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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

elia/LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

elia/README.md

@@ -0,0 +1,222 @@
+# LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
+Welcome to the official repository for the method presented in
+"LAVT: Language-Aware Vision Transformer for Referring Image Segmentation."
+
+
+![Pipeline Image](pipeline.jpg)
+
+Code in this repository is written using [PyTorch](https://pytorch.org/) and is organized in the following way (assuming the working directory is the root directory of this repository):
+* `./lib` contains files implementing the main network.
+* Inside `./lib`, `_utils.py` defines the highest-level model, which incorporates the backbone network
+defined in `backbone.py` and the simple mask decoder defined in `mask_predictor.py`.
+`segmentation.py` provides the model interface and initialization functions.
+* `./bert` contains files migrated from [Hugging Face Transformers v3.0.2](https://huggingface.co/transformers/v3.0.2/quicktour.html),
+which implement the BERT language model.
+We used Transformers v3.0.2 during development but it had a bug that would appear when using `DistributedDataParallel`.
+Therefore we maintain a copy of the relevant source files in this repository.
+This way, the bug is fixed and code in this repository is self-contained.
+* `./train.py` is invoked to train the model.
+* `./test.py` is invoked to run inference on the evaluation subsets after training.
+* `./refer` contains data pre-processing code and is also where data should be placed, including the images and all annotations.
+It is cloned from [refer](https://github.com/lichengunc/refer). 
+* `./data/dataset_refer_bert.py` is where the dataset class is defined.
+* `./utils.py` defines functions that track training statistics and setup
+functions for `DistributedDataParallel`.
+
+
+## Updates
+**June 21<sup>st</sup>, 2022**. Uploaded the training logs and trained
+model weights of lavt_one.
+
+**June 9<sup>th</sup>, 2022**.
+Added a more efficient implementation of LAVT.
+* To train this new model, specify `--model` as `lavt_one`
+(and `lavt` is still valid for specifying the old model).
+The rest of the configuration stays unchanged.
+* The difference between this version and the previous one
+is that the language model has been moved inside the overall model,
+so that `DistributedDataParallel` needs to be applied only once.
+Applying it twice (on the standalone language model and the main branch)
+as done in the old implementation led to low GPU utility,
+which prevented scaling up training speed with more GPUs.
+We recommend training this model on 8 GPUs
+(and same as before with batch size 32).
+
+## Setting Up
+### Preliminaries
+The code has been verified to work with PyTorch v1.7.1 and Python 3.7.
+1. Clone this repository.
+2. Change directory to root of this repository.
+### Package Dependencies
+1. Create a new Conda environment with Python 3.7 then activate it:
+```shell
+conda create -n lavt python==3.7
+conda activate lavt
+```
+
+2. Install PyTorch v1.7.1 with a CUDA version that works on your cluster/machine (CUDA 10.2 is used in this example):
+```shell
+conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch
+```
+
+3. Install the packages in `requirements.txt` via `pip`:
+```shell
+pip install -r requirements.txt
+```
+
+### Datasets
+1. Follow instructions in the `./refer` directory to set up subdirectories
+and download annotations.
+This directory is a git clone (minus two data files that we do not need)
+from the [refer](https://github.com/lichengunc/refer) public API.
+
+2. Download images from [COCO](https://cocodataset.org/#download).
+Please use the first downloading link *2014 Train images [83K/13GB]*, and extract
+the downloaded `train_2014.zip` file to `./refer/data/images/mscoco/images`.
+
+### The Initialization Weights for Training
+1. Create the `./pretrained_weights` directory where we will be storing the weights.
+```shell
+mkdir ./pretrained_weights
+```
+2. Download [pre-trained classification weights of
+the Swin Transformer](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth),
+and put the `pth` file in `./pretrained_weights`.
+These weights are needed for training to initialize the model.
+
+### Trained Weights of LAVT for Testing
+1. Create the `./checkpoints` directory where we will be storing the weights.
+```shell
+mkdir ./checkpoints
+```
+2. Download LAVT model weights (which are stored on Google Drive) using links below and put them in `./checkpoints`.
+
+| [RefCOCO](https://drive.google.com/file/d/13D-OeEOijV8KTC3BkFP-gOJymc6DLwVT/view?usp=sharing) | [RefCOCO+](https://drive.google.com/file/d/1B8Q44ZWsc8Pva2xD_M-KFh7-LgzeH2-2/view?usp=sharing) | [G-Ref (UMD)](https://drive.google.com/file/d/1BjUnPVpALurkGl7RXXvQiAHhA-gQYKvK/view?usp=sharing) | [G-Ref (Google)](https://drive.google.com/file/d/1weiw5UjbPfo3tCBPfB8tu6xFXCUG16yS/view?usp=sharing) |
+|---|---|---|---|
+
+3. Model weights and training logs of the new lavt_one implementation are below.
+
+| RefCOCO | RefCOCO+ | G-Ref (UMD) | G-Ref (Google) |
+|:-----:|:-----:|:-----:|:-----:|
+|[log](https://drive.google.com/file/d/1YIojIHqe3bxxsWOltifa2U9jH67hPHLM/view?usp=sharing) &#124; [weights](https://drive.google.com/file/d/1xFMEXr6AGU97Ypj1yr8oo00uObbeIQvJ/view?usp=sharing)|[log](https://drive.google.com/file/d/1Z34T4gEnWlvcSUQya7txOuM0zdLK7MRT/view?usp=sharing) &#124; [weights](https://drive.google.com/file/d/1HS8ZnGaiPJr-OmoUn4-4LVnVtD_zHY6w/view?usp=sharing)|[log](https://drive.google.com/file/d/14VAgahngOV8NA6noLZCqDoqaUrlW14v8/view?usp=sharing) &#124; [weights](https://drive.google.com/file/d/14g8NzgZn6HzC6tP_bsQuWmh5LnOcovsE/view?usp=sharing)|[log](https://drive.google.com/file/d/1JBXfmlwemWSvs92Rky0TlHcVuuLpt4Da/view?usp=sharing) &#124; [weights](https://drive.google.com/file/d/1IJeahFVLgKxu_BVmWacZs3oUzgTCeWcz/view?usp=sharing)|
+
+* The Prec@K, overall IoU and mean IoU numbers in the training logs will differ
+from the final results obtained by running `test.py`,
+because only one out of multiple annotated expressions is
+randomly selected and evaluated for each object during training.
+But these numbers give a good idea about the test performance.
+The two should be fairly close.
+
+
+## Training
+We use `DistributedDataParallel` from PyTorch.
+The released `lavt` weights were trained using 4 x 32G V100 cards (max mem on each card was about 26G).
+The released `lavt_one` weights were trained using 8 x 32G V100 cards (max mem on each card was about 13G).
+Using more cards was to accelerate training.
+To run on 4 GPUs (with IDs 0, 1, 2, and 3) on a single node:
+```shell
+mkdir ./models
+
+mkdir ./models/refcoco
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcoco --model_id refcoco --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/refcoco/output
+
+mkdir ./models/refcoco+
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcoco+ --model_id refcoco+ --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/refcoco+/output
+
+mkdir ./models/gref_umd
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcocog --splitBy umd --model_id gref_umd --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/gref_umd/output
+
+mkdir ./models/gref_google
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcocog --splitBy google --model_id gref_google --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/gref_google/output
+```
+* *--model* is a pre-defined model name. Options include `lavt` and `lavt_one`. See [Updates](#updates).
+* *--dataset* is the dataset name. One can choose from `refcoco`, `refcoco+`, and `refcocog`.
+* *--splitBy* needs to be specified if and only if the dataset is G-Ref (which is also called RefCOCOg).
+`umd` identifies the UMD partition and `google` identifies the Google partition.
+* *--model_id* is the model name one should define oneself (*e.g.*, customize it to contain training/model configurations, dataset information, experiment IDs, *etc*.).
+It is used in two ways: Training log will be saved as `./models/[args.model_id]/output` and the best checkpoint will be saved as `./checkpoints/model_best_[args.model_id].pth`.
+* *--swin_type* specifies the version of the Swin Transformer.
+One can choose from `tiny`, `small`, `base`, and `large`. The default is `base`.
+* *--pretrained_swin_weights* specifies the path to pre-trained Swin Transformer weights used for model initialization.
+* Note that currently we need to manually create the `./models/[args.model_id]` directory via `mkdir` before running `train.py`.
+This is because we use `tee` to redirect `stdout` and `stderr` to `./models/[args.model_id]/output` for logging.
+This is a nuisance and should be resolved in the future, *i.e.*, using a proper logger or a bash script for initiating training.
+
+## Testing
+For RefCOCO/RefCOCO+, run one of
+```shell
+python test.py --model lavt --swin_type base --dataset refcoco --split val --resume ./checkpoints/refcoco.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
+python test.py --model lavt --swin_type base --dataset refcoco+ --split val --resume ./checkpoints/refcoco+.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
+```
+* *--split* is the subset to evaluate, and one can choose from `val`, `testA`, and `testB`.
+* *--resume* is the path to the weights of a trained model.
+
+For G-Ref (UMD)/G-Ref (Google), run one of
+```shell
+python test.py --model lavt --swin_type base --dataset refcocog --splitBy umd --split val --resume ./checkpoints/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
+python test.py --model lavt --swin_type base --dataset refcocog --splitBy google --split val --resume ./checkpoints/gref_google.pth --workers 4 --ddp_trained_weights --window12 --img_size 480
+```
+* *--splitBy* specifies the partition to evaluate.
+One can choose from `umd` or `google`.
+* *--split* is the subset (according to the specified partition) to evaluate, and one can choose from `val` and `test` for the UMD partition, and only `val` for the Google partition..
+* *--resume* is the path to the weights of a trained model.
+
+## Results
+The complete test results of the released LAVT models are summarized as follows:
+
+|     Dataset     | P@0.5 | P@0.6 | P@0.7 | P@0.8 | P@0.9 | Overall IoU | Mean IoU |
+|:---------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----------:|:--------:|
+| RefCOCO val     | 84.46 | 80.90 | 75.28 | 64.71 | 34.30 |    72.73    |   74.46  |
+| RefCOCO test A  | 88.07 | 85.17 | 79.90 | 68.52 | 35.69 |    75.82    |   76.89  |
+| RefCOCO test B  | 79.12 | 74.94 | 69.17 | 59.37 | 34.45 |    68.79    |   70.94  |
+| RefCOCO+ val    | 74.44 | 70.91 | 65.58 | 56.34 | 30.23 |    62.14    |   65.81  |
+| RefCOCO+ test A | 80.68 | 77.96 | 72.90 | 62.21 | 32.36 |    68.38    |   70.97  |
+| RefCOCO+ test B | 65.66 | 61.85 | 55.94 | 47.56 | 27.24 |    55.10    |   59.23  |
+| G-Ref val (UMD) | 70.81 | 65.28 | 58.60 | 47.49 | 22.73 |    61.24    |   63.34  |
+| G-Ref test (UMD)| 71.54 | 66.38 | 59.00 | 48.21 | 23.10 |    62.09    |   63.62  |
+|G-Ref val (Goog.)| 71.16 | 67.21 | 61.76 | 51.98 | 27.30 |    60.50    |   63.66  |
+
+We have validated LAVT on RefCOCO with multiple runs.
+The overall IoU on the val set generally lies in the range of 72.73±0.5%.
+
+
+## Demo: Try LAVT on Your Own Image-text Pairs!
+One can run inference on a custom image-text pair
+and visualize the result by running the script `./demo_inference.py`.
+Choose your photos and expessions and have fun.
+
+
+## Citing LAVT
+```
+@inproceedings{yang2022lavt,
+  title={LAVT: Language-Aware Vision Transformer for Referring Image Segmentation},
+  author={Yang, Zhao and Wang, Jiaqi and Tang, Yansong and Chen, Kai and Zhao, Hengshuang and Torr, Philip HS},
+  booktitle={CVPR},
+  year={2022}
+}
+```
+
+
+## Contributing
+We appreciate all contributions.
+It helps the project if you could
+- report issues you are facing,
+- give a :+1: on issues reported by others that are relevant to you,
+- answer issues reported by others for which you have found solutions,
+- and implement helpful new features or improve the code otherwise with pull requests.
+
+## Acknowledgements
+Code in this repository is built upon several public repositories.
+Specifically,
+* data pre-processing leverages the [refer](https://github.com/lichengunc/refer) repository,
+* the backbone model is implemented based on code from [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation),
+* the training and testing pipelines are adapted from [RefVOS](https://github.com/miriambellver/refvos),
+* and implementation of the BERT model (files in the bert directory) is from [Hugging Face Transformers v3.0.2](https://github.com/huggingface/transformers/tree/v3.0.2)
+(we migrated over the relevant code to fix a bug and simplify the installation process).
+
+Some of these repositories in turn adapt code from [OpenMMLab](https://github.com/open-mmlab) and [TorchVision](https://github.com/pytorch/vision).
+We'd like to thank the authors/organizations of these repositories for open sourcing their projects.
+
+
+## License
+GNU GPLv3

