, ] that includes Rois's
num of each image in one batch. The N is the image's num. For example, the tensor has values [4,5] that represents
the first image has 4 Rois, the second image has 5 Rois. It only used in rcnn model.
'False' by default.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
tuple:
A tuple with format ``(rpn_rois, rpn_roi_probs)``.
- **rpn_rois**: The generated RoIs. 2-D Tensor with shape ``[N, 4]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
- **rpn_roi_probs**: The scores of generated RoIs. 2-D Tensor with shape ``[N, 1]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
Examples:
.. code-block:: python
import paddle
from ppdet.modeling import ops
paddle.enable_static()
scores = paddle.static.data(name='scores', shape=[None, 4, 5, 5], dtype='float32')
bbox_deltas = paddle.static.data(name='bbox_deltas', shape=[None, 16, 5, 5], dtype='float32')
im_shape = paddle.static.data(name='im_shape', shape=[None, 2], dtype='float32')
anchors = paddle.static.data(name='anchors', shape=[None, 5, 4, 4], dtype='float32')
variances = paddle.static.data(name='variances', shape=[None, 5, 10, 4], dtype='float32')
rois, roi_probs = ops.generate_proposals(scores, bbox_deltas,
im_shape, anchors, variances)
"""
if in_dynamic_mode():
assert return_rois_num, "return_rois_num should be True in dygraph mode."
attrs = ('pre_nms_topN', pre_nms_top_n, 'post_nms_topN', post_nms_top_n,
'nms_thresh', nms_thresh, 'min_size', min_size, 'eta', eta,
'pixel_offset', pixel_offset)
rpn_rois, rpn_roi_probs, rpn_rois_num = C_ops.generate_proposals_v2(
scores, bbox_deltas, im_shape, anchors, variances, *attrs)
if not return_rois_num:
rpn_rois_num = None
return rpn_rois, rpn_roi_probs, rpn_rois_num
else:
helper = LayerHelper('generate_proposals_v2', **locals())
check_variable_and_dtype(scores, 'scores', ['float32'],
'generate_proposals_v2')
check_variable_and_dtype(bbox_deltas, 'bbox_deltas', ['float32'],
'generate_proposals_v2')
check_variable_and_dtype(im_shape, 'im_shape', ['float32', 'float64'],
'generate_proposals_v2')
check_variable_and_dtype(anchors, 'anchors', ['float32'],
'generate_proposals_v2')
check_variable_and_dtype(variances, 'variances', ['float32'],
'generate_proposals_v2')
rpn_rois = helper.create_variable_for_type_inference(
dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference(
dtype=scores.dtype)
outputs = {
'RpnRois': rpn_rois,
'RpnRoiProbs': rpn_roi_probs,
}
if return_rois_num:
rpn_rois_num = helper.create_variable_for_type_inference(
dtype='int32')
rpn_rois_num.stop_gradient = True
outputs['RpnRoisNum'] = rpn_rois_num
helper.append_op(
type="generate_proposals_v2",
inputs={
'Scores': scores,
'BboxDeltas': bbox_deltas,
'ImShape': im_shape,
'Anchors': anchors,
'Variances': variances
},
attrs={
'pre_nms_topN': pre_nms_top_n,
'post_nms_topN': post_nms_top_n,
'nms_thresh': nms_thresh,
'min_size': min_size,
'eta': eta,
'pixel_offset': pixel_offset
},
outputs=outputs)
rpn_rois.stop_gradient = True
rpn_roi_probs.stop_gradient = True
if not return_rois_num:
rpn_rois_num = None
return rpn_rois, rpn_roi_probs, rpn_rois_num
def sigmoid_cross_entropy_with_logits(input,
label,
ignore_index=-100,
normalize=False):
output = F.binary_cross_entropy_with_logits(input, label, reduction='none')
mask_tensor = paddle.cast(label != ignore_index, 'float32')
output = paddle.multiply(output, mask_tensor)
if normalize:
sum_valid_mask = paddle.sum(mask_tensor)
output = output / sum_valid_mask
return output
def smooth_l1(input, label, inside_weight=None, outside_weight=None,
sigma=None):
input_new = paddle.multiply(input, inside_weight)
label_new = paddle.multiply(label, inside_weight)
delta = 1 / (sigma * sigma)
out = F.smooth_l1_loss(input_new, label_new, reduction='none', delta=delta)
out = paddle.multiply(out, outside_weight)
out = out / delta
out = paddle.reshape(out, shape=[out.shape[0], -1])
out = paddle.sum(out, axis=1)
return out
def channel_shuffle(x, groups):
batch_size, num_channels, height, width = x.shape[0:4]
assert num_channels % groups == 0, 'num_channels should be divisible by groups'
channels_per_group = num_channels // groups
x = paddle.reshape(
x=x, shape=[batch_size, groups, channels_per_group, height, width])
x = paddle.transpose(x=x, perm=[0, 2, 1, 3, 4])
x = paddle.reshape(x=x, shape=[batch_size, num_channels, height, width])
return x
def get_static_shape(tensor):
shape = paddle.shape(tensor)
shape.stop_gradient = True
return shape
| PaddleDetection/ppdet/modeling/ops.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/ops.py",
"repo_id": "PaddleDetection",
"token_count": 23581
} | 85 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import paddle
import paddle.nn.functional as F
from paddle import nn
from .resnet import ResNet50, ResNet101
from ppdet.core.workspace import register
__all__ = ['ResNetEmbedding']
@register
class ResNetEmbedding(nn.Layer):
in_planes = 2048
def __init__(self, model_name='ResNet50', last_stride=1):
super(ResNetEmbedding, self).__init__()
assert model_name in ['ResNet50', 'ResNet101'], "Unsupported ReID arch: {}".format(model_name)
self.base = eval(model_name)(last_conv_stride=last_stride)
self.gap = nn.AdaptiveAvgPool2D(output_size=1)
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.bn = nn.BatchNorm1D(self.in_planes, bias_attr=False)
def forward(self, x):
base_out = self.base(x)
global_feat = self.gap(base_out)
global_feat = self.flatten(global_feat)
global_feat = self.bn(global_feat)
return global_feat
| PaddleDetection/ppdet/modeling/reid/resnet_embedding.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/reid/resnet_embedding.py",
"repo_id": "PaddleDetection",
"token_count": 585
} | 86 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified from Deformable-DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Modified from detrex (https://github.com/IDEA-Research/detrex)
# Copyright 2022 The IDEA Authors. All rights reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from ppdet.core.workspace import register
from ..layers import MultiHeadAttention
from .position_encoding import PositionEmbedding
from ..heads.detr_head import MLP
from .deformable_transformer import (MSDeformableAttention,
DeformableTransformerEncoderLayer,
DeformableTransformerEncoder)
from ..initializer import (linear_init_, constant_, xavier_uniform_, normal_,
bias_init_with_prob)
from .utils import (_get_clones, get_valid_ratio,
get_contrastive_denoising_training_group,
get_sine_pos_embed, inverse_sigmoid)
__all__ = ['DINOTransformer']
class DINOTransformerDecoderLayer(nn.Layer):
def __init__(self,
d_model=256,
n_head=8,
dim_feedforward=1024,
dropout=0.,
activation="relu",
n_levels=4,
n_points=4,
lr_mult=1.0,
weight_attr=None,
bias_attr=None):
super(DINOTransformerDecoderLayer, self).__init__()
# self attention
self.self_attn = MultiHeadAttention(d_model, n_head, dropout=dropout)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(
d_model, weight_attr=weight_attr, bias_attr=bias_attr)
# cross attention
self.cross_attn = MSDeformableAttention(d_model, n_head, n_levels,
n_points, lr_mult)
self.dropout2 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(
d_model, weight_attr=weight_attr, bias_attr=bias_attr)
# ffn
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.activation = getattr(F, activation)
self.dropout3 = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.dropout4 = nn.Dropout(dropout)
self.norm3 = nn.LayerNorm(
d_model, weight_attr=weight_attr, bias_attr=bias_attr)
self._reset_parameters()
def _reset_parameters(self):
linear_init_(self.linear1)
linear_init_(self.linear2)
xavier_uniform_(self.linear1.weight)
xavier_uniform_(self.linear2.weight)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, tgt):
return self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
def forward(self,
tgt,
reference_points,
memory,
memory_spatial_shapes,
memory_level_start_index,
attn_mask=None,
memory_mask=None,
query_pos_embed=None):
# self attention
q = k = self.with_pos_embed(tgt, query_pos_embed)
if attn_mask is not None:
attn_mask = paddle.where(
attn_mask.astype('bool'),
paddle.zeros(attn_mask.shape, tgt.dtype),
paddle.full(attn_mask.shape, float("-inf"), tgt.dtype))
tgt2 = self.self_attn(q, k, value=tgt, attn_mask=attn_mask)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
# cross attention
tgt2 = self.cross_attn(
self.with_pos_embed(tgt, query_pos_embed), reference_points, memory,
memory_spatial_shapes, memory_level_start_index, memory_mask)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# ffn
tgt2 = self.forward_ffn(tgt)
tgt = tgt + self.dropout4(tgt2)
tgt = self.norm3(tgt)
return tgt
class DINOTransformerDecoder(nn.Layer):
def __init__(self,
hidden_dim,
decoder_layer,
num_layers,
weight_attr=None,
bias_attr=None):
super(DINOTransformerDecoder, self).__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.norm = nn.LayerNorm(
hidden_dim, weight_attr=weight_attr, bias_attr=bias_attr)
def forward(self,
tgt,
ref_points_unact,
memory,
memory_spatial_shapes,
memory_level_start_index,
bbox_head,
query_pos_head,
valid_ratios=None,
attn_mask=None,
memory_mask=None):
if valid_ratios is None:
valid_ratios = paddle.ones(
[memory.shape[0], memory_spatial_shapes.shape[0], 2])
output = tgt
intermediate = []
inter_bboxes = []
ref_points = F.sigmoid(ref_points_unact)
for i, layer in enumerate(self.layers):
reference_points_input = ref_points.detach().unsqueeze(
2) * valid_ratios.tile([1, 1, 2]).unsqueeze(1)
query_pos_embed = get_sine_pos_embed(
reference_points_input[..., 0, :], self.hidden_dim // 2)
query_pos_embed = query_pos_head(query_pos_embed)
output = layer(output, reference_points_input, memory,
memory_spatial_shapes, memory_level_start_index,
attn_mask, memory_mask, query_pos_embed)
ref_points = F.sigmoid(bbox_head[i](output) + inverse_sigmoid(
ref_points.detach()))
intermediate.append(self.norm(output))
inter_bboxes.append(ref_points)
return paddle.stack(intermediate), paddle.stack(inter_bboxes)
@register
class DINOTransformer(nn.Layer):
__shared__ = ['num_classes', 'hidden_dim']
def __init__(self,
num_classes=80,
hidden_dim=256,
num_queries=900,
position_embed_type='sine',
in_feats_channel=[512, 1024, 2048],
num_levels=4,
num_encoder_points=4,
num_decoder_points=4,
nhead=8,
num_encoder_layers=6,
num_decoder_layers=6,
dim_feedforward=1024,
dropout=0.,
activation="relu",
lr_mult=1.0,
pe_temperature=10000,
pe_offset=-0.5,
num_denoising=100,
label_noise_ratio=0.5,
box_noise_scale=1.0,
learnt_init_query=True,
eps=1e-2):
super(DINOTransformer, self).__init__()
assert position_embed_type in ['sine', 'learned'], \
f'ValueError: position_embed_type not supported {position_embed_type}!'
assert len(in_feats_channel) <= num_levels
self.hidden_dim = hidden_dim
self.nhead = nhead
self.num_levels = num_levels
self.num_classes = num_classes
self.num_queries = num_queries
self.eps = eps
self.num_decoder_layers = num_decoder_layers
weight_attr = ParamAttr(regularizer=L2Decay(0.0))
bias_attr = ParamAttr(regularizer=L2Decay(0.0))
# backbone feature projection
self._build_input_proj_layer(in_feats_channel, weight_attr, bias_attr)
# Transformer module
encoder_layer = DeformableTransformerEncoderLayer(
hidden_dim, nhead, dim_feedforward, dropout, activation, num_levels,
num_encoder_points, lr_mult, weight_attr, bias_attr)
self.encoder = DeformableTransformerEncoder(encoder_layer,
num_encoder_layers)
decoder_layer = DINOTransformerDecoderLayer(
hidden_dim, nhead, dim_feedforward, dropout, activation, num_levels,
num_decoder_points, lr_mult, weight_attr, bias_attr)
self.decoder = DINOTransformerDecoder(hidden_dim, decoder_layer,
num_decoder_layers, weight_attr,
bias_attr)
# denoising part
self.denoising_class_embed = nn.Embedding(
num_classes,
hidden_dim,
weight_attr=ParamAttr(initializer=nn.initializer.Normal()))
self.num_denoising = num_denoising
self.label_noise_ratio = label_noise_ratio
self.box_noise_scale = box_noise_scale
# position embedding
self.position_embedding = PositionEmbedding(
hidden_dim // 2,
temperature=pe_temperature,
normalize=True if position_embed_type == 'sine' else False,
embed_type=position_embed_type,
offset=pe_offset)
self.level_embed = nn.Embedding(num_levels, hidden_dim)
# decoder embedding
self.learnt_init_query = learnt_init_query
if learnt_init_query:
self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
self.query_pos_head = MLP(2 * hidden_dim,
hidden_dim,
hidden_dim,
num_layers=2)
# encoder head
self.enc_output = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.LayerNorm(
hidden_dim, weight_attr=weight_attr, bias_attr=bias_attr))
self.enc_score_head = nn.Linear(hidden_dim, num_classes)
self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, num_layers=3)
# decoder head
self.dec_score_head = nn.LayerList([
nn.Linear(hidden_dim, num_classes)
for _ in range(num_decoder_layers)
])
self.dec_bbox_head = nn.LayerList([
MLP(hidden_dim, hidden_dim, 4, num_layers=3)
for _ in range(num_decoder_layers)
])
self._reset_parameters()
def _reset_parameters(self):
# class and bbox head init
bias_cls = bias_init_with_prob(0.01)
linear_init_(self.enc_score_head)
constant_(self.enc_score_head.bias, bias_cls)
constant_(self.enc_bbox_head.layers[-1].weight)
constant_(self.enc_bbox_head.layers[-1].bias)
for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
linear_init_(cls_)
constant_(cls_.bias, bias_cls)
constant_(reg_.layers[-1].weight)
constant_(reg_.layers[-1].bias)
linear_init_(self.enc_output[0])
xavier_uniform_(self.enc_output[0].weight)
normal_(self.level_embed.weight)
if self.learnt_init_query:
xavier_uniform_(self.tgt_embed.weight)
xavier_uniform_(self.query_pos_head.layers[0].weight)
xavier_uniform_(self.query_pos_head.layers[1].weight)
for l in self.input_proj:
xavier_uniform_(l[0].weight)
constant_(l[0].bias)
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_feats_channel': [i.channels for i in input_shape], }
def _build_input_proj_layer(self,
in_feats_channel,
weight_attr=None,
bias_attr=None):
self.input_proj = nn.LayerList()
for in_channels in in_feats_channel:
self.input_proj.append(
nn.Sequential(
('conv', nn.Conv2D(
in_channels, self.hidden_dim, kernel_size=1)), (
'norm', nn.GroupNorm(
32,
self.hidden_dim,
weight_attr=weight_attr,
bias_attr=bias_attr))))
in_channels = in_feats_channel[-1]
for _ in range(self.num_levels - len(in_feats_channel)):
self.input_proj.append(
nn.Sequential(
('conv', nn.Conv2D(
in_channels,
self.hidden_dim,
kernel_size=3,
stride=2,
padding=1)), ('norm', nn.GroupNorm(
32,
self.hidden_dim,
weight_attr=weight_attr,
bias_attr=bias_attr))))
in_channels = self.hidden_dim
def _get_encoder_input(self, feats, pad_mask=None):
# get projection features
proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)]
if self.num_levels > len(proj_feats):
len_srcs = len(proj_feats)
for i in range(len_srcs, self.num_levels):
if i == len_srcs:
proj_feats.append(self.input_proj[i](feats[-1]))
else:
proj_feats.append(self.input_proj[i](proj_feats[-1]))
# get encoder inputs
feat_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
valid_ratios = []
for i, feat in enumerate(proj_feats):
bs, _, h, w = paddle.shape(feat)
spatial_shapes.append(paddle.stack([h, w]))
# [b,c,h,w] -> [b,h*w,c]
feat_flatten.append(feat.flatten(2).transpose([0, 2, 1]))
if pad_mask is not None:
mask = F.interpolate(pad_mask.unsqueeze(0), size=(h, w))[0]
else:
mask = paddle.ones([bs, h, w])
valid_ratios.append(get_valid_ratio(mask))
# [b, h*w, c]
pos_embed = self.position_embedding(mask).flatten(1, 2)
lvl_pos_embed = pos_embed + self.level_embed.weight[i]
lvl_pos_embed_flatten.append(lvl_pos_embed)
if pad_mask is not None:
# [b, h*w]
mask_flatten.append(mask.flatten(1))
# [b, l, c]
feat_flatten = paddle.concat(feat_flatten, 1)
# [b, l]
mask_flatten = None if pad_mask is None else paddle.concat(mask_flatten,
1)
# [b, l, c]
lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1)
# [num_levels, 2]
spatial_shapes = paddle.to_tensor(
paddle.stack(spatial_shapes).astype('int64'))
# [l] start index of each level
level_start_index = paddle.concat([
paddle.zeros(
[1], dtype='int64'), spatial_shapes.prod(1).cumsum(0)[:-1]
])
# [b, num_levels, 2]
valid_ratios = paddle.stack(valid_ratios, 1)
return (feat_flatten, spatial_shapes, level_start_index, mask_flatten,
lvl_pos_embed_flatten, valid_ratios)
def forward(self, feats, pad_mask=None, gt_meta=None):
# input projection and embedding
(feat_flatten, spatial_shapes, level_start_index, mask_flatten,
lvl_pos_embed_flatten,
valid_ratios) = self._get_encoder_input(feats, pad_mask)
# encoder
memory = self.encoder(feat_flatten, spatial_shapes, level_start_index,
mask_flatten, lvl_pos_embed_flatten, valid_ratios)
# prepare denoising training
if self.training:
denoising_class, denoising_bbox_unact, attn_mask, dn_meta = \
get_contrastive_denoising_training_group(gt_meta,
self.num_classes,
self.num_queries,
self.denoising_class_embed.weight,
self.num_denoising,
self.label_noise_ratio,
self.box_noise_scale)
else:
denoising_class, denoising_bbox_unact, attn_mask, dn_meta = None, None, None, None
target, init_ref_points_unact, enc_topk_bboxes, enc_topk_logits = \
self._get_decoder_input(
memory, spatial_shapes, mask_flatten, denoising_class,
denoising_bbox_unact)
# decoder
inter_feats, inter_bboxes = self.decoder(
target, init_ref_points_unact, memory, spatial_shapes,
level_start_index, self.dec_bbox_head, self.query_pos_head,
valid_ratios, attn_mask, mask_flatten)
out_bboxes = []
out_logits = []
for i in range(self.num_decoder_layers):
out_logits.append(self.dec_score_head[i](inter_feats[i]))
if i == 0:
out_bboxes.append(
F.sigmoid(self.dec_bbox_head[i](inter_feats[i]) +
init_ref_points_unact))
else:
out_bboxes.append(
F.sigmoid(self.dec_bbox_head[i](inter_feats[i]) +
inverse_sigmoid(inter_bboxes[i - 1])))
out_bboxes = paddle.stack(out_bboxes)
out_logits = paddle.stack(out_logits)
return (out_bboxes, out_logits, enc_topk_bboxes, enc_topk_logits,
dn_meta)
def _get_encoder_output_anchors(self,
memory,
spatial_shapes,
memory_mask=None,
grid_size=0.05):
output_anchors = []
idx = 0
for lvl, (h, w) in enumerate(spatial_shapes):
if memory_mask is not None:
mask_ = memory_mask[:, idx:idx + h * w].reshape([-1, h, w])
valid_H = paddle.sum(mask_[:, :, 0], 1)
valid_W = paddle.sum(mask_[:, 0, :], 1)
else:
valid_H, valid_W = h, w
grid_y, grid_x = paddle.meshgrid(
paddle.arange(end=h), paddle.arange(end=w))
grid_xy = paddle.stack([grid_x, grid_y], -1).astype(memory.dtype)
valid_WH = paddle.stack([valid_W, valid_H], -1).reshape(
[-1, 1, 1, 2]).astype(grid_xy.dtype)
grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH
wh = paddle.ones_like(grid_xy) * grid_size * (2.0**lvl)
output_anchors.append(
paddle.concat([grid_xy, wh], -1).reshape([-1, h * w, 4]))
idx += h * w
output_anchors = paddle.concat(output_anchors, 1)
valid_mask = ((output_anchors > self.eps) *
(output_anchors < 1 - self.eps)).all(-1, keepdim=True)
output_anchors = paddle.log(output_anchors / (1 - output_anchors))
if memory_mask is not None:
valid_mask = (valid_mask * (memory_mask.unsqueeze(-1) > 0)) > 0
output_anchors = paddle.where(valid_mask, output_anchors,
paddle.to_tensor(float("inf")))
memory = paddle.where(valid_mask, memory, paddle.to_tensor(0.))
output_memory = self.enc_output(memory)
return output_memory, output_anchors
def _get_decoder_input(self,
memory,
spatial_shapes,
memory_mask=None,
denoising_class=None,
denoising_bbox_unact=None):
bs, _, _ = memory.shape
# prepare input for decoder
output_memory, output_anchors = self._get_encoder_output_anchors(
memory, spatial_shapes, memory_mask)
enc_outputs_class = self.enc_score_head(output_memory)
enc_outputs_coord_unact = self.enc_bbox_head(
output_memory) + output_anchors
_, topk_ind = paddle.topk(
enc_outputs_class.max(-1), self.num_queries, axis=1)
# extract region proposal boxes
batch_ind = paddle.arange(end=bs).astype(topk_ind.dtype)
batch_ind = batch_ind.unsqueeze(-1).tile([1, self.num_queries])
topk_ind = paddle.stack([batch_ind, topk_ind], axis=-1)
reference_points_unact = paddle.gather_nd(enc_outputs_coord_unact,
topk_ind) # unsigmoided.
enc_topk_bboxes = F.sigmoid(reference_points_unact)
if denoising_bbox_unact is not None:
reference_points_unact = paddle.concat(
[denoising_bbox_unact, reference_points_unact], 1)
enc_topk_logits = paddle.gather_nd(enc_outputs_class, topk_ind)
# extract region features
if self.learnt_init_query:
target = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1])
else:
target = paddle.gather_nd(output_memory, topk_ind).detach()
if denoising_class is not None:
target = paddle.concat([denoising_class, target], 1)
return target, reference_points_unact.detach(
), enc_topk_bboxes, enc_topk_logits
| PaddleDetection/ppdet/modeling/transformers/dino_transformer.py/0 | {
"file_path": "PaddleDetection/ppdet/modeling/transformers/dino_transformer.py",
"repo_id": "PaddleDetection",
"token_count": 11738
} | 87 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import weakref
from copy import deepcopy
from .utils import get_bn_running_state_names
__all__ = ['ModelEMA', 'SimpleModelEMA']
class ModelEMA(object):
"""
Exponential Weighted Average for Deep Neutal Networks
Args:
model (nn.Layer): Detector of model.
decay (int): The decay used for updating ema parameter.
Ema's parameter are updated with the formula:
`ema_param = decay * ema_param + (1 - decay) * cur_param`.
Defaults is 0.9998.
ema_decay_type (str): type in ['threshold', 'normal', 'exponential'],
'threshold' as default.
cycle_epoch (int): The epoch of interval to reset ema_param and
step. Defaults is -1, which means not reset. Its function is to
add a regular effect to ema, which is set according to experience
and is effective when the total training epoch is large.
ema_black_list (set|list|tuple, optional): The custom EMA black_list.
Blacklist of weight names that will not participate in EMA
calculation. Default: None.
"""
def __init__(self,
model,
decay=0.9998,
ema_decay_type='threshold',
cycle_epoch=-1,
ema_black_list=None,
ema_filter_no_grad=False):
self.step = 0
self.epoch = 0
self.decay = decay
self.ema_decay_type = ema_decay_type
self.cycle_epoch = cycle_epoch
self.ema_black_list = self._match_ema_black_list(
model.state_dict().keys(), ema_black_list)
bn_states_names = get_bn_running_state_names(model)
if ema_filter_no_grad:
for n, p in model.named_parameters():
if p.stop_gradient and n not in bn_states_names:
self.ema_black_list.add(n)
self.state_dict = dict()
for k, v in model.state_dict().items():
if k in self.ema_black_list:
self.state_dict[k] = v
else:
self.state_dict[k] = paddle.zeros_like(v, dtype='float32')
self._model_state = {
k: weakref.ref(p)
for k, p in model.state_dict().items()
}
def reset(self):
self.step = 0
self.epoch = 0
for k, v in self.state_dict.items():
if k in self.ema_black_list:
self.state_dict[k] = v
else:
self.state_dict[k] = paddle.zeros_like(v)
def resume(self, state_dict, step=0):
for k, v in state_dict.items():
if k in self.state_dict:
if self.state_dict[k].dtype == v.dtype:
self.state_dict[k] = v
else:
self.state_dict[k] = v.astype(self.state_dict[k].dtype)
self.step = step
def update(self, model=None):
if self.ema_decay_type == 'threshold':
decay = min(self.decay, (1 + self.step) / (10 + self.step))
elif self.ema_decay_type == 'exponential':
decay = self.decay * (1 - math.exp(-(self.step + 1) / 2000))
else:
decay = self.decay
self._decay = decay
if model is not None:
model_dict = model.state_dict()
else:
model_dict = {k: p() for k, p in self._model_state.items()}
assert all(
[v is not None for _, v in model_dict.items()]), 'python gc.'
for k, v in self.state_dict.items():
if k not in self.ema_black_list:
v = decay * v + (1 - decay) * model_dict[k].astype('float32')
v.stop_gradient = True
self.state_dict[k] = v
self.step += 1
def apply(self):
if self.step == 0:
return self.state_dict
state_dict = dict()
model_dict = {k: p() for k, p in self._model_state.items()}
for k, v in self.state_dict.items():
if k in self.ema_black_list:
v.stop_gradient = True
state_dict[k] = v
else:
if self.ema_decay_type != 'exponential':
v = v / (1 - self._decay**self.step)
v = v.astype(model_dict[k].dtype)
v.stop_gradient = True
state_dict[k] = v
self.epoch += 1
if self.cycle_epoch > 0 and self.epoch == self.cycle_epoch:
self.reset()
return state_dict
def _match_ema_black_list(self, weight_name, ema_black_list=None):
out_list = set()
if ema_black_list:
for name in weight_name:
for key in ema_black_list:
if key in name:
out_list.add(name)
return out_list
class SimpleModelEMA(object):
"""
Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
Keep a moving average of everything in the model state_dict (parameters and buffers).
This is intended to allow functionality like
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
A smoothed version of the weights is necessary for some training schemes to perform well.
This class is sensitive where it is initialized in the sequence of model init,
GPU assignment and distributed training wrappers.
"""
def __init__(self, model=None, decay=0.9996):
"""
Args:
model (nn.Module): model to apply EMA.
decay (float): ema decay reate.
"""
self.model = deepcopy(model)
self.decay = decay
def update(self, model, decay=None):
if decay is None:
decay = self.decay
with paddle.no_grad():
state = {}
msd = model.state_dict()
for k, v in self.model.state_dict().items():
if paddle.is_floating_point(v):
v *= decay
v += (1.0 - decay) * msd[k].detach()
state[k] = v
self.model.set_state_dict(state)
def resume(self, state_dict, step=0):
state = {}
msd = state_dict
for k, v in self.model.state_dict().items():
if paddle.is_floating_point(v):
v = msd[k].detach()
state[k] = v
self.model.set_state_dict(state)
self.step = step
| PaddleDetection/ppdet/optimizer/ema.py/0 | {
"file_path": "PaddleDetection/ppdet/optimizer/ema.py",
"repo_id": "PaddleDetection",
"token_count": 3360
} | 88 |
import PIL
def imagedraw_textsize_c(draw, text, font=None):
if int(PIL.__version__.split('.')[0]) < 10:
tw, th = draw.textsize(text, font=font)
else:
left, top, right, bottom = draw.textbbox((0, 0), text, font=font)
tw, th = right - left, bottom - top
return tw, th
| PaddleDetection/ppdet/utils/compact.py/0 | {
"file_path": "PaddleDetection/ppdet/utils/compact.py",
"repo_id": "PaddleDetection",
"token_count": 135
} | 89 |
#!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="cpp_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet cpp_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]}")
filename_value=$(func_parser_value "${lines[3]}")
# export params
save_export_key=$(func_parser_key "${lines[5]}")
save_export_value=$(func_parser_value "${lines[5]}")
export_weight_key=$(func_parser_key "${lines[6]}")
export_weight_value=$(func_parser_value "${lines[6]}")
norm_export=$(func_parser_value "${lines[7]}")
pact_export=$(func_parser_value "${lines[8]}")
fpgm_export=$(func_parser_value "${lines[9]}")
distill_export=$(func_parser_value "${lines[10]}")
export_key1=$(func_parser_key "${lines[11]}")
export_value1=$(func_parser_value "${lines[11]}")
export_key2=$(func_parser_key "${lines[12]}")
export_value2=$(func_parser_value "${lines[12]}")
kl_quant_export=$(func_parser_value "${lines[13]}")
# parser cpp inference model
opencv_dir=$(func_parser_value "${lines[15]}")
cpp_infer_mode_list=$(func_parser_value "${lines[16]}")
cpp_infer_is_quant_list=$(func_parser_value "${lines[17]}")
# parser cpp inference
inference_cmd=$(func_parser_value "${lines[18]}")
cpp_use_gpu_key=$(func_parser_key "${lines[19]}")
cpp_use_gpu_list=$(func_parser_value "${lines[19]}")
cpp_use_mkldnn_key=$(func_parser_key "${lines[20]}")
cpp_use_mkldnn_list=$(func_parser_value "${lines[20]}")
cpp_cpu_threads_key=$(func_parser_key "${lines[21]}")
cpp_cpu_threads_list=$(func_parser_value "${lines[21]}")
cpp_batch_size_key=$(func_parser_key "${lines[22]}")
cpp_batch_size_list=$(func_parser_value "${lines[22]}")
cpp_use_trt_key=$(func_parser_key "${lines[23]}")
cpp_use_trt_list=$(func_parser_value "${lines[23]}")
cpp_precision_key=$(func_parser_key "${lines[24]}")
cpp_precision_list=$(func_parser_value "${lines[24]}")
cpp_infer_model_key=$(func_parser_key "${lines[25]}")
cpp_image_dir_key=$(func_parser_key "${lines[26]}")
cpp_infer_img_dir=$(func_parser_value "${lines[26]}")
cpp_benchmark_key=$(func_parser_key "${lines[27]}")
cpp_benchmark_value=$(func_parser_value "${lines[27]}")
cpp_infer_key1=$(func_parser_key "${lines[28]}")
cpp_infer_value1=$(func_parser_value "${lines[28]}")
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_cpp.log"
function func_cpp_inference(){
IFS='|'
_script=$1
_model_dir=$2
_log_path=$3
_img_dir=$4
_flag_quant=$5
# inference
for use_gpu in ${cpp_use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
continue
fi
for threads in ${cpp_cpu_threads_list[*]}; do
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_mode_paddle_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}"
done
done
done
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
for precision in ${cpp_precision_list[*]}; do
if [[ ${precision} != "paddle" ]]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} = "trt_int8" ]]; then
continue
fi
if [[ ${_flag_quant} = "True" ]] && [[ ${precision} != "trt_int8" ]]; then
continue
fi
fi
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/cpp_infer_gpu_mode_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}" "${model_name}" "${_save_log_path}"
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
cd ./deploy/cpp
# set OPENCV_DIR
if [ ${opencv_dir} = "default" ] || [ ${opencv_dir} = "null" ]; then
OPENCV_DIR=$(pwd)/deps/opencv-3.4.16_gcc8.2_ffmpeg
else
OPENCV_DIR=${opencv_dir}
fi
# build program
# TODO: set PADDLE_INFER_DIR and TENSORRT_ROOT
if [ -z $PADDLE_INFER_DIR ]; then
Paddle_Infer_Link=$2
if [ "" = "$Paddle_Infer_Link" ];then
wget -nc https://paddle-inference-lib.bj.bcebos.com/2.2.2/cxx_c/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.1_cudnn7.6.5_trt6.0.1.5/paddle_inference.tgz --no-check-certificate
tar zxf paddle_inference.tgz
PADDLE_INFER_DIR=$(pwd)/paddle_inference
else
wget -nc $Paddle_Infer_Link --no-check-certificate
tar zxf paddle_inference.tgz
PADDLE_INFER_DIR=$(pwd)/paddle_inference
if [ ! -d "paddle_inference" ]; then
PADDLE_INFER_DIR=$(pwd)/paddle_inference_install_dir
fi
fi
fi
if [ -z $TENSORRT_ROOT ]; then
TENSORRT_ROOT=/usr/local/TensorRT6-cuda10.1-cudnn7
fi
CUDA_LIB=$(dirname `find /usr -name libcudart.so`)
CUDNN_LIB=$(dirname `find /usr -name libcudnn.so`)
TENSORRT_LIB_DIR="${TENSORRT_ROOT}/lib"
TENSORRT_INC_DIR="${TENSORRT_ROOT}/include"
rm -rf build
mkdir -p build
cd ./build
cmake .. \
-DWITH_GPU=ON \
-DWITH_MKL=ON \
-DWITH_TENSORRT=OFF \
-DPADDLE_LIB_NAME=libpaddle_inference \
-DPADDLE_DIR=${PADDLE_INFER_DIR} \
-DCUDA_LIB=${CUDA_LIB} \
-DCUDNN_LIB=${CUDNN_LIB} \
-DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
-DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_KEYPOINT=ON \
-DWITH_MOT=ON
make -j8
cd ../../../
echo "################### build finished! ###################"
# set cuda device
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
eval $env
# run cpp infer
Count=0
IFS="|"
infer_quant_flag=(${cpp_infer_is_quant_list})
for infer_mode in ${cpp_infer_mode_list[*]}; do
if [ ${infer_mode} != "null" ]; then
# run export
case ${infer_mode} in
norm) run_export=${norm_export} ;;
quant) run_export=${pact_export} ;;
fpgm) run_export=${fpgm_export} ;;
distill) run_export=${distill_export} ;;
kl_quant) run_export=${kl_quant_export} ;;
*) echo "Undefined infer_mode!"; exit 1;
esac
set_export_weight=$(func_set_params "${export_weight_key}" "${export_weight_value}")
set_save_export_dir=$(func_set_params "${save_export_key}" "${save_export_value}")
set_filename=$(func_set_params "${filename_key}" "${model_name}")
export_log_path="${LOG_PATH}/export.log"
export_cmd="${python} ${run_export} ${set_export_weight} ${set_filename} ${set_save_export_dir} "
echo $export_cmd
eval "${export_cmd} > ${export_log_path} 2>&1"
status_export=$?