elia/app.py

@@ -0,0 +1,310 @@
+import gradio as gr
+
+image_path = './image001.png'
+sentence = 'spoon on the dish'
+weights = './checkpoints/model_best_refcoco_0508.pth'
+device = 'cpu'
+
+# pre-process the input image
+from PIL import Image
+import torchvision.transforms as T
+import numpy as np
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from bert.multimodal_bert import MultiModalBert
+import torchvision
+
+from lib import multimodal_segmentation_ppm
+#import transforms as T
+import utils
+
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+from modeling.MaskFormerModel import MaskFormerHead
+from addict import Dict
+#from bert.modeling_bert import BertLMPredictionHead, BertEncoder
+import cv2
+import textwrap
+
+class WrapperModel(nn.Module):
+    def __init__(self, image_model, language_model, classifier) :
+        super(WrapperModel, self).__init__()
+        self.image_model = image_model
+        self.language_model = language_model
+        self.classifier = classifier
+
+        config = Dict({
+          "architectures": [
+           "BertForMaskedLM"
+          ],
+          "attention_probs_dropout_prob": 0.1,
+          "gradient_checkpointing": False,
+          "hidden_act": "gelu",
+          "hidden_dropout_prob": 0.1,
+          "hidden_size": 512,
+          "initializer_range": 0.02,
+          "intermediate_size": 3072,
+          "layer_norm_eps": 1e-12,
+          #"max_position_embeddings": 16+20,
+          "model_type": "bert",
+          "num_attention_heads": 8,
+          "num_hidden_layers": 8,
+         "pad_token_id": 0,
+          "position_embedding_type": "absolute",
+          "transformers_version": "4.6.0.dev0",
+          "type_vocab_size": 2,
+          "use_cache": True,
+          "vocab_size": 30522
+        })
+
+
+
+    def _get_binary_mask(self, target):
+        # 返回每类的binary mask
+        y, x = target.size()
+        target_onehot = torch.zeros(self.num_classes + 1, y, x)
+        target_onehot = target_onehot.scatter(dim=0, index=target.unsqueeze(0), value=1)
+        return target_onehot[1:]
+
+    def semantic_inference(self, mask_cls, mask_pred):       
+        mask_cls = F.softmax(mask_cls, dim=1)[...,1:]
+        mask_pred = mask_pred.sigmoid()      
+        semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred)        
+        return semseg
+
+    def forward(self, image, sentences, attentions): 
+        print(image.sum(), sentences.sum(), attentions.sum())
+        input_shape = image.shape[-2:]
+        l_mask = attentions.unsqueeze(dim=-1)
+
+        i0, Wh, Ww = self.image_model.forward_stem(image)
+        l0, extended_attention_mask = self.language_model.forward_stem(sentences, attentions)
+
+        i1 = self.image_model.forward_stage1(i0, Wh, Ww)
+        l1 = self.language_model.forward_stage1(l0, extended_attention_mask)
+        i1_residual, H, W, i1_temp, Wh, Ww  = self.image_model.forward_pwam1(i1, Wh, Ww, l1, l_mask)
+        l1_residual, l1 = self.language_model.forward_pwam1(i1, l1, extended_attention_mask) 
+        i1 = i1_temp
+
+        i2 = self.image_model.forward_stage2(i1, Wh, Ww)
+        l2 = self.language_model.forward_stage2(l1, extended_attention_mask)
+        i2_residual, H, W, i2_temp, Wh, Ww  = self.image_model.forward_pwam2(i2, Wh, Ww, l2, l_mask)
+        l2_residual, l2 = self.language_model.forward_pwam2(i2, l2, extended_attention_mask) 
+        i2 = i2_temp
+
+        i3 = self.image_model.forward_stage3(i2, Wh, Ww)
+        l3 = self.language_model.forward_stage3(l2, extended_attention_mask)
+        i3_residual, H, W, i3_temp, Wh, Ww  = self.image_model.forward_pwam3(i3, Wh, Ww, l3, l_mask)
+        l3_residual, l3 = self.language_model.forward_pwam3(i3, l3, extended_attention_mask) 
+        i3 = i3_temp
+
+        i4 = self.image_model.forward_stage4(i3, Wh, Ww)
+        l4 = self.language_model.forward_stage4(l3, extended_attention_mask)
+        i4_residual, H, W, i4_temp, Wh, Ww  = self.image_model.forward_pwam4(i4, Wh, Ww, l4, l_mask)
+        l4_residual, l4 = self.language_model.forward_pwam4(i4, l4, extended_attention_mask) 
+        i4 = i4_temp
+
+        #i1_residual, i2_residual, i3_residual, i4_residual = features
+        #x = self.classifier(i4_residual, i3_residual, i2_residual, i1_residual)
+        #x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)
+        outputs = {}
+        outputs['s1'] = i1_residual
+        outputs['s2'] = i2_residual
+        outputs['s3'] = i3_residual
+        outputs['s4'] = i4_residual
+
+        predictions = self.classifier(outputs)
+        return predictions
+
+#img = Image.open(image_path).convert("RGB")
+
+# pre-process the raw sentence
+from bert.tokenization_bert import BertTokenizer
+import torch
+
+# initialize model and load weights
+#from bert.modeling_bert import BertModel
+#from lib import segmentation
+
+# construct a mini args class; like from a config file
+
+
+class args:
+    swin_type = 'base'
+    window12 = True
+    mha = ''
+    fusion_drop = 0.0
+
+
+#single_model = segmentation.__dict__['lavt'](pretrained='', args=args)
+single_model = multimodal_segmentation_ppm.__dict__['lavt'](pretrained='',args=args)
+single_model.to(device)
+model_class = MultiModalBert
+single_bert_model = model_class.from_pretrained('bert-base-uncased', embed_dim=single_model.backbone.embed_dim)
+single_bert_model.pooler = None
+
+input_shape = dict()
+input_shape['s1'] = Dict({'channel': 128,  'stride': 4})
+input_shape['s2'] = Dict({'channel': 256,  'stride': 8})
+input_shape['s3'] = Dict({'channel': 512,  'stride': 16})
+input_shape['s4'] = Dict({'channel': 1024, 'stride': 32})
+
+
+
+cfg = Dict()
+cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 
+cfg.MODEL.MASK_FORMER.NHEADS = 8
+cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 4
+cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256
+cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"]
+
+cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1
+cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 1
+cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
+cfg.MODEL.MASK_FORMER.DEC_LAYERS = 10
+cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+
+maskformer_head = MaskFormerHead(cfg, input_shape)
+
+
+model = WrapperModel(single_model.backbone, single_bert_model, maskformer_head)
+
+
+
+checkpoint = torch.load(weights, map_location='cpu')
+
+model.load_state_dict(checkpoint['model'], strict=False)
+model.to(device)
+model.eval()
+#single_bert_model.load_state_dict(checkpoint['bert_model'])
+#single_model.load_state_dict(checkpoint['model'])
+#model = single_model.to(device)
+#bert_model = single_bert_model.to(device)
+
+
+# inference
+#import torch.nn.functional as F
+#last_hidden_states = bert_model(padded_sent_toks, attention_mask=attention_mask)[0]
+#embedding = last_hidden_states.permute(0, 2, 1)
+#output = model(img, embedding, l_mask=attention_mask.unsqueeze(-1))
+#output = output.argmax(1, keepdim=True)  # (1, 1, 480, 480)
+#output = F.interpolate(output.float(), (original_h, original_w))  # 'nearest'; resize to the original image size
+#output = output.squeeze()  # (orig_h, orig_w)
+#output = output.cpu().data.numpy()  # (orig_h, orig_w)
+
+#output = pred_masks[0]
+
+#output = output.cpu()
+
+
+
+#print(output.shape)
+#output_mask = output.argmax(1).data.numpy()
+#output = (output > 0.5).data.cpu().numpy()
+
+
+# show/save results
+def overlay_davis(image, mask, colors=[[0, 0, 0], [255, 0, 0]], cscale=1, alpha=0.4):
+    from scipy.ndimage.morphology import binary_dilation
+
+    colors = np.reshape(colors, (-1, 3))
+    colors = np.atleast_2d(colors) * cscale
+
+    im_overlay = image.copy()
+    object_ids = np.unique(mask)
+
+    for object_id in object_ids[1:]:
+        # Overlay color on  binary mask
+        foreground = image*alpha + np.ones(image.shape)*(1-alpha) * np.array(colors[object_id])
+        binary_mask = mask == object_id
+
+        # Compose image
+        im_overlay[binary_mask] = foreground[binary_mask]
+
+        # countours = skimage.morphology.binary.binary_dilation(binary_mask) - binary_mask
+        countours = binary_dilation(binary_mask) ^ binary_mask
+        # countours = cv2.dilate(binary_mask, cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))) - binary_mask
+        im_overlay[countours, :] = 0
+
+    return im_overlay.astype(image.dtype)
+
+
+def run_model(img, sentence):
+
+#img = Image.open(image_path).convert("RGB")
+    img = Image.fromarray(img)
+    img = img.convert("RGB")
+    #print(img.shape)
+    img_ndarray = np.array(img)  # (orig_h, orig_w, 3); for visualization
+    original_w, original_h = img.size  # PIL .size returns width first and height second
+
+    image_transforms = T.Compose(
+        [
+         T.Resize((480, 480)),
+         T.ToTensor(),
+         T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ]
+    )
+
+    img = image_transforms(img).unsqueeze(0)  # (1, 3, 480, 480)
+    img = img.to(device)  # for inference (input)
+
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    sentence_tokenized = tokenizer.encode(text=sentence, add_special_tokens=True)
+    sentence_tokenized = sentence_tokenized[:20]  # if the sentence is longer than 20, then this truncates it to 20 words
+    # pad the tokenized sentence
+    padded_sent_toks = [0] * 20
+    padded_sent_toks[:len(sentence_tokenized)] = sentence_tokenized
+    # create a sentence token mask: 1 for real words; 0 for padded tokens
+    attention_mask = [0] * 20
+    attention_mask[:len(sentence_tokenized)] = [1]*len(sentence_tokenized)
+    # convert lists to tensors
+    padded_sent_toks = torch.tensor(padded_sent_toks).unsqueeze(0)  # (1, 20)
+    attention_mask = torch.tensor(attention_mask).unsqueeze(0)  # (1, 20)
+    padded_sent_toks = padded_sent_toks.to(device)  # for inference (input)
+    attention_mask = attention_mask.to(device)  # for inference (input)
+
+    output = model(img, padded_sent_toks, attention_mask)[0]
+    #print(output[0].keys())
+    #print(output[1].shape)
+    mask_cls_results = output["pred_logits"]
+    mask_pred_results = output["pred_masks"]
+
+    target_shape = img_ndarray.shape[:2]
+    #print(target_shape, mask_pred_results.shape)
+    mask_pred_results = F.interpolate(mask_pred_results, size=(480,480), mode='bilinear', align_corners=True)
+
+    pred_masks = model.semantic_inference(mask_cls_results, mask_pred_results)                
+
+    output = torch.nn.functional.interpolate(pred_masks, target_shape)
+    output = (output > 0.5).data.cpu().numpy()
+
+    output = output.astype(np.uint8)  # (orig_h, orig_w), np.uint8
+    # Overlay the mask on the image
+    print(img_ndarray.shape, output.shape)
+    visualization = overlay_davis(img_ndarray, output[0][0])  # red
+    visualization = Image.fromarray(visualization)
+    # show the visualization
+    #visualization.show()
+    # Save the visualization
+    #visualization.save('./demo/spoon_on_the_dish.jpg')
+    return visualization
+
+
+
+
+demo = gr.Interface(run_model, inputs=[gr.Image(), "text"], outputs=["image"])
+demo.launch()
+

elia/args.py

@@ -0,0 +1,74 @@
+import argparse
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='LAVT training and testing')
+    parser.add_argument('--amsgrad', action='store_true',
+                        help='if true, set amsgrad to True in an Adam or AdamW optimizer.')
+    parser.add_argument('-b', '--batch-size', default=8, type=int)
+    parser.add_argument('--bert_tokenizer', default='bert-base-uncased', help='BERT tokenizer')
+    parser.add_argument('--ck_bert', default='bert-base-uncased', help='pre-trained BERT weights')
+    parser.add_argument('--dataset', default='refcoco', help='refcoco, refcoco+, or refcocog')
+    parser.add_argument('--ddp_trained_weights', action='store_true',
+                        help='Only needs specified when testing,'
+                             'whether the weights to be loaded are from a DDP-trained model')
+    parser.add_argument('--device', default='cuda:0', help='device')  # only used when testing on a single machine
+    parser.add_argument('--epochs', default=40, type=int, metavar='N', help='number of total epochs to run')
+    parser.add_argument('--fusion_drop', default=0.0, type=float, help='dropout rate for PWAMs')
+    parser.add_argument('--img_size', default=480, type=int, help='input image size')
+    parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel')
+    parser.add_argument('--lr', default=0.00005, type=float, help='the initial learning rate')
+    parser.add_argument('--mha', default='', help='If specified, should be in the format of a-b-c-d, e.g., 4-4-4-4,'
+                                                  'where a, b, c, and d refer to the numbers of heads in stage-1,'
+                                                  'stage-2, stage-3, and stage-4 PWAMs')
+    parser.add_argument('--model', default='lavt', help='model: lavt, lavt_one')
+    parser.add_argument('--model_id', default='lavt', help='name to identify the model')
+    parser.add_argument('--output-dir', default='./checkpoints/', help='path where to save checkpoint weights')
+    parser.add_argument('--pin_mem', action='store_true',
+                        help='If true, pin memory when using the data loader.')
+    parser.add_argument('--pretrained_swin_weights', default='',
+                        help='path to pre-trained Swin backbone weights')
+    parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
+    parser.add_argument('--refer_data_root', default='./refer/data/', help='REFER dataset root directory')
+    parser.add_argument('--resume', default='auto', help='resume from checkpoint')
+    parser.add_argument('--split', default='test', help='only used when testing')
+    parser.add_argument('--splitBy', default='unc', help='change to umd or google when the dataset is G-Ref (RefCOCOg)')
+    parser.add_argument('--swin_type', default='base',
+                        help='tiny, small, base, or large variants of the Swin Transformer')
+    parser.add_argument('--wd', '--weight-decay', default=1e-2, type=float, metavar='W', help='weight decay',
+                        dest='weight_decay')
+    parser.add_argument('--window12', action='store_true',
+                        help='only needs specified when testing,'
+                             'when training, window size is inferred from pre-trained weights file name'
+                             '(containing \'window12\'). Initialize Swin with window size 12 instead of the default 7.')
+    parser.add_argument('-j', '--workers', default=16, type=int, metavar='N', help='number of data loading workers')
+    parser.add_argument('--seed', default=0, type=int)
+    parser.add_argument('--max_ckpt', default=2, type=int)
+    parser.add_argument('--num_object_queries', default=1, type=int)
+    parser.add_argument('--no_object_weight', default=0.0, type=float)
+    parser.add_argument('--class_weight', default=2.0, type=float)
+    parser.add_argument('--dice_weight', default=2.0, type=float)
+    parser.add_argument('--mask_weight', default=2.0, type=float)
+    parser.add_argument('--train_num_points', default=12544, type=int)
+    parser.add_argument('--dim_feedforward', default=2048, type=int)
+    parser.add_argument('--dec_layers', default=10, type=int)
+    parser.add_argument('--transformer_enc_layers', default=4, type=int)
+
+    parser.add_argument('--plic_pos_weight', default=0.5, type=float)
+    parser.add_argument('--plic_neg_weight', default=0.5, type=float)
+    parser.add_argument('--plic_lang_weight', default=0.5, type=float)
+    parser.add_argument('--plic_pos_alpha', default=0.0, type=float)
+    parser.add_argument('--plic_neg_alpha', default=0.0, type=float)
+    parser.add_argument('--plic_lang_alpha', default=0.0, type=float)
+    parser.add_argument('--plic_pos_temp', default=0.2, type=float)
+    parser.add_argument('--plic_neg_temp', default=0.2, type=float)
+    parser.add_argument('--plic_lang_temp', default=0.2, type=float)
+    parser.add_argument('--smlm_weight', default=1.0, type=float)
+    parser.add_argument('--vis_dir', default='./vis_dir')
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args_dict = parser.parse_args()

elia/bert/activations.py

@@ -0,0 +1,56 @@
+import logging
+import math
+
+import torch
+import torch.nn.functional as F
+
+
+logger = logging.getLogger(__name__)
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+def _gelu_python(x):
+    """ Original Implementation of the gelu activation function in Google Bert repo when initially created.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        This is now written in C in torch.nn.functional
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def gelu_new(x):
+    """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+
+
+if torch.__version__ < "1.4.0":
+    gelu = _gelu_python
+else:
+    gelu = F.gelu
+
+
+def gelu_fast(x):
+    return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))
+
+
+ACT2FN = {
+    "relu": F.relu,
+    "swish": swish,
+    "gelu": gelu,
+    "tanh": torch.tanh,
+    "gelu_new": gelu_new,
+    "gelu_fast": gelu_fast,
+}
+
+
+def get_activation(activation_string):
+    if activation_string in ACT2FN:
+        return ACT2FN[activation_string]
+    else:
+        raise KeyError("function {} not found in ACT2FN mapping {}".format(activation_string, list(ACT2FN.keys())))

elia/bert/configuration_bert.py

@@ -0,0 +1,143 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+""" BERT model configuration """
+
+
+import logging
+
+from .configuration_utils import PretrainedConfig
+
+
+logger = logging.getLogger(__name__)
+
+BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
+    "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
+    "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
+    "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
+    "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
+    "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
+    "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
+    "bert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
+    "bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
+    "bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
+    "bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
+    "bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
+    "bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
+    "bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json",
+    "bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json",
+    "cl-tohoku/bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese/config.json",
+    "cl-tohoku/bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking/config.json",
+    "cl-tohoku/bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char/config.json",
+    "cl-tohoku/bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking/config.json",
+    "TurkuNLP/bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json",
+    "TurkuNLP/bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json",
+    "wietsedv/bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+}
+
+
+class BertConfig(PretrainedConfig):
+    r"""
+        This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
+        It is used to instantiate an BERT model according to the specified arguments, defining the model
+        architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+        the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
+
+        Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+        to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+        for more information.
+
+
+        Args:
+            vocab_size (:obj:`int`, optional, defaults to 30522):
+                Vocabulary size of the BERT model. Defines the different tokens that
+                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
+            hidden_size (:obj:`int`, optional, defaults to 768):
+                Dimensionality of the encoder layers and the pooler layer.
+            num_hidden_layers (:obj:`int`, optional, defaults to 12):
+                Number of hidden layers in the Transformer encoder.
+            num_attention_heads (:obj:`int`, optional, defaults to 12):
+                Number of attention heads for each attention layer in the Transformer encoder.
+            intermediate_size (:obj:`int`, optional, defaults to 3072):
+                Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+            hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+                The non-linear activation function (function or string) in the encoder and pooler.
+                If string, "gelu", "relu", "swish" and "gelu_new" are supported.
+            hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+                The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+                The dropout ratio for the attention probabilities.
+            max_position_embeddings (:obj:`int`, optional, defaults to 512):
+                The maximum sequence length that this model might ever be used with.
+                Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+            type_vocab_size (:obj:`int`, optional, defaults to 2):
+                The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
+            initializer_range (:obj:`float`, optional, defaults to 0.02):
+                The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+                The epsilon used by the layer normalization layers.
+            gradient_checkpointing (:obj:`bool`, optional, defaults to False):
+                If True, use gradient checkpointing to save memory at the expense of slower backward pass.
+
+        Example::
+
+            >>> from transformers import BertModel, BertConfig
+
+            >>> # Initializing a BERT bert-base-uncased style configuration
+            >>> configuration = BertConfig()
+
+            >>> # Initializing a model from the bert-base-uncased style configuration
+            >>> model = BertModel(configuration)
+
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
+    """
+    model_type = "bert"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        gradient_checkpointing=False,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.gradient_checkpointing = gradient_checkpointing

elia/bert/configuration_utils.py

@@ -0,0 +1,408 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+""" Configuration base class and utilities."""
+
+
+import copy
+import json
+import logging
+import os
+from typing import Dict, Tuple
+
+from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
+
+
+logger = logging.getLogger(__name__)
+
+
+class PretrainedConfig(object):
+    r""" Base class for all configuration classes.
+        Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.
+
+        Note:
+            A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.
+            It only affects the model's configuration.
+
+        Class attributes (overridden by derived classes):
+            - ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.
+
+        Args:
+            finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):
+                Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
+            num_labels (:obj:`int`, `optional`, defaults to `2`):
+                Number of classes to use when the model is a classification model (sequences/tokens)
+            output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Should the model returns all hidden-states.
+            output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Should the model returns all attentions.
+            torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Is the model used with Torchscript (for PyTorch models).
+    """
+    model_type: str = ""
+
+    def __init__(self, **kwargs):
+        # Attributes with defaults
+        self.output_hidden_states = kwargs.pop("output_hidden_states", False)
+        self.output_attentions = kwargs.pop("output_attentions", False)
+        self.use_cache = kwargs.pop("use_cache", True)  # Not used by all models
+        self.torchscript = kwargs.pop("torchscript", False)  # Only used by PyTorch models
+        self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