cat ${export_log_path}
status_check $status_export "${export_cmd}" "${status_log}" "${model_name}" "${export_log_path}"
fi
#run inference
save_export_model_dir="${save_export_value}/${model_name}"
is_quant=${infer_quant_flag[Count]}
func_cpp_inference "${inference_cmd}" "${save_export_model_dir}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
eval "unset CUDA_VISIBLE_DEVICES"
| PaddleDetection/test_tipc/test_inference_cpp.sh/0 | {
"file_path": "PaddleDetection/test_tipc/test_inference_cpp.sh",
"repo_id": "PaddleDetection",
"token_count": 4660
} | 90 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
# add python path of PaddleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
# ignore warning log
import warnings
warnings.filterwarnings('ignore')
import paddle
from ppdet.core.workspace import load_config, merge_config
from ppdet.utils.check import check_gpu, check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.engine import Trainer
from ppdet.engine.trainer_ssod import Trainer_ARSL
from ppdet.slim import build_slim_model
from ppdet.utils.logger import setup_logger
logger = setup_logger('export_model')
def parse_args():
parser = ArgsParser()
parser.add_argument(
"--output_dir",
type=str,
default="output_inference",
help="Directory for storing the output model files.")
parser.add_argument(
"--export_serving_model",
type=bool,
default=False,
help="Whether to export serving model or not.")
parser.add_argument(
"--slim_config",
default=None,
type=str,
help="Configuration file of slim method.")
parser.add_argument("--for_fd", action='store_true')
args = parser.parse_args()
return args
def run(FLAGS, cfg):
ssod_method = cfg.get('ssod_method', None)
if ssod_method is not None and ssod_method == 'ARSL':
trainer = Trainer_ARSL(cfg, mode='test')
trainer.load_weights(cfg.weights, ARSL_eval=True)
# build detector
else:
trainer = Trainer(cfg, mode='test')
# load weights
if cfg.architecture in ['DeepSORT', 'ByteTrack']:
trainer.load_weights_sde(cfg.det_weights, cfg.reid_weights)
else:
trainer.load_weights(cfg.weights)
# export model
trainer.export(FLAGS.output_dir, for_fd=FLAGS.for_fd)
if FLAGS.export_serving_model:
assert not FLAGS.for_fd
from paddle_serving_client.io import inference_model_to_serving
model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0]
inference_model_to_serving(
dirname="{}/{}".format(FLAGS.output_dir, model_name),
serving_server="{}/{}/serving_server".format(FLAGS.output_dir,
model_name),
serving_client="{}/{}/serving_client".format(FLAGS.output_dir,
model_name),
model_filename="model.pdmodel",
params_filename="model.pdiparams")
def main():
paddle.set_device("cpu")
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check_config(cfg)
if 'use_gpu' not in cfg:
cfg.use_gpu = False
check_gpu(cfg.use_gpu)
check_version()
run(FLAGS, cfg)
if __name__ == '__main__':
main()
| PaddleDetection/tools/export_model.py/0 | {
"file_path": "PaddleDetection/tools/export_model.py",
"repo_id": "PaddleDetection",
"token_count": 1526
} | 91 |
.hf_cache/
.idea
*.md
.git*
.enditorconfig
models
converter
tests
.pytest_cache
| fauxpilot/triton.dockerignore/0 | {
"file_path": "fauxpilot/triton.dockerignore",
"repo_id": "fauxpilot",
"token_count": 37
} | 92 |
{
"pipeline": [
{
"input_folder": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/buckets', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)",
"output_folder": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/remove_ids', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)",
"config": {
"n_grams": 5,
"num_buckets": 14,
"hashes_per_bucket": 8,
"use_64bit_hashes": true,
"seed": 1,
"norm_config": {
"lowercase": true,
"norm_whitespace": true,
"remove_punctuation": true,
"norm_unicode_diacritics": true,
"norm_numbers": true,
"norm_weekdays": false,
"norm_monthnames": false
}
},
"save_cluster_id": false,
"ignore_index_matches": false,
"lines_to_buffer": 5
}
],
"logging_dir": "DataFolder(path='/home/ubuntu/wensimin-work/get-data/logs/2024-07-05_01-48-57_sekfz', fs=<fsspec.implementations.local.LocalFileSystem object at 0x7efddeeb6d10>)",
"skip_completed": true,
"tasks": 1,
"workers": 1,
"start_method": "forkserver",
"local_tasks": 1,
"local_rank_offset": 0,
"depends": null,
"_launched": true,
"world_size": 1
} | get-data/logs/2024-07-05_01-48-57_sekfz/executor.json/0 | {
"file_path": "get-data/logs/2024-07-05_01-48-57_sekfz/executor.json",
"repo_id": "get-data",
"token_count": 819
} | 93 |
# Face Alignment Datasets
(Updating)
## Training Datasets
### Menpo2D-Train
https://ibug.doc.ic.ac.uk/resources/2nd-facial-landmark-tracking-competition-menpo-ben/
### 300W-Train
https://ibug.doc.ic.ac.uk/resources/300-W/
### LFPW
https://neerajkumar.org/databases/lfpw/
### Helen
http://www.ifp.illinois.edu/~vuongle2/helen/
### AFW
### AFLW
https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/
### FDDB
### Face Synthetics
https://github.com/microsoft/FaceSynthetics
### 300W-LP (3D annotation)
http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm
## Test Datasets
### 300W-Test
https://ibug.doc.ic.ac.uk/resources/300-W/
### COFW
http://www.vision.caltech.edu/xpburgos/ICCV13/#dataset
### Menpo2D-Test
https://ibug.doc.ic.ac.uk/resources/2nd-facial-landmark-tracking-competition-menpo-ben/
### AFLW2000-3D (3D annotation)
http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm
| insightface/alignment/_datasets_/README.md/0 | {
"file_path": "insightface/alignment/_datasets_/README.md",
"repo_id": "insightface",
"token_count": 418
} | 94 |
from trainer_synthetics import FaceSynthetics
import sys
import glob
import torch
import os
import numpy as np
import cv2
import os.path as osp
import insightface
from insightface.app import FaceAnalysis
from insightface.utils import face_align
flip_parts = ([1, 17], [2, 16], [3, 15], [4, 14], [5, 13], [6, 12], [7, 11], [8, 10],
[18, 27], [19, 26], [20, 25], [21, 24], [22, 23],
[32, 36], [33, 35],
[37, 46], [38, 45], [39, 44], [40, 43], [41, 48], [42, 47],
[49, 55], [50, 54], [51, 53], [62, 64], [61, 65], [68, 66], [59, 57], [60, 56])
app = FaceAnalysis()
app.prepare(ctx_id=0, det_size=(224, 224))
input_size = 256
USE_FLIP = False
root = 'data/300W/Validation'
output_dir = 'outputs/'
if not osp.exists(output_dir):
os.makedirs(output_dir)
outf = open(osp.join(output_dir, 'pred.txt'), 'w')
model = FaceSynthetics.load_from_checkpoint(sys.argv[1]).cuda()
model.eval()
for line in open(osp.join(root, '300W_validation.txt'), 'r'):
line = line.strip().split()
img_path = osp.join(root, line[0])
gt = line[1:]
#print(len(gt))
name = img_path.split('/')[-1]
img = cv2.imread(img_path)
dimg = img.copy()
faces = app.get(img, max_num=1)
if len(faces)!=1:
continue
bbox = faces[0].bbox
w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
rotate = 0
_scale = input_size / (max(w, h)*1.5)
aimg, M = face_align.transform(img, center, input_size, _scale, rotate)
#cv2.imwrite("outputs/a_%s"%name, aimg)
aimg = cv2.cvtColor(aimg, cv2.COLOR_BGR2RGB)
kps = None
flips = [0, 1] if USE_FLIP else [0]
for flip in flips:
input = aimg.copy()
if flip:
input = input[:,::-1,:].copy()
input = np.transpose(input, (2, 0, 1))
input = np.expand_dims(input, 0)
imgs = torch.Tensor(input).cuda()
imgs.div_(255).sub_(0.5).div_(0.5)
pred = model(imgs).detach().cpu().numpy().flatten().reshape( (-1, 2) )
pred[:, 0:2] += 1
pred[:, 0:2] *= (input_size // 2)
if flip:
pred_flip = pred.copy()
pred_flip[:, 0] = input_size - 1 - pred_flip[:, 0]
for pair in flip_parts:
tmp = pred_flip[pair[0] - 1, :].copy()
pred_flip[pair[0] - 1, :] = pred_flip[pair[1] - 1, :]
pred_flip[pair[1] - 1, :] = tmp
pred = pred_flip
if kps is None:
kps = pred
else:
kps += pred
kps /= 2.0
#print(pred.shape)
IM = cv2.invertAffineTransform(M)
kps = face_align.trans_points(kps, IM)
outf.write(line[0])
outf.write(' ')
outf.write(' '.join(["%.5f"%x for x in kps.flatten()]))
outf.write("\n")
box = bbox.astype(np.int)
color = (0, 0, 255)
cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
kps = kps.astype(np.int)
#print(landmark.shape)
for l in range(kps.shape[0]):
color = (0, 0, 255)
cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color, 2)
cv2.imwrite("outputs/%s"%name, dimg)
#ret = np.argmax(feat)
#print(feat)
#outf.write("%s %.4f %.4f %.4f\n"%(line[0], feat[0], feat[1], feat[2]))
outf.close()
| insightface/alignment/synthetics/test_synthetics.py/0 | {
"file_path": "insightface/alignment/synthetics/test_synthetics.py",
"repo_id": "insightface",
"token_count": 1664
} | 95 |
BATCH_SIZE: 512
DATA:
EXP_TMC: true
EXP_TMC_DETERMINISTIC: true
EXP_TMC_INTERVAL: 3
NUM_FRAMES: 1
SCALE_MID_MEAN: 0.720643
SCALE_MID_STD: 0.058
USE_RANDOM_DIFF: true
NETWORK:
DIS_RES_BLOCKS: 2
DIS_TEMP_RES_BLOCKS: 2
DIS_USE_SPECTRAL_NORM: false
SCALER_INPUT_SIZE: 34
TRAIN:
BOUND_AZIM: 2.44346
BOUND_ELEV: 0.34906585
DIS_LR: 0.0001
LOSS_TYPE: ss_adv
LOSS_WEIGHTS:
- 0.5
- 5.0
- 1.0
- 1.0
MAINNET_CRITICS: 4
NUM_CRITICS: 3
NUM_CRITICS_TEMP: 3
POSE_LR: 0.0001
PRETRAIN_LIFTER: false
SCALE_LOSS_WEIGHTS:
- 0.01
- 1.0
SUBNET_CRITICS: 1
TEMP_LR: 0.0002
USE_CYCLE: true
USE_NEW_ROT: false
USE_NEW_TEMP: false
USE_SCALER: true
USE_GT: false
| insightface/body/human_pose/ambiguity_aware/cfg/pre_tmc_klbone.yaml/0 | {
"file_path": "insightface/body/human_pose/ambiguity_aware/cfg/pre_tmc_klbone.yaml",
"repo_id": "insightface",
"token_count": 372
} | 96 |
import os
import torch
import torch.optim as optim
import numpy as np
from sklearn.metrics import auc
joint_parents = [1, 2, 13, 13, 3, 4, 7, 8, 12, 12, 9, 10, 14, 13, 13, 12, 15]
def rigid_align(predicted, target):
assert predicted.shape == target.shape
muX = np.mean(target, axis=1, keepdims=True)
muY = np.mean(predicted, axis=1, keepdims=True)
X0 = target - muX
Y0 = predicted - muY
normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))
X0 /= normX
Y0 /= normY
H = np.matmul(X0.transpose(0, 2, 1), Y0)
U, s, Vt = np.linalg.svd(H)
V = Vt.transpose(0, 2, 1)
R = np.matmul(V, U.transpose(0, 2, 1))
# Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
V[:, :, -1] *= sign_detR
s[:, -1] *= sign_detR.flatten()
R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation
tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
a = tr * normX / normY # Scale
t = muX - a * np.matmul(muY, R) # Translation
# Perform rigid transformation on the input
predicted_aligned = a * np.matmul(predicted, R) + t
# Return MPJPE
return predicted_aligned
def p_mpjpe(predicted, target, rot=True, trans=True, scale=True):
"""
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
"""
assert predicted.shape == target.shape
muX = np.mean(target, axis=1, keepdims=True)
muY = np.mean(predicted, axis=1, keepdims=True)
X0 = target - muX
Y0 = predicted - muY
normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))
X0 /= normX
Y0 /= normY
H = np.matmul(X0.transpose(0, 2, 1), Y0)
U, s, Vt = np.linalg.svd(H)
V = Vt.transpose(0, 2, 1)
R = np.matmul(V, U.transpose(0, 2, 1))
# Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
V[:, :, -1] *= sign_detR
s[:, -1] *= sign_detR.flatten()
R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation
tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
a = tr * normX / normY # Scale
t = muX - a * np.matmul(muY, R) # Translation
# Perform rigid transformation on the input
if rot:
predicted_aligned = np.matmul(predicted, R)
else:
predicted_aligned = predicted
if scale:
predicted_aligned = a * predicted_aligned
if trans:
predicted_aligned = predicted_aligned + t
# predicted_aligned = a * np.matmul(predicted, R) + t
# Return MPJPE
return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1))
def get_rotation_y_v2(angle, is_mpi=False):
# first get the rod matrix
bs = angle.size(0)
cos, sin = torch.cos(angle), torch.sin(angle)
cos = cos.repeat(3, 1).view(3, bs).permute(1, 0).contiguous().view(-1, 1)
sin = sin.repeat(3, 1).view(3, bs).permute(1, 0).contiguous().view(-1, 1)
# if is_mpi:
# rx, ry, rz = -0.3189, 0.3282, 0.8891
# else:
rx, ry, rz = -0.01474, 0.96402, 0.261718
# rx, ry, rz = 0, 1, 0
r = torch.tensor([[rx, ry, rz]])
r_mat = r.t().matmul(r)
r_hat = torch.tensor([[0, -rz, ry], [rz, 0, -rx], [-ry, rx, 0]])
e1 = cos * torch.eye(3).repeat(bs, 1).type_as(cos)
e2 = (1 - cos) * r_mat.repeat(bs, 1).type_as(cos)
e3 = sin * r_hat.repeat(bs, 1).type_as(sin)
mat = e1 + e2 + e3
mat = mat.view(bs, 3, 3)
return mat
def get_rotation_y(angle):
bs = angle.size(0)
sin, cos = torch.sin(angle), torch.cos(angle)
mat = torch.zeros((bs * 3, 3)).type_as(sin)
mat[0:bs, 0:1], mat[0:bs, 2:3] = cos, sin
mat[bs:2*bs, 1] = 1.0
mat[bs*2:bs*3, 0:1], mat[bs*2:bs*3, 2:3] = -sin, cos
mat = mat.view(3, bs, 3).permute(1, 0, 2)
return mat
def get_rotation_x(angle):
bs = angle.size(0)
sin, cos = torch.sin(angle), torch.cos(angle)
mat = torch.zeros((bs * 3, 3)).type_as(sin)
mat[0:bs, 0] = 1.0
mat[bs:bs*2, 1:2], mat[bs:bs*2, 2:3] = cos, -sin
mat[bs*2:bs*3, 1:2], mat[bs*2:bs*3, 2:3] = sin, cos
mat = mat.view(3, bs, 3).permute(1, 0, 2)
return mat
def get_rotation_z(angle):
bs = angle.size(0)
sin, cos = torch.sin(angle), torch.cos(angle)
mat = torch.zeros((bs * 3, 3)).type_as(sin)
mat[2*bs:3*bs, 2] = 1.0
mat[0:bs, 0:1], mat[0:bs, 1:2] = cos, -sin
mat[bs:2*bs, 0:1], mat[bs:2*bs, 1:2] = sin, cos
mat = mat.view(3, bs, 3).permute(1, 0, 2)
return mat
def euler2rotmat(eulers):
# inputs' shape: (N, 3), tensors
# rotate in the order of z, x, y
n = eulers.size(0)
thetax, thetay, thetaz = eulers[:, 0:1], eulers[:, 1:2], eulers[:, 2:3]
matx = get_rotation_x(thetax)
maty = get_rotation_y(thetay)
matz = get_rotation_z(thetaz)
rotmat = matz.matmul(matx).matmul(maty)
# rotmat = maty.matmul(matx).matmul(matz)
return rotmat
def rotate(joints_3d, eulers):
rotmat = euler2rotmat(eulers)
root = joints_3d[:, 13:14] if joints_3d.shape[1] == 17 else joints_3d[:, 12:13]
joints_3d = joints_3d - root
joints_3d = joints_3d.matmul(rotmat)
# joints_3d = rotmat.matmul(joints_3d.permute(0, 2, 1))
# joints_3d = joints_3d.permute(0, 2, 1).contiguous()
joints_3d = joints_3d + root
return joints_3d
def rotate2(joints_3d, rotmat):
n = rotmat.size(0)
rotmat = rotmat.view(n, 3, 3)
joints_3d = joints_3d.matmul(rotmat)
# joints_3d = rotmat.matmul(joints_3d.permute(0, 2, 1)).permute(0, 2, 1)
return joints_3d
def transform_3d(inputs, rot_y, rot_x, is_reverse):
# rot_y/rot_x: N x 1
root3d = inputs[:,13:14].clone() if inputs.shape[1] == 17 else inputs[:, 12:13]
outputs = inputs - root3d
rot_y_mat = get_rotation_y_v2(rot_y)
rot_x_mat = get_rotation_x(rot_x)
rot_mat = rot_x_mat.bmm(rot_y_mat) if not is_reverse else rot_y_mat.bmm(rot_x_mat)
# N x 3 x 3 , ((N x J x 3) -> (N x 3 x J)) -> N x 3 x J
outputs = rot_mat.bmm(outputs.permute(0, 2, 1))
outputs = outputs.permute(0, 2, 1)
outputs += root3d
return outputs
# this is the transformation defined by the paper originally
def transform_3d_v2(inputs, rot_y, rot_x, shift, is_reverse, rot_z=None, use_new_rot=False, is_mpi=False):
shift = torch.FloatTensor([0.0, 0.0, shift]).type_as(inputs).view(1, 1, 3)
shift = shift.expand_as(inputs)
if is_reverse:
outputs = inputs - shift
else:
root3d = inputs[:, 13:14].clone() if inputs.shape[1] == 17 else inputs[:, 12:13]
outputs = inputs - root3d
if use_new_rot:
rot_y_mat = get_rotation_y_v2(rot_y, is_mpi=is_mpi)
else:
rot_y_mat = get_rotation_y(rot_y)
rot_x_mat = get_rotation_x(rot_x)
if rot_z is None:
rot_mat = rot_x_mat.bmm(rot_y_mat) if not is_reverse else rot_y_mat.bmm(rot_x_mat)
else:
rot_z_mat = get_rotation_z(rot_z)
rot_mat = rot_z_mat.bmm(rot_x_mat).bmm(rot_y_mat) if not is_reverse else rot_y_mat.bmm(rot_x_mat).bmm(rot_z_mat)
# N x 3 x 3 , ((N x J x 3) -> (N x 3 x J)) -> N x 3 x J
outputs = rot_mat.bmm(outputs.permute(0, 2, 1))
outputs = outputs.permute(0, 2, 1)
# add the shift instead of the root
if not is_reverse:
outputs += shift
return outputs
def Transform3DV1(inputs, rot_x, rot_y, isReverse):
## shape of inputs ==> (B, J, 3)
## shape of rot_x, rot_y ==> (B, 1)
root3d = inputs[:,13:14].clone() if inputs.shape[1] == 17 else inputs[:, 12:13]
inputs -= root3d
if not isReverse:
## first 3D poses are rotated with Y axis
inputs_ = inputs.clone()
inputs_[:,:,0] = torch.cos(rot_x) * inputs[:,:,0] + torch.sin(rot_x) * inputs[:,:,2]
inputs_[:,:,2] = -torch.sin(rot_x) * inputs[:,:,0] + torch.cos(rot_x) * inputs[:,:,2]
## second 3D poses are rotated with X axis
inputs = inputs_.clone()
inputs[:,:,1] = torch.cos(rot_y) * inputs_[:,:,1] - torch.sin(rot_y) * inputs_[:,:,2]
inputs[:,:,2] = torch.sin(rot_y) * inputs_[:,:,1] + torch.cos(rot_y) * inputs_[:,:,2]
else:
## first 3D poses are rotated with X axis
inputs_ = inputs.clone()
inputs_[:,:,1] = torch.cos(rot_y) * inputs[:,:,1] - torch.sin(rot_y) * inputs[:,:,2]
inputs_[:,:,2] = torch.sin(rot_y) * inputs[:,:,1] + torch.cos(rot_y) * inputs[:,:,2]
## second 3D poses are rotated with Y axis
inputs = inputs_.clone()
inputs[:,:,0] = torch.cos(rot_x) * inputs_[:,:,0] + torch.sin(rot_x) * inputs_[:,:,2]
inputs[:,:,2] = -torch.sin(rot_x) * inputs_[:,:,0] + torch.cos(rot_x) * inputs_[:,:,2]
inputs += root3d
return inputs
def Transform3D(inputs, rot_x, rot_y, isReverse):
## shape of inputs ==> (B, J, 3)
## shape of rot_x, rot_y ==> (B, 1)
root3d = inputs[:,13:14].clone() if inputs.shape[1] == 17 else inputs[:, 12:13]
inputs -= root3d
rot_x = rot_x.unsqueeze(-1)
rot_y = rot_y.unsqueeze(-1)
if not isReverse:
## first 3D poses are rotated with Y axis
inputs = \
torch.cat([torch.cos(rot_x) * inputs[:,:,0:1] - torch.sin(rot_x) * inputs[:,:,2:3],
inputs[:,:,1:2],
torch.sin(rot_x) * inputs[:,:,0:1] + torch.cos(rot_x) * inputs[:,:,2:3]], -1)
## second 3D poses are rotated with X axis
inputs = \
torch.cat([inputs[:,:,0:1],
torch.cos(rot_y) * inputs[:,:,1:2] + torch.sin(rot_y) * inputs[:,:,2:3],
-torch.sin(rot_y) * inputs[:,:,1:2] + torch.cos(rot_y) * inputs[:,:,2:3]], -1)
else:
## first 3D poses are rotated with X axis
inputs = \
torch.cat([inputs[:,:,0:1],
torch.cos(rot_y) * inputs[:,:,1:2] + torch.sin(rot_y) * inputs[:,:,2:3],
-torch.sin(rot_y) * inputs[:,:,1:2] + torch.cos(rot_y) * inputs[:,:,2:3]], -1)
## second 3D poses are rotated with Y axis
inputs = \
torch.cat([torch.cos(rot_x) * inputs[:,:,0:1] - torch.sin(rot_x) * inputs[:,:,2:3],
inputs[:,:,1:2],
torch.sin(rot_x) * inputs[:,:,0:1] + torch.cos(rot_x) * inputs[:,:,2:3]], -1)
inputs += root3d
return inputs
def get_optimizer(cfg, model, is_dis=False, is_temp=False):
optimizer = None
if is_dis:
lr = cfg.TRAIN.TEMP_LR if is_temp else cfg.TRAIN.DIS_LR
else:
lr = cfg.TRAIN.POSE_LR
if cfg.TRAIN.OPTIMIZER == 'sgd':
optimizer = optim.SGD(
model.parameters(),
lr=lr,
momentum=cfg.TRAIN.MOMENTUM,
weight_decay=cfg.TRAIN.WD,
nesterov=cfg.TRAIN.NESTEROV
)
elif cfg.TRAIN.OPTIMIZER == 'adam':
optimizer = optim.Adam(
model.parameters(),
lr=lr,
betas=(0.5, 0.9)
)
return optimizer
def load_checkpoint(model, optimizer, output_dir, filename='checkpoint.pth.tar'):
file = os.path.join(output_dir, filename)
if os.path.isfile(file):
checkpoint = torch.load(file)
start_epoch = checkpoint['epoch']
model.module.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print('=> load checkpoint {} (epoch {})'
.format(file, start_epoch))
return start_epoch, model, optimizer
else:
print('=> no checkpoint found at {}'.format(file))
return 0, model, optimizer
def save_checkpoint(states, is_best, output_dir,
filename='checkpoint.pth.tar'):
torch.save(states, os.path.join(output_dir, filename))
if is_best:
torch.save(states, os.path.join(output_dir, "model_best.pth.tar"))
def test_transform_3d():
x1 = torch.randn(100, 17, 3)
x2 = x1.clone()
rot_y = torch.randn(100, 1)
rot_x = torch.randn(100, 1)
is_reverse = True
r1 = transform_3d(x1, rot_y, rot_x, is_reverse)
r2 = Transform3DV1(x2, rot_y, rot_x, is_reverse)
if (r1 - r2 < 1e-5).all():
print("Passed.")
else:
raise ValueError("At least one computation is not corrected")
def test_transform_3d_v2():
bs = 100
x1 = torch.randn(bs, 17, 3)
rot_y = torch.randn(bs, 1)
rot_x = torch.randn(bs, 1)
rot_z = torch.randn(bs, 1)
root = x1[:, 13:14] if x1.shape[1] == 17 else x1[:, 12:13]
x2 = transform_3d_v2(x1, rot_y, rot_x, 10.0, False, rot_z, use_new_rot=True)
x22 = transform_3d_v2(x1, rot_y, rot_x, 10.0, False, rot_z, use_new_rot=False)
print((x22 - x2 < 1e-5).all().item())
x3 = transform_3d_v2(x2, -rot_y, -rot_x, 10.0, True, -rot_z, use_new_rot=True)
x3 = x3 + root
print((x1 - x3 < 1e-5).all().item())
def test_p_mpjpe():
x = np.random.randn(32, 17, 3)
y = np.random.randn(32, 17, 3)
err = p_mpjpe(x, y)
print(err)
def get_pck3d(joints_3d_pre, joints_3d_gt):
# about half of the head size
threshold = 150 / 2048
n, c, _ = joints_3d_pre.shape
cnt = (np.linalg.norm(joints_3d_pre - joints_3d_gt, axis=-1) < threshold).sum()
return cnt / (n * c)
def _scale_range(x, a, b):
m = x.min()
M = x.max()
return (x - M)/(m - M)*(a - b) + b
def calc_dists(joints_3d_pre, joints_3d_gt, head_size=300):
dists = 1000 / head_size * np.linalg.norm(joints_3d_pre - joints_3d_gt, axis=-1)
return dists
def calc_pck3d(dists, threshold=0.5):
n, c = dists.shape
return (dists < threshold).sum() / (n * c)
def calc_auc(joints_3d_pre, joints_3d_gt, head_size=300, is_mpi=True):
indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16]
# joints_3d_pre = joints_3d_pre[:, indices]
# joints_3d_gt = joints_3d_gt[:, indices]
dists = calc_dists(joints_3d_pre, joints_3d_gt, head_size=head_size)
x = np.arange(0.0, 0.51, 0.05)
pcks = []
pckh5 = 0.0
for thresh in x:
pck = calc_pck3d(dists, thresh)
if thresh == 0.50:
pckh5 = pck
pcks.append(pck)
# scale to 0~1
x = _scale_range(x, 0, 1)
auc_val = auc(x, np.array(pcks))
# the second output is the pckh@0.5
return auc_val, pckh5
def calc_auc_aligned(joints_3d_pre, joints_3d_gt, head_size=300, is_mpi=True):
joints_3d_pre = rigid_align(joints_3d_pre, joints_3d_gt)
indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16]
# joints_3d_pre = joints_3d_pre[:, indices]
# joints_3d_gt = joints_3d_gt[:, indices]
dists = calc_dists(joints_3d_pre, joints_3d_gt, head_size=head_size)
x = np.arange(0.0, 0.51, 0.05)
pcks = []
pckh5 = 0.0
for thresh in x:
pck = calc_pck3d(dists, thresh)
if thresh == 0.50:
pckh5 = pck
pcks.append(pck)
# scale to 0~1
x = _scale_range(x, 0, 1)
auc_val = auc(x, np.array(pcks))
# the second output is the pckh@0.5
return auc_val, pckh5
if __name__ == "__main__":
# test_transform_3d_v2()
# test_p_mpjpe()
x = torch.tensor([1.5708, 1.5708, -1.5708]).view(1, -1)
print(euler2rotmat(x))
| insightface/body/human_pose/ambiguity_aware/lib/utils/utils.py/0 | {
"file_path": "insightface/body/human_pose/ambiguity_aware/lib/utils/utils.py",
"repo_id": "insightface",
"token_count": 7636
} | 97 |
#!/usr/bin/env python3
# coding=utf-8
import h5py
import numpy as np
import pickle as pkl
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default='train')
parser.add_argument('--prefix', default="mpi")
args = parser.parse_args()
prefix = args.prefix
mode = args.mode
mpi2h36m = [10, 9, 8, 11, 12, 13, 4, 3, 2, 5, 6, 7, 1, 14, 15, 16, 0] if prefix == "mpi" else list(range(17))
readpath = f"../data/{prefix}_{mode}_pred3.h5"
savepath = f"../data/mpi_{mode}_scales.pkl"
f = h5py.File(readpath, "r")
joints_2d_gt = np.array(f['joint_2d_gt'])[:, mpi2h36m]
# joints_3d_pre = np.array(f['joint_3d_pre'])
joints_3d_gt = np.array(f['joint_3d_gt'])[:, mpi2h36m] / 1000.0
f.close()
if prefix == "mpi":
factors = 0.7577316 if mode == "valid" else 0.7286965902
else:
factors = 0.680019 if mode == "valid" else 0.6451607
joints_2d_gt[:, :, 0] = (joints_2d_gt[:, :, 0] - 1024.0) / 1024.0
joints_2d_gt[:, :, 1] = (joints_2d_gt[:, :, 1] - 1024.0) / 1024.0
root2d = joints_2d_gt[:, 13:14].copy()
joints_2d_gt = joints_2d_gt - root2d
joints_2d_gt[:, 13:14] = 1e-5
# factor_2d = 1 / 10 / np.linalg.norm(joints_2d_gt[:, -1] - joints_2d_gt[:, 13], axis=1).reshape(-1, 1, 1)
factor_2d = 1 / 10 / np.linalg.norm(joints_2d_gt[:, -1] - joints_2d_gt[:, 13], axis=1).reshape(-1, 1, 1)
# scale the 2d joints
# joints_2d_gt = joints_2d_gt * factor_2d * factors[:, 0:1, 0:1]
joints_2d_gt = joints_2d_gt * factor_2d
# then we project the 3d joints
# minus the root and shift to (0, 0, 10)
joints_3d_gt = joints_3d_gt - joints_3d_gt[:, 13:14].copy()
joints_3d_gt = joints_3d_gt / factors
shift = np.array([0, 0, 10]).reshape(1, 1, 3)
root3d_gt = joints_3d_gt[:, 13:14].copy()
joints_3d_gt = joints_3d_gt - root3d_gt + shift
# project the 3d joints
# N * J * 2
project_gt_2d = joints_3d_gt[..., :2] / joints_3d_gt[..., 2:]
x1_min, x1_max = joints_2d_gt[..., 0:1].min(axis=1, keepdims=True), joints_2d_gt[..., 0:1].max(axis=1, keepdims=True)
y1_min, y1_max = joints_2d_gt[..., 1:].min(axis=1, keepdims=True), joints_2d_gt[..., 1:].max(axis=1, keepdims=True)
x2_min, x2_max = project_gt_2d[..., 0:1].min(axis=1, keepdims=True), project_gt_2d[..., 0:1].max(axis=1, keepdims=True)
y2_min, y2_max = project_gt_2d[..., 1:].min(axis=1, keepdims=True), project_gt_2d[..., 1:].max(axis=1, keepdims=True)
scales = ((x2_max - x2_min) / (x1_max - x1_min) + (y2_max - y2_min) / (y1_max - y1_min)) / 2
heights, widths = y1_max - y1_min, x1_max - x1_min
scale_mids = (scales + (heights + widths) / 2) / 2
print("Mean/Std of scale mid: {:.3f}/{:.3f}".format(scale_mids.mean(), scale_mids.std()))
with open(savepath, "wb") as f:
pkl.dump({"scale": scales.reshape(-1), "scale_mid": scale_mids.reshape(-1)}, f)
err_gt = np.linalg.norm(project_gt_2d - joints_2d_gt, axis=-1).mean()
print("Projection GT error is: {:.4f}".format(err_gt))
# first descale, minus the root, and shift
# joints_3d_pre = joints_3d_pre / factors
# root3d_pre = joints_3d_pre[:, 13:14].copy()
# joints_3d_pre = joints_3d_pre - root3d_pre + shift
# project_pre_2d = joints_3d_pre[..., :2] / joints_3d_pre[..., 2:]
# err_pre = np.linalg.norm(project_pre_2d - joints_2d_gt, axis=-1).mean()
# print("Projection PRE error is: {:.4f}".format(err_pre))
| insightface/body/human_pose/ambiguity_aware/scripts/mpi_validate_project.py/0 | {
"file_path": "insightface/body/human_pose/ambiguity_aware/scripts/mpi_validate_project.py",
"repo_id": "insightface",
"token_count": 1543
} | 98 |
## pytorch 训练样例
[训练样例地址]()
### 下载数据集
* 下载 MS1MV3 [Link](https://github.com/deepinsight/insightface/tree/master/challenges/iccv19-lfr)
* 下载 Glint360K [Link](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download)
### 服务器提交地址
http://iccv21-mfr.com/
### 安装依赖
1. 安装 pytorch 1.7.1
假设你已经安装好了GPU驱动和CUDA，根据你的CUDA版本，来选择你要安装的pytorch命令。
查看CUDA版本的命令为: `/usr/local/cuda/bin/nvcc -V`。
Linux and Windows
```shell
# CUDA 11.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 10.2
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
# CUDA 10.1
pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 9.2
pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
```
你也可以安装pytorch的其他版本，例如1.6.0或者更高的版本。
2. 安装其他依赖
```shell
pip install -r requirement.txt
```
### 运行
根据你的服务器，选择你要运行的命令。
* 一台服务器，四张GPU运行
```shell
python -m torch.distributed.launch --nproc_per_node=4 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
```
* 一台服务器，八张GPU运行
```shell
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
```
* 多台服务器，每台服务器8张GPU
1. 节点0
```shell
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py
```
2. 节点1
```shell
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py
```
### 提交
1. 提交onnx模型
竞赛要求模型转换为`onnx`模型提交，arcface_torch工程在保存模型时，会自动转换成为onnx，其地址为`${cfg.output}/backbone.onnx`。
模型checkpoint介绍：
```shell
├── backbone.onnx # 需要提交的模型
├── backbone.pth # pytorch 保存的模型
├── rank_0_softmax_weight_mom.pt # 模型并行原因，每张卡保存softmax独有的参数
├── rank_0_softmax_weight.pt
├── rank_1_softmax_weight_mom.pt
├── rank_1_softmax_weight.pt
├── ... ...