+        self.pruned_heads = kwargs.pop("pruned_heads", {})
+
+        # Is decoder is used in encoder-decoder models to differentiate encoder from decoder
+        self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
+        self.is_decoder = kwargs.pop("is_decoder", False)
+
+        # Parameters for sequence generation
+        self.max_length = kwargs.pop("max_length", 20)
+        self.min_length = kwargs.pop("min_length", 0)
+        self.do_sample = kwargs.pop("do_sample", False)
+        self.early_stopping = kwargs.pop("early_stopping", False)
+        self.num_beams = kwargs.pop("num_beams", 1)
+        self.temperature = kwargs.pop("temperature", 1.0)
+        self.top_k = kwargs.pop("top_k", 50)
+        self.top_p = kwargs.pop("top_p", 1.0)
+        self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
+        self.length_penalty = kwargs.pop("length_penalty", 1.0)
+        self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
+        self.bad_words_ids = kwargs.pop("bad_words_ids", None)
+        self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
+
+        # Fine-tuning task arguments
+        self.architectures = kwargs.pop("architectures", None)
+        self.finetuning_task = kwargs.pop("finetuning_task", None)
+        self.id2label = kwargs.pop("id2label", None)
+        self.label2id = kwargs.pop("label2id", None)
+        if self.id2label is not None:
+            kwargs.pop("num_labels", None)
+            self.id2label = dict((int(key), value) for key, value in self.id2label.items())
+            # Keys are always strings in JSON so convert ids to int here.
+        else:
+            self.num_labels = kwargs.pop("num_labels", 2)
+
+        # Tokenizer arguments TODO: eventually tokenizer and models should share the same config
+        self.prefix = kwargs.pop("prefix", None)
+        self.bos_token_id = kwargs.pop("bos_token_id", None)
+        self.pad_token_id = kwargs.pop("pad_token_id", None)
+        self.eos_token_id = kwargs.pop("eos_token_id", None)
+        self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
+
+        # task specific arguments
+        self.task_specific_params = kwargs.pop("task_specific_params", None)
+
+        # TPU arguments
+        self.xla_device = kwargs.pop("xla_device", None)
+
+        # Additional attributes without default values
+        for key, value in kwargs.items():
+            try:
+                setattr(self, key, value)
+            except AttributeError as err:
+                logger.error("Can't set {} with value {} for {}".format(key, value, self))
+                raise err
+
+    @property
+    def num_labels(self):
+        return len(self.id2label)
+
+    @num_labels.setter
+    def num_labels(self, num_labels):
+        self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
+        self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
+
+    def save_pretrained(self, save_directory):
+        """
+        Save a configuration object to the directory `save_directory`, so that it
+        can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
+
+        Args:
+            save_directory (:obj:`string`):
+                Directory where the configuration JSON file will be saved.
+        """
+        if os.path.isfile(save_directory):
+            raise AssertionError("Provided path ({}) should be a directory, not a file".format(save_directory))
+        os.makedirs(save_directory, exist_ok=True)
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        self.to_json_file(output_config_file, use_diff=True)
+        logger.info("Configuration saved in {}".format(output_config_file))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig":
+        r"""
+
+        Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
+
+        Args:
+            pretrained_model_name_or_path (:obj:`string`):
+                either:
+                  - a string with the `shortcut name` of a pre-trained model configuration to load from cache or
+                    download, e.g.: ``bert-base-uncased``.
+                  - a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to
+                    our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+                  - a path to a `directory` containing a configuration file saved using the
+                    :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                  - a path or url to a saved configuration JSON `file`, e.g.:
+                    ``./my_model_directory/configuration.json``.
+            cache_dir (:obj:`string`, `optional`):
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+            kwargs (:obj:`Dict[str, any]`, `optional`):
+                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
+                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
+                controlled by the `return_unused_kwargs` keyword parameter.
+            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exist.
+            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
+            proxies (:obj:`Dict`, `optional`):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.:
+                :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`
+                The proxies are used on each request.
+            return_unused_kwargs: (`optional`) bool:
+                If False, then this function returns just the final configuration object.
+                If True, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs` is a
+                dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part
+                of kwargs which has not been used to update `config` and is otherwise ignored.
+
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object
+
+        Examples::
+
+            # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
+            # derived class: BertConfig
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+            config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
+            config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
+            assert config.output_attention == True
+            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
+                                                               foo=False, return_unused_kwargs=True)
+            assert config.output_attention == True
+            assert unused_kwargs == {'foo': False}
+
+        """
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        return cls.from_dict(config_dict, **kwargs)
+
+    @classmethod
+    def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict, Dict]:
+        """
+        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used
+        for instantiating a Config using `from_dict`.
+
+        Parameters:
+            pretrained_model_name_or_path (:obj:`string`):
+                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
+
+        Returns:
+            :obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.
+
+        """
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+
+        if os.path.isdir(pretrained_model_name_or_path):
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+            config_file = pretrained_model_name_or_path
+        else:
+            config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)
+
+        try:
+            # Load from URL or cache if already cached
+            resolved_config_file = cached_path(
+                config_file,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                local_files_only=local_files_only,
+            )
+            # Load config dict
+            if resolved_config_file is None:
+                raise EnvironmentError
+            config_dict = cls._dict_from_json_file(resolved_config_file)
+
+        except EnvironmentError:
+            msg = (
+                f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
+                f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
+                f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
+            )
+            raise EnvironmentError(msg)
+
+        except json.JSONDecodeError:
+            msg = (
+                "Couldn't reach server at '{}' to download configuration file or "
+                "configuration file is not a valid JSON file. "
+                "Please check network or file content here: {}.".format(config_file, resolved_config_file)
+            )
+            raise EnvironmentError(msg)
+
+        if resolved_config_file == config_file:
+            logger.info("loading configuration file {}".format(config_file))
+        else:
+            logger.info("loading configuration file {} from cache at {}".format(config_file, resolved_config_file))
+
+        return config_dict, kwargs
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict, **kwargs) -> "PretrainedConfig":
+        """
+        Constructs a `Config` from a Python dictionary of parameters.
+
+        Args:
+            config_dict (:obj:`Dict[str, any]`):
+                Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved
+                from a pre-trained checkpoint by leveraging the :func:`~transformers.PretrainedConfig.get_config_dict`
+                method.
+            kwargs (:obj:`Dict[str, any]`):
+                Additional parameters from which to initialize the configuration object.
+
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object
+        """
+        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
+
+        config = cls(**config_dict)
+
+        if hasattr(config, "pruned_heads"):
+            config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())
+
+        # Update config with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        logger.info("Model config %s", str(config))
+        if return_unused_kwargs:
+            return config, kwargs
+        else:
+            return config
+
+    @classmethod
+    def from_json_file(cls, json_file: str) -> "PretrainedConfig":
+        """
+        Constructs a `Config` from the path to a json file of parameters.
+
+        Args:
+            json_file (:obj:`string`):
+                Path to the JSON file containing the parameters.
+
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object
+
+        """
+        config_dict = cls._dict_from_json_file(json_file)
+        return cls(**config_dict)
+
+    @classmethod
+    def _dict_from_json_file(cls, json_file: str):
+        with open(json_file, "r", encoding="utf-8") as reader:
+            text = reader.read()
+        return json.loads(text)
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __repr__(self):
+        return "{} {}".format(self.__class__.__name__, self.to_json_string())
+
+    def to_diff_dict(self):
+        """
+        Removes all attributes from config which correspond to the default
+        config attributes for better readability and serializes to a Python
+        dictionary.
+
+        Returns:
+            :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        config_dict = self.to_dict()
+
+        # get the default config dict
+        default_config_dict = PretrainedConfig().to_dict()
+
+        serializable_config_dict = {}
+
+        # only serialize values that differ from the default config
+        for key, value in config_dict.items():
+            if key not in default_config_dict or value != default_config_dict[key]:
+                serializable_config_dict[key] = value
+
+        return serializable_config_dict
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary.
+
+        Returns:
+            :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        if hasattr(self.__class__, "model_type"):
+            output["model_type"] = self.__class__.model_type
+        return output
+
+    def to_json_string(self, use_diff=True):
+        """
+        Serializes this instance to a JSON string.
+
+        Args:
+            use_diff (:obj:`bool`):
+                If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON string.
+
+        Returns:
+            :obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.
+        """
+        if use_diff is True:
+            config_dict = self.to_diff_dict()
+        else:
+            config_dict = self.to_dict()
+        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path, use_diff=True):
+        """
+        Save this instance to a json file.
+
+        Args:
+            json_file_path (:obj:`string`):
+                Path to the JSON file in which this configuration instance's parameters will be saved.
+            use_diff (:obj:`bool`):
+                If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON file.
+        """
+        with open(json_file_path, "w", encoding="utf-8") as writer:
+            writer.write(self.to_json_string(use_diff=use_diff))
+
+    def update(self, config_dict: Dict):
+        """
+        Updates attributes of this class
+        with attributes from `config_dict`.
+
+        Args:
+            :obj:`Dict[str, any]`: Dictionary of attributes that shall be updated for this class.
+        """
+        for key, value in config_dict.items():
+            setattr(self, key, value)

elia/bert/file_utils.py

@@ -0,0 +1,808 @@
+"""
+Utilities for working with the local dataset cache.
+This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+Copyright by the AllenNLP authors.
+"""
+
+import fnmatch
+import json
+import logging
+import os
+import shutil
+import sys
+import tarfile
+import tempfile
+from contextlib import contextmanager
+from functools import partial, wraps
+from hashlib import sha256
+from pathlib import Path
+from typing import Dict, Optional, Union
+from urllib.parse import urlparse
+from zipfile import ZipFile, is_zipfile
+
+import requests
+from filelock import FileLock
+from tqdm.auto import tqdm
+
+#from . import __version__
+__version__ = "3.0.2"
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+try:
+    USE_TF = os.environ.get("USE_TF", "AUTO").upper()
+    USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
+    if USE_TORCH in ("1", "ON", "YES", "AUTO") and USE_TF not in ("1", "ON", "YES"):
+        import torch
+
+        _torch_available = True  # pylint: disable=invalid-name
+        logger.info("PyTorch version {} available.".format(torch.__version__))
+    else:
+        logger.info("Disabling PyTorch because USE_TF is set")
+        _torch_available = False
+except ImportError:
+    _torch_available = False  # pylint: disable=invalid-name
+
+try:
+    USE_TF = os.environ.get("USE_TF", "AUTO").upper()
+    USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
+
+    if USE_TF in ("1", "ON", "YES", "AUTO") and USE_TORCH not in ("1", "ON", "YES"):
+        import tensorflow as tf
+
+        assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
+        _tf_available = True  # pylint: disable=invalid-name
+        logger.info("TensorFlow version {} available.".format(tf.__version__))
+    else:
+        logger.info("Disabling Tensorflow because USE_TORCH is set")
+        _tf_available = False
+except (ImportError, AssertionError):
+    _tf_available = False  # pylint: disable=invalid-name
+
+
+try:
+    from torch.hub import _get_torch_home
+
+    torch_cache_home = _get_torch_home()
+except ImportError:
+    torch_cache_home = os.path.expanduser(
+        os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
+    )
+
+
+try:
+    import torch_xla.core.xla_model as xm  # noqa: F401
+
+    if _torch_available:
+        _torch_tpu_available = True  # pylint: disable=
+    else:
+        _torch_tpu_available = False
+except ImportError:
+    _torch_tpu_available = False
+
+
+try:
+    import psutil  # noqa: F401
+
+    _psutil_available = True
+
+except ImportError:
+    _psutil_available = False
+
+
+try:
+    import py3nvml  # noqa: F401
+
+    _py3nvml_available = True
+
+except ImportError:
+    _py3nvml_available = False
+
+
+try:
+    from apex import amp  # noqa: F401
+
+    _has_apex = True
+except ImportError:
+    _has_apex = False
+
+default_cache_path = os.path.join(torch_cache_home, "transformers")
+
+
+PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
+PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
+TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
+
+WEIGHTS_NAME = "pytorch_model.bin"
+TF2_WEIGHTS_NAME = "tf_model.h5"
+TF_WEIGHTS_NAME = "model.ckpt"
+CONFIG_NAME = "config.json"
+MODEL_CARD_NAME = "modelcard.json"
+
+
+MULTIPLE_CHOICE_DUMMY_INPUTS = [[[0], [1]], [[0], [1]]]
+DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
+DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
+
+S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
+CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
+
+
+def is_torch_available():
+    return _torch_available
+
+
+def is_tf_available():
+    return _tf_available
+
+
+def is_torch_tpu_available():
+    return _torch_tpu_available
+
+
+def is_psutil_available():
+    return _psutil_available
+
+
+def is_py3nvml_available():
+    return _py3nvml_available
+
+
+def is_apex_available():
+    return _has_apex
+
+
+def add_start_docstrings(*docstr):
+    def docstring_decorator(fn):
+        fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
+        return fn
+
+    return docstring_decorator
+
+
+def add_start_docstrings_to_callable(*docstr):
+    def docstring_decorator(fn):
+        class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
+        intro = "   The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
+        note = r"""
+
+    .. note::
+        Although the recipe for forward pass needs to be defined within
+        this function, one should call the :class:`Module` instance afterwards
+        instead of this since the former takes care of running the
+        pre and post processing steps while the latter silently ignores them.
+        """
+        fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
+        return fn
+
+    return docstring_decorator
+
+
+def add_end_docstrings(*docstr):
+    def docstring_decorator(fn):
+        fn.__doc__ = fn.__doc__ + "".join(docstr)
+        return fn
+
+    return docstring_decorator
+
+
+PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1
+
+        >>> outputs = model(**inputs, labels=labels)
+        >>> loss, scores = outputs[:2]
+"""
+
+PT_QUESTION_ANSWERING_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> start_positions = torch.tensor([1])
+        >>> end_positions = torch.tensor([3])
+
+        >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
+        >>> loss, start_scores, end_scores = outputs[:3]
+"""
+
+PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(**inputs, labels=labels)
+        >>> loss, logits = outputs[:2]
+"""
+
+PT_MASKED_LM_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> input_ids = tokenizer("Hello, my dog is cute", return_tensors="pt")["input_ids"]
+
+        >>> outputs = model(input_ids, labels=input_ids)
+        >>> loss, prediction_scores = outputs[:2]
+"""
+
+PT_BASE_MODEL_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+"""
+
+PT_MULTIPLE_CHOICE_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import torch
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        >>> choice0 = "It is eaten with a fork and a knife."
+        >>> choice1 = "It is eaten while held in the hand."
+        >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
+
+        >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
+        >>> outputs = model(**{{k: v.unsqueeze(0) for k,v in encoding.items()}}, labels=labels)  # batch size is 1
+
+        >>> # the linear classifier still needs to be trained
+        >>> loss, logits = outputs[:2]
+"""
+
+PT_CAUSAL_LM_SAMPLE = r"""
+    Example::
+
+        >>> import torch
+        >>> from transformers import {tokenizer_class}, {model_class}
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs, labels=inputs["input_ids"])
+        >>> loss, logits = outputs[:2]
+"""
+
+TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
+        >>> input_ids = inputs["input_ids"]
+        >>> inputs["labels"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1
+
+        >>> outputs = model(inputs)
+        >>> loss, scores = outputs[:2]
+"""
+
+TF_QUESTION_ANSWERING_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+        >>> input_dict = tokenizer(question, text, return_tensors='tf')
+        >>> start_scores, end_scores = model(input_dict)
+
+        >>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
+        >>> answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
+"""
+
+TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
+        >>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
+
+        >>> outputs = model(inputs)
+        >>> loss, logits = outputs[:2]
+"""
+
+TF_MASKED_LM_SAMPLE = r"""
+    Example::
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
+
+        >>> outputs = model(input_ids)
+        >>> prediction_scores = outputs[0]
+"""
+
+TF_BASE_MODEL_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
+        >>> outputs = model(inputs)
+
+        >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+"""
+
+TF_MULTIPLE_CHOICE_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        >>> choice0 = "It is eaten with a fork and a knife."
+        >>> choice1 = "It is eaten while held in the hand."
+
+        >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='tf', padding=True)
+        >>> inputs = {{k: tf.expand_dims(v, 0) for k, v in encoding.items()}}
+        >>> outputs = model(inputs)  # batch size is 1
+
+        >>> # the linear classifier still needs to be trained
+        >>> logits = outputs[0]
+"""
+
+TF_CAUSAL_LM_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {tokenizer_class}, {model_class}
+        >>> import tensorflow as tf
+
+        >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
+        >>> outputs = model(inputs)
+        >>> logits = outputs[0]
+"""
+
+
+def add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None):
+    def docstring_decorator(fn):
+        model_class = fn.__qualname__.split(".")[0]
+        is_tf_class = model_class[:2] == "TF"
+
+        if "SequenceClassification" in model_class:
+            code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE
+        elif "QuestionAnswering" in model_class:
+            code_sample = TF_QUESTION_ANSWERING_SAMPLE if is_tf_class else PT_QUESTION_ANSWERING_SAMPLE
+        elif "TokenClassification" in model_class:
+            code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE
+        elif "MultipleChoice" in model_class:
+            code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE
+        elif "MaskedLM" in model_class:
+            code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE
+        elif "LMHead" in model_class:
+            code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE
+        elif "Model" in model_class:
+            code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE
+        else:
+            raise ValueError(f"Docstring can't be built for model {model_class}")
+
+        built_doc = code_sample.format(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)
+        fn.__doc__ = (fn.__doc__ or "") + "".join(docstr) + built_doc
+        return fn
+
+    return docstring_decorator
+
+
+def is_remote_url(url_or_filename):
+    parsed = urlparse(url_or_filename)
+    return parsed.scheme in ("http", "https")
+
+
+def hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:
+    """
+    Resolve a model identifier, and a file name, to a HF-hosted url
+    on either S3 or Cloudfront (a Content Delivery Network, or CDN).
+
+    Cloudfront is replicated over the globe so downloads are way faster
+    for the end user (and it also lowers our bandwidth costs). However, it
+    is more aggressively cached by default, so may not always reflect the
+    latest changes to the underlying file (default TTL is 24 hours).
+
+    In terms of client-side caching from this library, even though
+    Cloudfront relays the ETags from S3, using one or the other
+    (or switching from one to the other) will affect caching: cached files
+    are not shared between the two because the cached file's name contains
+    a hash of the url.
+    """
+    endpoint = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
+    legacy_format = "/" not in model_id
+    if legacy_format:
+        return f"{endpoint}/{model_id}-{filename}"
+    else:
+        return f"{endpoint}/{model_id}/{filename}"
+
+
+def url_to_filename(url, etag=None):
+    """
+    Convert `url` into a hashed filename in a repeatable way.
+    If `etag` is specified, append its hash to the url's, delimited
+    by a period.
+    If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name
+    so that TF 2.0 can identify it as a HDF5 file
+    (see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
+    """
+    url_bytes = url.encode("utf-8")
+    url_hash = sha256(url_bytes)
+    filename = url_hash.hexdigest()
+
+    if etag:
+        etag_bytes = etag.encode("utf-8")
+        etag_hash = sha256(etag_bytes)
+        filename += "." + etag_hash.hexdigest()
+
+    if url.endswith(".h5"):
+        filename += ".h5"
+
+    return filename
+
+
+def filename_to_url(filename, cache_dir=None):
+    """
+    Return the url and etag (which may be ``None``) stored for `filename`.
+    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
+    """
+    if cache_dir is None:
+        cache_dir = TRANSFORMERS_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    cache_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(cache_path):
+        raise EnvironmentError("file {} not found".format(cache_path))
+
+    meta_path = cache_path + ".json"
+    if not os.path.exists(meta_path):
+        raise EnvironmentError("file {} not found".format(meta_path))
+
+    with open(meta_path, encoding="utf-8") as meta_file:
+        metadata = json.load(meta_file)
+    url = metadata["url"]
+    etag = metadata["etag"]
+
+    return url, etag
+
+
+def cached_path(
+    url_or_filename,
+    cache_dir=None,
+    force_download=False,
+    proxies=None,
+    resume_download=False,
+    user_agent: Union[Dict, str, None] = None,
+    extract_compressed_file=False,
+    force_extract=False,
+    local_files_only=False,
+) -> Optional[str]:
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    Args:
+        cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
+        force_download: if True, re-dowload the file even if it's already cached in the cache dir.
+        resume_download: if True, resume the download if incompletly recieved file is found.
+        user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
+        extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
+            file in a folder along the archive.
+        force_extract: if True when extract_compressed_file is True and the archive was already extracted,
+            re-extract the archive and overide the folder where it was extracted.
+
+    Return:
+        None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
+        Local path (string) otherwise
+    """
+    if cache_dir is None:
+        cache_dir = TRANSFORMERS_CACHE
+    if isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if is_remote_url(url_or_filename):
+        # URL, so get it from the cache (downloading if necessary)
+        output_path = get_from_cache(
+            url_or_filename,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            resume_download=resume_download,
+            user_agent=user_agent,
+            local_files_only=local_files_only,
+        )
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        output_path = url_or_filename
+    elif urlparse(url_or_filename).scheme == "":
+        # File, but it doesn't exist.
+        raise EnvironmentError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
+
+    if extract_compressed_file:
+        if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
+            return output_path
+
+        # Path where we extract compressed archives
+        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
+        output_dir, output_file = os.path.split(output_path)
+        output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
+        output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
+
+        if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
+            return output_path_extracted
+
+        # Prevent parallel extractions
+        lock_path = output_path + ".lock"
+        with FileLock(lock_path):
+            shutil.rmtree(output_path_extracted, ignore_errors=True)
+            os.makedirs(output_path_extracted)
+            if is_zipfile(output_path):
+                with ZipFile(output_path, "r") as zip_file:
+                    zip_file.extractall(output_path_extracted)
+                    zip_file.close()
+            elif tarfile.is_tarfile(output_path):
+                tar_file = tarfile.open(output_path)
+                tar_file.extractall(output_path_extracted)
+                tar_file.close()
+            else:
+                raise EnvironmentError("Archive format of {} could not be identified".format(output_path))
+
+        return output_path_extracted
+
+    return output_path
+
+
+def http_get(url, temp_file, proxies=None, resume_size=0, user_agent: Union[Dict, str, None] = None):
+    ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
+    if is_torch_available():
+        ua += "; torch/{}".format(torch.__version__)
+    if is_tf_available():
+        ua += "; tensorflow/{}".format(tf.__version__)
+    if isinstance(user_agent, dict):
+        ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
+    elif isinstance(user_agent, str):
+        ua += "; " + user_agent
+    headers = {"user-agent": ua}
+    if resume_size > 0:
+        headers["Range"] = "bytes=%d-" % (resume_size,)
+    response = requests.get(url, stream=True, proxies=proxies, headers=headers)
+    if response.status_code == 416:  # Range not satisfiable
+        return
+    content_length = response.headers.get("Content-Length")
+    total = resume_size + int(content_length) if content_length is not None else None
+    progress = tqdm(
+        unit="B",
+        unit_scale=True,
+        total=total,
+        initial=resume_size,
+        desc="Downloading",
+        disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
+    )
+    for chunk in response.iter_content(chunk_size=1024):
+        if chunk:  # filter out keep-alive new chunks
+            progress.update(len(chunk))
+            temp_file.write(chunk)
+    progress.close()
+
+
+def get_from_cache(
+    url,
+    cache_dir=None,
+    force_download=False,
+    proxies=None,
+    etag_timeout=10,
+    resume_download=False,
+    user_agent: Union[Dict, str, None] = None,
+    local_files_only=False,
+) -> Optional[str]:
+    """
+    Given a URL, look for the corresponding file in the local cache.
+    If it's not there, download it. Then return the path to the cached file.
+
+    Return:
+        None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
+        Local path (string) otherwise
+    """
+    if cache_dir is None:
+        cache_dir = TRANSFORMERS_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    os.makedirs(cache_dir, exist_ok=True)
+
+    etag = None
+    if not local_files_only:
+        try:
+            response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
+            if response.status_code == 200:
+                etag = response.headers.get("ETag")
+        except (EnvironmentError, requests.exceptions.Timeout):
+            # etag is already None
+            pass
+
+    filename = url_to_filename(url, etag)
+
+    # get cache path to put the file
+    cache_path = os.path.join(cache_dir, filename)
+
+    # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
+    # try to get the last downloaded one
+    if etag is None:
+        if os.path.exists(cache_path):
+            return cache_path
+        else:
+            matching_files = [
+                file
+                for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
+                if not file.endswith(".json") and not file.endswith(".lock")
+            ]
+            if len(matching_files) > 0:
+                return os.path.join(cache_dir, matching_files[-1])
+            else:
+                # If files cannot be found and local_files_only=True,
+                # the models might've been found if local_files_only=False
+                # Notify the user about that
+                if local_files_only:
+                    raise ValueError(
+                        "Cannot find the requested files in the cached path and outgoing traffic has been"
+                        " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
+                        " to False."
+                    )
+                return None
+
+    # From now on, etag is not None.
+    if os.path.exists(cache_path) and not force_download:
+        return cache_path
+
+    # Prevent parallel downloads of the same file with a lock.
+    lock_path = cache_path + ".lock"
+    with FileLock(lock_path):
+
+        # If the download just completed while the lock was activated.
+        if os.path.exists(cache_path) and not force_download:
+            # Even if returning early like here, the lock will be released.
+            return cache_path
+
+        if resume_download:
+            incomplete_path = cache_path + ".incomplete"
+
+            @contextmanager
+            def _resumable_file_manager():
+                with open(incomplete_path, "a+b") as f:
+                    yield f
+
+            temp_file_manager = _resumable_file_manager
+            if os.path.exists(incomplete_path):
+                resume_size = os.stat(incomplete_path).st_size
+            else:
+                resume_size = 0
+        else:
+            temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
+            resume_size = 0
+
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with temp_file_manager() as temp_file:
+            logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
+
+            http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
+
+        logger.info("storing %s in cache at %s", url, cache_path)
+        os.replace(temp_file.name, cache_path)
+
+        logger.info("creating metadata file for %s", cache_path)
+        meta = {"url": url, "etag": etag}
+        meta_path = cache_path + ".json"
+        with open(meta_path, "w") as meta_file:
+            json.dump(meta, meta_file)
+
+    return cache_path
+
+
+class cached_property(property):
+    """
+    Descriptor that mimics @property but caches output in member variable.
+
+    From tensorflow_datasets
+
+    Built-in in functools from Python 3.8.
+    """
+
+    def __get__(self, obj, objtype=None):
+        # See docs.python.org/3/howto/descriptor.html#properties
+        if obj is None:
+            return self
+        if self.fget is None:
+            raise AttributeError("unreadable attribute")
+        attr = "__cached_" + self.fget.__name__
+        cached = getattr(obj, attr, None)
+        if cached is None:
+            cached = self.fget(obj)
+            setattr(obj, attr, cached)
+        return cached
+
+
+def torch_required(func):
+    # Chose a different decorator name than in tests so it's clear they are not the same.
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if is_torch_available():
+            return func(*args, **kwargs)
+        else:
+            raise ImportError(f"Method `{func.__name__}` requires PyTorch.")
+
+    return wrapper
+
+
+def tf_required(func):
+    # Chose a different decorator name than in tests so it's clear they are not the same.
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if is_tf_available():
+            return func(*args, **kwargs)
+        else:
+            raise ImportError(f"Method `{func.__name__}` requires TF.")
+
+    return wrapper