└── training.log # 训练日志
```
2. 检查onnx模型是否规范
提交模型前检查一下提交的模型是否规范，并测试模型的推理时间
测试命令：
```shell
python onnx_helper_sample.py --model_root ms1mv3_arcface_r50/
```
也可以先测试一下onnx模型在公开测试集IJBC上的性能：
https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/onnx_ijbc.py
测试命令：
```shell
CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50
```
3. 模型大小参考
推理时间是在`Tesla V100 GPU`中测试, 其中 onnxruntime-gpu==1.6。
| 模型名称 | 大小/MB | 推理时间/ms |
| ------- | ---------- | ----------- |
| R50 | 166 | 4.262 |
| R100 | 248 | 7.031 |
| R200 | 476 | 13.48 |
### 提示与技巧
1. 训练加速-混合精度训练
当时使用图灵架构的GPU时候，强烈建议开启混合精度训练模型，在`config.py`中，将`config.fp16`设置为True，可以节省大量显存和提升训练速度，例如：
训练设置：
MS1MV3(SSD) + 4*V100 + R100 + BatchSize 4*128
- 开启混合精度训练前
```python3
# training log
Training: 2021-05-12 00:00:42,110-Speed 884.42 samples/sec Loss 47.2532 Epoch: 0 Global Step: 100
Training: 2021-05-12 00:01:10,979-Speed 886.77 samples/sec Loss 47.3550 Epoch: 0 Global Step: 150
Training: 2021-05-12 00:01:43,936-Speed 776.80 samples/sec Loss 47.0214 Epoch: 0 Global Step: 200
Training: 2021-05-12 00:02:16,064-Speed 796.83 samples/sec Loss 46.7781 Epoch: 0 Global Step: 250
Training: 2021-05-12 00:02:45,018-Speed 884.18 samples/sec Loss 46.3187 Epoch: 0 Global Step: 300
# gpustat -i
[0] Tesla V100-SXM2-32GB | 67 C, 99 % | 17844 / 32510 MB
[1] Tesla V100-SXM2-32GB | 64 C, 98 % | 17844 / 32510 MB
[2] Tesla V100-SXM2-32GB | 65 C, 93 % | 17916 / 32510 MB
[3] Tesla V100-SXM2-32GB | 72 C, 82 % | 17910 / 32510 MB
```
- 开启混合精度训练后
```python3
# training log
Training: 2021-05-12 00:04:27,869-Speed 1604.59 samples/sec Loss 47.6050 Epoch: 0 Global Step: 100
Training: 2021-05-12 00:04:43,681-Speed 1619.08 samples/sec Loss 47.5865 Epoch: 0 Global Step: 150
Training: 2021-05-12 00:04:59,460-Speed 1622.39 samples/sec Loss 47.2380 Epoch: 0 Global Step: 200
Training: 2021-05-12 00:05:15,271-Speed 1619.25 samples/sec Loss 46.9030 Epoch: 0 Global Step: 250
Training: 2021-05-12 00:05:31,065-Speed 1620.86 samples/sec Loss 46.4425 Epoch: 0 Global Step: 300
# gpustat -i
[0] Tesla V100-SXM2-32GB | 64 C, 96 % | 10664 / 32510 M
[1] Tesla V100-SXM2-32GB | 63 C, 96 % | 10630 / 32510 MB
[2] Tesla V100-SXM2-32GB | 63 C, 79 % | 10736 / 32510 MB
[3] Tesla V100-SXM2-32GB | 70 C, 86 % | 10736 / 32510 MB
```
2. 训练加速-将数据挂载到内存盘来提升训练速度
使用如下的命令：
```shell
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=40G tmpfs /train_tmp
```
让后将训练集拷贝到目录`/train_tmp`下，然后开始训练。
| insightface/challenges/iccv21-mfr/tutorial_pytorch_cn.md/0 | {
"file_path": "insightface/challenges/iccv21-mfr/tutorial_pytorch_cn.md",
"repo_id": "insightface",
"token_count": 3149
} | 99 |
import mxnet as mx
def do_checkpoint(prefix, means, stds):
def _callback(iter_no, sym, arg, aux):
if 'bbox_pred_weight' in arg:
arg['bbox_pred_weight_test'] = (arg['bbox_pred_weight'].T *
mx.nd.array(stds)).T
arg['bbox_pred_bias_test'] = arg['bbox_pred_bias'] * mx.nd.array(
stds) + mx.nd.array(means)
mx.model.save_checkpoint(prefix, iter_no + 1, sym, arg, aux)
if 'bbox_pred_weight' in arg:
arg.pop('bbox_pred_weight_test')
arg.pop('bbox_pred_bias_test')
return _callback
| insightface/detection/retinaface/rcnn/core/callback.py/0 | {
"file_path": "insightface/detection/retinaface/rcnn/core/callback.py",
"repo_id": "insightface",
"token_count": 336
} | 100 |
import numpy as np
def unique_boxes(boxes, scale=1.0):
""" return indices of unique boxes """
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v).astype(np.int)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)
def filter_small_boxes(boxes, min_size):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = np.where((w >= min_size) & (h > min_size))[0]
return keep
| insightface/detection/retinaface/rcnn/dataset/ds_utils.py/0 | {
"file_path": "insightface/detection/retinaface/rcnn/dataset/ds_utils.py",
"repo_id": "insightface",
"token_count": 195
} | 101 |
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#define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
#define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
#define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
#define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
#define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
#define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
#define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))
#endif
#if CYTHON_COMPILING_IN_PYPY
#define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
#define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
#else
#define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
#define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
#define PyUnicode_Contains(u, s) PySequence_Contains(u, s)
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
#define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)
#endif
#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
#define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
#endif
#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
#else
#define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
#endif
#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
#define PyObject_ASCII(o) PyObject_Repr(o)
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBaseString_Type PyUnicode_Type
#define PyStringObject PyUnicodeObject
#define PyString_Type PyUnicode_Type
#define PyString_Check PyUnicode_Check
#define PyString_CheckExact PyUnicode_CheckExact
#define PyObject_Unicode PyObject_Str
#endif
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
#define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
#else
#define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
#define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
#endif
#ifndef PySet_CheckExact
#define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
#endif
#if CYTHON_ASSUME_SAFE_MACROS
#define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq)
#else
#define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq)
#endif
#if PY_MAJOR_VERSION >= 3
#define PyIntObject PyLongObject
#define PyInt_Type PyLong_Type
#define PyInt_Check(op) PyLong_Check(op)
#define PyInt_CheckExact(op) PyLong_CheckExact(op)
#define PyInt_FromString PyLong_FromString
#define PyInt_FromUnicode PyLong_FromUnicode
#define PyInt_FromLong PyLong_FromLong
#define PyInt_FromSize_t PyLong_FromSize_t
#define PyInt_FromSsize_t PyLong_FromSsize_t
#define PyInt_AsLong PyLong_AsLong
#define PyInt_AS_LONG PyLong_AS_LONG
#define PyInt_AsSsize_t PyLong_AsSsize_t
#define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
#define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
#define PyNumber_Int PyNumber_Long
#endif
#if PY_MAJOR_VERSION >= 3
#define PyBoolObject PyLongObject
#endif
#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
#ifndef PyUnicode_InternFromString
#define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
#endif
#endif
#if PY_VERSION_HEX < 0x030200A4
typedef long Py_hash_t;
#define __Pyx_PyInt_FromHash_t PyInt_FromLong
#define __Pyx_PyInt_AsHash_t PyInt_AsLong
#else
#define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
#define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
#endif
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func))
#else
#define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
#endif
#if CYTHON_USE_ASYNC_SLOTS
#if PY_VERSION_HEX >= 0x030500B1
#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
#else
#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
#endif
#else
#define __Pyx_PyType_AsAsync(obj) NULL
#endif
#ifndef __Pyx_PyAsyncMethodsStruct
typedef struct {
unaryfunc am_await;
unaryfunc am_aiter;
unaryfunc am_anext;
} __Pyx_PyAsyncMethodsStruct;
#endif
#if defined(WIN32) || defined(MS_WINDOWS)
#define _USE_MATH_DEFINES
#endif
#include <math.h>
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
#else
static CYTHON_INLINE float __PYX_NAN() {
float value;
memset(&value, 0xFF, sizeof(value));
return value;
}
#endif
#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
#define __Pyx_truncl trunc
#else
#define __Pyx_truncl truncl
#endif
#define __PYX_ERR(f_index, lineno, Ln_error) \
{ \
__pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
}
#ifndef __PYX_EXTERN_C
#ifdef __cplusplus
#define __PYX_EXTERN_C extern "C"
#else
#define __PYX_EXTERN_C extern
#endif
#endif
#define __PYX_HAVE___mask
#define __PYX_HAVE_API___mask
/* Early includes */
#include <string.h>
#include <stdio.h>
#include "numpy/arrayobject.h"
#include "numpy/ufuncobject.h"
#include <stdlib.h>
#include "maskApi.h"
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */
#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
#define CYTHON_WITHOUT_ASSERTIONS
#endif
typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
#define __PYX_DEFAULT_STRING_ENCODING ""
#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#define __Pyx_uchar_cast(c) ((unsigned char)c)
#define __Pyx_long_cast(x) ((long)x)
#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\
(sizeof(type) < sizeof(Py_ssize_t)) ||\
(sizeof(type) > sizeof(Py_ssize_t) &&\
likely(v < (type)PY_SSIZE_T_MAX ||\
v == (type)PY_SSIZE_T_MAX) &&\
(!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
v == (type)PY_SSIZE_T_MIN))) ||\
(sizeof(type) == sizeof(Py_ssize_t) &&\
(is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
v == (type)PY_SSIZE_T_MAX))) )
#if defined (__cplusplus) && __cplusplus >= 201103L
#include <cstdlib>
#define __Pyx_sst_abs(value) std::abs(value)
#elif SIZEOF_INT >= SIZEOF_SIZE_T
#define __Pyx_sst_abs(value) abs(value)
#elif SIZEOF_LONG >= SIZEOF_SIZE_T
#define __Pyx_sst_abs(value) labs(value)
#elif defined (_MSC_VER)
#define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
#define __Pyx_sst_abs(value) llabs(value)
#elif defined (__GNUC__)
#define __Pyx_sst_abs(value) __builtin_llabs(value)
#else
#define __Pyx_sst_abs(value) ((value<0) ? -value : value)
#endif
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
#define __Pyx_PyBytes_FromString PyBytes_FromString
#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
#if PY_MAJOR_VERSION < 3
#define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#else
#define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
#define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
#endif
#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))
#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))
#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
const Py_UNICODE *u_end = u;
while (*u_end++) ;
return (size_t)(u_end - u - 1);
}
#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
#define __Pyx_PySequence_Tuple(obj)\
(likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_ASSUME_SAFE_MACROS
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
#else
#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
#endif
#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
PyObject* sys;
PyObject* default_encoding = NULL;
PyObject* ascii_chars_u = NULL;
PyObject* ascii_chars_b = NULL;
const char* default_encoding_c;
sys = PyImport_ImportModule("sys");
if (!sys) goto bad;
default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
Py_DECREF(sys);
if (!default_encoding) goto bad;
default_encoding_c = PyBytes_AsString(default_encoding);
if (!default_encoding_c) goto bad;
if (strcmp(default_encoding_c, "ascii") == 0) {
__Pyx_sys_getdefaultencoding_not_ascii = 0;
} else {
char ascii_chars[128];
int c;
for (c = 0; c < 128; c++) {
ascii_chars[c] = c;
}
__Pyx_sys_getdefaultencoding_not_ascii = 1;
ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
if (!ascii_chars_u) goto bad;
ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
PyErr_Format(
PyExc_ValueError,
"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
default_encoding_c);
goto bad;
}
Py_DECREF(ascii_chars_u);
Py_DECREF(ascii_chars_b);
}
Py_DECREF(default_encoding);
return 0;
bad:
Py_XDECREF(default_encoding);
Py_XDECREF(ascii_chars_u);
Py_XDECREF(ascii_chars_b);
return -1;
}
#endif
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
#else
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
static char* __PYX_DEFAULT_STRING_ENCODING;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
PyObject* sys;
PyObject* default_encoding = NULL;
char* default_encoding_c;
sys = PyImport_ImportModule("sys");
if (!sys) goto bad;
default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
Py_DECREF(sys);
if (!default_encoding) goto bad;
default_encoding_c = PyBytes_AsString(default_encoding);
if (!default_encoding_c) goto bad;
__PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
Py_DECREF(default_encoding);
return 0;
bad:
Py_XDECREF(default_encoding);
return -1;
}
#endif
#endif
/* Test for GCC > 2.95 */
#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
#define likely(x) __builtin_expect(!!(x), 1)
#define unlikely(x) __builtin_expect(!!(x), 0)
#else /* !__GNUC__ or GCC < 2.95 */
#define likely(x) (x)
#define unlikely(x) (x)
#endif /* __GNUC__ */
static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
static PyObject *__pyx_m = NULL;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_cython_runtime = NULL;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static PyObject *__pyx_empty_unicode;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;
/* Header.proto */
#if !defined(CYTHON_CCOMPLEX)
#if defined(__cplusplus)
#define CYTHON_CCOMPLEX 1
#elif defined(_Complex_I)
#define CYTHON_CCOMPLEX 1
#else
#define CYTHON_CCOMPLEX 0
#endif
#endif
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
#include <complex>
#else
#include <complex.h>
#endif
#endif
#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
#undef _Complex_I
#define _Complex_I 1.0fj
#endif
static const char *__pyx_f[] = {
"_mask.pyx",
"stringsource",
"__init__.pxd",
"type.pxd",
};
/* BufferFormatStructs.proto */
#define IS_UNSIGNED(type) (((type) -1) > 0)
struct __Pyx_StructField_;
#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
typedef struct {
const char* name;
struct __Pyx_StructField_* fields;
size_t size;
size_t arraysize[8];
int ndim;
char typegroup;
char is_unsigned;
int flags;
} __Pyx_TypeInfo;
typedef struct __Pyx_StructField_ {
__Pyx_TypeInfo* type;
const char* name;
size_t offset;
} __Pyx_StructField;
typedef struct {
__Pyx_StructField* field;
size_t parent_offset;
} __Pyx_BufFmt_StackElem;
typedef struct {
__Pyx_StructField root;
__Pyx_BufFmt_StackElem* head;
size_t fmt_offset;
size_t new_count, enc_count;
size_t struct_alignment;
int is_complex;
char enc_type;
char new_packmode;
char enc_packmode;
char is_valid_array;
} __Pyx_BufFmt_Context;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730
* # in Cython to enable them only on the right systems.
*
* ctypedef npy_int8 int8_t # <<<<<<<<<<<<<<
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t
*/
typedef npy_int8 __pyx_t_5numpy_int8_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731
*
* ctypedef npy_int8 int8_t
* ctypedef npy_int16 int16_t # <<<<<<<<<<<<<<
* ctypedef npy_int32 int32_t
* ctypedef npy_int64 int64_t
*/
typedef npy_int16 __pyx_t_5numpy_int16_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732
* ctypedef npy_int8 int8_t
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t # <<<<<<<<<<<<<<
* ctypedef npy_int64 int64_t
* #ctypedef npy_int96 int96_t
*/
typedef npy_int32 __pyx_t_5numpy_int32_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733
* ctypedef npy_int16 int16_t
* ctypedef npy_int32 int32_t
* ctypedef npy_int64 int64_t # <<<<<<<<<<<<<<
* #ctypedef npy_int96 int96_t
* #ctypedef npy_int128 int128_t
*/
typedef npy_int64 __pyx_t_5numpy_int64_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737
* #ctypedef npy_int128 int128_t
*
* ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<<
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t
*/
typedef npy_uint8 __pyx_t_5numpy_uint8_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738
*
* ctypedef npy_uint8 uint8_t
* ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<<
* ctypedef npy_uint32 uint32_t
* ctypedef npy_uint64 uint64_t
*/
typedef npy_uint16 __pyx_t_5numpy_uint16_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739
* ctypedef npy_uint8 uint8_t
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<<
* ctypedef npy_uint64 uint64_t
* #ctypedef npy_uint96 uint96_t
*/
typedef npy_uint32 __pyx_t_5numpy_uint32_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740
* ctypedef npy_uint16 uint16_t
* ctypedef npy_uint32 uint32_t
* ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<<
* #ctypedef npy_uint96 uint96_t
* #ctypedef npy_uint128 uint128_t
*/
typedef npy_uint64 __pyx_t_5numpy_uint64_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":744
* #ctypedef npy_uint128 uint128_t
*
* ctypedef npy_float32 float32_t # <<<<<<<<<<<<<<
* ctypedef npy_float64 float64_t
* #ctypedef npy_float80 float80_t
*/
typedef npy_float32 __pyx_t_5numpy_float32_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":745
*
* ctypedef npy_float32 float32_t
* ctypedef npy_float64 float64_t # <<<<<<<<<<<<<<
* #ctypedef npy_float80 float80_t
* #ctypedef npy_float128 float128_t
*/
typedef npy_float64 __pyx_t_5numpy_float64_t;
/* "../../../../../../../root/anaconda2/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754
* # The int types are mapped a bit surprising --
* # numpy.int corresponds to 'l' and numpy.long to 'q'
* ctypedef npy_long int_t # <<<<<<<<<<<<<<
* ctypedef npy_longlong long_t
* ctypedef npy_longlong longlong_t
*/
typedef npy_long __pyx_t_5numpy_int_t;
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static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
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static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
PyObject *dict);
static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
PyObject *dict);
static int __pyx_CyFunction_init(void);
/* BufferFallbackError.proto */
static void __Pyx_RaiseBufferFallbackError(void);
/* None.proto */
static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);
/* BufferIndexError.proto */
static void __Pyx_RaiseBufferIndexError(int axis);
#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
/* PySequenceContains.proto */
static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
int result = PySequence_Contains(seq, item);
return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
}
/* RaiseTooManyValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
/* RaiseNeedMoreValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
/* RaiseNoneIterError.proto */
static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
/* SaveResetException.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
#else
#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)
#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)
#endif
/* PyErrExceptionMatches.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
#else
#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)
#endif
/* GetException.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
#else
static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
#endif
/* PyObject_GenericGetAttrNoDict.proto */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
#else
#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
#endif
/* PyObject_GenericGetAttr.proto */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
#else
#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
#endif
/* SetupReduce.proto */
static int __Pyx_setup_reduce(PyObject* type_obj);
/* Import.proto */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
/* CLineInTraceback.proto */
#ifdef CYTHON_CLINE_IN_TRACEBACK
#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
#else
static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
#endif
/* CodeObjectCache.proto */
typedef struct {
PyCodeObject* code_object;
int code_line;
} __Pyx_CodeObjectCacheEntry;
struct __Pyx_CodeObjectCache {
int count;
int max_count;
__Pyx_CodeObjectCacheEntry* entries;
};
static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
static PyCodeObject *__pyx_find_code_object(int code_line);
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
/* AddTraceback.proto */
static void __Pyx_AddTraceback(const char *funcname, int c_line,
int py_line, const char *filename);
/* BufferStructDeclare.proto */
typedef struct {
Py_ssize_t shape, strides, suboffsets;
} __Pyx_Buf_DimInfo;
typedef struct {
size_t refcount;
Py_buffer pybuffer;
} __Pyx_Buffer;
typedef struct {
__Pyx_Buffer *rcbuffer;
char *data;
__Pyx_Buf_DimInfo diminfo[8];
} __Pyx_LocalBuf_ND;
#if PY_MAJOR_VERSION < 3
static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
static void __Pyx_ReleaseBuffer(Py_buffer *view);
#else
#define __Pyx_GetBuffer PyObject_GetBuffer
#define __Pyx_ReleaseBuffer PyBuffer_Release
#endif
/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value);
/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value);
/* RealImag.proto */
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
#define __Pyx_CREAL(z) ((z).real())
#define __Pyx_CIMAG(z) ((z).imag())
#else
#define __Pyx_CREAL(z) (__real__(z))
#define __Pyx_CIMAG(z) (__imag__(z))
#endif
#else
#define __Pyx_CREAL(z) ((z).real)
#define __Pyx_CIMAG(z) ((z).imag)
#endif
#if defined(__cplusplus) && CYTHON_CCOMPLEX\
&& (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
#define __Pyx_SET_CREAL(z,x) ((z).real(x))
#define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
#else
#define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
#define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
#endif
/* Arithmetic.proto */
#if CYTHON_CCOMPLEX
#define __Pyx_c_eq_float(a, b) ((a)==(b))
#define __Pyx_c_sum_float(a, b) ((a)+(b))
#define __Pyx_c_diff_float(a, b) ((a)-(b))
#define __Pyx_c_prod_float(a, b) ((a)*(b))
#define __Pyx_c_quot_float(a, b) ((a)/(b))
#define __Pyx_c_neg_float(a) (-(a))
#ifdef __cplusplus
#define __Pyx_c_is_zero_float(z) ((z)==(float)0)
#define __Pyx_c_conj_float(z) (::std::conj(z))
#if 1
#define __Pyx_c_abs_float(z) (::std::abs(z))
#define __Pyx_c_pow_float(a, b) (::std::pow(a, b))
#endif
#else
#define __Pyx_c_is_zero_float(z) ((z)==0)
#define __Pyx_c_conj_float(z) (conjf(z))
#if 1
#define __Pyx_c_abs_float(z) (cabsf(z))
#define __Pyx_c_pow_float(a, b) (cpowf(a, b))
#endif
#endif
#else
static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
#if 1
static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
#endif
#endif
/* Arithmetic.proto */
#if CYTHON_CCOMPLEX
#define __Pyx_c_eq_double(a, b) ((a)==(b))
#define __Pyx_c_sum_double(a, b) ((a)+(b))
#define __Pyx_c_diff_double(a, b) ((a)-(b))
#define __Pyx_c_prod_double(a, b) ((a)*(b))
#define __Pyx_c_quot_double(a, b) ((a)/(b))
#define __Pyx_c_neg_double(a) (-(a))
#ifdef __cplusplus
#define __Pyx_c_is_zero_double(z) ((z)==(double)0)
#define __Pyx_c_conj_double(z) (::std::conj(z))
#if 1
#define __Pyx_c_abs_double(z) (::std::abs(z))
#define __Pyx_c_pow_double(a, b) (::std::pow(a, b))
#endif
#else
#define __Pyx_c_is_zero_double(z) ((z)==0)
#define __Pyx_c_conj_double(z) (conj(z))
#if 1
#define __Pyx_c_abs_double(z) (cabs(z))
#define __Pyx_c_pow_double(a, b) (cpow(a, b))
#endif
#endif
#else
static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
#if 1
static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
#endif
#endif
/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);
/* CIntFromPy.proto */
static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *);
/* CIntFromPy.proto */
static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *);
/* CIntFromPy.proto */
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
/* CIntFromPy.proto */
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
/* FastTypeChecks.proto */
#if CYTHON_COMPILING_IN_CPYTHON
#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
#else
#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
#endif
#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
/* CheckBinaryVersion.proto */
static int __Pyx_check_binary_version(void);
/* PyIdentifierFromString.proto */
#if !defined(__Pyx_PyIdentifier_FromString)
#if PY_MAJOR_VERSION < 3
#define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
#else
#define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
#endif
#endif
/* ModuleImport.proto */
static PyObject *__Pyx_ImportModule(const char *name);
/* TypeImport.proto */
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);
/* InitStrings.proto */
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
/* Module declarations from 'cpython.buffer' */
/* Module declarations from 'libc.string' */
/* Module declarations from 'libc.stdio' */
/* Module declarations from '__builtin__' */
/* Module declarations from 'cpython.type' */
static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
/* Module declarations from 'cpython' */
/* Module declarations from 'cpython.object' */
/* Module declarations from 'cpython.ref' */
/* Module declarations from 'cpython.mem' */
/* Module declarations from 'numpy' */
/* Module declarations from 'numpy' */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/
/* Module declarations from 'libc.stdlib' */
/* Module declarations from '_mask' */
static PyTypeObject *__pyx_ptype_5_mask_RLEs = 0;
static PyTypeObject *__pyx_ptype_5_mask_Masks = 0;
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint8_t = { "uint8_t", NULL, sizeof(__pyx_t_5numpy_uint8_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint8_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint8_t), 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_double_t = { "double_t", NULL, sizeof(__pyx_t_5numpy_double_t), { 0 }, 0, 'R', 0, 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint32_t = { "uint32_t", NULL, sizeof(__pyx_t_5numpy_uint32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint32_t), 0 };
#define __Pyx_MODULE_NAME "_mask"
extern int __pyx_module_is_main__mask;
int __pyx_module_is_main__mask = 0;
/* Implementation of '_mask' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_AttributeError;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_enumerate;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_RuntimeError;
static PyObject *__pyx_builtin_ImportError;
static const char __pyx_k_F[] = "F";
static const char __pyx_k_N[] = "N";
static const char __pyx_k_R[] = "R";
static const char __pyx_k_a[] = "_a";
static const char __pyx_k_h[] = "h";
static const char __pyx_k_i[] = "i";
static const char __pyx_k_j[] = "j";
static const char __pyx_k_m[] = "m";
static const char __pyx_k_n[] = "n";
static const char __pyx_k_p[] = "p";
static const char __pyx_k_w[] = "w";
static const char __pyx_k_Rs[] = "Rs";
static const char __pyx_k_bb[] = "bb";
static const char __pyx_k_dt[] = "dt";
static const char __pyx_k_gt[] = "gt";
static const char __pyx_k_np[] = "np";
static const char __pyx_k_a_2[] = "a";
static const char __pyx_k_all[] = "all";
static const char __pyx_k_iou[] = "_iou";
static const char __pyx_k_len[] = "_len";
static const char __pyx_k_obj[] = "obj";
static const char __pyx_k_sys[] = "sys";
static const char __pyx_k_area[] = "area";
static const char __pyx_k_bb_2[] = "_bb";
static const char __pyx_k_cnts[] = "cnts";
static const char __pyx_k_data[] = "data";
static const char __pyx_k_main[] = "__main__";
static const char __pyx_k_mask[] = "_mask";
static const char __pyx_k_name[] = "__name__";
static const char __pyx_k_objs[] = "objs";
static const char __pyx_k_poly[] = "poly";
static const char __pyx_k_size[] = "size";
static const char __pyx_k_test[] = "__test__";
static const char __pyx_k_utf8[] = "utf8";
static const char __pyx_k_array[] = "array";
static const char __pyx_k_bbIou[] = "_bbIou";
static const char __pyx_k_dtype[] = "dtype";
static const char __pyx_k_iou_2[] = "iou";
static const char __pyx_k_isbox[] = "isbox";
static const char __pyx_k_isrle[] = "isrle";
static const char __pyx_k_masks[] = "masks";
static const char __pyx_k_merge[] = "merge";
static const char __pyx_k_numpy[] = "numpy";
static const char __pyx_k_order[] = "order";
static const char __pyx_k_pyobj[] = "pyobj";
static const char __pyx_k_range[] = "range";
static const char __pyx_k_shape[] = "shape";
static const char __pyx_k_uint8[] = "uint8";
static const char __pyx_k_zeros[] = "zeros";
static const char __pyx_k_astype[] = "astype";
static const char __pyx_k_author[] = "__author__";
static const char __pyx_k_counts[] = "counts";
static const char __pyx_k_decode[] = "decode";
static const char __pyx_k_double[] = "double";
static const char __pyx_k_encode[] = "encode";
static const char __pyx_k_frBbox[] = "frBbox";
static const char __pyx_k_frPoly[] = "frPoly";
static const char __pyx_k_import[] = "__import__";
static const char __pyx_k_iouFun[] = "_iouFun";
static const char __pyx_k_mask_2[] = "mask";
static const char __pyx_k_reduce[] = "__reduce__";
static const char __pyx_k_rleIou[] = "_rleIou";
static const char __pyx_k_toBbox[] = "toBbox";
static const char __pyx_k_ucRles[] = "ucRles";
static const char __pyx_k_uint32[] = "uint32";
static const char __pyx_k_iscrowd[] = "iscrowd";
static const char __pyx_k_np_poly[] = "np_poly";
static const char __pyx_k_preproc[] = "_preproc";
static const char __pyx_k_reshape[] = "reshape";
static const char __pyx_k_rleObjs[] = "rleObjs";
static const char __pyx_k_tsungyi[] = "tsungyi";
static const char __pyx_k_c_string[] = "c_string";
static const char __pyx_k_frString[] = "_frString";
static const char __pyx_k_getstate[] = "__getstate__";
static const char __pyx_k_mask_pyx[] = "_mask.pyx";
static const char __pyx_k_setstate[] = "__setstate__";
static const char __pyx_k_toString[] = "_toString";
static const char __pyx_k_TypeError[] = "TypeError";
static const char __pyx_k_enumerate[] = "enumerate";
static const char __pyx_k_intersect[] = "intersect";
static const char __pyx_k_py_string[] = "py_string";
static const char __pyx_k_pyiscrowd[] = "pyiscrowd";
static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
static const char __pyx_k_ValueError[] = "ValueError";
static const char __pyx_k_ImportError[] = "ImportError";
static const char __pyx_k_frPyObjects[] = "frPyObjects";
static const char __pyx_k_RuntimeError[] = "RuntimeError";
static const char __pyx_k_version_info[] = "version_info";
static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
static const char __pyx_k_AttributeError[] = "AttributeError";
static const char __pyx_k_PYTHON_VERSION[] = "PYTHON_VERSION";
static const char __pyx_k_iou_locals__len[] = "iou.<locals>._len";
static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
static const char __pyx_k_frUncompressedRLE[] = "frUncompressedRLE";
static const char __pyx_k_iou_locals__bbIou[] = "iou.<locals>._bbIou";
static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
static const char __pyx_k_iou_locals__rleIou[] = "iou.<locals>._rleIou";
static const char __pyx_k_iou_locals__preproc[] = "iou.<locals>._preproc";
static const char __pyx_k_input_data_type_not_allowed[] = "input data type not allowed.";
static const char __pyx_k_input_type_is_not_supported[] = "input type is not supported.";
static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
static const char __pyx_k_Python_version_must_be_2_or_3[] = "Python version must be 2 or 3";
static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
static const char __pyx_k_numpy_ndarray_input_is_only_for[] = "numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension";
static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
static const char __pyx_k_unrecognized_type_The_following[] = "unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.";
static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
static const char __pyx_k_The_dt_and_gt_should_have_the_sa[] = "The dt and gt should have the same data type, either RLEs, list or np.ndarray";
static const char __pyx_k_list_input_can_be_bounding_box_N[] = "list input can be bounding box (Nx4) or RLEs ([RLE])";
static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
static PyObject *__pyx_n_s_AttributeError;
static PyObject *__pyx_n_s_F;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
static PyObject *__pyx_n_s_ImportError;
static PyObject *__pyx_n_s_N;
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{"__reduce_cython__", (PyCFunction)__pyx_pw_5_mask_4RLEs_7__reduce_cython__, METH_NOARGS, 0},
{"__setstate_cython__", (PyCFunction)__pyx_pw_5_mask_4RLEs_9__setstate_cython__, METH_O, 0},
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__pyx_methods_5_mask_RLEs, /*tp_methods*/
0, /*tp_members*/
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NULL, /* m_reload */
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NULL, /* m_traverse */
NULL, /* m_clear */
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* def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
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return PyObject_RichCompareBool(s1, s2, equals);
#else
if (s1 == s2) {
return (equals == Py_EQ);
} else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
const char *ps1, *ps2;
Py_ssize_t length = PyBytes_GET_SIZE(s1);
if (length != PyBytes_GET_SIZE(s2))
return (equals == Py_NE);
ps1 = PyBytes_AS_STRING(s1);
ps2 = PyBytes_AS_STRING(s2);
if (ps1[0] != ps2[0]) {
return (equals == Py_NE);
} else if (length == 1) {
return (equals == Py_EQ);
} else {
int result;
#if CYTHON_USE_UNICODE_INTERNALS
Py_hash_t hash1, hash2;
hash1 = ((PyBytesObject*)s1)->ob_shash;
hash2 = ((PyBytesObject*)s2)->ob_shash;
if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
return (equals == Py_NE);
}
#endif
result = memcmp(ps1, ps2, (size_t)length);
return (equals == Py_EQ) ? (result == 0) : (result != 0);
}
} else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
return (equals == Py_NE);
} else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
return (equals == Py_NE);
} else {
int result;
PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
if (!py_result)
return -1;
result = __Pyx_PyObject_IsTrue(py_result);
Py_DECREF(py_result);
return result;
}
#endif
}
/* UnicodeEquals */
static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
#if CYTHON_COMPILING_IN_PYPY
return PyObject_RichCompareBool(s1, s2, equals);
#else
#if PY_MAJOR_VERSION < 3
PyObject* owned_ref = NULL;
#endif
int s1_is_unicode, s2_is_unicode;
if (s1 == s2) {
goto return_eq;
}
s1_is_unicode = PyUnicode_CheckExact(s1);
s2_is_unicode = PyUnicode_CheckExact(s2);
#if PY_MAJOR_VERSION < 3
if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
owned_ref = PyUnicode_FromObject(s2);
if (unlikely(!owned_ref))
return -1;
s2 = owned_ref;
s2_is_unicode = 1;
} else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
owned_ref = PyUnicode_FromObject(s1);
if (unlikely(!owned_ref))
return -1;
s1 = owned_ref;
s1_is_unicode = 1;
} else if (((!s2_is_unicode) & (!s1_is_unicode))) {
return __Pyx_PyBytes_Equals(s1, s2, equals);
}
#endif
if (s1_is_unicode & s2_is_unicode) {
Py_ssize_t length;
int kind;
void *data1, *data2;
if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
return -1;
length = __Pyx_PyUnicode_GET_LENGTH(s1);
if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
goto return_ne;
}
#if CYTHON_USE_UNICODE_INTERNALS
{
Py_hash_t hash1, hash2;
#if CYTHON_PEP393_ENABLED
hash1 = ((PyASCIIObject*)s1)->hash;
hash2 = ((PyASCIIObject*)s2)->hash;
#else
hash1 = ((PyUnicodeObject*)s1)->hash;
hash2 = ((PyUnicodeObject*)s2)->hash;
#endif
if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
goto return_ne;
}
}
#endif
kind = __Pyx_PyUnicode_KIND(s1);
if (kind != __Pyx_PyUnicode_KIND(s2)) {
goto return_ne;
}
data1 = __Pyx_PyUnicode_DATA(s1);
data2 = __Pyx_PyUnicode_DATA(s2);
if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
goto return_ne;
} else if (length == 1) {
goto return_eq;
} else {
int result = memcmp(data1, data2, (size_t)(length * kind));
#if PY_MAJOR_VERSION < 3
Py_XDECREF(owned_ref);
#endif
return (equals == Py_EQ) ? (result == 0) : (result != 0);
}
} else if ((s1 == Py_None) & s2_is_unicode) {
goto return_ne;
} else if ((s2 == Py_None) & s1_is_unicode) {
goto return_ne;
} else {
int result;
PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
#if PY_MAJOR_VERSION < 3
Py_XDECREF(owned_ref);
#endif
if (!py_result)
return -1;
result = __Pyx_PyObject_IsTrue(py_result);
Py_DECREF(py_result);
return result;
}
return_eq:
#if PY_MAJOR_VERSION < 3
Py_XDECREF(owned_ref);
#endif
return (equals == Py_EQ);
return_ne:
#if PY_MAJOR_VERSION < 3
Py_XDECREF(owned_ref);
#endif
return (equals == Py_NE);
#endif
}
/* PyCFunctionFastCall */
#if CYTHON_FAST_PYCCALL
static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
PyCFunction meth = PyCFunction_GET_FUNCTION(func);
PyObject *self = PyCFunction_GET_SELF(func);
int flags = PyCFunction_GET_FLAGS(func);
assert(PyCFunction_Check(func));
assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)));
assert(nargs >= 0);
assert(nargs == 0 || args != NULL);
/* _PyCFunction_FastCallDict() must not be called with an exception set,
because it may clear it (directly or indirectly) and so the
caller loses its exception */
assert(!PyErr_Occurred());
if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL);
} else {
return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs);
}
}
#endif
/* PyFunctionFastCall */
#if CYTHON_FAST_PYCALL
#include "frameobject.h"
static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
PyObject *globals) {
PyFrameObject *f;
PyThreadState *tstate = __Pyx_PyThreadState_Current;
PyObject **fastlocals;
Py_ssize_t i;
PyObject *result;
assert(globals != NULL);
/* XXX Perhaps we should create a specialized
PyFrame_New() that doesn't take locals, but does
take builtins without sanity checking them.