elia/bert/generation_utils.py

@@ -0,0 +1,993 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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 logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import Tensor
+from torch.nn import functional as F
+
+
+logger = logging.getLogger(__name__)
+
+
+class GenerationMixin:
+    """
+    A class contraining all of the functions supporting generation, to be used as a mixin in PreTrainedModel.
+    """
+
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}
+
+    def adjust_logits_during_generation(self, logits, **kwargs):
+        return logits
+
+    def _use_cache(self, outputs, use_cache):
+        """During generation, decide whether to pass the `past` variable to the next forward pass."""
+        if len(outputs) <= 1 or use_cache is False:
+            return False
+        if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
+            return False
+        return True
+
+    def enforce_repetition_penalty_(self, lprobs, batch_size, num_beams, prev_output_tokens, repetition_penalty):
+        """repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). """
+        for i in range(batch_size * num_beams):
+            for previous_token in set(prev_output_tokens[i].tolist()):
+                # if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
+                if lprobs[i, previous_token] < 0:
+                    lprobs[i, previous_token] *= repetition_penalty
+                else:
+                    lprobs[i, previous_token] /= repetition_penalty
+
+    def postprocess_next_token_scores(
+        self,
+        scores,
+        input_ids,
+        no_repeat_ngram_size,
+        bad_words_ids,
+        cur_len,
+        min_length,
+        max_length,
+        eos_token_id,
+        repetition_penalty,
+        batch_size,
+        num_beams,
+    ):
+        # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
+        if repetition_penalty != 1.0:
+            self.enforce_repetition_penalty_(
+                scores, batch_size, num_beams, input_ids, repetition_penalty,
+            )
+
+        # set eos token prob to zero if min_length is not reached
+        if eos_token_id is not None and cur_len < min_length:
+            scores[:, eos_token_id] = -float("inf")
+
+        if no_repeat_ngram_size > 0:
+            # calculate a list of banned tokens to prevent repetitively generating the same ngrams
+            num_batch_hypotheses = batch_size * num_beams
+            # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
+            banned_batch_tokens = calc_banned_ngram_tokens(
+                input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
+            )
+            for i, banned_tokens in enumerate(banned_batch_tokens):
+                scores[i, banned_tokens] = -float("inf")
+
+        if bad_words_ids is not None:
+            # calculate a list of banned tokens according to bad words
+            banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)
+
+            for i, banned_tokens in enumerate(banned_tokens):
+                scores[i, banned_tokens] = -float("inf")
+
+        return scores
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        max_length: Optional[int] = None,
+        min_length: Optional[int] = None,
+        do_sample: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        num_beams: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        repetition_penalty: Optional[float] = None,
+        bad_words_ids: Optional[Iterable[int]] = None,
+        bos_token_id: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[int] = None,
+        length_penalty: Optional[float] = None,
+        no_repeat_ngram_size: Optional[int] = None,
+        num_return_sequences: Optional[int] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        decoder_start_token_id: Optional[int] = None,
+        use_cache: Optional[bool] = None,
+        **model_specific_kwargs
+    ) -> torch.LongTensor:
+        r""" Generates sequences for models with a LM head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.
+
+        Adapted in part from `Facebook's XLM beam search code`_.
+
+        .. _`Facebook's XLM beam search code`:
+           https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529
+
+
+        Parameters:
+
+            input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)`
+                The sequence used as a prompt for the generation. If `None` the method initializes
+                it as an empty `torch.LongTensor` of shape `(1,)`.
+
+            max_length: (`optional`) int
+                The max length of the sequence to be generated.  Between `min_length` and infinity. Default to 20.
+
+            min_length: (`optional`) int
+                The min length of the sequence to be generated.  Between 0 and infinity. Default to 0.
+
+            do_sample: (`optional`) bool
+                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.
+
+            early_stopping: (`optional`) bool
+                if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.
+
+            num_beams: (`optional`) int
+                Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.
+
+            temperature: (`optional`) float
+                The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
+
+            top_k: (`optional`) int
+                The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
+
+            top_p: (`optional`) float
+                The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
+
+            repetition_penalty: (`optional`) float
+                The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.
+
+            pad_token_id: (`optional`) int
+                Padding token. Default to specicic model pad_token_id or None if it does not exist.
+
+            bos_token_id: (`optional`) int
+                BOS token. Defaults to `bos_token_id` as defined in the models config.
+
+            eos_token_id: (`optional`) int
+                EOS token. Defaults to `eos_token_id` as defined in the models config.
+
+            length_penalty: (`optional`) float
+                Exponential penalty to the length. Default to 1.
+
+            no_repeat_ngram_size: (`optional`) int
+                If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.
+            bad_words_ids: (`optional`) list of lists of int
+                `bad_words_ids` contains tokens that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
+
+            num_return_sequences: (`optional`) int
+                The number of independently computed returned sequences for each element in the batch. Default to 1.
+
+            attention_mask (`optional`) obj: `torch.LongTensor` of same shape as `input_ids`
+                Mask to avoid performing attention on padding token indices.
+                Mask values selected in ``[0, 1]``:
+                ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+                Defaults to `None`.
+
+                `What are attention masks? <../glossary.html#attention-mask>`__
+
+            decoder_start_token_id=None: (`optional`) int
+                If an encoder-decoder model starts decoding with a different token than BOS.
+                Defaults to `None` and is changed to `BOS` later.
+
+            use_cache: (`optional`) bool
+                If `use_cache` is True, past key values are used to speed up decoding if applicable to model. Defaults to `True`.
+
+            model_specific_kwargs: (`optional`) dict
+                Additional model specific kwargs will be forwarded to the `forward` function of the model.
+
+        Return:
+
+            output: `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`
+                sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`
+
+        Examples::
+
+            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            outputs = model.generate(max_length=40)  # do greedy decoding
+            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
+
+            tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
+            input_context = 'The dog'
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
+            for i in range(3): #  3 output sequences were generated
+                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
+
+            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            input_context = 'The dog'
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3)  # 3 generate sequences using by sampling
+            for i in range(3): #  3 output sequences were generated
+                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
+
+            tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
+            input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
+            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
+
+            tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
+            model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
+            input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
+            bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
+            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
+            outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated
+        """
+
+        # We cannot generate if the model does not have a LM head
+        if self.get_output_embeddings() is None:
+            raise AttributeError(
+                "You tried to generate sequences with a model that does not have a LM Head."
+                "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )"
+            )
+
+        max_length = max_length if max_length is not None else self.config.max_length
+        min_length = min_length if min_length is not None else self.config.min_length
+        do_sample = do_sample if do_sample is not None else self.config.do_sample
+        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        num_beams = num_beams if num_beams is not None else self.config.num_beams
+        temperature = temperature if temperature is not None else self.config.temperature
+        top_k = top_k if top_k is not None else self.config.top_k
+        top_p = top_p if top_p is not None else self.config.top_p
+        repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
+        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
+        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
+        no_repeat_ngram_size = (
+            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
+        )
+        bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
+        num_return_sequences = (
+            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
+        )
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
+        )
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]  # overriden by the input batch_size
+        else:
+            batch_size = 1
+
+        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
+        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
+        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
+        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
+        assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
+        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
+        assert temperature > 0, "`temperature` should be strictly positive."
+        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
+        assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
+        assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
+        assert input_ids is not None or (
+            isinstance(bos_token_id, int) and bos_token_id >= 0
+        ), "If input_ids is not defined, `bos_token_id` should be a positive integer."
+        assert pad_token_id is None or (
+            isinstance(pad_token_id, int) and (pad_token_id >= 0)
+        ), "`pad_token_id` should be a positive integer."
+        assert (eos_token_id is None) or (
+            isinstance(eos_token_id, int) and (eos_token_id >= 0)
+        ), "`eos_token_id` should be a positive integer."
+        assert length_penalty > 0, "`length_penalty` should be strictly positive."
+        assert (
+            isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
+        ), "`no_repeat_ngram_size` should be a positive integer."
+        assert (
+            isinstance(num_return_sequences, int) and num_return_sequences > 0
+        ), "`num_return_sequences` should be a strictly positive integer."
+        assert (
+            bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
+        ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
+
+        if input_ids is None:
+            assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
+                "you should either supply a context to complete as `input_ids` input "
+                "or a `bos_token_id` (integer >= 0) as a first token to start the generation."
+            )
+            input_ids = torch.full(
+                (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,
+            )
+        else:
+            assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."
+
+        # not allow to duplicate outputs when greedy decoding
+        if do_sample is False:
+            if num_beams == 1:
+                # no_beam_search greedy generation conditions
+                assert (
+                    num_return_sequences == 1
+                ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"
+
+            else:
+                # beam_search greedy generation conditions
+                assert (
+                    num_beams >= num_return_sequences
+                ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"
+
+        # create attention mask if necessary
+        # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
+        if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
+            attention_mask = input_ids.ne(pad_token_id).long()
+        elif attention_mask is None:
+            attention_mask = input_ids.new_ones(input_ids.shape)
+
+        # set pad_token_id to eos_token_id if not set. Important that this is done after
+        # attention_mask is created
+        if pad_token_id is None and eos_token_id is not None:
+            logger.warning(
+                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
+            )
+            pad_token_id = eos_token_id
+
+        # current position and vocab size
+        if hasattr(self.config, "vocab_size"):
+            vocab_size = self.config.vocab_size
+        elif (
+            self.config.is_encoder_decoder
+            and hasattr(self.config, "decoder")
+            and hasattr(self.config.decoder, "vocab_size")
+        ):
+            vocab_size = self.config.decoder.vocab_size
+
+        # set effective batch size and effective batch multiplier according to do_sample
+        if do_sample:
+            effective_batch_size = batch_size * num_return_sequences
+            effective_batch_mult = num_return_sequences
+        else:
+            effective_batch_size = batch_size
+            effective_batch_mult = 1
+
+        if self.config.is_encoder_decoder:
+            if decoder_start_token_id is None:
+                decoder_start_token_id = bos_token_id
+
+            assert (
+                decoder_start_token_id is not None
+            ), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
+            assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
+            assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)
+
+            # get encoder and store encoder outputs
+            encoder = self.get_encoder()
+
+            encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask)
+
+        # Expand input ids if num_beams > 1 or num_return_sequences > 1
+        if num_return_sequences > 1 or num_beams > 1:
+            input_ids_len = input_ids.shape[-1]
+            input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)
+            attention_mask = attention_mask.unsqueeze(1).expand(
+                batch_size, effective_batch_mult * num_beams, input_ids_len
+            )
+
+            input_ids = input_ids.contiguous().view(
+                effective_batch_size * num_beams, input_ids_len
+            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
+            attention_mask = attention_mask.contiguous().view(
+                effective_batch_size * num_beams, input_ids_len
+            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
+
+        if self.config.is_encoder_decoder:
+            # create empty decoder_input_ids
+            input_ids = torch.full(
+                (effective_batch_size * num_beams, 1),
+                decoder_start_token_id,
+                dtype=torch.long,
+                device=next(self.parameters()).device,
+            )
+            cur_len = 1
+
+            assert (
+                batch_size == encoder_outputs[0].shape[0]
+            ), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} "
+
+            # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)
+            expanded_batch_idxs = (
+                torch.arange(batch_size)
+                .view(-1, 1)
+                .repeat(1, num_beams * effective_batch_mult)
+                .view(-1)
+                .to(input_ids.device)
+            )
+            # expand encoder_outputs
+            encoder_outputs = (encoder_outputs[0].index_select(0, expanded_batch_idxs), *encoder_outputs[1:])
+
+        else:
+            encoder_outputs = None
+            cur_len = input_ids.shape[-1]
+
+        assert (
+            cur_len < max_length
+        ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`"
+
+        if num_beams > 1:
+            output = self._generate_beam_search(
+                input_ids,
+                cur_len=cur_len,
+                max_length=max_length,
+                min_length=min_length,
+                do_sample=do_sample,
+                early_stopping=early_stopping,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                batch_size=effective_batch_size,
+                num_return_sequences=num_return_sequences,
+                length_penalty=length_penalty,
+                num_beams=num_beams,
+                vocab_size=vocab_size,
+                encoder_outputs=encoder_outputs,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                model_specific_kwargs=model_specific_kwargs,
+            )
+        else:
+            output = self._generate_no_beam_search(
+                input_ids,
+                cur_len=cur_len,
+                max_length=max_length,
+                min_length=min_length,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                batch_size=effective_batch_size,
+                encoder_outputs=encoder_outputs,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                model_specific_kwargs=model_specific_kwargs,
+            )
+
+        return output
+
+    def _generate_no_beam_search(
+        self,
+        input_ids,
+        cur_len,
+        max_length,
+        min_length,
+        do_sample,
+        temperature,
+        top_k,
+        top_p,
+        repetition_penalty,
+        no_repeat_ngram_size,
+        bad_words_ids,
+        pad_token_id,
+        eos_token_id,
+        batch_size,
+        encoder_outputs,
+        attention_mask,
+        use_cache,
+        model_specific_kwargs,
+    ):
+        """ Generate sequences for each example without beam search (num_beams == 1).