*/
assert(tstate != NULL);
f = PyFrame_New(tstate, co, globals, NULL);
if (f == NULL) {
return NULL;
}
fastlocals = f->f_localsplus;
for (i = 0; i < na; i++) {
Py_INCREF(*args);
fastlocals[i] = *args++;
}
result = PyEval_EvalFrameEx(f,0);
++tstate->recursion_depth;
Py_DECREF(f);
--tstate->recursion_depth;
return result;
}
#if 1 || PY_VERSION_HEX < 0x030600B1
static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {
PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
PyObject *globals = PyFunction_GET_GLOBALS(func);
PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
PyObject *closure;
#if PY_MAJOR_VERSION >= 3
PyObject *kwdefs;
#endif
PyObject *kwtuple, **k;
PyObject **d;
Py_ssize_t nd;
Py_ssize_t nk;
PyObject *result;
assert(kwargs == NULL || PyDict_Check(kwargs));
nk = kwargs ? PyDict_Size(kwargs) : 0;
if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
return NULL;
}
if (
#if PY_MAJOR_VERSION >= 3
co->co_kwonlyargcount == 0 &&
#endif
likely(kwargs == NULL || nk == 0) &&
co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
if (argdefs == NULL && co->co_argcount == nargs) {
result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
goto done;
}
else if (nargs == 0 && argdefs != NULL
&& co->co_argcount == Py_SIZE(argdefs)) {
/* function called with no arguments, but all parameters have
a default value: use default values as arguments .*/
args = &PyTuple_GET_ITEM(argdefs, 0);
result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
goto done;
}
}
if (kwargs != NULL) {
Py_ssize_t pos, i;
kwtuple = PyTuple_New(2 * nk);
if (kwtuple == NULL) {
result = NULL;
goto done;
}
k = &PyTuple_GET_ITEM(kwtuple, 0);
pos = i = 0;
while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
Py_INCREF(k[i]);
Py_INCREF(k[i+1]);
i += 2;
}
nk = i / 2;
}
else {
kwtuple = NULL;
k = NULL;
}
closure = PyFunction_GET_CLOSURE(func);
#if PY_MAJOR_VERSION >= 3
kwdefs = PyFunction_GET_KW_DEFAULTS(func);
#endif
if (argdefs != NULL) {
d = &PyTuple_GET_ITEM(argdefs, 0);
nd = Py_SIZE(argdefs);
}
else {
d = NULL;
nd = 0;
}
#if PY_MAJOR_VERSION >= 3
result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
args, nargs,
k, (int)nk,
d, (int)nd, kwdefs, closure);
#else
result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
args, nargs,
k, (int)nk,
d, (int)nd, closure);
#endif
Py_XDECREF(kwtuple);
done:
Py_LeaveRecursiveCall();
return result;
}
#endif
#endif
/* PyObjectCall */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
PyObject *result;
ternaryfunc call = func->ob_type->tp_call;
if (unlikely(!call))
return PyObject_Call(func, arg, kw);
if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
return NULL;
result = (*call)(func, arg, kw);
Py_LeaveRecursiveCall();
if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
PyErr_SetString(
PyExc_SystemError,
"NULL result without error in PyObject_Call");
}
return result;
}
#endif
/* PyObjectCallMethO */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
PyObject *self, *result;
PyCFunction cfunc;
cfunc = PyCFunction_GET_FUNCTION(func);
self = PyCFunction_GET_SELF(func);
if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
return NULL;
result = cfunc(self, arg);
Py_LeaveRecursiveCall();
if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
PyErr_SetString(
PyExc_SystemError,
"NULL result without error in PyObject_Call");
}
return result;
}
#endif
/* PyObjectCallOneArg */
#if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
PyObject *result;
PyObject *args = PyTuple_New(1);
if (unlikely(!args)) return NULL;
Py_INCREF(arg);
PyTuple_SET_ITEM(args, 0, arg);
result = __Pyx_PyObject_Call(func, args, NULL);
Py_DECREF(args);
return result;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
#if CYTHON_FAST_PYCALL
if (PyFunction_Check(func)) {
return __Pyx_PyFunction_FastCall(func, &arg, 1);
}
#endif
if (likely(PyCFunction_Check(func))) {
if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
return __Pyx_PyObject_CallMethO(func, arg);
#if CYTHON_FAST_PYCCALL
} else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
return __Pyx_PyCFunction_FastCall(func, &arg, 1);
#endif
}
}
return __Pyx__PyObject_CallOneArg(func, arg);
}
#else
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
PyObject *result;
PyObject *args = PyTuple_Pack(1, arg);
if (unlikely(!args)) return NULL;
result = __Pyx_PyObject_Call(func, args, NULL);
Py_DECREF(args);
return result;
}
#endif
/* PyErrFetchRestore */
#if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
PyObject *tmp_type, *tmp_value, *tmp_tb;
tmp_type = tstate->curexc_type;
tmp_value = tstate->curexc_value;
tmp_tb = tstate->curexc_traceback;
tstate->curexc_type = type;
tstate->curexc_value = value;
tstate->curexc_traceback = tb;
Py_XDECREF(tmp_type);
Py_XDECREF(tmp_value);
Py_XDECREF(tmp_tb);
}
static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
*type = tstate->curexc_type;
*value = tstate->curexc_value;
*tb = tstate->curexc_traceback;
tstate->curexc_type = 0;
tstate->curexc_value = 0;
tstate->curexc_traceback = 0;
}
#endif
/* RaiseException */
#if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
CYTHON_UNUSED PyObject *cause) {
__Pyx_PyThreadState_declare
Py_XINCREF(type);
if (!value || value == Py_None)
value = NULL;
else
Py_INCREF(value);
if (!tb || tb == Py_None)
tb = NULL;
else {
Py_INCREF(tb);
if (!PyTraceBack_Check(tb)) {
PyErr_SetString(PyExc_TypeError,
"raise: arg 3 must be a traceback or None");
goto raise_error;
}
}
if (PyType_Check(type)) {
#if CYTHON_COMPILING_IN_PYPY
if (!value) {
Py_INCREF(Py_None);
value = Py_None;
}
#endif
PyErr_NormalizeException(&type, &value, &tb);
} else {
if (value) {
PyErr_SetString(PyExc_TypeError,
"instance exception may not have a separate value");
goto raise_error;
}
value = type;
type = (PyObject*) Py_TYPE(type);
Py_INCREF(type);
if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
PyErr_SetString(PyExc_TypeError,
"raise: exception class must be a subclass of BaseException");
goto raise_error;
}
}
__Pyx_PyThreadState_assign
__Pyx_ErrRestore(type, value, tb);
return;
raise_error:
Py_XDECREF(value);
Py_XDECREF(type);
Py_XDECREF(tb);
return;
}
#else
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
PyObject* owned_instance = NULL;
if (tb == Py_None) {
tb = 0;
} else if (tb && !PyTraceBack_Check(tb)) {
PyErr_SetString(PyExc_TypeError,
"raise: arg 3 must be a traceback or None");
goto bad;
}
if (value == Py_None)
value = 0;
if (PyExceptionInstance_Check(type)) {
if (value) {
PyErr_SetString(PyExc_TypeError,
"instance exception may not have a separate value");
goto bad;
}
value = type;
type = (PyObject*) Py_TYPE(value);
} else if (PyExceptionClass_Check(type)) {
PyObject *instance_class = NULL;
if (value && PyExceptionInstance_Check(value)) {
instance_class = (PyObject*) Py_TYPE(value);
if (instance_class != type) {
int is_subclass = PyObject_IsSubclass(instance_class, type);
if (!is_subclass) {
instance_class = NULL;
} else if (unlikely(is_subclass == -1)) {
goto bad;
} else {
type = instance_class;
}
}
}
if (!instance_class) {
PyObject *args;
if (!value)
args = PyTuple_New(0);
else if (PyTuple_Check(value)) {
Py_INCREF(value);
args = value;
} else
args = PyTuple_Pack(1, value);
if (!args)
goto bad;
owned_instance = PyObject_Call(type, args, NULL);
Py_DECREF(args);
if (!owned_instance)
goto bad;
value = owned_instance;
if (!PyExceptionInstance_Check(value)) {
PyErr_Format(PyExc_TypeError,
"calling %R should have returned an instance of "
"BaseException, not %R",
type, Py_TYPE(value));
goto bad;
}
}
} else {
PyErr_SetString(PyExc_TypeError,
"raise: exception class must be a subclass of BaseException");
goto bad;
}
if (cause) {
PyObject *fixed_cause;
if (cause == Py_None) {
fixed_cause = NULL;
} else if (PyExceptionClass_Check(cause)) {
fixed_cause = PyObject_CallObject(cause, NULL);
if (fixed_cause == NULL)
goto bad;
} else if (PyExceptionInstance_Check(cause)) {
fixed_cause = cause;
Py_INCREF(fixed_cause);
} else {
PyErr_SetString(PyExc_TypeError,
"exception causes must derive from "
"BaseException");
goto bad;
}
PyException_SetCause(value, fixed_cause);
}
PyErr_SetObject(type, value);
if (tb) {
#if CYTHON_COMPILING_IN_PYPY
PyObject *tmp_type, *tmp_value, *tmp_tb;
PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
Py_INCREF(tb);
PyErr_Restore(tmp_type, tmp_value, tb);
Py_XDECREF(tmp_tb);
#else
PyThreadState *tstate = __Pyx_PyThreadState_Current;
PyObject* tmp_tb = tstate->curexc_traceback;
if (tb != tmp_tb) {
Py_INCREF(tb);
tstate->curexc_traceback = tb;
Py_XDECREF(tmp_tb);
}
#endif
}
bad:
Py_XDECREF(owned_instance);
return;
}
#endif
/* ExtTypeTest */
static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
if (unlikely(!type)) {
PyErr_SetString(PyExc_SystemError, "Missing type object");
return 0;
}
if (likely(__Pyx_TypeCheck(obj, type)))
return 1;
PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
Py_TYPE(obj)->tp_name, type->tp_name);
return 0;
}
/* ArgTypeTest */
static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
{
if (unlikely(!type)) {
PyErr_SetString(PyExc_SystemError, "Missing type object");
return 0;
}
else if (exact) {
#if PY_MAJOR_VERSION == 2
if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
#endif
}
else {
if (likely(__Pyx_TypeCheck(obj, type))) return 1;
}
PyErr_Format(PyExc_TypeError,
"Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
name, type->tp_name, Py_TYPE(obj)->tp_name);
return 0;
}
/* PyIntBinop */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_CheckExact(op1))) {
const long b = intval;
long x;
long a = PyInt_AS_LONG(op1);
x = (long)((unsigned long)a + b);
if (likely((x^a) >= 0 || (x^b) >= 0))
return PyInt_FromLong(x);
return PyLong_Type.tp_as_number->nb_add(op1, op2);
}
#endif
#if CYTHON_USE_PYLONG_INTERNALS
if (likely(PyLong_CheckExact(op1))) {
const long b = intval;
long a, x;
#ifdef HAVE_LONG_LONG
const PY_LONG_LONG llb = intval;
PY_LONG_LONG lla, llx;
#endif
const digit* digits = ((PyLongObject*)op1)->ob_digit;
const Py_ssize_t size = Py_SIZE(op1);
if (likely(__Pyx_sst_abs(size) <= 1)) {
a = likely(size) ? digits[0] : 0;
if (size == -1) a = -a;
} else {
switch (size) {
case -2:
if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
case 2:
if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
case -3:
if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
case 3:
if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
case -4:
if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
case 4:
if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
#ifdef HAVE_LONG_LONG
} else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
goto long_long;
#endif
}
CYTHON_FALLTHROUGH;
default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
}
}
x = a + b;
return PyLong_FromLong(x);
#ifdef HAVE_LONG_LONG
long_long:
llx = lla + llb;
return PyLong_FromLongLong(llx);
#endif
}
#endif
if (PyFloat_CheckExact(op1)) {
const long b = intval;
double a = PyFloat_AS_DOUBLE(op1);
double result;
PyFPE_START_PROTECT("add", return NULL)
result = ((double)a) + (double)b;
PyFPE_END_PROTECT(result)
return PyFloat_FromDouble(result);
}
return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
}
#endif
/* PyIntBinop */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
if (op1 == op2) {
Py_RETURN_TRUE;
}
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_CheckExact(op1))) {
const long b = intval;
long a = PyInt_AS_LONG(op1);
if (a == b) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
}
#endif
#if CYTHON_USE_PYLONG_INTERNALS
if (likely(PyLong_CheckExact(op1))) {
const long b = intval;
long a;
const digit* digits = ((PyLongObject*)op1)->ob_digit;
const Py_ssize_t size = Py_SIZE(op1);
if (likely(__Pyx_sst_abs(size) <= 1)) {
a = likely(size) ? digits[0] : 0;
if (size == -1) a = -a;
} else {
switch (size) {
case -2:
if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
case 2:
if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
case -3:
if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
case 3:
if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
case -4:
if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
case 4:
if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
break;
}
CYTHON_FALLTHROUGH;
#if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ);
#else
default: Py_RETURN_FALSE;
#endif
}
}
if (a == b) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
}
#endif
if (PyFloat_CheckExact(op1)) {
const long b = intval;
double a = PyFloat_AS_DOUBLE(op1);
if ((double)a == (double)b) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
}
return PyObject_RichCompare(op1, op2, Py_EQ);
}
#endif
/* GetModuleGlobalName */
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
PyObject *result;
#if !CYTHON_AVOID_BORROWED_REFS
#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
if (likely(result)) {
Py_INCREF(result);
} else if (unlikely(PyErr_Occurred())) {
result = NULL;
} else {
#else
result = PyDict_GetItem(__pyx_d, name);
if (likely(result)) {
Py_INCREF(result);
} else {
#endif
#else
result = PyObject_GetItem(__pyx_d, name);
if (!result) {
PyErr_Clear();
#endif
result = __Pyx_GetBuiltinName(name);
}
return result;
}
/* DictGetItem */
#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
PyObject *value;
value = PyDict_GetItemWithError(d, key);
if (unlikely(!value)) {
if (!PyErr_Occurred()) {
PyObject* args = PyTuple_Pack(1, key);
if (likely(args))
PyErr_SetObject(PyExc_KeyError, args);
Py_XDECREF(args);
}
return NULL;
}
Py_INCREF(value);
return value;
}
#endif
/* GetItemInt */
static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
PyObject *r;
if (!j) return NULL;
r = PyObject_GetItem(o, j);
Py_DECREF(j);
return r;
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
Py_ssize_t wrapped_i = i;
if (wraparound & unlikely(i < 0)) {
wrapped_i += PyList_GET_SIZE(o);
}
if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {
PyObject *r = PyList_GET_ITEM(o, wrapped_i);
Py_INCREF(r);
return r;
}
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
Py_ssize_t wrapped_i = i;
if (wraparound & unlikely(i < 0)) {
wrapped_i += PyTuple_GET_SIZE(o);
}
if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {
PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
Py_INCREF(r);
return r;
}
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
CYTHON_NCP_UNUSED int wraparound,
CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
if (is_list || PyList_CheckExact(o)) {
Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
PyObject *r = PyList_GET_ITEM(o, n);
Py_INCREF(r);
return r;
}
}
else if (PyTuple_CheckExact(o)) {
Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
PyObject *r = PyTuple_GET_ITEM(o, n);
Py_INCREF(r);
return r;
}
} else {
PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
if (likely(m && m->sq_item)) {
if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
Py_ssize_t l = m->sq_length(o);
if (likely(l >= 0)) {
i += l;
} else {
if (!PyErr_ExceptionMatches(PyExc_OverflowError))
return NULL;
PyErr_Clear();
}
}
return m->sq_item(o, i);
}
}
#else
if (is_list || PySequence_Check(o)) {
return PySequence_GetItem(o, i);
}
#endif
return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
}
/* IsLittleEndian */
static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
{
union {
uint32_t u32;
uint8_t u8[4];
} S;
S.u32 = 0x01020304;
return S.u8[0] == 4;
}
/* BufferFormatCheck */
static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
__Pyx_BufFmt_StackElem* stack,
__Pyx_TypeInfo* type) {
stack[0].field = &ctx->root;
stack[0].parent_offset = 0;
ctx->root.type = type;
ctx->root.name = "buffer dtype";
ctx->root.offset = 0;
ctx->head = stack;
ctx->head->field = &ctx->root;
ctx->fmt_offset = 0;
ctx->head->parent_offset = 0;
ctx->new_packmode = '@';
ctx->enc_packmode = '@';
ctx->new_count = 1;
ctx->enc_count = 0;
ctx->enc_type = 0;
ctx->is_complex = 0;
ctx->is_valid_array = 0;
ctx->struct_alignment = 0;
while (type->typegroup == 'S') {
++ctx->head;
ctx->head->field = type->fields;
ctx->head->parent_offset = 0;
type = type->fields->type;
}
}
static int __Pyx_BufFmt_ParseNumber(const char** ts) {
int count;
const char* t = *ts;
if (*t < '0' || *t > '9') {
return -1;
} else {
count = *t++ - '0';
while (*t >= '0' && *t < '9') {
count *= 10;
count += *t++ - '0';
}
}
*ts = t;
return count;
}
static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
int number = __Pyx_BufFmt_ParseNumber(ts);
if (number == -1)
PyErr_Format(PyExc_ValueError,\
"Does not understand character buffer dtype format string ('%c')", **ts);
return number;
}
static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
PyErr_Format(PyExc_ValueError,
"Unexpected format string character: '%c'", ch);
}
static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
switch (ch) {
case 'c': return "'char'";
case 'b': return "'signed char'";
case 'B': return "'unsigned char'";
case 'h': return "'short'";
case 'H': return "'unsigned short'";
case 'i': return "'int'";
case 'I': return "'unsigned int'";
case 'l': return "'long'";
case 'L': return "'unsigned long'";
case 'q': return "'long long'";
case 'Q': return "'unsigned long long'";
case 'f': return (is_complex ? "'complex float'" : "'float'");
case 'd': return (is_complex ? "'complex double'" : "'double'");
case 'g': return (is_complex ? "'complex long double'" : "'long double'");
case 'T': return "a struct";
case 'O': return "Python object";
case 'P': return "a pointer";
case 's': case 'p': return "a string";
case 0: return "end";
default: return "unparseable format string";
}
}
static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return 2;
case 'i': case 'I': case 'l': case 'L': return 4;
case 'q': case 'Q': return 8;
case 'f': return (is_complex ? 8 : 4);
case 'd': return (is_complex ? 16 : 8);
case 'g': {
PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
return 0;
}
case 'O': case 'P': return sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
switch (ch) {
case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(short);
case 'i': case 'I': return sizeof(int);
case 'l': case 'L': return sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(float) * (is_complex ? 2 : 1);
case 'd': return sizeof(double) * (is_complex ? 2 : 1);
case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
case 'O': case 'P': return sizeof(void*);
default: {
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
}
typedef struct { char c; short x; } __Pyx_st_short;
typedef struct { char c; int x; } __Pyx_st_int;
typedef struct { char c; long x; } __Pyx_st_long;
typedef struct { char c; float x; } __Pyx_st_float;
typedef struct { char c; double x; } __Pyx_st_double;
typedef struct { char c; long double x; } __Pyx_st_longdouble;
typedef struct { char c; void *x; } __Pyx_st_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
/* These are for computing the padding at the end of the struct to align
on the first member of the struct. This will probably the same as above,
but we don't have any guarantees.