+            All returned sequence are generated independantly.
+        """
+        # length of generated sentences / unfinished sentences
+        unfinished_sents = input_ids.new(batch_size).fill_(1)
+        sent_lengths = input_ids.new(batch_size).fill_(max_length)
+
+        past = (encoder_outputs, None) if encoder_outputs is not None else None
+
+        while cur_len < max_length:
+            model_inputs = self.prepare_inputs_for_generation(
+                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs
+            )
+
+            outputs = self(**model_inputs)
+            next_token_logits = outputs[0][:, -1, :]
+
+            scores = self.postprocess_next_token_scores(
+                scores=next_token_logits,
+                input_ids=input_ids,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                cur_len=cur_len,
+                min_length=min_length,
+                max_length=max_length,
+                eos_token_id=eos_token_id,
+                repetition_penalty=repetition_penalty,
+                batch_size=batch_size,
+                num_beams=1,
+            )
+
+            # if model has past, then set the past variable to speed up decoding
+            if self._use_cache(outputs, use_cache):
+                past = outputs[1]
+
+            if do_sample:
+                # Temperature (higher temperature => more likely to sample low probability tokens)
+                if temperature != 1.0:
+                    scores = scores / temperature
+                # Top-p/top-k filtering
+                next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p)
+                # Sample
+                probs = F.softmax(next_token_logscores, dim=-1)
+                next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:
+                # Greedy decoding
+                next_token = torch.argmax(next_token_logits, dim=-1)
+
+            # update generations and finished sentences
+            if eos_token_id is not None:
+                # pad finished sentences if eos_token_id exist
+                tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
+            else:
+                tokens_to_add = next_token
+
+            # add token and increase length by one
+            input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)
+            cur_len = cur_len + 1
+
+            if eos_token_id is not None:
+                eos_in_sents = tokens_to_add == eos_token_id
+                # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
+                is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
+                sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)
+                # unfinished_sents is set to zero if eos in sentence
+                unfinished_sents.mul_((~eos_in_sents).long())
+
+            # stop when there is a </s> in each sentence, or if we exceed the maximul length
+            if unfinished_sents.max() == 0:
+                break
+
+            # extend attention_mask for new generated input if only decoder
+            if self.config.is_encoder_decoder is False:
+                attention_mask = torch.cat(
+                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                )
+
+        return input_ids
+
+    def _generate_beam_search(
+        self,
+        input_ids,
+        cur_len,
+        max_length,
+        min_length,
+        do_sample,
+        early_stopping,
+        temperature,
+        top_k,
+        top_p,
+        repetition_penalty,
+        no_repeat_ngram_size,
+        bad_words_ids,
+        pad_token_id,
+        eos_token_id,
+        batch_size,
+        num_return_sequences,
+        length_penalty,
+        num_beams,
+        vocab_size,
+        encoder_outputs,
+        attention_mask,
+        use_cache,
+        model_specific_kwargs,
+    ):
+        """ Generate sequences for each example with beam search.
+        """
+
+        # generated hypotheses
+        generated_hyps = [
+            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
+            for _ in range(batch_size)
+        ]
+
+        # scores for each sentence in the beam
+        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
+
+        # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
+        if do_sample is False:
+            beam_scores[:, 1:] = -1e9
+        beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)
+
+        # cache compute states
+        past = (encoder_outputs, None) if encoder_outputs is not None else None
+
+        # done sentences
+        done = [False for _ in range(batch_size)]
+
+        while cur_len < max_length:
+            model_inputs = self.prepare_inputs_for_generation(
+                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs
+            )
+            outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
+            next_token_logits = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)
+
+            # if model has past, then set the past variable to speed up decoding
+            if self._use_cache(outputs, use_cache):
+                past = outputs[1]
+            if self.config.is_encoder_decoder and do_sample is False:
+                # TODO (PVP) still a bit hacky here - there might be a better solution
+                next_token_logits = self.adjust_logits_during_generation(
+                    next_token_logits, cur_len=cur_len, max_length=max_length
+                )
+
+            scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
+
+            scores = self.postprocess_next_token_scores(
+                scores=scores,
+                input_ids=input_ids,
+                no_repeat_ngram_size=no_repeat_ngram_size,
+                bad_words_ids=bad_words_ids,
+                cur_len=cur_len,
+                min_length=min_length,
+                max_length=max_length,
+                eos_token_id=eos_token_id,
+                repetition_penalty=repetition_penalty,
+                batch_size=batch_size,
+                num_beams=num_beams,
+            )
+
+            assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
+                scores.shape, (batch_size * num_beams, vocab_size)
+            )
+
+            if do_sample:
+                _scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+                # Temperature
+                if temperature != 1.0:
+                    _scores = _scores / temperature
+                # Top-p/top-k filtering
+                _scores = top_k_top_p_filtering(
+                    _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
+                )  # (batch_size * num_beams, vocab_size)
+                # re-organize to group the beam together to sample from all beam_idxs
+                _scores = _scores.contiguous().view(
+                    batch_size, num_beams * vocab_size
+                )  # (batch_size, num_beams * vocab_size)
+
+                # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
+                probs = F.softmax(_scores, dim=-1)
+                next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)  # (batch_size, num_beams * 2)
+                # Compute next scores
+                next_scores = torch.gather(_scores, -1, next_tokens)  # (batch_size, num_beams * 2)
+                # sort the sampled vector to make sure that the first num_beams samples are the best
+                next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
+                next_tokens = torch.gather(next_tokens, -1, next_scores_indices)  # (batch_size, num_beams * 2)
+
+            else:
+                next_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+
+                # re-organize to group the beam together (we are keeping top hypothesis accross beams)
+                next_scores = next_scores.view(
+                    batch_size, num_beams * vocab_size
+                )  # (batch_size, num_beams * vocab_size)
+
+                next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True)
+
+            assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)
+
+            # next batch beam content
+            next_batch_beam = []
+
+            # for each sentence
+            for batch_idx in range(batch_size):
+
+                # if we are done with this sentence, add a pad token
+                if done[batch_idx]:
+                    assert (
+                        len(generated_hyps[batch_idx]) >= num_beams
+                    ), "Batch can only be done if at least {} beams have been generated".format(num_beams)
+                    assert (
+                        eos_token_id is not None and pad_token_id is not None
+                    ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
+                    next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams)  # pad the batch
+                    continue
+
+                # next sentence beam content, this will get added to next_batch_beam
+                next_sent_beam = []
+
+                # next tokens for this sentence
+                for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
+                    zip(next_tokens[batch_idx], next_scores[batch_idx])
+                ):
+                    # get beam and token IDs
+                    beam_id = beam_token_id // vocab_size
+                    token_id = beam_token_id % vocab_size
+
+                    effective_beam_id = batch_idx * num_beams + beam_id
+                    # add to generated hypotheses if end of sentence
+                    if (eos_token_id is not None) and (token_id.item() == eos_token_id):
+                        # if beam_token does not belong to top num_beams tokens, it should not be added
+                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
+                        if is_beam_token_worse_than_top_num_beams:
+                            continue
+                        generated_hyps[batch_idx].add(
+                            input_ids[effective_beam_id].clone(), beam_token_score.item(),
+                        )
+                    else:
+                        # add next predicted token since it is not eos_token
+                        next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
+
+                    # once the beam for next step is full, don't add more tokens to it.
+                    if len(next_sent_beam) == num_beams:
+                        break
+
+                # Check if we are done so that we can save a pad step if all(done)
+                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
+                    next_scores[batch_idx].max().item(), cur_len
+                )
+
+                # update next beam content
+                assert len(next_sent_beam) == num_beams, "Beam should always be full"
+                next_batch_beam.extend(next_sent_beam)
+                assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step"
+
+            # stop when we are done with each sentence
+            if all(done):
+                break
+
+            # sanity check / prepare next batch
+            assert len(next_batch_beam) == batch_size * num_beams
+            beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
+            beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
+            beam_idx = input_ids.new([x[2] for x in next_batch_beam])
+
+            # re-order batch and update current length
+            input_ids = input_ids[beam_idx, :]
+            input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
+            cur_len = cur_len + 1
+
+            # re-order internal states
+            if past is not None:
+                past = self._reorder_cache(past, beam_idx)
+
+            # extend attention_mask for new generated input if only decoder
+            if self.config.is_encoder_decoder is False:
+                attention_mask = torch.cat(
+                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                )
+
+        # finalize all open beam hypotheses and add to generated hypotheses
+        for batch_idx in range(batch_size):
+            if done[batch_idx]:
+                continue
+
+            # test that beam scores match previously calculated scores if not eos and batch_idx not done
+            if eos_token_id is not None and all(
+                (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx]
+            ):
+                assert torch.all(
+                    next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
+                ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
+                    next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],
+                )
+
+            # need to add best num_beams hypotheses to generated hyps
+            for beam_id in range(num_beams):
+                effective_beam_id = batch_idx * num_beams + beam_id
+                final_score = beam_scores[effective_beam_id].item()
+                final_tokens = input_ids[effective_beam_id]
+                generated_hyps[batch_idx].add(final_tokens, final_score)
+
+        # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
+        output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
+        output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences
+
+        # select the best hypotheses
+        sent_lengths = input_ids.new(output_batch_size)
+        best = []
+
+        # retrieve best hypotheses
+        for i, hypotheses in enumerate(generated_hyps):
+            sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
+            for j in range(output_num_return_sequences_per_batch):
+                effective_batch_idx = output_num_return_sequences_per_batch * i + j
+                best_hyp = sorted_hyps.pop()[1]
+                sent_lengths[effective_batch_idx] = len(best_hyp)
+                best.append(best_hyp)
+
+        # shorter batches are padded
+        if sent_lengths.min().item() != sent_lengths.max().item():
+            assert pad_token_id is not None, "`Pad_token_id` has to be defined"
+            sent_max_len = min(sent_lengths.max().item() + 1, max_length)
+            decoded = input_ids.new(output_batch_size, sent_max_len).fill_(pad_token_id)
+
+            # fill with hypothesis and eos_token_id if necessary
+            for i, hypo in enumerate(best):
+                decoded[i, : sent_lengths[i]] = hypo
+                if sent_lengths[i] < max_length:
+                    decoded[i, sent_lengths[i]] = eos_token_id
+        else:
+            # none of the hypotheses have an eos_token
+            assert (len(hypo) == max_length for hypo in best)
+            decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)
+
+        return decoded
+
+    @staticmethod
+    def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:
+        return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)
+
+
+def calc_banned_ngram_tokens(prev_input_ids: Tensor, num_hypos: int, no_repeat_ngram_size: int, cur_len: int) -> None:
+    """Copied from fairseq for no_repeat_ngram in beam_search"""
+    if cur_len + 1 < no_repeat_ngram_size:
+        # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
+        return [[] for _ in range(num_hypos)]
+    generated_ngrams = [{} for _ in range(num_hypos)]
+    for idx in range(num_hypos):
+        gen_tokens = prev_input_ids[idx].tolist()
+        generated_ngram = generated_ngrams[idx]
+        for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
+            prev_ngram_tuple = tuple(ngram[:-1])
+            generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
+
+    def _get_generated_ngrams(hypo_idx):
+        # Before decoding the next token, prevent decoding of ngrams that have already appeared
+        start_idx = cur_len + 1 - no_repeat_ngram_size
+        ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
+        return generated_ngrams[hypo_idx].get(ngram_idx, [])
+
+    banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
+    return banned_tokens
+
+
+def calc_banned_bad_words_ids(prev_input_ids: Iterable[int], bad_words_ids: Iterable[int]) -> Iterable[int]:
+    banned_tokens = []
+
+    def _tokens_match(prev_tokens, tokens):
+        if len(tokens) == 0:
+            # if bad word tokens is just one token always ban it
+            return True
+        if len(tokens) > len(prev_input_ids):
+            # if bad word tokens are longer then prev input_ids they can't be equal
+            return False
+
+        if prev_tokens[-len(tokens) :] == tokens:
+            # if tokens match
+            return True
+        else:
+            return False
+
+    for prev_input_ids_slice in prev_input_ids:
+        banned_tokens_slice = []
+
+        for banned_token_seq in bad_words_ids:
+            assert len(banned_token_seq) > 0, "Banned words token sequences {} cannot have an empty list".format(
+                bad_words_ids
+            )
+
+            if _tokens_match(prev_input_ids_slice.tolist(), banned_token_seq[:-1]) is False:
+                # if tokens do not match continue
+                continue
+
+            banned_tokens_slice.append(banned_token_seq[-1])
+
+        banned_tokens.append(banned_tokens_slice)
+
+    return banned_tokens
+
+
+def top_k_top_p_filtering(
+    logits: Tensor,
+    top_k: int = 0,
+    top_p: float = 1.0,
+    filter_value: float = -float("Inf"),
+    min_tokens_to_keep: int = 1,
+) -> Tensor:
+    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+        Args:
+            logits: logits distribution shape (batch size, vocabulary size)
+            if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+            if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+            Make sure we keep at least min_tokens_to_keep per batch example in the output
+        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        logits[indices_to_remove] = filter_value
+    return logits
+
+
+class BeamHypotheses(object):
+    def __init__(self, num_beams, max_length, length_penalty, early_stopping):
+        """
+        Initialize n-best list of hypotheses.
+        """
+        self.max_length = max_length - 1  # ignoring bos_token
+        self.length_penalty = length_penalty
+        self.early_stopping = early_stopping
+        self.num_beams = num_beams
+        self.beams = []
+        self.worst_score = 1e9
+
+    def __len__(self):
+        """
+        Number of hypotheses in the list.
+        """
+        return len(self.beams)
+
+    def add(self, hyp, sum_logprobs):
+        """
+        Add a new hypothesis to the list.
+        """
+        score = sum_logprobs / len(hyp) ** self.length_penalty
+        if len(self) < self.num_beams or score > self.worst_score:
+            self.beams.append((score, hyp))
+            if len(self) > self.num_beams:
+                sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
+                del self.beams[sorted_scores[0][1]]
+                self.worst_score = sorted_scores[1][0]
+            else:
+                self.worst_score = min(score, self.worst_score)
+
+    def is_done(self, best_sum_logprobs, cur_len):
+        """
+        If there are enough hypotheses and that none of the hypotheses being generated
+        can become better than the worst one in the heap, then we are done with this sentence.
+        """
+
+        if len(self) < self.num_beams:
+            return False
+        elif self.early_stopping:
+            return True
+        else:
+            cur_score = best_sum_logprobs / cur_len ** self.length_penalty
+            ret = self.worst_score >= cur_score
+            return ret