*/
typedef struct { short x; char c; } __Pyx_pad_short;
typedef struct { int x; char c; } __Pyx_pad_int;
typedef struct { long x; char c; } __Pyx_pad_long;
typedef struct { float x; char c; } __Pyx_pad_float;
typedef struct { double x; char c; } __Pyx_pad_double;
typedef struct { long double x; char c; } __Pyx_pad_longdouble;
typedef struct { void *x; char c; } __Pyx_pad_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
switch (ch) {
case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
#endif
case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
default:
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
switch (ch) {
case 'c':
return 'H';
case 'b': case 'h': case 'i':
case 'l': case 'q': case 's': case 'p':
return 'I';
case 'B': case 'H': case 'I': case 'L': case 'Q':
return 'U';
case 'f': case 'd': case 'g':
return (is_complex ? 'C' : 'R');
case 'O':
return 'O';
case 'P':
return 'P';
default: {
__Pyx_BufFmt_RaiseUnexpectedChar(ch);
return 0;
}
}
}
static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
if (ctx->head == NULL || ctx->head->field == &ctx->root) {
const char* expected;
const char* quote;
if (ctx->head == NULL) {
expected = "end";
quote = "";
} else {
expected = ctx->head->field->type->name;
quote = "'";
}
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch, expected %s%s%s but got %s",
quote, expected, quote,
__Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
} else {
__Pyx_StructField* field = ctx->head->field;
__Pyx_StructField* parent = (ctx->head - 1)->field;
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
parent->type->name, field->name);
}
}
static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
char group;
size_t size, offset, arraysize = 1;
if (ctx->enc_type == 0) return 0;
if (ctx->head->field->type->arraysize[0]) {
int i, ndim = 0;
if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
ctx->is_valid_array = ctx->head->field->type->ndim == 1;
ndim = 1;
if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
PyErr_Format(PyExc_ValueError,
"Expected a dimension of size %zu, got %zu",
ctx->head->field->type->arraysize[0], ctx->enc_count);
return -1;
}
}
if (!ctx->is_valid_array) {
PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
ctx->head->field->type->ndim, ndim);
return -1;
}
for (i = 0; i < ctx->head->field->type->ndim; i++) {
arraysize *= ctx->head->field->type->arraysize[i];
}
ctx->is_valid_array = 0;
ctx->enc_count = 1;
}
group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
do {
__Pyx_StructField* field = ctx->head->field;
__Pyx_TypeInfo* type = field->type;
if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
} else {
size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
}
if (ctx->enc_packmode == '@') {
size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
size_t align_mod_offset;
if (align_at == 0) return -1;
align_mod_offset = ctx->fmt_offset % align_at;
if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
if (ctx->struct_alignment == 0)
ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
ctx->is_complex);
}
if (type->size != size || type->typegroup != group) {
if (type->typegroup == 'C' && type->fields != NULL) {
size_t parent_offset = ctx->head->parent_offset + field->offset;
++ctx->head;
ctx->head->field = type->fields;
ctx->head->parent_offset = parent_offset;
continue;
}
if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
} else {
__Pyx_BufFmt_RaiseExpected(ctx);
return -1;
}
}
offset = ctx->head->parent_offset + field->offset;
if (ctx->fmt_offset != offset) {
PyErr_Format(PyExc_ValueError,
"Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
(Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
return -1;
}
ctx->fmt_offset += size;
if (arraysize)
ctx->fmt_offset += (arraysize - 1) * size;
--ctx->enc_count;
while (1) {
if (field == &ctx->root) {
ctx->head = NULL;
if (ctx->enc_count != 0) {
__Pyx_BufFmt_RaiseExpected(ctx);
return -1;
}
break;
}
ctx->head->field = ++field;
if (field->type == NULL) {
--ctx->head;
field = ctx->head->field;
continue;
} else if (field->type->typegroup == 'S') {
size_t parent_offset = ctx->head->parent_offset + field->offset;
if (field->type->fields->type == NULL) continue;
field = field->type->fields;
++ctx->head;
ctx->head->field = field;
ctx->head->parent_offset = parent_offset;
break;
} else {
break;
}
}
} while (ctx->enc_count);
ctx->enc_type = 0;
ctx->is_complex = 0;
return 0;
}
static PyObject *
__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
{
const char *ts = *tsp;
int i = 0, number;
int ndim = ctx->head->field->type->ndim;
;
++ts;
if (ctx->new_count != 1) {
PyErr_SetString(PyExc_ValueError,
"Cannot handle repeated arrays in format string");
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
while (*ts && *ts != ')') {
switch (*ts) {
case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue;
default: break;
}
number = __Pyx_BufFmt_ExpectNumber(&ts);
if (number == -1) return NULL;
if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
return PyErr_Format(PyExc_ValueError,
"Expected a dimension of size %zu, got %d",
ctx->head->field->type->arraysize[i], number);
if (*ts != ',' && *ts != ')')
return PyErr_Format(PyExc_ValueError,
"Expected a comma in format string, got '%c'", *ts);
if (*ts == ',') ts++;
i++;
}
if (i != ndim)
return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
ctx->head->field->type->ndim, i);
if (!*ts) {
PyErr_SetString(PyExc_ValueError,
"Unexpected end of format string, expected ')'");
return NULL;
}
ctx->is_valid_array = 1;
ctx->new_count = 1;
*tsp = ++ts;
return Py_None;
}
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
int got_Z = 0;
while (1) {
switch(*ts) {
case 0:
if (ctx->enc_type != 0 && ctx->head == NULL) {
__Pyx_BufFmt_RaiseExpected(ctx);
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
if (ctx->head != NULL) {
__Pyx_BufFmt_RaiseExpected(ctx);
return NULL;
}
return ts;
case ' ':
case '\r':
case '\n':
++ts;
break;
case '<':
if (!__Pyx_Is_Little_Endian()) {
PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
return NULL;
}
ctx->new_packmode = '=';
++ts;
break;
case '>':
case '!':
if (__Pyx_Is_Little_Endian()) {
PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
return NULL;
}
ctx->new_packmode = '=';
++ts;
break;
case '=':
case '@':
case '^':
ctx->new_packmode = *ts++;
break;
case 'T':
{
const char* ts_after_sub;
size_t i, struct_count = ctx->new_count;
size_t struct_alignment = ctx->struct_alignment;
ctx->new_count = 1;
++ts;
if (*ts != '{') {
PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
return NULL;
}
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_type = 0;
ctx->enc_count = 0;
ctx->struct_alignment = 0;
++ts;
ts_after_sub = ts;
for (i = 0; i != struct_count; ++i) {
ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
if (!ts_after_sub) return NULL;
}
ts = ts_after_sub;
if (struct_alignment) ctx->struct_alignment = struct_alignment;
}
break;
case '}':
{
size_t alignment = ctx->struct_alignment;
++ts;
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_type = 0;
if (alignment && ctx->fmt_offset % alignment) {
ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
}
}
return ts;
case 'x':
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->fmt_offset += ctx->new_count;
ctx->new_count = 1;
ctx->enc_count = 0;
ctx->enc_type = 0;
ctx->enc_packmode = ctx->new_packmode;
++ts;
break;
case 'Z':
got_Z = 1;
++ts;
if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
__Pyx_BufFmt_RaiseUnexpectedChar('Z');
return NULL;
}
CYTHON_FALLTHROUGH;
case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
case 'l': case 'L': case 'q': case 'Q':
case 'f': case 'd': case 'g':
case 'O': case 'p':
if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
ctx->enc_packmode == ctx->new_packmode) {
ctx->enc_count += ctx->new_count;
ctx->new_count = 1;
got_Z = 0;
++ts;
break;
}
CYTHON_FALLTHROUGH;
case 's':
if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
ctx->enc_count = ctx->new_count;
ctx->enc_packmode = ctx->new_packmode;
ctx->enc_type = *ts;
ctx->is_complex = got_Z;
++ts;
ctx->new_count = 1;
got_Z = 0;
break;
case ':':
++ts;
while(*ts != ':') ++ts;
++ts;
break;
case '(':
if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
break;
default:
{
int number = __Pyx_BufFmt_ExpectNumber(&ts);
if (number == -1) return NULL;
ctx->new_count = (size_t)number;
}
}
}
}
/* BufferGetAndValidate */
static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
if (unlikely(info->buf == NULL)) return;
if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
__Pyx_ReleaseBuffer(info);
}
static void __Pyx_ZeroBuffer(Py_buffer* buf) {
buf->buf = NULL;
buf->obj = NULL;
buf->strides = __Pyx_zeros;
buf->shape = __Pyx_zeros;
buf->suboffsets = __Pyx_minusones;
}
static int __Pyx__GetBufferAndValidate(
Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags,
int nd, int cast, __Pyx_BufFmt_StackElem* stack)
{
buf->buf = NULL;
if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {
__Pyx_ZeroBuffer(buf);
return -1;
}
if (unlikely(buf->ndim != nd)) {
PyErr_Format(PyExc_ValueError,
"Buffer has wrong number of dimensions (expected %d, got %d)",
nd, buf->ndim);
goto fail;
}
if (!cast) {
__Pyx_BufFmt_Context ctx;
__Pyx_BufFmt_Init(&ctx, stack, dtype);
if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
}
if (unlikely((unsigned)buf->itemsize != dtype->size)) {
PyErr_Format(PyExc_ValueError,
"Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
buf->itemsize, (buf->itemsize > 1) ? "s" : "",
dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
goto fail;
}
if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
return 0;
fail:;
__Pyx_SafeReleaseBuffer(buf);
return -1;
}
/* FetchCommonType */
static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
PyObject* fake_module;
PyTypeObject* cached_type = NULL;
fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
if (!fake_module) return NULL;
Py_INCREF(fake_module);
cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
if (cached_type) {
if (!PyType_Check((PyObject*)cached_type)) {
PyErr_Format(PyExc_TypeError,
"Shared Cython type %.200s is not a type object",
type->tp_name);
goto bad;
}
if (cached_type->tp_basicsize != type->tp_basicsize) {
PyErr_Format(PyExc_TypeError,
"Shared Cython type %.200s has the wrong size, try recompiling",
type->tp_name);
goto bad;
}
} else {
if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
PyErr_Clear();
if (PyType_Ready(type) < 0) goto bad;
if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
goto bad;
Py_INCREF(type);
cached_type = type;
}
done:
Py_DECREF(fake_module);
return cached_type;
bad:
Py_XDECREF(cached_type);
cached_type = NULL;
goto done;
}
/* CythonFunction */
#include <structmember.h>
static PyObject *
__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
{
if (unlikely(op->func_doc == NULL)) {
if (op->func.m_ml->ml_doc) {
#if PY_MAJOR_VERSION >= 3
op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc);
#else
op->func_doc = PyString_FromString(op->func.m_ml->ml_doc);
#endif
if (unlikely(op->func_doc == NULL))
return NULL;
} else {
Py_INCREF(Py_None);
return Py_None;
}
}
Py_INCREF(op->func_doc);
return op->func_doc;
}
static int
__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value)
{
PyObject *tmp = op->func_doc;
if (value == NULL) {
value = Py_None;
}
Py_INCREF(value);
op->func_doc = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op)
{
if (unlikely(op->func_name == NULL)) {
#if PY_MAJOR_VERSION >= 3
op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name);
#else
op->func_name = PyString_InternFromString(op->func.m_ml->ml_name);
#endif
if (unlikely(op->func_name == NULL))
return NULL;
}
Py_INCREF(op->func_name);
return op->func_name;
}
static int
__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value)
{
PyObject *tmp;
#if PY_MAJOR_VERSION >= 3
if (unlikely(value == NULL || !PyUnicode_Check(value))) {
#else
if (unlikely(value == NULL || !PyString_Check(value))) {
#endif
PyErr_SetString(PyExc_TypeError,
"__name__ must be set to a string object");
return -1;
}
tmp = op->func_name;
Py_INCREF(value);
op->func_name = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op)
{
Py_INCREF(op->func_qualname);
return op->func_qualname;
}
static int
__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value)
{
PyObject *tmp;
#if PY_MAJOR_VERSION >= 3
if (unlikely(value == NULL || !PyUnicode_Check(value))) {
#else
if (unlikely(value == NULL || !PyString_Check(value))) {
#endif
PyErr_SetString(PyExc_TypeError,
"__qualname__ must be set to a string object");
return -1;
}
tmp = op->func_qualname;
Py_INCREF(value);
op->func_qualname = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure)
{
PyObject *self;
self = m->func_closure;
if (self == NULL)
self = Py_None;
Py_INCREF(self);
return self;
}
static PyObject *
__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op)
{
if (unlikely(op->func_dict == NULL)) {
op->func_dict = PyDict_New();
if (unlikely(op->func_dict == NULL))
return NULL;
}
Py_INCREF(op->func_dict);
return op->func_dict;
}
static int
__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value)
{
PyObject *tmp;
if (unlikely(value == NULL)) {
PyErr_SetString(PyExc_TypeError,
"function's dictionary may not be deleted");
return -1;
}
if (unlikely(!PyDict_Check(value))) {
PyErr_SetString(PyExc_TypeError,
"setting function's dictionary to a non-dict");
return -1;
}
tmp = op->func_dict;
Py_INCREF(value);
op->func_dict = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op)
{
Py_INCREF(op->func_globals);
return op->func_globals;
}
static PyObject *
__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op)
{
Py_INCREF(Py_None);
return Py_None;
}
static PyObject *
__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op)
{
PyObject* result = (op->func_code) ? op->func_code : Py_None;
Py_INCREF(result);
return result;
}
static int
__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
int result = 0;
PyObject *res = op->defaults_getter((PyObject *) op);
if (unlikely(!res))
return -1;
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
Py_INCREF(op->defaults_tuple);
op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
Py_INCREF(op->defaults_kwdict);
#else
op->defaults_tuple = PySequence_ITEM(res, 0);
if (unlikely(!op->defaults_tuple)) result = -1;
else {
op->defaults_kwdict = PySequence_ITEM(res, 1);
if (unlikely(!op->defaults_kwdict)) result = -1;
}
#endif
Py_DECREF(res);
return result;
}
static int
__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) {
PyObject* tmp;
if (!value) {
value = Py_None;
} else if (value != Py_None && !PyTuple_Check(value)) {
PyErr_SetString(PyExc_TypeError,
"__defaults__ must be set to a tuple object");
return -1;
}
Py_INCREF(value);
tmp = op->defaults_tuple;
op->defaults_tuple = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) {
PyObject* result = op->defaults_tuple;
if (unlikely(!result)) {
if (op->defaults_getter) {
if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
result = op->defaults_tuple;
} else {
result = Py_None;
}
}
Py_INCREF(result);
return result;
}
static int
__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) {
PyObject* tmp;
if (!value) {
value = Py_None;
} else if (value != Py_None && !PyDict_Check(value)) {
PyErr_SetString(PyExc_TypeError,
"__kwdefaults__ must be set to a dict object");
return -1;
}
Py_INCREF(value);
tmp = op->defaults_kwdict;
op->defaults_kwdict = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) {
PyObject* result = op->defaults_kwdict;
if (unlikely(!result)) {
if (op->defaults_getter) {
if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
result = op->defaults_kwdict;
} else {
result = Py_None;
}
}
Py_INCREF(result);
return result;
}
static int
__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) {
PyObject* tmp;
if (!value || value == Py_None) {
value = NULL;
} else if (!PyDict_Check(value)) {
PyErr_SetString(PyExc_TypeError,
"__annotations__ must be set to a dict object");
return -1;
}
Py_XINCREF(value);
tmp = op->func_annotations;
op->func_annotations = value;
Py_XDECREF(tmp);
return 0;
}
static PyObject *
__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) {
PyObject* result = op->func_annotations;
if (unlikely(!result)) {
result = PyDict_New();
if (unlikely(!result)) return NULL;
op->func_annotations = result;
}
Py_INCREF(result);
return result;
}
static PyGetSetDef __pyx_CyFunction_getsets[] = {
{(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
{(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
{(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
{(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
{(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
{(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0},
{(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
{(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
{(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
{(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
{(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
{(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
{(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
{(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
{(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
{(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
{(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
{(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
{0, 0, 0, 0, 0}
};
static PyMemberDef __pyx_CyFunction_members[] = {
{(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0},
{0, 0, 0, 0, 0}
};
static PyObject *
__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args)
{
#if PY_MAJOR_VERSION >= 3
return PyUnicode_FromString(m->func.m_ml->ml_name);
#else
return PyString_FromString(m->func.m_ml->ml_name);
#endif
}
static PyMethodDef __pyx_CyFunction_methods[] = {
{"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
{0, 0, 0, 0}
};
#if PY_VERSION_HEX < 0x030500A0
#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
#else
#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist)
#endif
static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname,
PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
__pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type);
if (op == NULL)
return NULL;
op->flags = flags;
__Pyx_CyFunction_weakreflist(op) = NULL;
op->func.m_ml = ml;
op->func.m_self = (PyObject *) op;
Py_XINCREF(closure);
op->func_closure = closure;
Py_XINCREF(module);
op->func.m_module = module;
op->func_dict = NULL;
op->func_name = NULL;
Py_INCREF(qualname);
op->func_qualname = qualname;
op->func_doc = NULL;
op->func_classobj = NULL;
op->func_globals = globals;
Py_INCREF(op->func_globals);
Py_XINCREF(code);
op->func_code = code;
op->defaults_pyobjects = 0;
op->defaults = NULL;
op->defaults_tuple = NULL;
op->defaults_kwdict = NULL;
op->defaults_getter = NULL;
op->func_annotations = NULL;
PyObject_GC_Track(op);
return (PyObject *) op;
}
static int
__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
{
Py_CLEAR(m->func_closure);
Py_CLEAR(m->func.m_module);
Py_CLEAR(m->func_dict);
Py_CLEAR(m->func_name);
Py_CLEAR(m->func_qualname);
Py_CLEAR(m->func_doc);
Py_CLEAR(m->func_globals);
Py_CLEAR(m->func_code);
Py_CLEAR(m->func_classobj);
Py_CLEAR(m->defaults_tuple);
Py_CLEAR(m->defaults_kwdict);
Py_CLEAR(m->func_annotations);
if (m->defaults) {
PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
int i;
for (i = 0; i < m->defaults_pyobjects; i++)
Py_XDECREF(pydefaults[i]);
PyObject_Free(m->defaults);
m->defaults = NULL;
}
return 0;
}
static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
{
if (__Pyx_CyFunction_weakreflist(m) != NULL)
PyObject_ClearWeakRefs((PyObject *) m);
__Pyx_CyFunction_clear(m);
PyObject_GC_Del(m);
}
static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
{
PyObject_GC_UnTrack(m);
__Pyx__CyFunction_dealloc(m);
}
static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
{
Py_VISIT(m->func_closure);
Py_VISIT(m->func.m_module);
Py_VISIT(m->func_dict);
Py_VISIT(m->func_name);
Py_VISIT(m->func_qualname);
Py_VISIT(m->func_doc);
Py_VISIT(m->func_globals);
Py_VISIT(m->func_code);
Py_VISIT(m->func_classobj);
Py_VISIT(m->defaults_tuple);
Py_VISIT(m->defaults_kwdict);
if (m->defaults) {
PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
int i;
for (i = 0; i < m->defaults_pyobjects; i++)
Py_VISIT(pydefaults[i]);
}
return 0;
}
static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type)
{
__pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) {
Py_INCREF(func);
return func;
}
if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) {
if (type == NULL)
type = (PyObject *)(Py_TYPE(obj));
return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type)));
}
if (obj == Py_None)
obj = NULL;
return __Pyx_PyMethod_New(func, obj, type);
}
static PyObject*
__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
{
#if PY_MAJOR_VERSION >= 3
return PyUnicode_FromFormat("<cyfunction %U at %p>",
op->func_qualname, (void *)op);
#else
return PyString_FromFormat("<cyfunction %s at %p>",
PyString_AsString(op->func_qualname), (void *)op);
#endif
}
static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
PyCFunctionObject* f = (PyCFunctionObject*)func;
PyCFunction meth = f->m_ml->ml_meth;
Py_ssize_t size;
switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
case METH_VARARGS:
if (likely(kw == NULL || PyDict_Size(kw) == 0))
return (*meth)(self, arg);
break;
case METH_VARARGS | METH_KEYWORDS:
return (*(PyCFunctionWithKeywords)meth)(self, arg, kw);
case METH_NOARGS:
if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
size = PyTuple_GET_SIZE(arg);
if (likely(size == 0))
return (*meth)(self, NULL);
PyErr_Format(PyExc_TypeError,
"%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
f->m_ml->ml_name, size);
return NULL;
}
break;
case METH_O:
if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
size = PyTuple_GET_SIZE(arg);
if (likely(size == 1)) {
PyObject *result, *arg0;
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
arg0 = PyTuple_GET_ITEM(arg, 0);
#else
arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
#endif
result = (*meth)(self, arg0);
#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
Py_DECREF(arg0);
#endif
return result;
}
PyErr_Format(PyExc_TypeError,
"%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
f->m_ml->ml_name, size);
return NULL;
}
break;
default:
PyErr_SetString(PyExc_SystemError, "Bad call flags in "
"__Pyx_CyFunction_Call. METH_OLDARGS is no "
"longer supported!");
return NULL;
}
PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
f->m_ml->ml_name);
return NULL;
}
static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
}
static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
PyObject *result;
__pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
Py_ssize_t argc;
PyObject *new_args;
PyObject *self;
argc = PyTuple_GET_SIZE(args);
new_args = PyTuple_GetSlice(args, 1, argc);
if (unlikely(!new_args))
return NULL;
self = PyTuple_GetItem(args, 0);
if (unlikely(!self)) {
Py_DECREF(new_args);
return NULL;
}
result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
Py_DECREF(new_args);
} else {
result = __Pyx_CyFunction_Call(func, args, kw);
}
return result;
}
static PyTypeObject __pyx_CyFunctionType_type = {
PyVarObject_HEAD_INIT(0, 0)
"cython_function_or_method",
sizeof(__pyx_CyFunctionObject),
0,
(destructor) __Pyx_CyFunction_dealloc,
0,
0,
0,
#if PY_MAJOR_VERSION < 3
0,
#else
0,
#endif
(reprfunc) __Pyx_CyFunction_repr,
0,
0,
0,
0,
__Pyx_CyFunction_CallAsMethod,
0,
0,
0,
0,
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
0,
(traverseproc) __Pyx_CyFunction_traverse,
(inquiry) __Pyx_CyFunction_clear,
0,
#if PY_VERSION_HEX < 0x030500A0
offsetof(__pyx_CyFunctionObject, func_weakreflist),
#else
offsetof(PyCFunctionObject, m_weakreflist),
#endif
0,
0,
__pyx_CyFunction_methods,
__pyx_CyFunction_members,
__pyx_CyFunction_getsets,
0,
0,
__Pyx_CyFunction_descr_get,
0,
offsetof(__pyx_CyFunctionObject, func_dict),
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
#if PY_VERSION_HEX >= 0x030400a1
0,
#endif
};
static int __pyx_CyFunction_init(void) {
__pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
if (unlikely(__pyx_CyFunctionType == NULL)) {
return -1;
}
return 0;
}
static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
__pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
m->defaults = PyObject_Malloc(size);
if (unlikely(!m->defaults))
return PyErr_NoMemory();
memset(m->defaults, 0, size);
m->defaults_pyobjects = pyobjects;
return m->defaults;
}
static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
__pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
m->defaults_tuple = tuple;
Py_INCREF(tuple);
}
static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
__pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
m->defaults_kwdict = dict;
Py_INCREF(dict);
}
static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
__pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
m->func_annotations = dict;
Py_INCREF(dict);
}
/* BufferFallbackError */
static void __Pyx_RaiseBufferFallbackError(void) {
PyErr_SetString(PyExc_ValueError,
"Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!");
}
/* None */
static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
Py_ssize_t q = a / b;
Py_ssize_t r = a - q*b;
q -= ((r != 0) & ((r ^ b) < 0));
return q;
}
/* BufferIndexError */
static void __Pyx_RaiseBufferIndexError(int axis) {
PyErr_Format(PyExc_IndexError,
"Out of bounds on buffer access (axis %d)", axis);
}
/* RaiseTooManyValuesToUnpack */
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
PyErr_Format(PyExc_ValueError,
"too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}
/* RaiseNeedMoreValuesToUnpack */
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
PyErr_Format(PyExc_ValueError,
"need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
index, (index == 1) ? "" : "s");
}
/* RaiseNoneIterError */
static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
}
/* SaveResetException */
#if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
#if PY_VERSION_HEX >= 0x030700A3
*type = tstate->exc_state.exc_type;
*value = tstate->exc_state.exc_value;
*tb = tstate->exc_state.exc_traceback;
#else
*type = tstate->exc_type;
*value = tstate->exc_value;
*tb = tstate->exc_traceback;
#endif
Py_XINCREF(*type);
Py_XINCREF(*value);
Py_XINCREF(*tb);
}
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
PyObject *tmp_type, *tmp_value, *tmp_tb;
#if PY_VERSION_HEX >= 0x030700A3
tmp_type = tstate->exc_state.exc_type;
tmp_value = tstate->exc_state.exc_value;
tmp_tb = tstate->exc_state.exc_traceback;
tstate->exc_state.exc_type = type;
tstate->exc_state.exc_value = value;
tstate->exc_state.exc_traceback = tb;
#else
tmp_type = tstate->exc_type;
tmp_value = tstate->exc_value;
tmp_tb = tstate->exc_traceback;
tstate->exc_type = type;
tstate->exc_value = value;
tstate->exc_traceback = tb;
#endif
Py_XDECREF(tmp_type);
Py_XDECREF(tmp_value);
Py_XDECREF(tmp_tb);
}
#endif
/* PyErrExceptionMatches */
#if CYTHON_FAST_THREAD_STATE
static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
Py_ssize_t i, n;
n = PyTuple_GET_SIZE(tuple);
#if PY_MAJOR_VERSION >= 3
for (i=0; i<n; i++) {
if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
}
#endif
for (i=0; i<n; i++) {
if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
}
return 0;
}
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
PyObject *exc_type = tstate->curexc_type;
if (exc_type == err) return 1;
if (unlikely(!exc_type)) return 0;
if (unlikely(PyTuple_Check(err)))
return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
}
#endif
/* GetException */
#if CYTHON_FAST_THREAD_STATE
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
#else
static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
#endif
PyObject *local_type, *local_value, *local_tb;
#if CYTHON_FAST_THREAD_STATE
PyObject *tmp_type, *tmp_value, *tmp_tb;
local_type = tstate->curexc_type;
local_value = tstate->curexc_value;
local_tb = tstate->curexc_traceback;
tstate->curexc_type = 0;
tstate->curexc_value = 0;
tstate->curexc_traceback = 0;
#else
PyErr_Fetch(&local_type, &local_value, &local_tb);
#endif
PyErr_NormalizeException(&local_type, &local_value, &local_tb);
#if CYTHON_FAST_THREAD_STATE
if (unlikely(tstate->curexc_type))
#else
if (unlikely(PyErr_Occurred()))
#endif
goto bad;
#if PY_MAJOR_VERSION >= 3
if (local_tb) {
if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
goto bad;
}
#endif
Py_XINCREF(local_tb);
Py_XINCREF(local_type);
Py_XINCREF(local_value);
*type = local_type;
*value = local_value;
*tb = local_tb;
#if CYTHON_FAST_THREAD_STATE
#if PY_VERSION_HEX >= 0x030700A3
tmp_type = tstate->exc_state.exc_type;
tmp_value = tstate->exc_state.exc_value;
tmp_tb = tstate->exc_state.exc_traceback;
tstate->exc_state.exc_type = local_type;
tstate->exc_state.exc_value = local_value;
tstate->exc_state.exc_traceback = local_tb;
#else
tmp_type = tstate->exc_type;
tmp_value = tstate->exc_value;
tmp_tb = tstate->exc_traceback;
tstate->exc_type = local_type;
tstate->exc_value = local_value;
tstate->exc_traceback = local_tb;
#endif
Py_XDECREF(tmp_type);
Py_XDECREF(tmp_value);
Py_XDECREF(tmp_tb);
#else
PyErr_SetExcInfo(local_type, local_value, local_tb);
#endif
return 0;
bad:
*type = 0;
*value = 0;
*tb = 0;
Py_XDECREF(local_type);
Py_XDECREF(local_value);
Py_XDECREF(local_tb);
return -1;
}
/* PyObject_GenericGetAttrNoDict */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
PyErr_Format(PyExc_AttributeError,
#if PY_MAJOR_VERSION >= 3
"'%.50s' object has no attribute '%U'",
tp->tp_name, attr_name);
#else
"'%.50s' object has no attribute '%.400s'",
tp->tp_name, PyString_AS_STRING(attr_name));
#endif
return NULL;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
PyObject *descr;
PyTypeObject *tp = Py_TYPE(obj);
if (unlikely(!PyString_Check(attr_name))) {
return PyObject_GenericGetAttr(obj, attr_name);
}
assert(!tp->tp_dictoffset);
descr = _PyType_Lookup(tp, attr_name);
if (unlikely(!descr)) {
return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
}
Py_INCREF(descr);
#if PY_MAJOR_VERSION < 3
if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
#endif
{
descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
if (unlikely(f)) {
PyObject *res = f(descr, obj, (PyObject *)tp);
Py_DECREF(descr);
return res;
}
}
return descr;
}
#endif
/* PyObject_GenericGetAttr */
#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
return PyObject_GenericGetAttr(obj, attr_name);
}
return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
}
#endif
/* SetupReduce */
static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
int ret;
PyObject *name_attr;
name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name);
if (likely(name_attr)) {
ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
} else {
ret = -1;
}
if (unlikely(ret < 0)) {
PyErr_Clear();
ret = 0;
}
Py_XDECREF(name_attr);
return ret;
}
static int __Pyx_setup_reduce(PyObject* type_obj) {
int ret = 0;
PyObject *object_reduce = NULL;
PyObject *object_reduce_ex = NULL;
PyObject *reduce = NULL;
PyObject *reduce_ex = NULL;
PyObject *reduce_cython = NULL;
PyObject *setstate = NULL;
PyObject *setstate_cython = NULL;
#if CYTHON_USE_PYTYPE_LOOKUP
if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD;
#else
if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD;
#endif
#if CYTHON_USE_PYTYPE_LOOKUP
object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
#else
object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
#endif
reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD;
if (reduce_ex == object_reduce_ex) {
#if CYTHON_USE_PYTYPE_LOOKUP
object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
#else
object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
#endif
reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD;
if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD;
ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD;
ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD;
setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
if (!setstate) PyErr_Clear();
if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD;
ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD;
ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD;
}
PyType_Modified((PyTypeObject*)type_obj);
}
}
goto GOOD;
BAD:
if (!PyErr_Occurred())
PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
ret = -1;
GOOD:
#if !CYTHON_USE_PYTYPE_LOOKUP
Py_XDECREF(object_reduce);
Py_XDECREF(object_reduce_ex);
#endif
Py_XDECREF(reduce);
Py_XDECREF(reduce_ex);
Py_XDECREF(reduce_cython);
Py_XDECREF(setstate);
Py_XDECREF(setstate_cython);
return ret;
}
/* Import */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
PyObject *empty_list = 0;
PyObject *module = 0;
PyObject *global_dict = 0;
PyObject *empty_dict = 0;
PyObject *list;
#if PY_MAJOR_VERSION < 3
PyObject *py_import;
py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
if (!py_import)
goto bad;
#endif
if (from_list)
list = from_list;
else {
empty_list = PyList_New(0);
if (!empty_list)
goto bad;
list = empty_list;
}
global_dict = PyModule_GetDict(__pyx_m);
if (!global_dict)
goto bad;
empty_dict = PyDict_New();
if (!empty_dict)
goto bad;
{
#if PY_MAJOR_VERSION >= 3
if (level == -1) {
if (strchr(__Pyx_MODULE_NAME, '.')) {
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, 1);
if (!module) {
if (!PyErr_ExceptionMatches(PyExc_ImportError))
goto bad;
PyErr_Clear();
}
}
level = 0;
}
#endif
if (!module) {
#if PY_MAJOR_VERSION < 3
PyObject *py_level = PyInt_FromLong(level);
if (!py_level)
goto bad;
module = PyObject_CallFunctionObjArgs(py_import,
name, global_dict, empty_dict, list, py_level, NULL);
Py_DECREF(py_level);
#else
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, level);
#endif
}
}
bad:
#if PY_MAJOR_VERSION < 3
Py_XDECREF(py_import);
#endif
Py_XDECREF(empty_list);
Py_XDECREF(empty_dict);
return module;
}
/* CLineInTraceback */
#ifndef CYTHON_CLINE_IN_TRACEBACK
static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {
PyObject *use_cline;
PyObject *ptype, *pvalue, *ptraceback;
#if CYTHON_COMPILING_IN_CPYTHON
PyObject **cython_runtime_dict;
#endif
if (unlikely(!__pyx_cython_runtime)) {
return c_line;
}
__Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
#if CYTHON_COMPILING_IN_CPYTHON
cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
if (likely(cython_runtime_dict)) {
use_cline = __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);
} else
#endif
{
PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
if (use_cline_obj) {
use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
Py_DECREF(use_cline_obj);
} else {
PyErr_Clear();
use_cline = NULL;
}
}
if (!use_cline) {
c_line = 0;
PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
}
else if (PyObject_Not(use_cline) != 0) {
c_line = 0;
}
__Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
return c_line;
}
#endif
/* CodeObjectCache */
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
int start = 0, mid = 0, end = count - 1;
if (end >= 0 && code_line > entries[end].code_line) {
return count;
}
while (start < end) {
mid = start + (end - start) / 2;
if (code_line < entries[mid].code_line) {
end = mid;
} else if (code_line > entries[mid].code_line) {
start = mid + 1;
} else {
return mid;
}
}
if (code_line <= entries[mid].code_line) {
return mid;
} else {
return mid + 1;
}
}
static PyCodeObject *__pyx_find_code_object(int code_line) {
PyCodeObject* code_object;
int pos;
if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
return NULL;
}
pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
return NULL;
}
code_object = __pyx_code_cache.entries[pos].code_object;
Py_INCREF(code_object);
return code_object;
}
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
int pos, i;
__Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
if (unlikely(!code_line)) {
return;
}
if (unlikely(!entries)) {
entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
if (likely(entries)) {
__pyx_code_cache.entries = entries;
__pyx_code_cache.max_count = 64;
__pyx_code_cache.count = 1;
entries[0].code_line = code_line;
entries[0].code_object = code_object;
Py_INCREF(code_object);
}
return;
}
pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
PyCodeObject* tmp = entries[pos].code_object;
entries[pos].code_object = code_object;
Py_DECREF(tmp);
return;
}
if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
int new_max = __pyx_code_cache.max_count + 64;
entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
__pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
if (unlikely(!entries)) {
return;
}
__pyx_code_cache.entries = entries;
__pyx_code_cache.max_count = new_max;
}
for (i=__pyx_code_cache.count; i>pos; i--) {
entries[i] = entries[i-1];
}
entries[pos].code_line = code_line;
entries[pos].code_object = code_object;
__pyx_code_cache.count++;
Py_INCREF(code_object);
}
/* AddTraceback */
#include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
const char *funcname, int c_line,
int py_line, const char *filename) {
PyCodeObject *py_code = 0;
PyObject *py_srcfile = 0;
PyObject *py_funcname = 0;
#if PY_MAJOR_VERSION < 3
py_srcfile = PyString_FromString(filename);
#else
py_srcfile = PyUnicode_FromString(filename);
#endif
if (!py_srcfile) goto bad;
if (c_line) {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
#else
py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
#endif
}
else {
#if PY_MAJOR_VERSION < 3
py_funcname = PyString_FromString(funcname);
#else
py_funcname = PyUnicode_FromString(funcname);
#endif
}
if (!py_funcname) goto bad;
py_code = __Pyx_PyCode_New(
0,
0,
0,
0,
0,
__pyx_empty_bytes, /*PyObject *code,*/
__pyx_empty_tuple, /*PyObject *consts,*/
__pyx_empty_tuple, /*PyObject *names,*/
__pyx_empty_tuple, /*PyObject *varnames,*/
__pyx_empty_tuple, /*PyObject *freevars,*/
__pyx_empty_tuple, /*PyObject *cellvars,*/
py_srcfile, /*PyObject *filename,*/
py_funcname, /*PyObject *name,*/
py_line,
__pyx_empty_bytes /*PyObject *lnotab*/
);
Py_DECREF(py_srcfile);
Py_DECREF(py_funcname);
return py_code;
bad:
Py_XDECREF(py_srcfile);
Py_XDECREF(py_funcname);
return NULL;
}
static void __Pyx_AddTraceback(const char *funcname, int c_line,
int py_line, const char *filename) {
PyCodeObject *py_code = 0;
PyFrameObject *py_frame = 0;
PyThreadState *tstate = __Pyx_PyThreadState_Current;
if (c_line) {
c_line = __Pyx_CLineForTraceback(tstate, c_line);
}
py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
if (!py_code) {
py_code = __Pyx_CreateCodeObjectForTraceback(
funcname, c_line, py_line, filename);
if (!py_code) goto bad;
__pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
}
py_frame = PyFrame_New(
tstate, /*PyThreadState *tstate,*/
py_code, /*PyCodeObject *code,*/
__pyx_d, /*PyObject *globals,*/
0 /*PyObject *locals*/
);
if (!py_frame) goto bad;
__Pyx_PyFrame_SetLineNumber(py_frame, py_line);
PyTraceBack_Here(py_frame);
bad:
Py_XDECREF(py_code);
Py_XDECREF(py_frame);
}
#if PY_MAJOR_VERSION < 3
static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
return -1;
}
static void __Pyx_ReleaseBuffer(Py_buffer *view) {
PyObject *obj = view->obj;
if (!obj) return;
if (PyObject_CheckBuffer(obj)) {
PyBuffer_Release(view);
return;
}
if ((0)) {}
else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view);
view->obj = NULL;
Py_DECREF(obj);
}
#endif
/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
const long neg_one = (long) -1, const_zero = (long) 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(long) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(long) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
}
} else {
if (sizeof(long) <= sizeof(long)) {
return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(long),
little, !is_unsigned);
}
}
/* CIntFromPyVerify */
#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
__PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
__PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
{\
func_type value = func_value;\
if (sizeof(target_type) < sizeof(func_type)) {\
if (unlikely(value != (func_type) (target_type) value)) {\
func_type zero = 0;\
if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
return (target_type) -1;\
if (is_unsigned && unlikely(value < zero))\
goto raise_neg_overflow;\
else\
goto raise_overflow;\
}\
}\
return (target_type) value;\
}
/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value) {
const siz neg_one = (siz) -1, const_zero = (siz) 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(siz) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(siz) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
}
} else {
if (sizeof(siz) <= sizeof(long)) {
return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(siz),
little, !is_unsigned);
}
}
/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) {
const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(Py_intptr_t) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
}
} else {
if (sizeof(Py_intptr_t) <= sizeof(long)) {
return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t),
little, !is_unsigned);
}
}
/* Declarations */
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
return ::std::complex< float >(x, y);
}
#else
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
return x + y*(__pyx_t_float_complex)_Complex_I;
}
#endif
#else
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
__pyx_t_float_complex z;
z.real = x;
z.imag = y;
return z;
}
#endif
/* Arithmetic */
#if CYTHON_CCOMPLEX
#else
static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
return (a.real == b.real) && (a.imag == b.imag);
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real + b.real;
z.imag = a.imag + b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real - b.real;
z.imag = a.imag - b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
#if 1
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
if (b.imag == 0) {
return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
} else if (fabsf(b.real) >= fabsf(b.imag)) {
if (b.real == 0 && b.imag == 0) {
return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
} else {
float r = b.imag / b.real;
float s = 1.0 / (b.real + b.imag * r);
return __pyx_t_float_complex_from_parts(
(a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
}
} else {
float r = b.real / b.imag;
float s = 1.0 / (b.imag + b.real * r);
return __pyx_t_float_complex_from_parts(
(a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
}
}
#else
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
if (b.imag == 0) {
return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
} else {
float denom = b.real * b.real + b.imag * b.imag;
return __pyx_t_float_complex_from_parts(
(a.real * b.real + a.imag * b.imag) / denom,
(a.imag * b.real - a.real * b.imag) / denom);
}
}
#endif
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
__pyx_t_float_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
return (a.real == 0) && (a.imag == 0);
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
__pyx_t_float_complex z;
z.real = a.real;
z.imag = -a.imag;
return z;
}
#if 1
static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
#if !defined(HAVE_HYPOT) || defined(_MSC_VER)
return sqrtf(z.real*z.real + z.imag*z.imag);
#else
return hypotf(z.real, z.imag);
#endif
}
static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
__pyx_t_float_complex z;
float r, lnr, theta, z_r, z_theta;
if (b.imag == 0 && b.real == (int)b.real) {
if (b.real < 0) {
float denom = a.real * a.real + a.imag * a.imag;
a.real = a.real / denom;
a.imag = -a.imag / denom;
b.real = -b.real;
}
switch ((int)b.real) {
case 0:
z.real = 1;
z.imag = 0;
return z;
case 1:
return a;
case 2:
z = __Pyx_c_prod_float(a, a);
return __Pyx_c_prod_float(a, a);
case 3:
z = __Pyx_c_prod_float(a, a);
return __Pyx_c_prod_float(z, a);
case 4:
z = __Pyx_c_prod_float(a, a);
return __Pyx_c_prod_float(z, z);
}
}
if (a.imag == 0) {
if (a.real == 0) {
return a;
} else if (b.imag == 0) {
z.real = powf(a.real, b.real);
z.imag = 0;
return z;
} else if (a.real > 0) {
r = a.real;
theta = 0;
} else {
r = -a.real;
theta = atan2f(0, -1);
}
} else {
r = __Pyx_c_abs_float(a);
theta = atan2f(a.imag, a.real);
}
lnr = logf(r);
z_r = expf(lnr * b.real - theta * b.imag);
z_theta = theta * b.real + lnr * b.imag;
z.real = z_r * cosf(z_theta);
z.imag = z_r * sinf(z_theta);
return z;
}
#endif
#endif
/* Declarations */
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
return ::std::complex< double >(x, y);
}
#else
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
return x + y*(__pyx_t_double_complex)_Complex_I;
}
#endif
#else
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
__pyx_t_double_complex z;
z.real = x;
z.imag = y;
return z;
}
#endif
/* Arithmetic */
#if CYTHON_CCOMPLEX
#else
static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
return (a.real == b.real) && (a.imag == b.imag);
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real + b.real;
z.imag = a.imag + b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real - b.real;
z.imag = a.imag - b.imag;
return z;
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
z.real = a.real * b.real - a.imag * b.imag;
z.imag = a.real * b.imag + a.imag * b.real;
return z;
}
#if 1
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
if (b.imag == 0) {
return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
} else if (fabs(b.real) >= fabs(b.imag)) {
if (b.real == 0 && b.imag == 0) {
return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
} else {
double r = b.imag / b.real;
double s = 1.0 / (b.real + b.imag * r);
return __pyx_t_double_complex_from_parts(
(a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
}
} else {
double r = b.real / b.imag;
double s = 1.0 / (b.imag + b.real * r);
return __pyx_t_double_complex_from_parts(
(a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
}
}
#else
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
if (b.imag == 0) {
return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
} else {
double denom = b.real * b.real + b.imag * b.imag;
return __pyx_t_double_complex_from_parts(
(a.real * b.real + a.imag * b.imag) / denom,
(a.imag * b.real - a.real * b.imag) / denom);
}
}
#endif
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
__pyx_t_double_complex z;
z.real = -a.real;
z.imag = -a.imag;
return z;
}
static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
return (a.real == 0) && (a.imag == 0);
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
__pyx_t_double_complex z;
z.real = a.real;
z.imag = -a.imag;
return z;
}
#if 1
static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
#if !defined(HAVE_HYPOT) || defined(_MSC_VER)
return sqrt(z.real*z.real + z.imag*z.imag);
#else
return hypot(z.real, z.imag);
#endif
}
static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
__pyx_t_double_complex z;
double r, lnr, theta, z_r, z_theta;
if (b.imag == 0 && b.real == (int)b.real) {
if (b.real < 0) {
double denom = a.real * a.real + a.imag * a.imag;
a.real = a.real / denom;
a.imag = -a.imag / denom;
b.real = -b.real;
}
switch ((int)b.real) {
case 0:
z.real = 1;
z.imag = 0;
return z;
case 1:
return a;
case 2:
z = __Pyx_c_prod_double(a, a);
return __Pyx_c_prod_double(a, a);
case 3:
z = __Pyx_c_prod_double(a, a);
return __Pyx_c_prod_double(z, a);
case 4:
z = __Pyx_c_prod_double(a, a);
return __Pyx_c_prod_double(z, z);
}
}
if (a.imag == 0) {
if (a.real == 0) {
return a;
} else if (b.imag == 0) {
z.real = pow(a.real, b.real);
z.imag = 0;
return z;
} else if (a.real > 0) {
r = a.real;
theta = 0;
} else {
r = -a.real;
theta = atan2(0, -1);
}
} else {
r = __Pyx_c_abs_double(a);
theta = atan2(a.imag, a.real);
}
lnr = log(r);
z_r = exp(lnr * b.real - theta * b.imag);
z_theta = theta * b.real + lnr * b.imag;
z.real = z_r * cos(z_theta);
z.imag = z_r * sin(z_theta);
return z;
}
#endif
#endif
/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
const int neg_one = (int) -1, const_zero = (int) 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(int) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(int) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
}
} else {
if (sizeof(int) <= sizeof(long)) {
return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(int),
little, !is_unsigned);
}
}
/* CIntToPy */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {
const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;
const int is_unsigned = neg_one > const_zero;
if (is_unsigned) {
if (sizeof(enum NPY_TYPES) < sizeof(long)) {
return PyInt_FromLong((long) value);
} else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) {
return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) {
return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
}
} else {
if (sizeof(enum NPY_TYPES) <= sizeof(long)) {
return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
} else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) {
return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
}
}
{
int one = 1; int little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&value;
return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES),
little, !is_unsigned);
}
}
/* CIntFromPy */
static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *x) {
const siz neg_one = (siz) -1, const_zero = (siz) 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(siz) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(siz, long, PyInt_AS_LONG(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
return (siz) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (siz) 0;
case 1: __PYX_VERIFY_RETURN_INT(siz, digit, digits[0])
case 2:
if (8 * sizeof(siz) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) >= 2 * PyLong_SHIFT) {
return (siz) (((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
}
}
break;
case 3:
if (8 * sizeof(siz) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) >= 3 * PyLong_SHIFT) {
return (siz) (((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
}
}
break;
case 4:
if (8 * sizeof(siz) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) >= 4 * PyLong_SHIFT) {
return (siz) (((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
}
}
break;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (siz) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(siz) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT_EXC(siz, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(siz, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
}
} else {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (siz) 0;
case -1: __PYX_VERIFY_RETURN_INT(siz, sdigit, (sdigit) (-(sdigit)digits[0]))
case 1: __PYX_VERIFY_RETURN_INT(siz, digit, +digits[0])
case -2:
if (8 * sizeof(siz) - 1 > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
return (siz) (((siz)-1)*(((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
case 2:
if (8 * sizeof(siz) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
return (siz) ((((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
case -3:
if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
return (siz) (((siz)-1)*(((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
case 3:
if (8 * sizeof(siz) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
return (siz) ((((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
case -4:
if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) {
return (siz) (((siz)-1)*(((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
case 4:
if (8 * sizeof(siz) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) {
return (siz) ((((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
}
}
break;
}
#endif
if (sizeof(siz) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT_EXC(siz, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(siz, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
siz val;
PyObject *v = __Pyx_PyNumber_IntOrLong(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (siz) -1;
}
} else {
siz val;
PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
if (!tmp) return (siz) -1;
val = __Pyx_PyInt_As_siz(tmp);
Py_DECREF(tmp);
return val;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to siz");
return (siz) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to siz");
return (siz) -1;
}
/* CIntFromPy */
static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) {
const size_t neg_one = (size_t) -1, const_zero = (size_t) 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(size_t) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
return (size_t) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (size_t) 0;
case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0])
case 2:
if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) {
return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
}
break;
case 3:
if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) {
return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
}
break;
case 4:
if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) {
return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
}
break;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (size_t) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(size_t) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
}
} else {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (size_t) 0;
case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0]))
case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, +digits[0])
case -2:
if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
case 2:
if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
case -3:
if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
case 3:
if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
case -4:
if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
case 4:
if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
}
}
break;
}
#endif
if (sizeof(size_t) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
size_t val;
PyObject *v = __Pyx_PyNumber_IntOrLong(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (size_t) -1;
}
} else {
size_t val;
PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
if (!tmp) return (size_t) -1;
val = __Pyx_PyInt_As_size_t(tmp);
Py_DECREF(tmp);
return val;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to size_t");
return (size_t) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to size_t");
return (size_t) -1;
}
/* CIntFromPy */
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
const int neg_one = (int) -1, const_zero = (int) 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(int) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
return (int) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (int) 0;
case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
case 2:
if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
}
}
break;
case 3:
if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
}
}
break;
case 4:
if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
}
}
break;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (int) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(int) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
}
} else {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (int) 0;
case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0])
case -2:
if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
case 2:
if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
case -3:
if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
case 3:
if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
case -4:
if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
case 4:
if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
}
}
break;
}
#endif
if (sizeof(int) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
int val;
PyObject *v = __Pyx_PyNumber_IntOrLong(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (int) -1;
}
} else {
int val;
PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
if (!tmp) return (int) -1;
val = __Pyx_PyInt_As_int(tmp);
Py_DECREF(tmp);
return val;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to int");
return (int) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to int");
return (int) -1;
}
/* CIntFromPy */
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
const long neg_one = (long) -1, const_zero = (long) 0;
const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x))) {
if (sizeof(long) < sizeof(long)) {
__PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
} else {
long val = PyInt_AS_LONG(x);
if (is_unsigned && unlikely(val < 0)) {
goto raise_neg_overflow;
}
return (long) val;
}
} else
#endif
if (likely(PyLong_Check(x))) {
if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (long) 0;
case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
case 2:
if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
}
}
break;
case 3:
if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
}
}
break;
case 4:
if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
}
}
break;
}
#endif
#if CYTHON_COMPILING_IN_CPYTHON
if (unlikely(Py_SIZE(x) < 0)) {
goto raise_neg_overflow;
}
#else
{
int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
if (unlikely(result < 0))
return (long) -1;
if (unlikely(result == 1))
goto raise_neg_overflow;
}
#endif
if (sizeof(long) <= sizeof(unsigned long)) {
__PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
}
} else {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)x)->ob_digit;
switch (Py_SIZE(x)) {
case 0: return (long) 0;
case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0])
case -2:
if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
case 2:
if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
case -3:
if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
case 3:
if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
case -4:
if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
case 4:
if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
__PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
} else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
}
}
break;
}
#endif
if (sizeof(long) <= sizeof(long)) {
__PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
} else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
__PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
}
}
{
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
PyErr_SetString(PyExc_RuntimeError,
"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
long val;
PyObject *v = __Pyx_PyNumber_IntOrLong(x);
#if PY_MAJOR_VERSION < 3
if (likely(v) && !PyLong_Check(v)) {
PyObject *tmp = v;
v = PyNumber_Long(tmp);
Py_DECREF(tmp);
}
#endif
if (likely(v)) {
int one = 1; int is_little = (int)*(unsigned char *)&one;
unsigned char *bytes = (unsigned char *)&val;
int ret = _PyLong_AsByteArray((PyLongObject *)v,
bytes, sizeof(val),
is_little, !is_unsigned);
Py_DECREF(v);
if (likely(!ret))
return val;
}
#endif
return (long) -1;
}
} else {
long val;
PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
if (!tmp) return (long) -1;
val = __Pyx_PyInt_As_long(tmp);
Py_DECREF(tmp);
return val;
}
raise_overflow:
PyErr_SetString(PyExc_OverflowError,
"value too large to convert to long");
return (long) -1;
raise_neg_overflow:
PyErr_SetString(PyExc_OverflowError,
"can't convert negative value to long");
return (long) -1;
}
/* FastTypeChecks */
#if CYTHON_COMPILING_IN_CPYTHON
static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
while (a) {
a = a->tp_base;
if (a == b)
return 1;
}
return b == &PyBaseObject_Type;
}
static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
PyObject *mro;
if (a == b) return 1;
mro = a->tp_mro;
if (likely(mro)) {
Py_ssize_t i, n;
n = PyTuple_GET_SIZE(mro);
for (i = 0; i < n; i++) {
if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
return 1;
}
return 0;
}
return __Pyx_InBases(a, b);
}
#if PY_MAJOR_VERSION == 2
static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
PyObject *exception, *value, *tb;
int res;
__Pyx_PyThreadState_declare
__Pyx_PyThreadState_assign
__Pyx_ErrFetch(&exception, &value, &tb);
res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
if (unlikely(res == -1)) {
PyErr_WriteUnraisable(err);
res = 0;
}
if (!res) {
res = PyObject_IsSubclass(err, exc_type2);
if (unlikely(res == -1)) {
PyErr_WriteUnraisable(err);
res = 0;
}
}
__Pyx_ErrRestore(exception, value, tb);
return res;
}
#else
static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
if (!res) {
res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
}
return res;
}
#endif
static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
Py_ssize_t i, n;
assert(PyExceptionClass_Check(exc_type));
n = PyTuple_GET_SIZE(tuple);
#if PY_MAJOR_VERSION >= 3
for (i=0; i<n; i++) {
if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
}
#endif
for (i=0; i<n; i++) {
PyObject *t = PyTuple_GET_ITEM(tuple, i);
#if PY_MAJOR_VERSION < 3
if (likely(exc_type == t)) return 1;
#endif
if (likely(PyExceptionClass_Check(t))) {
if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
} else {
}
}
return 0;
}
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
if (likely(err == exc_type)) return 1;
if (likely(PyExceptionClass_Check(err))) {
if (likely(PyExceptionClass_Check(exc_type))) {
return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
} else if (likely(PyTuple_Check(exc_type))) {
return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
} else {
}
}
return PyErr_GivenExceptionMatches(err, exc_type);
}
static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
assert(PyExceptionClass_Check(exc_type1));
assert(PyExceptionClass_Check(exc_type2));
if (likely(err == exc_type1 || err == exc_type2)) return 1;
if (likely(PyExceptionClass_Check(err))) {
return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
}
return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
}
#endif
/* CheckBinaryVersion */
static int __Pyx_check_binary_version(void) {
char ctversion[4], rtversion[4];
PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
char message[200];
PyOS_snprintf(message, sizeof(message),
"compiletime version %s of module '%.100s' "
"does not match runtime version %s",
ctversion, __Pyx_MODULE_NAME, rtversion);
return PyErr_WarnEx(NULL, message, 1);
}
return 0;
}
/* ModuleImport */
#ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
PyObject *py_name = 0;
PyObject *py_module = 0;
py_name = __Pyx_PyIdentifier_FromString(name);
if (!py_name)
goto bad;
py_module = PyImport_Import(py_name);
Py_DECREF(py_name);
return py_module;
bad:
Py_XDECREF(py_name);
return 0;
}
#endif
/* TypeImport */
#ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
size_t size, int strict)
{
PyObject *py_module = 0;
PyObject *result = 0;
PyObject *py_name = 0;
char warning[200];
Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
PyObject *py_basicsize;
#endif
py_module = __Pyx_ImportModule(module_name);
if (!py_module)
goto bad;
py_name = __Pyx_PyIdentifier_FromString(class_name);
if (!py_name)
goto bad;
result = PyObject_GetAttr(py_module, py_name);
Py_DECREF(py_name);
py_name = 0;
Py_DECREF(py_module);
py_module = 0;
if (!result)
goto bad;
if (!PyType_Check(result)) {
PyErr_Format(PyExc_TypeError,
"%.200s.%.200s is not a type object",
module_name, class_name);
goto bad;
}
#ifndef Py_LIMITED_API
basicsize = ((PyTypeObject *)result)->tp_basicsize;
#else
py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
if (!py_basicsize)
goto bad;
basicsize = PyLong_AsSsize_t(py_basicsize);
Py_DECREF(py_basicsize);
py_basicsize = 0;
if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
goto bad;
#endif
if (!strict && (size_t)basicsize > size) {
PyOS_snprintf(warning, sizeof(warning),
"%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd",
module_name, class_name, basicsize, size);
if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
}
else if ((size_t)basicsize != size) {
PyErr_Format(PyExc_ValueError,
"%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd",
module_name, class_name, basicsize, size);
goto bad;
}
return (PyTypeObject *)result;
bad:
Py_XDECREF(py_module);
Py_XDECREF(result);
return NULL;
}
#endif
/* InitStrings */
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
while (t->p) {
#if PY_MAJOR_VERSION < 3
if (t->is_unicode) {
*t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
} else if (t->intern) {
*t->p = PyString_InternFromString(t->s);
} else {
*t->p = PyString_FromStringAndSize(t->s, t->n - 1);
}
#else
if (t->is_unicode | t->is_str) {
if (t->intern) {
*t->p = PyUnicode_InternFromString(t->s);
} else if (t->encoding) {
*t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
} else {
*t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
}
} else {
*t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
}
#endif
if (!*t->p)
return -1;
if (PyObject_Hash(*t->p) == -1)
return -1;
++t;
}
return 0;
}
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
}
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
Py_ssize_t ignore;
return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
#if !CYTHON_PEP393_ENABLED
static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
char* defenc_c;
PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
if (!defenc) return NULL;
defenc_c = PyBytes_AS_STRING(defenc);
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
{
char* end = defenc_c + PyBytes_GET_SIZE(defenc);
char* c;
for (c = defenc_c; c < end; c++) {
if ((unsigned char) (*c) >= 128) {
PyUnicode_AsASCIIString(o);
return NULL;
}
}
}
#endif
*length = PyBytes_GET_SIZE(defenc);
return defenc_c;
}
#else
static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
if (likely(PyUnicode_IS_ASCII(o))) {
*length = PyUnicode_GET_LENGTH(o);
return PyUnicode_AsUTF8(o);
} else {
PyUnicode_AsASCIIString(o);
return NULL;
}
#else
return PyUnicode_AsUTF8AndSize(o, length);
#endif
}
#endif
#endif
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
if (
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
__Pyx_sys_getdefaultencoding_not_ascii &&
#endif
PyUnicode_Check(o)) {
return __Pyx_PyUnicode_AsStringAndSize(o, length);
} else
#endif
#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
if (PyByteArray_Check(o)) {
*length = PyByteArray_GET_SIZE(o);
return PyByteArray_AS_STRING(o);
} else
#endif
{
char* result;
int r = PyBytes_AsStringAndSize(o, &result, length);
if (unlikely(r < 0)) {
return NULL;
} else {
return result;
}
}
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
int is_true = x == Py_True;
if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
else return PyObject_IsTrue(x);
}
static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
#if PY_MAJOR_VERSION >= 3
if (PyLong_Check(result)) {
if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
"__int__ returned non-int (type %.200s). "
"The ability to return an instance of a strict subclass of int "
"is deprecated, and may be removed in a future version of Python.",
Py_TYPE(result)->tp_name)) {
Py_DECREF(result);
return NULL;
}
return result;
}
#endif
PyErr_Format(PyExc_TypeError,
"__%.4s__ returned non-%.4s (type %.200s)",
type_name, type_name, Py_TYPE(result)->tp_name);
Py_DECREF(result);
return NULL;
}
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
#if CYTHON_USE_TYPE_SLOTS
PyNumberMethods *m;
#endif
const char *name = NULL;
PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_Check(x) || PyLong_Check(x)))
#else
if (likely(PyLong_Check(x)))
#endif
return __Pyx_NewRef(x);
#if CYTHON_USE_TYPE_SLOTS
m = Py_TYPE(x)->tp_as_number;
#if PY_MAJOR_VERSION < 3
if (m && m->nb_int) {
name = "int";
res = m->nb_int(x);
}
else if (m && m->nb_long) {
name = "long";
res = m->nb_long(x);
}
#else
if (likely(m && m->nb_int)) {
name = "int";
res = m->nb_int(x);
}
#endif
#else
if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
res = PyNumber_Int(x);
}
#endif
if (likely(res)) {
#if PY_MAJOR_VERSION < 3
if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
#else
if (unlikely(!PyLong_CheckExact(res))) {
#endif
return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
}
}
else if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_TypeError,
"an integer is required");
}
return res;
}
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
Py_ssize_t ival;
PyObject *x;
#if PY_MAJOR_VERSION < 3
if (likely(PyInt_CheckExact(b))) {
if (sizeof(Py_ssize_t) >= sizeof(long))
return PyInt_AS_LONG(b);
else
return PyInt_AsSsize_t(x);
}
#endif
if (likely(PyLong_CheckExact(b))) {
#if CYTHON_USE_PYLONG_INTERNALS
const digit* digits = ((PyLongObject*)b)->ob_digit;
const Py_ssize_t size = Py_SIZE(b);
if (likely(__Pyx_sst_abs(size) <= 1)) {
ival = likely(size) ? digits[0] : 0;
if (size == -1) ival = -ival;
return ival;
} else {
switch (size) {
case 2:
if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
case -2:
if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
case 3:
if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
case -3:
if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
case 4:
if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
case -4:
if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
}
break;
}
}
#endif
return PyLong_AsSsize_t(b);
}
x = PyNumber_Index(b);
if (!x) return -1;
ival = PyInt_AsSsize_t(x);
Py_DECREF(x);
return ival;
}
static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
}
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
return PyInt_FromSize_t(ival);
}
#endif /* Py_PYTHON_H */
| insightface/detection/retinaface/rcnn/pycocotools/_mask.c/0 | {
"file_path": "insightface/detection/retinaface/rcnn/pycocotools/_mask.c",
"repo_id": "insightface",
"token_count": 385752
} | 102 |
import mxnet as mx
import numpy as np
from rcnn.config import config
from rcnn.PY_OP import rpn_fpn_ohem3
FPN = False
USE_DCN = False
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", bias_wd_mult=0.0, dcn=False):
weight = mx.symbol.Variable(name="{}_weight".format(name),
init=mx.init.Normal(0.01),
attr={'__lr_mult__': '1.0'})
bias = mx.symbol.Variable(name="{}_bias".format(name),
init=mx.init.Constant(0.0),
attr={
'__lr_mult__': '2.0',
'__wd_mult__': str(bias_wd_mult)
})
if not dcn:
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}".format(name), weight = weight, bias=bias)
else:
assert kernel[0] == 3 and kernel[1] == 3
num_group = 1
f = num_group * 18
offset_weight = mx.symbol.Variable(
name="{}_offset_weight".format(name),
init=mx.init.Constant(0.0),
attr={'__lr_mult__': '1.0'})
offset_bias = mx.symbol.Variable(name="{}_offset_bias".format(name),
init=mx.init.Constant(0.0),
attr={
'__lr_mult__': '2.0',
'__wd_mult__': str(bias_wd_mult)
})
conv_offset = mx.symbol.Convolution(name=name + '_offset',
data=from_layer,
weight=offset_weight,
bias=offset_bias,
num_filter=f,
pad=(1, 1),
kernel=(3, 3),
stride=(1, 1))
conv = mx.contrib.symbol.DeformableConvolution(
name=name,
data=from_layer,
offset=conv_offset,
weight=weight,
bias=bias,
num_filter=num_filter,
pad=(1, 1),
kernel=(3, 3),
num_deformable_group=num_group,
stride=(1, 1),
no_bias=False)
if len(act_type) > 0:
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
else:
relu = conv
return relu
def ssh_context_module(body, num_filters, name):
conv_dimred = conv_act_layer(body,
name + '_conv1',
num_filters,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu',
dcn=False)
conv5x5 = conv_act_layer(conv_dimred,
name + '_conv2',
num_filters,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='',
dcn=USE_DCN)
conv7x7_1 = conv_act_layer(conv_dimred,
name + '_conv3_1',
num_filters,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu',
dcn=False)
conv7x7 = conv_act_layer(conv7x7_1,
name + '_conv3_2',
num_filters,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='',
dcn=USE_DCN)
return (conv5x5, conv7x7)
def ssh_detection_module(body, num_filters, name):
conv3x3 = conv_act_layer(body,
name + '_conv1',
num_filters,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='',
dcn=USE_DCN)
conv5x5, conv7x7 = ssh_context_module(body, num_filters // 2,
name + '_context')
ret = mx.sym.concat(*[conv3x3, conv5x5, conv7x7],
dim=1,
name=name + '_concat')
ret = mx.symbol.Activation(data=ret,
act_type='relu',
name=name + '_concat_relu')
return ret
def conv_bn(input, filter, ksize, stride, padding, act_type='relu', name=''):
conv = mx.symbol.Convolution(data=input, kernel=(ksize,ksize), pad=(padding,padding), \
stride=(stride,stride), num_filter=filter, name=name+"_conv")
ret = mx.sym.BatchNorm(data=conv,
fix_gamma=False,
eps=2e-5,
momentum=0.9,
name=name + '_bn')
if act_type is not None:
ret = mx.symbol.Activation(data=ret, act_type=act_type, \
name="{}_{}".format(name, act_type))
return ret
def cpm(input, name):
# residual
branch1 = conv_bn(input,
1024,
1,
1,
0,
act_type=None,
name=name + "_branch1")
branch2a = conv_bn(input,
256,
1,
1,
0,
act_type='relu',
name=name + "_branch2a")
branch2b = conv_bn(branch2a,
256,
3,
1,
1,
act_type='relu',
name=name + "_branch2b")
branch2c = conv_bn(branch2b,
1024,
1,
1,
0,
act_type=None,
name=name + "_branch2c")
sum = branch1 + branch2c
rescomb = mx.symbol.Activation(data=sum,
act_type='relu',
name="%s_relu2" % (name))
ssh_out = ssh_detection_module(rescomb, 256, name=name + "_ssh")
return ssh_out
def get_feat_down(conv_feat):
#P5 = mx.symbol.Convolution(data=conv_feat[0], kernel=(1, 1), num_filter=256, name="P5_lateral")
P5 = conv_act_layer(conv_feat[0],
'P5_lateral',
256,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu')
# P5 2x upsampling + C4 = P4
P5_up = mx.symbol.UpSampling(P5,
scale=2,
sample_type='nearest',
workspace=512,
name='P5_upsampling',
num_args=1)
#P4_la = mx.symbol.Convolution(data=conv_feat[1], kernel=(1, 1), num_filter=256, name="P4_lateral")
P4_la = conv_act_layer(conv_feat[1],
'P4_lateral',
256,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu')
P5_clip = mx.symbol.Crop(*[P5_up, P4_la], name="P4_clip")
P4 = mx.sym.ElementWiseSum(*[P5_clip, P4_la], name="P4_sum")
#P4 = mx.symbol.Convolution(data=P4, kernel=(3, 3), pad=(1, 1), num_filter=256, name="P4_aggregate")
P4 = conv_act_layer(P4,
'P4_aggregate',
256,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
# P4 2x upsampling + C3 = P3
P4_up = mx.symbol.UpSampling(P4,
scale=2,
sample_type='nearest',
workspace=512,
name='P4_upsampling',
num_args=1)
#P3_la = mx.symbol.Convolution(data=conv_feat[2], kernel=(1, 1), num_filter=256, name="P3_lateral")
P3_la = conv_act_layer(conv_feat[2],
'P3_lateral',
256,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu')
P4_clip = mx.symbol.Crop(*[P4_up, P3_la], name="P3_clip")
P3 = mx.sym.ElementWiseSum(*[P4_clip, P3_la], name="P3_sum")
#P3 = mx.symbol.Convolution(data=P3, kernel=(3, 3), pad=(1, 1), num_filter=256, name="P3_aggregate")
P3 = conv_act_layer(P3,
'P3_aggregate',
256,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
return P3, P4, P5
def get_ssh_conv(data):
"""
shared convolutional layers
:param data: Symbol
:return: Symbol
"""
# group 1
#conv1_1 = mx.symbol.Convolution(
# data=data, kernel=(3, 3), pad=(1, 1), num_filter=64, workspace=2048, name="conv1_1")
#relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1")
relu1_1 = conv_act_layer(data,
'conv1_1',
64,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv1_2 = mx.symbol.Convolution(
# data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, workspace=2048, name="conv1_2")
#relu1_2 = mx.symbol.Activation(data=conv1_2, act_type="relu", name="relu1_2")
relu1_2 = conv_act_layer(relu1_1,
'conv1_2',
64,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
pool1 = mx.symbol.Pooling(data=relu1_2,
pool_type="max",
kernel=(2, 2),
stride=(2, 2),
name="pool1")
# group 2
#conv2_1 = mx.symbol.Convolution(
# data=pool1, kernel=(3, 3), pad=(1, 1), num_filter=128, workspace=2048, name="conv2_1")
#relu2_1 = mx.symbol.Activation(data=conv2_1, act_type="relu", name="relu2_1")
relu2_1 = conv_act_layer(pool1,
'conv2_1',
128,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv2_2 = mx.symbol.Convolution(
# data=relu2_1, kernel=(3, 3), pad=(1, 1), num_filter=128, workspace=2048, name="conv2_2")
#relu2_2 = mx.symbol.Activation(data=conv2_2, act_type="relu", name="relu2_2")
relu2_2 = conv_act_layer(relu2_1,
'conv2_2',
128,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
pool2 = mx.symbol.Pooling(data=relu2_2,
pool_type="max",
kernel=(2, 2),
stride=(2, 2),
name="pool2")
# group 3
#conv3_1 = mx.symbol.Convolution(
# data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=256, workspace=2048, name="conv3_1")
#relu3_1 = mx.symbol.Activation(data=conv3_1, act_type="relu", name="relu3_1")
relu3_1 = conv_act_layer(pool2,
'conv3_1',
256,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv3_2 = mx.symbol.Convolution(
# data=relu3_1, kernel=(3, 3), pad=(1, 1), num_filter=256, workspace=2048, name="conv3_2")
#relu3_2 = mx.symbol.Activation(data=conv3_2, act_type="relu", name="relu3_2")
relu3_2 = conv_act_layer(relu3_1,
'conv3_2',
256,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv3_3 = mx.symbol.Convolution(
# data=relu3_2, kernel=(3, 3), pad=(1, 1), num_filter=256, workspace=2048, name="conv3_3")
#relu3_3 = mx.symbol.Activation(data=conv3_3, act_type="relu", name="relu3_3")
relu3_3 = conv_act_layer(relu3_2,
'conv3_3',
256,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
pool3 = mx.symbol.Pooling(data=relu3_3,
pool_type="max",
kernel=(2, 2),
stride=(2, 2),
name="pool3")
# group 4
#conv4_1 = mx.symbol.Convolution(
# data=pool3, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv4_1")
#relu4_1 = mx.symbol.Activation(data=conv4_1, act_type="relu", name="relu4_1")
relu4_1 = conv_act_layer(pool3,
'conv4_1',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv4_2 = mx.symbol.Convolution(
# data=relu4_1, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv4_2")
#relu4_2 = mx.symbol.Activation(data=conv4_2, act_type="relu", name="relu4_2")
relu4_2 = conv_act_layer(relu4_1,
'conv4_2',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv4_3 = mx.symbol.Convolution(
# data=relu4_2, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv4_3")
#relu4_3 = mx.symbol.Activation(data=conv4_3, act_type="relu", name="relu4_3")
relu4_3 = conv_act_layer(relu4_2,
'conv4_3',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
pool4 = mx.symbol.Pooling(data=relu4_3,
pool_type="max",
kernel=(2, 2),
stride=(2, 2),
name="pool4")
# group 5
#conv5_1 = mx.symbol.Convolution(
# data=pool4, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv5_1")
#relu5_1 = mx.symbol.Activation(data=conv5_1, act_type="relu", name="relu5_1")
relu5_1 = conv_act_layer(pool4,
'conv5_1',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv5_2 = mx.symbol.Convolution(
# data=relu5_1, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv5_2")
#relu5_2 = mx.symbol.Activation(data=conv5_2, act_type="relu", name="relu5_2")
relu5_2 = conv_act_layer(relu5_1,
'conv5_2',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
#conv5_3 = mx.symbol.Convolution(
# data=relu5_2, kernel=(3, 3), pad=(1, 1), num_filter=512, workspace=2048, name="conv5_3")
#relu5_3 = mx.symbol.Activation(data=conv5_3, act_type="relu", name="relu5_3")
relu5_3 = conv_act_layer(relu5_2,
'conv5_3',
512,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu')
m3_pool = mx.sym.Pooling(data=relu5_3,
kernel=(2, 2),
stride=(2, 2),
pad=(0, 0),
pool_type='max')
if config.SSH_MODE <= 5:
#if FPN:
# relu4_3, relu5_3, m3_pool = get_feat_down([m3_pool, relu5_3, relu4_3])
F1 = 256
F2 = 128
if config.SSH_MODE == 1:
F2 = 256
_bwm = 1.0
conv4_128 = conv_act_layer(relu4_3,
'ssh_m1_red_conv',
F2,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
conv5_128 = conv_act_layer(relu5_3,
'ssh_m2_red_conv',
F2,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
conv5_128_up = mx.symbol.Deconvolution(data=conv5_128,
num_filter=F2,
kernel=(4, 4),
stride=(2, 2),
pad=(1, 1),
num_group=F2,
no_bias=True,
attr={
'__lr_mult__': '0.0',
'__wd_mult__': '0.0'
},
name='ssh_m2_red_upsampling')
#conv5_128_up = mx.symbol.UpSampling(conv5_128, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1)
conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up])
#conv5_128_up = mx.symbol.Crop(*[conv5_128_up, conv4_128])
conv_sum = conv4_128 + conv5_128_up
#conv_sum = conv_1x1
m1_conv = conv_act_layer(conv_sum,
'ssh_m1_conv',
F2,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
m1 = ssh_detection_module(m1_conv, F2, 'ssh_m1_det')
m2 = ssh_detection_module(relu5_3, F1, 'ssh_m2_det')
m3 = ssh_detection_module(m3_pool, F1, 'ssh_m3_det')
return {8: m1, 16: m2, 32: m3}
else:
F1 = 256
F2 = 256
_bwm = 1.0
conv4_128 = conv_act_layer(relu4_3,
'ssh_m1_red_conv',
F2,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
conv5_128 = conv_act_layer(relu5_3,
'ssh_m2_red_conv',
F2,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
conv5_128_up = mx.symbol.Deconvolution(data=conv5_128,
num_filter=F2,
kernel=(4, 4),
stride=(2, 2),
pad=(1, 1),
num_group=F2,
no_bias=True,
attr={
'__lr_mult__': '0.0',
'__wd_mult__': '0.0'
},
name='ssh_m2_red_upsampling')
#conv5_128_up = mx.symbol.UpSampling(conv5_128, scale=2, sample_type='nearest', workspace=512, name='ssh_m2_red_up', num_args=1)
conv4_128 = mx.symbol.Crop(*[conv4_128, conv5_128_up])
#conv5_128_up = mx.symbol.Crop(*[conv5_128_up, conv4_128])
conv_sum = conv4_128 + conv5_128_up
m1_conv = conv_act_layer(conv_sum,
'ssh_m1_conv',
F2,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
act_type='relu',
bias_wd_mult=_bwm)
m1 = cpm(m1_conv, 'ssh_m1_det')
m2 = cpm(relu5_3, 'ssh_m2_det')
m3 = cpm(m3_pool, 'ssh_m3_det')
return {8: m1, 16: m2, 32: m3}
def get_out(conv_fpn_feat, prefix, stride, landmark=False, lr_mult=1.0):
A = config.NUM_ANCHORS
ret_group = []
num_anchors = config.RPN_ANCHOR_CFG[str(stride)]['NUM_ANCHORS']
label = mx.symbol.Variable(name='%s_label_stride%d' % (prefix, stride))
bbox_target = mx.symbol.Variable(name='%s_bbox_target_stride%d' %
(prefix, stride))
bbox_weight = mx.symbol.Variable(name='%s_bbox_weight_stride%d' %
(prefix, stride))
if landmark:
landmark_target = mx.symbol.Variable(
name='%s_landmark_target_stride%d' % (prefix, stride))
landmark_weight = mx.symbol.Variable(
name='%s_landmark_weight_stride%d' % (prefix, stride))
rpn_relu = conv_fpn_feat[stride]
maxout_stat = 0
if config.USE_MAXOUT >= 1 and stride == config.RPN_FEAT_STRIDE[-1]:
maxout_stat = 1
if config.USE_MAXOUT >= 2 and stride != config.RPN_FEAT_STRIDE[-1]:
maxout_stat = 2
if maxout_stat == 0:
rpn_cls_score = conv_act_layer(rpn_relu,
'%s_rpn_cls_score_stride%d' %
(prefix, stride),
2 * num_anchors,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
elif maxout_stat == 1:
cls_list = []
for a in range(num_anchors):
rpn_cls_score_bg = conv_act_layer(
rpn_relu,
'%s_rpn_cls_score_stride%d_anchor%d_bg' % (prefix, stride, a),
3,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
rpn_cls_score_bg = mx.sym.max(rpn_cls_score_bg,
axis=1,
keepdims=True)
cls_list.append(rpn_cls_score_bg)
rpn_cls_score_fg = conv_act_layer(
rpn_relu,
'%s_rpn_cls_score_stride%d_anchor%d_fg' % (prefix, stride, a),
1,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
cls_list.append(rpn_cls_score_fg)
rpn_cls_score = mx.sym.concat(*cls_list,
dim=1,
name='%s_rpn_cls_score_stride%d' %
(prefix, stride))
else:
cls_list = []
for a in range(num_anchors):
rpn_cls_score_bg = conv_act_layer(
rpn_relu,
'%s_rpn_cls_score_stride%d_anchor%d_bg' % (prefix, stride, a),
1,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
cls_list.append(rpn_cls_score_bg)
rpn_cls_score_fg = conv_act_layer(
rpn_relu,
'%s_rpn_cls_score_stride%d_anchor%d_fg' % (prefix, stride, a),
3,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
rpn_cls_score_fg = mx.sym.max(rpn_cls_score_fg,
axis=1,
keepdims=True)
cls_list.append(rpn_cls_score_fg)
rpn_cls_score = mx.sym.concat(*cls_list,
dim=1,
name='%s_rpn_cls_score_stride%d' %
(prefix, stride))
rpn_bbox_pred = conv_act_layer(rpn_relu,
'%s_rpn_bbox_pred_stride%d' %
(prefix, stride),
4 * num_anchors,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
# prepare rpn data
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score,
shape=(0, 2, -1),
name="%s_rpn_cls_score_reshape_stride%s" % (prefix, stride))
rpn_bbox_pred_reshape = mx.symbol.Reshape(
data=rpn_bbox_pred,
shape=(0, 0, -1),
name="%s_rpn_bbox_pred_reshape_stride%s" % (prefix, stride))
if landmark:
rpn_landmark_pred = conv_act_layer(rpn_relu,
'%s_rpn_landmark_pred_stride%d' %
(prefix, stride),
10 * num_anchors,
kernel=(1, 1),
pad=(0, 0),
stride=(1, 1),
act_type='')
rpn_landmark_pred_reshape = mx.symbol.Reshape(
data=rpn_landmark_pred,
shape=(0, 0, -1),
name="%s_rpn_landmark_pred_reshape_stride%s" % (prefix, stride))
if config.TRAIN.RPN_ENABLE_OHEM >= 2:
label, anchor_weight = mx.sym.Custom(op_type='rpn_fpn_ohem3',
stride=int(stride),
network=config.network,
dataset=config.dataset,
prefix=prefix,
cls_score=rpn_cls_score_reshape,
labels=label)
_bbox_weight = mx.sym.tile(anchor_weight, (1, 1, 4))
_bbox_weight = _bbox_weight.reshape((0, -1, A * 4)).transpose(
(0, 2, 1))
bbox_weight = mx.sym.elemwise_mul(bbox_weight,
_bbox_weight,
name='%s_bbox_weight_mul_stride%s' %
(prefix, stride))
if landmark:
_landmark_weight = mx.sym.tile(anchor_weight, (1, 1, 10))
_landmark_weight = _landmark_weight.reshape(
(0, -1, A * 10)).transpose((0, 2, 1))
landmark_weight = mx.sym.elemwise_mul(
landmark_weight,
_landmark_weight,
name='%s_landmark_weight_mul_stride%s' % (prefix, stride))
#if not config.FACE_LANDMARK:
# label, bbox_weight = mx.sym.Custom(op_type='rpn_fpn_ohem', stride=int(stride), cls_score=rpn_cls_score_reshape, bbox_weight = bbox_weight , labels = label)
#else:
# label, bbox_weight, landmark_weight = mx.sym.Custom(op_type='rpn_fpn_ohem2', stride=int(stride), cls_score=rpn_cls_score_reshape, bbox_weight = bbox_weight, landmark_weight=landmark_weight, labels = label)
#cls loss
rpn_cls_prob = mx.symbol.SoftmaxOutput(data=rpn_cls_score_reshape,
label=label,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=lr_mult,
name='%s_rpn_cls_prob_stride%d' %
(prefix, stride))
ret_group.append(rpn_cls_prob)
ret_group.append(mx.sym.BlockGrad(label))
#bbox loss
bbox_diff = rpn_bbox_pred_reshape - bbox_target
bbox_diff = bbox_diff * bbox_weight
rpn_bbox_loss_ = mx.symbol.smooth_l1(name='%s_rpn_bbox_loss_stride%d_' %
(prefix, stride),
scalar=3.0,
data=bbox_diff)
rpn_bbox_loss = mx.sym.MakeLoss(
name='%s_rpn_bbox_loss_stride%d' % (prefix, stride),
data=rpn_bbox_loss_,
grad_scale=1.0 * lr_mult / (config.TRAIN.RPN_BATCH_SIZE))
ret_group.append(rpn_bbox_loss)
ret_group.append(mx.sym.BlockGrad(bbox_weight))
#landmark loss
if landmark:
landmark_diff = rpn_landmark_pred_reshape - landmark_target
landmark_diff = landmark_diff * landmark_weight
rpn_landmark_loss_ = mx.symbol.smooth_l1(
name='%s_rpn_landmark_loss_stride%d_' % (prefix, stride),
scalar=3.0,
data=landmark_diff)
rpn_landmark_loss = mx.sym.MakeLoss(
name='%s_rpn_landmark_loss_stride%d' % (prefix, stride),
data=rpn_landmark_loss_,
grad_scale=0.5 * lr_mult / (config.TRAIN.RPN_BATCH_SIZE))
ret_group.append(rpn_landmark_loss)
ret_group.append(mx.sym.BlockGrad(landmark_weight))
return ret_group
def get_ssh_train():
"""
Region Proposal Network with VGG
:return: Symbol
"""
data = mx.symbol.Variable(name="data")
# shared convolutional layers
conv_fpn_feat = get_ssh_conv(data)
ret_group = []
for stride in config.RPN_FEAT_STRIDE:
ret = get_out(conv_fpn_feat,
'face',
stride,
config.FACE_LANDMARK,
lr_mult=1.0)
ret_group += ret
if config.HEAD_BOX:
ret = get_out(conv_fpn_feat, 'head', stride, False, lr_mult=1.0)
ret_group += ret
return mx.sym.Group(ret_group)
| insightface/detection/retinaface/rcnn/symbol/symbol_ssh.py/0 | {
"file_path": "insightface/detection/retinaface/rcnn/symbol/symbol_ssh.py",
"repo_id": "insightface",
"token_count": 21827
} | 103 |
# RetinaFace Anti Cov Face Detector
## Introduction
RetinaFace-Anti-Cov is a customized one stage face detector to help people protect themselves from CovID-19.
## Testing
Please check ``test.py`` for testing.
Make sure that you set ``network='net3l'`` instead of ``'net3'`` for 'mnet_cov2' model, otherwise you will get incorrect landmarks.
## Pretrained Models
~~MobileNet0.25([baidu cloud](https://pan.baidu.com/s/1p8n4R2W-9WmmBWxYQEFcWg),code: fmfm)~~
Better: MobileNet0.25 ([baidu cloud](https://pan.baidu.com/s/16ihzPxjTObdbv0D6P6LmEQ), code: j3b6, [dropbox](https://www.dropbox.com/s/6rhhxsbh2qik65k/cov2.zip?dl=0))
## References
```
@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}
}
```
| insightface/detection/retinaface_anticov/README.md/0 | {
"file_path": "insightface/detection/retinaface_anticov/README.md",
"repo_id": "insightface",
"token_count": 383
} | 104 |
# model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False)
test_cfg = dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)
| insightface/detection/scrfd/configs/_base_/models/retinanet_r50_fpn.py/0 | {
"file_path": "insightface/detection/scrfd/configs/_base_/models/retinanet_r50_fpn.py",
"repo_id": "insightface",
"token_count": 948
} | 105 |
import random
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer)
from mmcv.utils import build_from_cfg
from mmdet.core import DistEvalHook, EvalHook
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
from mmdet.utils import get_root_logger
def set_random_seed(seed, deterministic=False):
"""Set random seed.
Args:
seed (int): Seed to be used.
deterministic (bool): Whether to set the deterministic option for
CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
to True and `torch.backends.cudnn.benchmark` to False.
Default: False.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if deterministic:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
if 'imgs_per_gpu' in cfg.data:
logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
'Please use "samples_per_gpu" instead')
if 'samples_per_gpu' in cfg.data:
logger.warning(
f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
f'={cfg.data.imgs_per_gpu} is used in this experiments')
else:
logger.warning(
'Automatically set "samples_per_gpu"="imgs_per_gpu"='
f'{cfg.data.imgs_per_gpu} in this experiments')
cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed) for ds in dataset
]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
model = MMDataParallel(
model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = EpochBasedRunner(
model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
# an ugly workaround to make .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=distributed)
elif distributed and 'type' not in cfg.optimizer_config:
optimizer_config = OptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = cfg.optimizer_config
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
if distributed:
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
# Support batch_size > 1 in validation
val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
if val_samples_per_gpu > 1:
# Replace 'ImageToTensor' to 'DefaultFormatBundle'
cfg.data.val.pipeline = replace_ImageToTensor(
cfg.data.val.pipeline)
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=val_samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
# user-defined hooks
if cfg.get('custom_hooks', None):
custom_hooks = cfg.custom_hooks
assert isinstance(custom_hooks, list), \
f'custom_hooks expect list type, but got {type(custom_hooks)}'
for hook_cfg in cfg.custom_hooks:
assert isinstance(hook_cfg, dict), \
'Each item in custom_hooks expects dict type, but got ' \
f'{type(hook_cfg)}'
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
| insightface/detection/scrfd/mmdet/apis/train.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/apis/train.py",
"repo_id": "insightface",
"token_count": 2739
} | 106 |
import torch
from ..builder import BBOX_ASSIGNERS
from ..iou_calculators import build_iou_calculator
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class MaxIoUAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, or a semi-positive integer
indicating the ground truth index.
- -1: negative sample, no assigned gt
- semi-positive integer: positive sample, index (0-based) of assigned gt
Args:
pos_iou_thr (float): IoU threshold for positive bboxes.
neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
min_pos_iou (float): Minimum iou for a bbox to be considered as a
positive bbox. Positive samples can have smaller IoU than
pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
gt_max_assign_all (bool): Whether to assign all bboxes with the same
highest overlap with some gt to that gt.
ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
`gt_bboxes_ignore` is specified). Negative values mean not
ignoring any bboxes.
ignore_wrt_candidates (bool): Whether to compute the iof between
`bboxes` and `gt_bboxes_ignore`, or the contrary.
match_low_quality (bool): Whether to allow low quality matches. This is
usually allowed for RPN and single stage detectors, but not allowed
in the second stage. Details are demonstrated in Step 4.
gpu_assign_thr (int): The upper bound of the number of GT for GPU
assign. When the number of gt is above this threshold, will assign
on CPU device. Negative values mean not assign on CPU.
"""
def __init__(self,
pos_iou_thr,
neg_iou_thr,
min_pos_iou=.0,
gt_max_assign_all=True,
ignore_iof_thr=-1,
ignore_wrt_candidates=True,
match_low_quality=True,
gpu_assign_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D')):
self.pos_iou_thr = pos_iou_thr
self.neg_iou_thr = neg_iou_thr
self.min_pos_iou = min_pos_iou
self.gt_max_assign_all = gt_max_assign_all
self.ignore_iof_thr = ignore_iof_thr
self.ignore_wrt_candidates = ignore_wrt_candidates
self.gpu_assign_thr = gpu_assign_thr
self.match_low_quality = match_low_quality
self.iou_calculator = build_iou_calculator(iou_calculator)
def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
"""Assign gt to bboxes.
This method assign a gt bbox to every bbox (proposal/anchor), each bbox
will be assigned with -1, or a semi-positive number. -1 means negative
sample, semi-positive number is the index (0-based) of assigned gt.
The assignment is done in following steps, the order matters.
1. assign every bbox to the background
2. assign proposals whose iou with all gts < neg_iou_thr to 0
3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
assign it to that bbox
4. for each gt bbox, assign its nearest proposals (may be more than
one) to itself
Args:
bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
labelled as `ignored`, e.g., crowd boxes in COCO.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
Example:
>>> self = MaxIoUAssigner(0.5, 0.5)
>>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]])
>>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]])
>>> assign_result = self.assign(bboxes, gt_bboxes)
>>> expected_gt_inds = torch.LongTensor([1, 0])
>>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
"""
assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
gt_bboxes.shape[0] > self.gpu_assign_thr) else False
# compute overlap and assign gt on CPU when number of GT is large
if assign_on_cpu:
device = bboxes.device
bboxes = bboxes.cpu()
gt_bboxes = gt_bboxes.cpu()
if gt_bboxes_ignore is not None:
gt_bboxes_ignore = gt_bboxes_ignore.cpu()
if gt_labels is not None:
gt_labels = gt_labels.cpu()
overlaps = self.iou_calculator(gt_bboxes, bboxes)
if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
if self.ignore_wrt_candidates:
ignore_overlaps = self.iou_calculator(
bboxes, gt_bboxes_ignore, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
ignore_overlaps = self.iou_calculator(
gt_bboxes_ignore, bboxes, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
if assign_on_cpu:
assign_result.gt_inds = assign_result.gt_inds.to(device)
assign_result.max_overlaps = assign_result.max_overlaps.to(device)
if assign_result.labels is not None:
assign_result.labels = assign_result.labels.to(device)
return assign_result
def assign_wrt_overlaps(self, overlaps, gt_labels=None):
"""Assign w.r.t. the overlaps of bboxes with gts.
Args:
overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
shape(k, n).
gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
# 1. assign -1 by default
assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-1,
dtype=torch.long)
if num_gts == 0 or num_bboxes == 0:
# No ground truth or boxes, return empty assignment
max_overlaps = overlaps.new_zeros((num_bboxes, ))
if num_gts == 0:
# No truth, assign everything to background
assigned_gt_inds[:] = 0
if gt_labels is None:
assigned_labels = None
else:
assigned_labels = overlaps.new_full((num_bboxes, ),
-1,
dtype=torch.long)
return AssignResult(
num_gts,
assigned_gt_inds,
max_overlaps,
labels=assigned_labels)
# for each anchor, which gt best overlaps with it
# for each anchor, the max iou of all gts
max_overlaps, argmax_overlaps = overlaps.max(dim=0)
# for each gt, which anchor best overlaps with it
# for each gt, the max iou of all proposals
gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
# 2. assign negative: below
# the negative inds are set to be 0
if isinstance(self.neg_iou_thr, float):
assigned_gt_inds[(max_overlaps >= 0)
& (max_overlaps < self.neg_iou_thr)] = 0
elif isinstance(self.neg_iou_thr, tuple):
assert len(self.neg_iou_thr) == 2
assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0])
& (max_overlaps < self.neg_iou_thr[1])] = 0
# 3. assign positive: above positive IoU threshold
pos_inds = max_overlaps >= self.pos_iou_thr
assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
if self.match_low_quality:
# Low-quality matching will overwirte the assigned_gt_inds assigned
# in Step 3. Thus, the assigned gt might not be the best one for
# prediction.
# For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2,
# bbox 1 will be assigned as the best target for bbox A in step 3.
# However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's
# assigned_gt_inds will be overwritten to be bbox B.
# This might be the reason that it is not used in ROI Heads.
for i in range(num_gts):
if gt_max_overlaps[i] >= self.min_pos_iou:
if self.gt_max_assign_all:
max_iou_inds = overlaps[i, :] == gt_max_overlaps[i]
assigned_gt_inds[max_iou_inds] = i + 1
else:
assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
if gt_labels is not None:
assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
pos_inds = torch.nonzero(
assigned_gt_inds > 0, as_tuple=False).squeeze()
if pos_inds.numel() > 0:
assigned_labels[pos_inds] = gt_labels[
assigned_gt_inds[pos_inds] - 1]
else:
assigned_labels = None
return AssignResult(
num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
| insightface/detection/scrfd/mmdet/core/bbox/assigners/max_iou_assigner.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/core/bbox/assigners/max_iou_assigner.py",
"repo_id": "insightface",
"token_count": 4863
} | 107 |
from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
"""Base class of samplers."""
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
self.num = num
self.pos_fraction = pos_fraction
self.neg_pos_ub = neg_pos_ub
self.add_gt_as_proposals = add_gt_as_proposals
self.pos_sampler = self
self.neg_sampler = self
@abstractmethod
def _sample_pos(self, assign_result, num_expected, **kwargs):
"""Sample positive samples."""
pass
@abstractmethod
def _sample_neg(self, assign_result, num_expected, **kwargs):
"""Sample negative samples."""
pass
def sample(self,
assign_result,
bboxes,
gt_bboxes,
gt_labels=None,
**kwargs):
"""Sample positive and negative bboxes.
This is a simple implementation of bbox sampling given candidates,
assigning results and ground truth bboxes.
Args:
assign_result (:obj:`AssignResult`): Bbox assigning results.
bboxes (Tensor): Boxes to be sampled from.
gt_bboxes (Tensor): Ground truth bboxes.
gt_labels (Tensor, optional): Class labels of ground truth bboxes.
Returns:
:obj:`SamplingResult`: Sampling result.
Example:
>>> from mmdet.core.bbox import RandomSampler
>>> from mmdet.core.bbox import AssignResult
>>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes
>>> rng = ensure_rng(None)
>>> assign_result = AssignResult.random(rng=rng)
>>> bboxes = random_boxes(assign_result.num_preds, rng=rng)
>>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng)
>>> gt_labels = None
>>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1,
>>> add_gt_as_proposals=False)
>>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels)
"""
if len(bboxes.shape) < 2:
bboxes = bboxes[None, :]
bboxes = bboxes[:, :4]
gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
if self.add_gt_as_proposals and len(gt_bboxes) > 0:
if gt_labels is None:
raise ValueError(
'gt_labels must be given when add_gt_as_proposals is True')
bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
assign_result.add_gt_(gt_labels)
gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
gt_flags = torch.cat([gt_ones, gt_flags])
num_expected_pos = int(self.num * self.pos_fraction)
pos_inds = self.pos_sampler._sample_pos(
assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
# We found that sampled indices have duplicated items occasionally.
# (may be a bug of PyTorch)
pos_inds = pos_inds.unique()
num_sampled_pos = pos_inds.numel()
num_expected_neg = self.num - num_sampled_pos
if self.neg_pos_ub >= 0:
_pos = max(1, num_sampled_pos)
neg_upper_bound = int(self.neg_pos_ub * _pos)
if num_expected_neg > neg_upper_bound:
num_expected_neg = neg_upper_bound
neg_inds = self.neg_sampler._sample_neg(
assign_result, num_expected_neg, bboxes=bboxes, **kwargs)
neg_inds = neg_inds.unique()
sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
assign_result, gt_flags)
return sampling_result
| insightface/detection/scrfd/mmdet/core/bbox/samplers/base_sampler.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/core/bbox/samplers/base_sampler.py",
"repo_id": "insightface",
"token_count": 1926
} | 108 |
"""
WiderFace evaluation code
author: wondervictor
mail: tianhengcheng@gmail.com
copyright@wondervictor
"""
from __future__ import absolute_import
import os
import tqdm
import pickle
import datetime
import argparse
import numpy as np
from scipy.io import loadmat
#from facedet.evaluation.box_utils import jaccard
#from facedet.evaluation.bbox import bbox_overlaps
#import torch
#from mmdet.core.bbox import bbox_overlaps
#def intersect(box_a, box_b):
# A = box_a.size(0)
# B = box_b.size(0)
# max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
# box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
# min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
# box_b[:, :2].unsqueeze(0).expand(A, B, 2))
# inter = torch.clamp((max_xy - min_xy), min=0)
# return inter[:, :, 0] * inter[:, :, 1]
#
#def jaccard(box_a, box_b):
# inter = intersect(box_a, box_b)
# #torch.cuda.empty_cache()
# if not inter.is_cuda:
# box_a_cpu = box_a.cpu()
# box_b_cpu = box_b.cpu()
# area_a_cpu = ((box_a_cpu[:, 2]-box_a_cpu[:, 0]) *
# (box_a_cpu[:, 3]-box_a_cpu[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
# area_b_cpu = ((box_b_cpu[:, 2]-box_b_cpu[:, 0]) *
# (box_b_cpu[:, 3]-box_b_cpu[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
# union_cpu = area_a_cpu + area_b_cpu - inter.cpu()
# return inter / union_cpu
# else:
# area_a = ((box_a[:, 2]-box_a[:, 0]) *
# (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
# area_b = ((box_b[:, 2]-box_b[:, 0]) *
# (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
# union = area_a + area_b - inter
#
# return inter / union # [A,B]
#
def bbox_overlaps(boxes, query_boxes):
n_ = boxes.shape[0]
k_ = query_boxes.shape[0]
overlaps = np.zeros((n_, k_), dtype=np.float)
for k in range(k_):
query_box_area = (query_boxes[k, 2] - query_boxes[k, 0] +
1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1)
for n in range(n_):
iw = min(boxes[n, 2], query_boxes[k, 2]) - max(
boxes[n, 0], query_boxes[k, 0]) + 1
if iw > 0:
ih = min(boxes[n, 3], query_boxes[k, 3]) - max(
boxes[n, 1], query_boxes[k, 1]) + 1
if ih > 0:
box_area = (boxes[n, 2] - boxes[n, 0] +
1) * (boxes[n, 3] - boxes[n, 1] + 1)
all_area = float(box_area + query_box_area - iw * ih)
overlaps[n, k] = iw * ih / all_area
return overlaps
def bbox_overlap(a, b):
x1 = np.maximum(a[:,0], b[0])
y1 = np.maximum(a[:,1], b[1])
x2 = np.minimum(a[:,2], b[2])
y2 = np.minimum(a[:,3], b[3])
w = x2-x1+1
h = y2-y1+1
inter = w*h
aarea = (a[:,2]-a[:,0]+1) * (a[:,3]-a[:,1]+1)
barea = (b[2]-b[0]+1) * (b[3]-b[1]+1)
o = inter / (aarea+barea-inter)
o[w<=0] = 0
o[h<=0] = 0
return o
def __bbox_overlap(a, b):
x1 = torch.max(a[:,0], b[0])
y1 = torch.max(a[:,1], b[1])
x2 = torch.min(a[:,2], b[2])
y2 = torch.min(a[:,3], b[3])
w = x2-x1+1
h = y2-y1+1
inter = w*h
aarea = (a[:,2]-a[:,0]+1) * (a[:,3]-a[:,1]+1)
barea = (b[2]-b[0]+1) * (b[3]-b[1]+1)
o = inter / (aarea+barea-inter)
o[w<=0] = 0
o[h<=0] = 0
return o
def np_around(array, num_decimals=0):
#return array
return np.around(array, decimals=num_decimals)
#def compute_iou(box_a, box_b):
# x0 = np.maximum(box_a[:,0], box_b[0])
# y0 = np.maximum(box_a[:,1], box_b[1])
# x1 = np.minimum(box_a[:,2], box_b[2])
# y1 = np.minimum(box_a[:,3], box_b[3])
# #print ('x0', x0[0], x1[0], y0[0], y1[0], box_a[0], box_b[:])
# #w = np.maximum(x1 - x0 + 1, 0)
# w = np_around(x1 - x0 + 1)
# #h = np.maximum(y1 - y0 + 1, 0)
# h = np_around(y1 - y0 + 1)
# inter = np_around(w * h)
# area_a = (box_a[:,2] - box_a[:,0] + 1) * (box_a[:,3] - box_a[:,1] + 1)
# area_a = np_around(area_a)
# area_b = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)
# area_b = np_around(area_b)
# iou = inter / (area_a + area_b - inter)
# iou[w <= 0] = 0
# iou[h <=0] = 0
# return iou
def np_round(val, decimals=4):
return val
#if isinstance(val, np.ndarray):
# val = np.around(val, decimals=decimals)
#return val
def get_gt_boxes(gt_dir):
""" gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)"""
gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat'))
hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat'))
medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat'))
easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat'))
facebox_list = gt_mat['face_bbx_list']
event_list = gt_mat['event_list']
file_list = gt_mat['file_list']
hard_gt_list = hard_mat['gt_list']
medium_gt_list = medium_mat['gt_list']
easy_gt_list = easy_mat['gt_list']
return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list
def get_gt_boxes_from_txt(gt_path, cache_dir):
cache_file = os.path.join(cache_dir, 'gt_cache.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as f:
boxes = pickle.load(f)
return boxes
f = open(gt_path, 'r')
state = 0
lines = f.readlines()
lines = list(map(lambda x: x.rstrip('\r\n'), lines))
boxes = {}
#print(len(lines))
f.close()
current_boxes = []
current_name = None
for line in lines:
if state == 0 and '--' in line:
state = 1
current_name = line
continue
if state == 1:
state = 2
continue
if state == 2 and '--' in line:
state = 1
boxes[current_name] = np.array(current_boxes).astype('float32')
current_name = line
current_boxes = []
continue
if state == 2:
box = [float(x) for x in line.split(' ')[:4]]
current_boxes.append(box)
continue
with open(cache_file, 'wb') as f:
pickle.dump(boxes, f)
return boxes
def read_pred_file(filepath):
with open(filepath, 'r') as f:
lines = f.readlines()
img_file = lines[0].rstrip('\n\r')
lines = lines[2:]
boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float')
return img_file.split('/')[-1], boxes
def get_preds(pred_dir):
events = os.listdir(pred_dir)
boxes = dict()
pbar = tqdm.tqdm(events)
for event in pbar:
pbar.set_description('Reading Predictions ')
event_dir = os.path.join(pred_dir, event)
event_images = os.listdir(event_dir)
current_event = dict()
for imgtxt in event_images:
imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))
current_event[imgname.rstrip('.jpg')] = _boxes
boxes[event] = current_event
return boxes
def norm_score(pred):
""" norm score
pred {key: [[x1,y1,x2,y2,s]]}
"""
max_score = -1
min_score = 2
for _, k in pred.items():
for _, v in k.items():
if len(v) == 0:
continue
_min = np.min(v[:, -1])
_max = np.max(v[:, -1])
max_score = max(_max, max_score)
min_score = min(_min, min_score)
diff = max_score - min_score
for _, k in pred.items():
for _, v in k.items():
if len(v) == 0:
continue
v[:, -1] = (v[:, -1] - min_score).astype(np.float64)/diff
return pred
def image_eval(pred, gt, ignore, iou_thresh, mpp):
""" single image evaluation
pred: Nx5
gt: Nx4
ignore:
"""
_pred = pred.copy()
_gt = gt.copy()
pred_recall = np.zeros(_pred.shape[0])
recall_list = np.zeros(_gt.shape[0])
proposal_list = np.ones(_pred.shape[0])
_pred[:, 2] = _pred[:, 2] + _pred[:, 0]
_pred[:, 3] = _pred[:, 3] + _pred[:, 1]
_gt[:, 2] = _gt[:, 2] + _gt[:, 0]
_gt[:, 3] = _gt[:, 3] + _gt[:, 1]
gt_overlap_list = mpp.starmap(bbox_overlap, zip([_gt]*_pred.shape[0],[_pred[h] for h in range(_pred.shape[0])]))
#use_cuda = True
#if use_cuda:
# _pred = torch.cuda.FloatTensor(_pred[:,:4])
# _gt = torch.cuda.FloatTensor(_gt)
#else:
# _pred = torch.FloatTensor(_pred[:,:4])
# _gt = torch.FloatTensor(_gt)
#overlaps = jaccard(_pred, _gt).cpu().numpy()
#overlaps = compute_iou((_pred[:, :4]), (_gt))
#overlaps = bbox_overlaps(_pred, _gt)
#if use_cuda:
# overlaps = overlaps.cpu().numpy()
#else:
# overlaps = overlaps.numpy()
for h in range(_pred.shape[0]):
#gt_overlap = overlaps[h]
#gt_overlap = bbox_overlap(_gt, _pred[h])
gt_overlap = gt_overlap_list[h]
#if use_cuda:
# gt_overlap = gt_overlap.cpu().numpy()
#else:
# gt_overlap = gt_overlap.numpy()
#max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
#gt_overlap = compute_iou(_gt, _pred[h, :4])
#exit()
#exit()
#print ('overlap', gt_overlap)
max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
if max_overlap >= iou_thresh:
if ignore[max_idx] == 0:
recall_list[max_idx] = -1
proposal_list[h] = -1
elif recall_list[max_idx] == 0:
recall_list[max_idx] = 1
r_keep_index = np.where(recall_list == 1)[0]
pred_recall[h] = len(r_keep_index)
return pred_recall, proposal_list
def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
pr_info = np.zeros((thresh_num, 2)).astype('float')
fp = np.zeros((pred_info.shape[0],), dtype=np.int)
last_info = [-1, -1]
for t in range(thresh_num):
thresh = 1 - (t+1)/thresh_num
r_index = np.where(pred_info[:, 4] >= thresh)[0]
if len(r_index) == 0:
pr_info[t, 0] = 0
pr_info[t, 1] = 0
else:
r_index = r_index[-1]
p_index = np.where(proposal_list[:r_index+1] == 1)[0]
pr_info[t, 0] = len(p_index) #valid pred number
pr_info[t, 1] = pred_recall[r_index] # valid gt number
if t>0 and pr_info[t, 0] > pr_info[t-1,0] and pr_info[t, 1]==pr_info[t-1,1]:
fp[r_index] = 1
#if thresh>=0.85:
# print(thresh, t, pr_info[t])
#print(pr_info[:10,0])
#print(pr_info[:10,1])
return pr_info, fp
def dataset_pr_info(thresh_num, pr_curve, count_face):
_pr_curve = np.zeros((thresh_num, 2))
for i in range(thresh_num):
#_pr_curve[i, 0] = round(pr_curve[i, 1] / pr_curve[i, 0], 4)
#_pr_curve[i, 1] = round(pr_curve[i, 1] / count_face, 4)
_pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]
_pr_curve[i, 1] = pr_curve[i, 1] / count_face
return _pr_curve
def voc_ap(rec, prec):
# correct AP calculation
# first append sentinel values at the end
#print ('rec:', rec)
#print ('pre:', prec)
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np_round(np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]))
return ap
def wider_evaluation(pred, gt_path, iou_thresh=0.5, debug=False):
#pred = get_preds(pred)
pred = norm_score(pred)
thresh_num = 1000
#thresh_num = 2000
facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
event_num = len(event_list)
settings = ['easy', 'medium', 'hard']
setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
from multiprocessing import Pool
#from multiprocessing.pool import ThreadPool
mpp = Pool(8)
aps = [-1.0, -1.0, -1.0]
meta = {}
#setting_id = 2
print('')
for setting_id in range(3):
#for setting_id in range(1):
ta = datetime.datetime.now()
# different setting
#iou_th = 0.5 #+ 0.05 * idx
iou_th = iou_thresh
# different setting
gt_list = setting_gts[setting_id]
count_face = 0
pr_curve = np.zeros((thresh_num, 2)).astype('float')
# [hard, medium, easy]
#pbar = tqdm.tqdm(range(event_num))
#for i in pbar:
high_score_count = 0
high_score_fp_count = 0
for i in range(event_num):
#pbar.set_description('Processing {}'.format(settings[setting_id]))
event_name = str(event_list[i][0][0])
img_list = file_list[i][0]
pred_list = pred[event_name]
sub_gt_list = gt_list[i][0]
# img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
gt_bbx_list = facebox_list[i][0]
for j in range(len(img_list)):
img_name = str(img_list[j][0][0])
pred_info = pred_list[img_name]
gt_boxes = gt_bbx_list[j][0].astype('float')
keep_index = sub_gt_list[j][0]
#print ('keep_index', keep_index)
count_face += len(keep_index)
if len(gt_boxes) == 0 or len(pred_info) == 0:
continue
#ignore = np.zeros(gt_boxes.shape[0])
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#assert len(keep_index)>0
ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
if len(keep_index) != 0:
ignore[keep_index-1] = 1
pred_info = np_round(pred_info,1)
#print('ignore:', len(ignore), len(np.where(ignore==1)[0]))
#pred_sort_idx= np.argsort(pred_info[:,4])
#pred_info = pred_info[pred_sort_idx][::-1]
#print ('pred_info', pred_info[:20, 4])
#exit()
gt_boxes = np_round(gt_boxes)
#ignore = np_round(ignore)
pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_th, mpp)
#print(pred_recall[:10], proposal_list[:10])
#print('1 stage', pred_recall, proposal_list)
#print(pred_info.shape, pred_recall.shape)
_img_pr_info, fp = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)
#for f in range(pred_info.shape[0]):
# _score = pred_info[f,4]
# if _score<0.929:
# break
# high_score_count+=1
# if fp[f]==1:
# w = pred_info[f, 2]
# h = pred_info[f, 3]
# print('fp:', event_name, img_name, _score, w, h)
# high_score_fp_count+=1
pr_curve += _img_pr_info
#print ('pr_curve', pr_curve, count_face)
pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
#print(pr_curve.shape)
propose = pr_curve[:, 0]
recall = pr_curve[:, 1]
#for f in range(thresh_num):
# print('R-P:', recall[f], propose[f])
for srecall in np.arange(0.1, 1.0001, 0.1):
rindex = len(np.where(recall<=srecall)[0])-1
rthresh = 1.0 - float(rindex)/thresh_num
print('Recall-Precision-Thresh:', recall[rindex], propose[rindex], rthresh)
ap = voc_ap(recall, propose)
aps[setting_id] = ap
tb = datetime.datetime.now()
#print('high score count:', high_score_count)
#print('high score fp count:', high_score_fp_count)
print('%s cost %.4f seconds, ap: %.5f'%(settings[setting_id], (tb-ta).total_seconds(), ap))
return aps
def get_widerface_gts(gt_path):
facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
event_num = len(event_list)
settings = ['easy', 'medium', 'hard']
setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
all_results = []
for setting_id in range(3):
results = {}
gt_list = setting_gts[setting_id]
count_face = 0
# [hard, medium, easy]
#pbar = tqdm.tqdm(range(event_num))
#for i in pbar:
for i in range(event_num):
#pbar.set_description('Processing {}'.format(settings[setting_id]))
event_name = str(event_list[i][0][0])
img_list = file_list[i][0]
sub_gt_list = gt_list[i][0]
# img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
gt_bbx_list = facebox_list[i][0]
results[event_name] = {}
for j in range(len(img_list)):
gt_boxes = gt_bbx_list[j][0].astype('float').copy()
gt_boxes[:,2] += gt_boxes[:,0]
gt_boxes[:,3] += gt_boxes[:,1]
keep_index = sub_gt_list[j][0].copy()
#print ('keep_index', keep_index.shape)
count_face += len(keep_index)
if len(gt_boxes) == 0:
results[event_name][str(img_list[j][0][0])] = np.empty( (0,4) )
continue
keep_index -= 1
keep_index = keep_index.flatten()
#ignore = np.zeros(gt_boxes.shape[0])
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#assert len(keep_index)>0
#ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#print('ignore:', len(ignore), len(np.where(ignore==1)[0]))
#pred_sort_idx= np.argsort(pred_info[:,4])
#pred_info = pred_info[pred_sort_idx][::-1]
#print ('pred_info', pred_info[:20, 4])
#exit()
#if setting_id==2 and len(keep_index)<gt_boxes.shape[0]:
# print(gt_boxes.shape, keep_index.shape)
gt_boxes = np_round(gt_boxes)[keep_index,:]
results[event_name][str(img_list[j][0][0])] = gt_boxes
all_results.append(results)
return all_results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--pred', default='')
parser.add_argument('-g', '--gt', default='./ground_truth/')
args = parser.parse_args()
evaluation(args.pred, args.gt)
| insightface/detection/scrfd/mmdet/core/evaluation/widerface.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/core/evaluation/widerface.py",
"repo_id": "insightface",
"token_count": 10224
} | 109 |
import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issues/973
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
hard_limit = rlimit[1]
soft_limit = min(4096, hard_limit)
resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
DATASETS = Registry('dataset')
PIPELINES = Registry('pipeline')
def _concat_dataset(cfg, default_args=None):
from .dataset_wrappers import ConcatDataset
ann_files = cfg['ann_file']
img_prefixes = cfg.get('img_prefix', None)
seg_prefixes = cfg.get('seg_prefix', None)
proposal_files = cfg.get('proposal_file', None)
separate_eval = cfg.get('separate_eval', True)
datasets = []
num_dset = len(ann_files)
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
# pop 'separate_eval' since it is not a valid key for common datasets.
if 'separate_eval' in data_cfg:
data_cfg.pop('separate_eval')
data_cfg['ann_file'] = ann_files[i]
if isinstance(img_prefixes, (list, tuple)):
data_cfg['img_prefix'] = img_prefixes[i]
if isinstance(seg_prefixes, (list, tuple)):
data_cfg['seg_prefix'] = seg_prefixes[i]
if isinstance(proposal_files, (list, tuple)):
data_cfg['proposal_file'] = proposal_files[i]
datasets.append(build_dataset(data_cfg, default_args))
return ConcatDataset(datasets, separate_eval)
def build_dataset(cfg, default_args=None):
from .dataset_wrappers import (ConcatDataset, RepeatDataset,
ClassBalancedDataset)
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
elif cfg['type'] == 'ConcatDataset':
dataset = ConcatDataset(
[build_dataset(c, default_args) for c in cfg['datasets']],
cfg.get('separate_eval', True))
elif cfg['type'] == 'RepeatDataset':
dataset = RepeatDataset(
build_dataset(cfg['dataset'], default_args), cfg['times'])
elif cfg['type'] == 'ClassBalancedDataset':
dataset = ClassBalancedDataset(
build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
elif isinstance(cfg.get('ann_file'), (list, tuple)):
dataset = _concat_dataset(cfg, default_args)
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
def build_dataloader(dataset,
samples_per_gpu,
workers_per_gpu,
num_gpus=1,
dist=True,
shuffle=True,
seed=None,
**kwargs):
"""Build PyTorch DataLoader.
In distributed training, each GPU/process has a dataloader.
In non-distributed training, there is only one dataloader for all GPUs.
Args:
dataset (Dataset): A PyTorch dataset.
samples_per_gpu (int): Number of training samples on each GPU, i.e.,
batch size of each GPU.
workers_per_gpu (int): How many subprocesses to use for data loading
for each GPU.
num_gpus (int): Number of GPUs. Only used in non-distributed training.
dist (bool): Distributed training/test or not. Default: True.
shuffle (bool): Whether to shuffle the data at every epoch.
Default: True.
kwargs: any keyword argument to be used to initialize DataLoader
Returns:
DataLoader: A PyTorch dataloader.
"""
rank, world_size = get_dist_info()
if dist:
# DistributedGroupSampler will definitely shuffle the data to satisfy
# that images on each GPU are in the same group
if shuffle:
sampler = DistributedGroupSampler(dataset, samples_per_gpu,
world_size, rank)
else:
sampler = DistributedSampler(
dataset, world_size, rank, shuffle=False)
batch_size = samples_per_gpu
num_workers = workers_per_gpu
else:
sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None
batch_size = num_gpus * samples_per_gpu
num_workers = num_gpus * workers_per_gpu
init_fn = partial(
worker_init_fn, num_workers=num_workers, rank=rank,
seed=seed) if seed is not None else None
data_loader = DataLoader(
dataset,
batch_size=batch_size,
sampler=sampler,
num_workers=num_workers,
collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
pin_memory=False,
worker_init_fn=init_fn,
**kwargs)
return data_loader
def worker_init_fn(worker_id, num_workers, rank, seed):
# The seed of each worker equals to
# num_worker * rank + worker_id + user_seed
worker_seed = num_workers * rank + worker_id + seed
np.random.seed(worker_seed)
random.seed(worker_seed)
| insightface/detection/scrfd/mmdet/datasets/builder.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/datasets/builder.py",
"repo_id": "insightface",
"token_count": 2315
} | 110 |
from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']
| insightface/detection/scrfd/mmdet/datasets/samplers/__init__.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/datasets/samplers/__init__.py",
"repo_id": "insightface",
"token_count": 54
} | 111 |
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottle2neck(_Bottleneck):
expansion = 4
def __init__(self,
inplanes,
planes,
scales=4,
base_width=26,
base_channels=64,
stage_type='normal',
**kwargs):
"""Bottle2neck block for Res2Net.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
width = int(math.floor(self.planes * (base_width / base_channels)))
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width * scales, postfix=1)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width * scales,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
if stage_type == 'stage' and self.conv2_stride != 1:
self.pool = nn.AvgPool2d(
kernel_size=3, stride=self.conv2_stride, padding=1)
convs = []
bns = []
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
for i in range(scales - 1):
convs.append(
build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
for i in range(scales - 1):
convs.append(
build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = build_conv_layer(
self.conv_cfg,
width * scales,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.stage_type = stage_type
self.scales = scales
self.width = width
delattr(self, 'conv2')
delattr(self, self.norm2_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
spx = torch.split(out, self.width, 1)
sp = self.convs[0](spx[0].contiguous())
sp = self.relu(self.bns[0](sp))
out = sp
for i in range(1, self.scales - 1):
if self.stage_type == 'stage':
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp.contiguous())
sp = self.relu(self.bns[i](sp))
out = torch.cat((out, sp), 1)
if self.stage_type == 'normal' or self.conv2_stride == 1:
out = torch.cat((out, spx[self.scales - 1]), 1)
elif self.stage_type == 'stage':
out = torch.cat((out, self.pool(spx[self.scales - 1])), 1)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
class Res2Layer(nn.Sequential):
"""Res2Layer to build Res2Net style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottle2neck. Default: False
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
scales (int): Scales used in Res2Net. Default: 4
base_width (int): Basic width of each scale. Default: 26
"""
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=True,
conv_cfg=None,
norm_cfg=dict(type='BN'),
scales=4,
base_width=26,
**kwargs):
self.block = block
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False),
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=1,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
scales=scales,
base_width=base_width,
stage_type='stage',
**kwargs))
inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
scales=scales,
base_width=base_width,
**kwargs))
super(Res2Layer, self).__init__(*layers)
@BACKBONES.register_module()
class Res2Net(ResNet):
"""Res2Net backbone.
Args:
scales (int): Scales used in Res2Net. Default: 4
base_width (int): Basic width of each scale. Default: 26
depth (int): Depth of res2net, from {50, 101, 152}.
in_channels (int): Number of input image channels. Default: 3.
num_stages (int): Res2net stages. Default: 4.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottle2neck.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
norm_cfg (dict): Dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
plugins (list[dict]): List of plugins for stages, each dict contains:
- cfg (dict, required): Cfg dict to build plugin.
- position (str, required): Position inside block to insert
plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
- stages (tuple[bool], optional): Stages to apply plugin, length
should be same as 'num_stages'.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from mmdet.models import Res2Net
>>> import torch
>>> self = Res2Net(depth=50, scales=4, base_width=26)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 256, 8, 8)
(1, 512, 4, 4)
(1, 1024, 2, 2)
(1, 2048, 1, 1)
"""
arch_settings = {
50: (Bottle2neck, (3, 4, 6, 3)),
101: (Bottle2neck, (3, 4, 23, 3)),
152: (Bottle2neck, (3, 8, 36, 3))
}
def __init__(self,
scales=4,
base_width=26,
style='pytorch',
deep_stem=True,
avg_down=True,
**kwargs):
self.scales = scales
self.base_width = base_width
super(Res2Net, self).__init__(
style='pytorch', deep_stem=True, avg_down=True, **kwargs)
def make_res_layer(self, **kwargs):
return Res2Layer(
scales=self.scales,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs)
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottle2neck):
# dcn in Res2Net bottle2neck is in ModuleList
for n in m.convs:
if hasattr(n, 'conv_offset'):
constant_init(n.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottle2neck):
constant_init(m.norm3, 0)
else:
raise TypeError('pretrained must be a str or None')
| insightface/detection/scrfd/mmdet/models/backbones/res2net.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/models/backbones/res2net.py",
"repo_id": "insightface",
"token_count": 6688
} | 112 |
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import DeformConv2d
from mmdet.core import multi_apply, multiclass_nms
from ..builder import HEADS
from .anchor_free_head import AnchorFreeHead
INF = 1e8
class FeatureAlign(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deform_groups=4):
super(FeatureAlign, self).__init__()
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
4, deform_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deform_groups=deform_groups)
self.relu = nn.ReLU(inplace=True)
def init_weights(self):
normal_init(self.conv_offset, std=0.1)
normal_init(self.conv_adaption, std=0.01)
def forward(self, x, shape):
offset = self.conv_offset(shape)
x = self.relu(self.conv_adaption(x, offset))
return x
@HEADS.register_module()
class FoveaHead(AnchorFreeHead):
"""FoveaBox: Beyond Anchor-based Object Detector
https://arxiv.org/abs/1904.03797
"""
def __init__(self,
num_classes,
in_channels,
base_edge_list=(16, 32, 64, 128, 256),
scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128,
512)),
sigma=0.4,
with_deform=False,
deform_groups=4,
**kwargs):
self.base_edge_list = base_edge_list
self.scale_ranges = scale_ranges
self.sigma = sigma
self.with_deform = with_deform
self.deform_groups = deform_groups
super().__init__(num_classes, in_channels, **kwargs)
def _init_layers(self):
# box branch
super()._init_reg_convs()
self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
# cls branch
if not self.with_deform:
super()._init_cls_convs()
self.conv_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
else:
self.cls_convs = nn.ModuleList()
self.cls_convs.append(
ConvModule(
self.feat_channels, (self.feat_channels * 4),
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.cls_convs.append(
ConvModule((self.feat_channels * 4), (self.feat_channels * 4),
1,
stride=1,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.feature_adaption = FeatureAlign(
self.feat_channels,
self.feat_channels,
kernel_size=3,
deform_groups=self.deform_groups)
self.conv_cls = nn.Conv2d(
int(self.feat_channels * 4),
self.cls_out_channels,
3,
padding=1)
def init_weights(self):
super().init_weights()
if self.with_deform:
self.feature_adaption.init_weights()
def forward_single(self, x):
cls_feat = x
reg_feat = x
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
bbox_pred = self.conv_reg(reg_feat)
if self.with_deform:
cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
cls_score = self.conv_cls(cls_feat)
return cls_score, bbox_pred
def _get_points_single(self, *args, **kwargs):
y, x = super()._get_points_single(*args, **kwargs)
return y + 0.5, x + 0.5
def loss(self,
cls_scores,
bbox_preds,
gt_bbox_list,
gt_label_list,
img_metas,
gt_bboxes_ignore=None):
assert len(cls_scores) == len(bbox_preds)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
bbox_preds[0].device)
num_imgs = cls_scores[0].size(0)
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
flatten_labels, flatten_bbox_targets = self.get_targets(
gt_bbox_list, gt_label_list, featmap_sizes, points)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
pos_inds = ((flatten_labels >= 0)
& (flatten_labels < self.num_classes)).nonzero().view(-1)
num_pos = len(pos_inds)
loss_cls = self.loss_cls(
flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs)
if num_pos > 0:
pos_bbox_preds = flatten_bbox_preds[pos_inds]
pos_bbox_targets = flatten_bbox_targets[pos_inds]
pos_weights = pos_bbox_targets.new_zeros(
pos_bbox_targets.size()) + 1.0
loss_bbox = self.loss_bbox(
pos_bbox_preds,
pos_bbox_targets,
pos_weights,
avg_factor=num_pos)
else:
loss_bbox = torch.tensor(
0,
dtype=flatten_bbox_preds.dtype,
device=flatten_bbox_preds.device)
return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points):
label_list, bbox_target_list = multi_apply(
self._get_target_single,
gt_bbox_list,
gt_label_list,
featmap_size_list=featmap_sizes,
point_list=points)
flatten_labels = [
torch.cat([
labels_level_img.flatten() for labels_level_img in labels_level
]) for labels_level in zip(*label_list)
]
flatten_bbox_targets = [
torch.cat([
bbox_targets_level_img.reshape(-1, 4)
for bbox_targets_level_img in bbox_targets_level
]) for bbox_targets_level in zip(*bbox_target_list)
]
flatten_labels = torch.cat(flatten_labels)
flatten_bbox_targets = torch.cat(flatten_bbox_targets)
return flatten_labels, flatten_bbox_targets
def _get_target_single(self,
gt_bboxes_raw,
gt_labels_raw,
featmap_size_list=None,
point_list=None):
gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
label_list = []
bbox_target_list = []
# for each pyramid, find the cls and box target
for base_len, (lower_bound, upper_bound), stride, featmap_size, \
(y, x) in zip(self.base_edge_list, self.scale_ranges,
self.strides, featmap_size_list, point_list):
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes
bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1],
4) + 1
# scale assignment
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero().flatten()
if len(hit_indices) == 0:
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
continue
_, hit_index_order = torch.sort(-gt_areas[hit_indices])
hit_indices = hit_indices[hit_index_order]
gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride
gt_labels = gt_labels_raw[hit_indices]
half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0])
half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1])
# valid fovea area: left, right, top, down
pos_left = torch.ceil(
gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long().\
clamp(0, featmap_size[1] - 1)
pos_right = torch.floor(
gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long().\
clamp(0, featmap_size[1] - 1)
pos_top = torch.ceil(
gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long().\
clamp(0, featmap_size[0] - 1)
pos_down = torch.floor(
gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long().\
clamp(0, featmap_size[0] - 1)
for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \
zip(pos_left, pos_top, pos_right, pos_down, gt_labels,
gt_bboxes_raw[hit_indices, :]):
labels[py1:py2 + 1, px1:px2 + 1] = label
bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \
(stride * x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \
(stride * y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \
(gt_x2 - stride * x[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \
(gt_y2 - stride * y[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.)
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
return label_list, bbox_target_list
def get_bboxes(self,
cls_scores,
bbox_preds,
img_metas,
cfg=None,
rescale=None):
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
points = self.get_points(
featmap_sizes,
bbox_preds[0].dtype,
bbox_preds[0].device,
flatten=True)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
det_bboxes = self._get_bboxes_single(cls_score_list,
bbox_pred_list, featmap_sizes,
points, img_shape,
scale_factor, cfg, rescale)
result_list.append(det_bboxes)
return result_list
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
featmap_sizes,
point_list,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(point_list)
det_bboxes = []
det_scores = []
for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \
in zip(cls_scores, bbox_preds, featmap_sizes, self.strides,
self.base_edge_list, point_list):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp()
nms_pre = cfg.get('nms_pre', -1)
if (nms_pre > 0) and (scores.shape[0] > nms_pre):
max_scores, _ = scores.max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
y = y[topk_inds]
x = x[topk_inds]
x1 = (stride * x - base_len * bbox_pred[:, 0]).\
clamp(min=0, max=img_shape[1] - 1)
y1 = (stride * y - base_len * bbox_pred[:, 1]).\
clamp(min=0, max=img_shape[0] - 1)
x2 = (stride * x + base_len * bbox_pred[:, 2]).\
clamp(min=0, max=img_shape[1] - 1)
y2 = (stride * y + base_len * bbox_pred[:, 3]).\
clamp(min=0, max=img_shape[0] - 1)
bboxes = torch.stack([x1, y1, x2, y2], -1)
det_bboxes.append(bboxes)
det_scores.append(scores)
det_bboxes = torch.cat(det_bboxes)
if rescale:
det_bboxes /= det_bboxes.new_tensor(scale_factor)
det_scores = torch.cat(det_scores)
padding = det_scores.new_zeros(det_scores.shape[0], 1)
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
det_scores = torch.cat([det_scores, padding], dim=1)
det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
| insightface/detection/scrfd/mmdet/models/dense_heads/fovea_head.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/models/dense_heads/fovea_head.py",
"repo_id": "insightface",
"token_count": 8469
} | 113 |
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, images_to_levels,
multi_apply, multiclass_nms, unmap)
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .guided_anchor_head import GuidedAnchorHead
@HEADS.register_module()
class SABLRetinaHead(BaseDenseHead):
"""Side-Aware Boundary Localization (SABL) for RetinaNet.
The anchor generation, assigning and sampling in SABLRetinaHead
are the same as GuidedAnchorHead for guided anchoring.
Please refer to https://arxiv.org/abs/1912.04260 for more details.
Args:
num_classes (int): Number of classes.
in_channels (int): Number of channels in the input feature map.
stacked_convs (int): Number of Convs for classification \
and regression branches. Defaults to 4.
feat_channels (int): Number of hidden channels. \
Defaults to 256.
approx_anchor_generator (dict): Config dict for approx generator.
square_anchor_generator (dict): Config dict for square generator.
conv_cfg (dict): Config dict for ConvModule. Defaults to None.
norm_cfg (dict): Config dict for Norm Layer. Defaults to None.
bbox_coder (dict): Config dict for bbox coder.
reg_decoded_bbox (bool): Whether to regress decoded bbox. \
Defaults to False.
train_cfg (dict): Training config of SABLRetinaHead.
test_cfg (dict): Testing config of SABLRetinaHead.
loss_cls (dict): Config of classification loss.
loss_bbox_cls (dict): Config of classification loss for bbox branch.
loss_bbox_reg (dict): Config of regression loss for bbox branch.
"""
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
conv_cfg=None,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=3.0),
reg_decoded_bbox=False,
train_cfg=None,
test_cfg=None,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)):
super(SABLRetinaHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.num_buckets = bbox_coder['num_buckets']
self.side_num = int(np.ceil(self.num_buckets / 2))
assert (approx_anchor_generator['octave_base_scale'] ==
square_anchor_generator['scales'][0])
assert (approx_anchor_generator['strides'] ==
square_anchor_generator['strides'])
self.approx_anchor_generator = build_anchor_generator(
approx_anchor_generator)
self.square_anchor_generator = build_anchor_generator(
square_anchor_generator)
self.approxs_per_octave = (
self.approx_anchor_generator.num_base_anchors[0])
# one anchor per location
self.num_anchors = 1
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.reg_decoded_bbox = reg_decoded_bbox
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.sampling = loss_cls['type'] not in [
'FocalLoss', 'GHMC', 'QualityFocalLoss'
]
if self.use_sigmoid_cls:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox_cls = build_loss(loss_bbox_cls)
self.loss_bbox_reg = build_loss(loss_bbox_reg)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.fp16_enabled = False
self._init_layers()
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.retina_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
self.retina_bbox_reg = nn.Conv2d(
self.feat_channels, self.side_num * 4, 3, padding=1)
self.retina_bbox_cls = nn.Conv2d(
self.feat_channels, self.side_num * 4, 3, padding=1)
def init_weights(self):
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_bbox_reg, std=0.01)
normal_init(self.retina_bbox_cls, std=0.01)
def forward_single(self, x):
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
reg_feat = reg_conv(reg_feat)
cls_score = self.retina_cls(cls_feat)
bbox_cls_pred = self.retina_bbox_cls(reg_feat)
bbox_reg_pred = self.retina_bbox_reg(reg_feat)
bbox_pred = (bbox_cls_pred, bbox_reg_pred)
return cls_score, bbox_pred
def forward(self, feats):
return multi_apply(self.forward_single, feats)
def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
"""Get squares according to feature map sizes and guided anchors.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
img_metas (list[dict]): Image meta info.
device (torch.device | str): device for returned tensors
Returns:
tuple: square approxs of each image
"""
num_imgs = len(img_metas)
# since feature map sizes of all images are the same, we only compute
# squares for one time
multi_level_squares = self.square_anchor_generator.grid_anchors(
featmap_sizes, device=device)
squares_list = [multi_level_squares for _ in range(num_imgs)]
return squares_list
def get_target(self,
approx_list,
inside_flag_list,
square_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=None,
sampling=True,
unmap_outputs=True):
"""Compute bucketing targets.
Args:
approx_list (list[list]): Multi level approxs of each image.
inside_flag_list (list[list]): Multi level inside flags of each
image.
square_list (list[list]): Multi level squares of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
label_channels (int): Channel of label.
sampling (bool): Sample Anchors or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple: Returns a tuple containing learning targets.
- labels_list (list[Tensor]): Labels of each level.
- label_weights_list (list[Tensor]): Label weights of each \
level.
- bbox_cls_targets_list (list[Tensor]): BBox cls targets of \
each level.
- bbox_cls_weights_list (list[Tensor]): BBox cls weights of \
each level.
- bbox_reg_targets_list (list[Tensor]): BBox reg targets of \
each level.
- bbox_reg_weights_list (list[Tensor]): BBox reg weights of \
each level.
- num_total_pos (int): Number of positive samples in all \
images.
- num_total_neg (int): Number of negative samples in all \
images.
"""
num_imgs = len(img_metas)
assert len(approx_list) == len(inside_flag_list) == len(
square_list) == num_imgs
# anchor number of multi levels
num_level_squares = [squares.size(0) for squares in square_list[0]]
# concat all level anchors and flags to a single tensor
inside_flag_flat_list = []
approx_flat_list = []
square_flat_list = []
for i in range(num_imgs):
assert len(square_list[i]) == len(inside_flag_list[i])
inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
approx_flat_list.append(torch.cat(approx_list[i]))
square_flat_list.append(torch.cat(square_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
(all_labels, all_label_weights, all_bbox_cls_targets,
all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights,
pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single,
approx_flat_list,
inside_flag_flat_list,
square_flat_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
sampling=sampling,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
labels_list = images_to_levels(all_labels, num_level_squares)
label_weights_list = images_to_levels(all_label_weights,
num_level_squares)
bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets,
num_level_squares)
bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights,
num_level_squares)
bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets,
num_level_squares)
bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights,
num_level_squares)
return (labels_list, label_weights_list, bbox_cls_targets_list,
bbox_cls_weights_list, bbox_reg_targets_list,
bbox_reg_weights_list, num_total_pos, num_total_neg)
def _get_target_single(self,
flat_approxs,
inside_flags,
flat_squares,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=None,
sampling=True,
unmap_outputs=True):
"""Compute regression and classification targets for anchors in a
single image.
Args:
flat_approxs (Tensor): flat approxs of a single image,
shape (n, 4)
inside_flags (Tensor): inside flags of a single image,
shape (n, ).
flat_squares (Tensor): flat squares of a single image,
shape (approxs_per_octave * n, 4)
gt_bboxes (Tensor): Ground truth bboxes of a single image, \
shape (num_gts, 4).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
img_meta (dict): Meta info of the image.
label_channels (int): Channel of label.
sampling (bool): Sample Anchors or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple:
- labels_list (Tensor): Labels in a single image
- label_weights (Tensor): Label weights in a single image
- bbox_cls_targets (Tensor): BBox cls targets in a single image
- bbox_cls_weights (Tensor): BBox cls weights in a single image
- bbox_reg_targets (Tensor): BBox reg targets in a single image
- bbox_reg_weights (Tensor): BBox reg weights in a single image
- num_total_pos (int): Number of positive samples \
in a single image
- num_total_neg (int): Number of negative samples \
in a single image
"""
if not inside_flags.any():
return (None, ) * 8
# assign gt and sample anchors
expand_inside_flags = inside_flags[:, None].expand(
-1, self.approxs_per_octave).reshape(-1)
approxs = flat_approxs[expand_inside_flags, :]
squares = flat_squares[inside_flags, :]
assign_result = self.assigner.assign(approxs, squares,
self.approxs_per_octave,
gt_bboxes, gt_bboxes_ignore)
sampling_result = self.sampler.sample(assign_result, squares,
gt_bboxes)
num_valid_squares = squares.shape[0]
bbox_cls_targets = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_cls_weights = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_reg_targets = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_reg_weights = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
labels = squares.new_full((num_valid_squares, ),
self.num_classes,
dtype=torch.long)
label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
(pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets,
pos_bbox_cls_weights) = self.bbox_coder.encode(
sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets
bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets
bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights
bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights
if gt_labels is None:
# Only rpn gives gt_labels as None
# Foreground is the first class
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[
sampling_result.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg.pos_weight
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_squares.size(0)
labels = unmap(
labels, num_total_anchors, inside_flags, fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors,
inside_flags)
bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors,
inside_flags)
bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors,
inside_flags)
bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors,
inside_flags)
bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors,
inside_flags)
return (labels, label_weights, bbox_cls_targets, bbox_cls_weights,
bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds)
def loss_single(self, cls_score, bbox_pred, labels, label_weights,
bbox_cls_targets, bbox_cls_weights, bbox_reg_targets,
bbox_reg_weights, num_total_samples):
# classification loss
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
cls_score = cls_score.permute(0, 2, 3,
1).reshape(-1, self.cls_out_channels)
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
# regression loss
bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4)
bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4)
bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4)
bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4)
(bbox_cls_pred, bbox_reg_pred) = bbox_pred
bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(
-1, self.side_num * 4)
bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape(
-1, self.side_num * 4)
loss_bbox_cls = self.loss_bbox_cls(
bbox_cls_pred,
bbox_cls_targets.long(),
bbox_cls_weights,
avg_factor=num_total_samples * 4 * self.side_num)
loss_bbox_reg = self.loss_bbox_reg(
bbox_reg_pred,
bbox_reg_targets,
bbox_reg_weights,
avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk)
return loss_cls, loss_bbox_cls, loss_bbox_reg
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
device = cls_scores[0].device
# get sampled approxes
approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs(
self, featmap_sizes, img_metas, device=device)
square_list = self.get_anchors(featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_target(
approxs_list,
inside_flag_list,
square_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=self.sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_cls_targets_list,
bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos + num_total_neg if self.sampling else num_total_pos)
losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_cls_targets_list,
bbox_cls_weights_list,
bbox_reg_targets_list,
bbox_reg_weights_list,
num_total_samples=num_total_samples)
return dict(
loss_cls=losses_cls,
loss_bbox_cls=losses_bbox_cls,
loss_bbox_reg=losses_bbox_reg)
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def get_bboxes(self,
cls_scores,
bbox_preds,
img_metas,
cfg=None,
rescale=False):
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
device = cls_scores[0].device
mlvl_anchors = self.get_anchors(
featmap_sizes, img_metas, device=device)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_cls_pred_list = [
bbox_preds[i][0][img_id].detach() for i in range(num_levels)
]
bbox_reg_pred_list = [
bbox_preds[i][1][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
proposals = self.get_bboxes_single(cls_score_list,
bbox_cls_pred_list,
bbox_reg_pred_list,
mlvl_anchors[img_id], img_shape,
scale_factor, cfg, rescale)
result_list.append(proposals)
return result_list
def get_bboxes_single(self,
cls_scores,
bbox_cls_preds,
bbox_reg_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
mlvl_bboxes = []
mlvl_scores = []
mlvl_confids = []
assert len(cls_scores) == len(bbox_cls_preds) == len(
bbox_reg_preds) == len(mlvl_anchors)
for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_cls_pred.size(
)[-2:] == bbox_reg_pred.size()[-2::]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
max_scores, _ = scores[:, :-1].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_cls_pred = bbox_cls_pred[topk_inds, :]
bbox_reg_pred = bbox_reg_pred[topk_inds, :]
scores = scores[topk_inds, :]
bbox_preds = [
bbox_cls_pred.contiguous(),
bbox_reg_pred.contiguous()
]
bboxes, confids = self.bbox_coder.decode(
anchors.contiguous(), bbox_preds, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_confids.append(confids)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
mlvl_confids = torch.cat(mlvl_confids)
if self.use_sigmoid_cls:
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
det_bboxes, det_labels = multiclass_nms(
mlvl_bboxes,
mlvl_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=mlvl_confids)
return det_bboxes, det_labels
| insightface/detection/scrfd/mmdet/models/dense_heads/sabl_retina_head.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/models/dense_heads/sabl_retina_head.py",
"repo_id": "insightface",
"token_count": 14826
} | 114 |
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FOVEA(SingleStageDetector):
"""Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg,
test_cfg, pretrained)
| insightface/detection/scrfd/mmdet/models/detectors/fovea.py/0 | {
"file_path": "insightface/detection/scrfd/mmdet/models/detectors/fovea.py",
"repo_id": "insightface",
"token_count": 292
} | 115 |
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