elia/bert/modeling_bert.py

@@ -0,0 +1,1569 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""PyTorch BERT model. """
+
+
+import logging
+import math
+import os
+import warnings
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss, MSELoss
+
+from .activations import gelu, gelu_new, swish
+from .configuration_bert import BertConfig
+from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
+from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+
+
+logger = logging.getLogger(__name__)
+
+_TOKENIZER_FOR_DOC = "BertTokenizer"
+
+BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "bert-base-uncased",
+    "bert-large-uncased",
+    "bert-base-cased",
+    "bert-large-cased",
+    "bert-base-multilingual-uncased",
+    "bert-base-multilingual-cased",
+    "bert-base-chinese",
+    "bert-base-german-cased",
+    "bert-large-uncased-whole-word-masking",
+    "bert-large-cased-whole-word-masking",
+    "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "bert-large-cased-whole-word-masking-finetuned-squad",
+    "bert-base-cased-finetuned-mrpc",
+    "bert-base-german-dbmdz-cased",
+    "bert-base-german-dbmdz-uncased",
+    "cl-tohoku/bert-base-japanese",
+    "cl-tohoku/bert-base-japanese-whole-word-masking",
+    "cl-tohoku/bert-base-japanese-char",
+    "cl-tohoku/bert-base-japanese-char-whole-word-masking",
+    "TurkuNLP/bert-base-finnish-cased-v1",
+    "TurkuNLP/bert-base-finnish-uncased-v1",
+    "wietsedv/bert-base-dutch-cased",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+]
+
+
+def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
+    """ Load tf checkpoints in a pytorch model.
+    """
+    try:
+        import re
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        logger.error(
+            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split("/")
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(
+            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+            for n in name
+        ):
+            logger.info("Skipping {}".format("/".join(name)))
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+                scope_names = re.split(r"_(\d+)", m_name)
+            else:
+                scope_names = [m_name]
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+                pointer = getattr(pointer, "bias")
+            elif scope_names[0] == "output_weights":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "squad":
+                pointer = getattr(pointer, "classifier")
+            else:
+                try:
+                    pointer = getattr(pointer, scope_names[0])
+                except AttributeError:
+                    logger.info("Skipping {}".format("/".join(name)))
+                    continue
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
+                pointer = pointer[num]
+        if m_name[-11:] == "_embeddings":
+            pointer = getattr(pointer, "weight")
+        elif m_name == "kernel":
+            array = np.transpose(array)
+        try:
+            assert pointer.shape == array.shape
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        logger.info("Initialize PyTorch weight {}".format(name))
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+def mish(x):
+    return x * torch.tanh(nn.functional.softplus(x))
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
+
+
+BertLayerNorm = torch.nn.LayerNorm
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        if position_ids is None:
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).expand(input_shape)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        if encoder_hidden_states is not None:
+            mixed_key_layer = self.key(encoder_hidden_states)
+            mixed_value_layer = self.value(encoder_hidden_states)
+            attention_mask = encoder_attention_mask
+        else:
+            mixed_key_layer = self.key(hidden_states)
+            mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = BertSelfAttention(config)
+        self.output = BertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention = BertAttention(config)
+        self.is_decoder = config.is_decoder
+        if self.is_decoder:
+            self.crossattention = BertAttention(config)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_attention_outputs = self.attention(
+            hidden_states, attention_mask, head_mask, output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        if self.is_decoder and encoder_hidden_states is not None:
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
+
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        outputs = (layer_output,) + outputs
+        return outputs
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+    ):
+        all_hidden_states = ()
+        all_attentions = ()
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        outputs = (hidden_states,)
+        if output_hidden_states:
+            outputs = outputs + (all_hidden_states,)
+        if output_attentions:
+            outputs = outputs + (all_attentions,)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """ An abstract class to handle weights initialization and
+        a simple interface for downloading and loading pretrained models.
+    """
+
+    config_class = BertConfig
+    load_tf_weights = load_tf_weights_in_bert
+    base_model_prefix = "bert"
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, BertLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+BERT_START_DOCSTRING = r"""
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
+    usage and behavior.
+
+    Parameters:
+        config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+BERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`transformers.BertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.__call__` for details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
+            Mask to avoid performing attention on padding token indices.
+            Mask values selected in ``[0, 1]``:
+            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
+            Segment token indices to indicate first and second portions of the inputs.
+            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
+            corresponds to a `sentence B` token
+
+            `What are token type IDs? <../glossary.html#token-type-ids>`_
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
+            Indices of positions of each input sequence tokens in the position embeddings.
+            Selected in the range ``[0, config.max_position_embeddings - 1]``.
+
+            `What are position IDs? <../glossary.html#position-ids>`_
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
+            Mask to nullify selected heads of the self-attention modules.
+            Mask values selected in ``[0, 1]``:
+            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
+        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+            than the model's internal embedding lookup matrix.
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+            if the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask
+            is used in the cross-attention if the model is configured as a decoder.
+            Mask values selected in ``[0, 1]``:
+            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
+            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
+"""
+
+
+@add_start_docstrings(
+    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
+    BERT_START_DOCSTRING,
+)
+class BertModel(BertPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention) as well
+    as a decoder, in which case a layer of cross-attention is added between
+    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
+    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the
+    :obj:`is_decoder` argument of the configuration set to :obj:`True`; an
+    :obj:`encoder_hidden_states` is expected as an input to the forward pass.
+
+    .. _`Attention is all you need`:
+        https://arxiv.org/abs/1706.03762
+
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config)
+        self.pooler = BertPooler(config)
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """ Prunes heads of the model.
+            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+            See base class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+    Return:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
+            Last layer hidden-state of the first token of the sequence (classification token)
+            further processed by a Linear layer and a Tanh activation function. The Linear
+            layer weights are trained from the next sentence prediction (classification)
+            objective during pre-training.
+
+            This output is usually *not* a good summary
+            of the semantic content of the input, you're often better with averaging or pooling
+            the sequence of hidden-states for the whole input sequence.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
+
+        # If a 2D ou 3D attention mask is provided for the cross-attention
+        # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        outputs = (sequence_output, pooled_output,) + encoder_outputs[
+            1:
+        ]  # add hidden_states and attentions if they are here
+        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model with two heads on top as done during the pre-training: a `masked language modeling` head and
+    a `next sentence prediction (classification)` head. """,
+    BERT_START_DOCSTRING,
+)
+class BertForPreTraining(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.cls = BertPreTrainingHeads(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        **kwargs
+    ):
+        r"""
+        labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
+            Labels for computing the masked language modeling loss.
+            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+            in ``[0, ..., config.vocab_size]``
+        next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`, defaults to :obj:`None`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`input_ids` docstring)
+            Indices should be in ``[0, 1]``.
+            ``0`` indicates sequence B is a continuation of sequence A,
+            ``1`` indicates sequence B is a random sequence.
+        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
+            Prediction scores of the next sequence prediction (classification) head (scores of True/False
+            continuation before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+
+
+    Examples::
+
+        >>> from transformers import BertTokenizer, BertForPreTraining
+        >>> import torch
+
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> prediction_scores, seq_relationship_scores = outputs[:2]
+
+        """
+        if "masked_lm_labels" in kwargs:
+            warnings.warn(
+                "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
+                DeprecationWarning,
+            )
+            labels = kwargs.pop("masked_lm_labels")
+        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output, pooled_output = outputs[:2]
+        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
+
+        outputs = (prediction_scores, seq_relationship_score,) + outputs[
+            2:
+        ]  # add hidden states and attention if they are here
+
+        if labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+            total_loss = masked_lm_loss + next_sentence_loss
+            outputs = (total_loss,) + outputs
+
+        return outputs  # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model with a `language modeling` head on top for CLM fine-tuning. """, BERT_START_DOCSTRING
+)
+class BertLMHeadModel(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        assert config.is_decoder, "If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True`."
+
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        **kwargs
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the left-to-right language modeling loss (next word prediction).
+            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+            in ``[0, ..., config.vocab_size]``
+        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Next token prediction loss.
+        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+
+    Example::
+
+        >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+        >>> import torch
+
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+        >>> config = BertConfig.from_pretrained("bert-base-cased")
+        >>> config.is_decoder = True
+        >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+        """
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
+
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            ltr_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            outputs = (ltr_lm_loss,) + outputs
+
+        return outputs  # (ltr_lm_loss), prediction_scores, (hidden_states), (attentions)
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
+class BertForMaskedLM(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        assert (
+            not config.is_decoder
+        ), "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention."
+
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        **kwargs
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the masked language modeling loss.
+            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+            in ``[0, ..., config.vocab_size]``
+        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Masked language modeling loss.
+        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+        if "masked_lm_labels" in kwargs:
+            warnings.warn(
+                "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
+                DeprecationWarning,
+            )
+            labels = kwargs.pop("masked_lm_labels")
+        assert "lm_labels" not in kwargs, "Use `BertWithLMHead` for autoregressive language modeling task."
+        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            outputs = (masked_lm_loss,) + outputs
+
+        return outputs  # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        #  add a dummy token
+        assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
+        attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
+        dummy_token = torch.full(
+            (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
+        )
+        input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+@add_start_docstrings(
+    """Bert Model with a `next sentence prediction (classification)` head on top. """, BERT_START_DOCSTRING,
+)
+class BertForNextSentencePrediction(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring)
+            Indices should be in ``[0, 1]``.
+            ``0`` indicates sequence B is a continuation of sequence A,
+            ``1`` indicates sequence B is a random sequence.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
+            Next sequence prediction (classification) loss.
+        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
+            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+
+    Examples::
+
+        >>> from transformers import BertTokenizer, BertForNextSentencePrediction
+        >>> import torch
+
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+
+        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+        >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
+
+        >>> loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1]))
+        >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
+        """
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = outputs[1]
+
+        seq_relationship_score = self.cls(pooled_output)
+
+        outputs = (seq_relationship_score,) + outputs[2:]  # add hidden states and attention if they are here
+        if next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss()
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+            outputs = (next_sentence_loss,) + outputs
+
+        return outputs  # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of
+    the pooled output) e.g. for GLUE tasks. """,
+    BERT_START_DOCSTRING,
+)
+class BertForSequenceClassification(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the sequence classification/regression loss.
+            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
+            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
+
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), logits, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model with a multiple choice classification head on top (a linear layer on top of
+    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
+    BERT_START_DOCSTRING,
+)
+class BertForMultipleChoice(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the multiple choice classification loss.
+            Indices should be in ``[0, ..., num_choices-1]`` where `num_choices` is the size of the second dimension
+            of the input tensors. (see `input_ids` above)
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
+            `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
+
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model with a token classification head on top (a linear layer on top of
+    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
+    BERT_START_DOCSTRING,
+)
+class BertForTokenClassification(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the token classification loss.
+            Indices should be in ``[0, ..., config.num_labels - 1]``.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
+            Classification loss.
+        scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)
+                active_labels = torch.where(
+                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
+                )
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), scores, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+    layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """,
+    BERT_START_DOCSTRING,
+)
+class BertForQuestionAnswering(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="bert-base-uncased")
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`).
+            Position outside of the sequence are not taken into account for computing the loss.
+        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`).
+            Position outside of the sequence are not taken into account for computing the loss.
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+        start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
+            Span-start scores (before SoftMax).
+        end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
+            Span-end scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        outputs = (start_logits, end_logits,) + outputs[2:]
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            outputs = (total_loss,) + outputs
+
+        return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)

elia/bert/modeling_utils.py

@@ -0,0 +1,1268 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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 inspect
+import logging
+import os
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+from torch import Tensor, device, dtype, nn
+from torch.nn import CrossEntropyLoss
+from torch.nn import functional as F
+
+from .activations import get_activation
+from .configuration_utils import PretrainedConfig
+from .file_utils import (
+    DUMMY_INPUTS,
+    TF2_WEIGHTS_NAME,
+    TF_WEIGHTS_NAME,
+    WEIGHTS_NAME,
+    cached_path,
+    hf_bucket_url,
+    is_remote_url,
+)
+from .generation_utils import GenerationMixin
+
+
+logger = logging.getLogger(__name__)
+
+
+try:
+    from torch.nn import Identity
+except ImportError:
+    # Older PyTorch compatibility
+    class Identity(nn.Module):
+        r"""A placeholder identity operator that is argument-insensitive.
+        """
+
+        def __init__(self, *args, **kwargs):
+            super().__init__()
+
+        def forward(self, input):
+            return input
+
+
+def find_pruneable_heads_and_indices(
+    heads: List, n_heads: int, head_size: int, already_pruned_heads: set
+) -> Tuple[set, "torch.LongTensor"]:
+    mask = torch.ones(n_heads, head_size)
+    heads = set(heads) - already_pruned_heads  # Convert to set and remove already pruned heads
+    for head in heads:
+        # Compute how many pruned heads are before the head and move the index accordingly
+        head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
+        mask[head] = 0
+    mask = mask.view(-1).contiguous().eq(1)
+    index: torch.LongTensor = torch.arange(len(mask))[mask].long()
+    return heads, index
+
+
+class ModuleUtilsMixin:
+    """
+    A few utilities for torch.nn.Modules, to be used as a mixin.
+    """
+
+    def num_parameters(self, only_trainable: bool = False) -> int:
+        """
+        Get number of (optionally, trainable) parameters in the module.
+        """
+        params = filter(lambda x: x.requires_grad, self.parameters()) if only_trainable else self.parameters()
+        return sum(p.numel() for p in params)
+
+    @staticmethod
+    def _hook_rss_memory_pre_forward(module, *args, **kwargs):
+        try:
+            import psutil
+        except (ImportError):
+            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
+
+        process = psutil.Process(os.getpid())
+        mem = process.memory_info()
+        module.mem_rss_pre_forward = mem.rss
+        return None
+
+    @staticmethod
+    def _hook_rss_memory_post_forward(module, *args, **kwargs):
+        try:
+            import psutil
+        except (ImportError):
+            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
+
+        process = psutil.Process(os.getpid())
+        mem = process.memory_info()
+        module.mem_rss_post_forward = mem.rss
+        mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
+        module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
+        return None
+
+    def add_memory_hooks(self):
+        """ Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.
+            Increase in memory consumption is stored in a `mem_rss_diff` attribute for each module and can be reset to zero with `model.reset_memory_hooks_state()`
+        """
+        for module in self.modules():
+            module.register_forward_pre_hook(self._hook_rss_memory_pre_forward)
+            module.register_forward_hook(self._hook_rss_memory_post_forward)
+        self.reset_memory_hooks_state()
+
+    def reset_memory_hooks_state(self):
+        for module in self.modules():
+            module.mem_rss_diff = 0
+            module.mem_rss_post_forward = 0
+            module.mem_rss_pre_forward = 0
+
+    @property
+    def device(self) -> device:
+        """
+        Get torch.device from module, assuming that the whole module has one device.
+        """
+        try:
+            return next(self.parameters()).device
+        except StopIteration:
+            # For nn.DataParallel compatibility in PyTorch 1.5
+
+            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
+                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+                return tuples
+
+            gen = self._named_members(get_members_fn=find_tensor_attributes)
+            first_tuple = next(gen)
+            return first_tuple[1].device
+
+    @property
+    def dtype(self) -> dtype:
+        """
+        Get torch.dtype from module, assuming that the whole module has one dtype.
+        """
+        try:
+            return next(self.parameters()).dtype
+        except StopIteration:
+            # For nn.DataParallel compatibility in PyTorch 1.5
+
+            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
+                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+                return tuples
+
+            gen = self._named_members(get_members_fn=find_tensor_attributes)
+            first_tuple = next(gen)
+            return first_tuple[1].dtype
+
+    def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
+        """type: torch.Tensor -> torch.Tensor"""
+        if encoder_attention_mask.dim() == 3:
+            encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
+        if encoder_attention_mask.dim() == 2:
+            encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
+        # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
+        # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
+        # /transformer/transformer_layers.py#L270
+        # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
+        # encoder_extended_attention_mask.transpose(-1, -2))
+        encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+
+        if self.dtype == torch.float16:
+            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
+        elif self.dtype == torch.float32:
+            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
+        else:
+            raise ValueError(
+                "{} not recognized. `dtype` should be set to either `torch.float32` or `torch.float16`".format(
+                    self.dtype
+                )
+            )
+
+        return encoder_extended_attention_mask
+
+    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple, device: device) -> Tensor:
+        """Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored.
+
+        Arguments:
+            attention_mask: torch.Tensor with 1 indicating tokens to ATTEND to
+            input_shape: tuple, shape of input_ids
+            device: torch.Device, usually self.device
+
+        Returns:
+            torch.Tensor with dtype of attention_mask.dtype
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if self.config.is_decoder:
+                batch_size, seq_length = input_shape
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+
+    def get_head_mask(self, head_mask: Tensor, num_hidden_layers: int, is_attention_chunked: bool = False) -> Tensor:
+        """
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        attention_probs has shape bsz x n_heads x N x N
+        Arguments:
+            head_mask: torch.Tensor or None: has shape [num_heads] or [num_hidden_layers x num_heads]
+            num_hidden_layers: int
+        Returns:
+             Tensor of shape shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+             or list with [None] for each layer
+        """
+        if head_mask is not None:
+            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
+            if is_attention_chunked is True:
+                head_mask = head_mask.unsqueeze(-1)
+        else:
+            head_mask = [None] * num_hidden_layers
+
+        return head_mask
+
+    def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
+        """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
+        if head_mask.dim() == 1:
+            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+            head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
+        elif head_mask.dim() == 2:
+            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
+        assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
+        head_mask = head_mask.to(dtype=self.dtype)  # switch to fload if need + fp16 compatibility
+        return head_mask
+
+
+class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
+    r""" Base class for all models.
+
+        :class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
+        as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
+
+        Class attributes (overridden by derived classes):
+            - ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
+            - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
+
+                - ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
+                - ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
+                - ``path``: a path (string) to the TensorFlow checkpoint.
+
+            - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
+    """
+    config_class = None
+    base_model_prefix = ""
+
+    @property
+    def dummy_inputs(self):
+        """ Dummy inputs to do a forward pass in the network.
+
+        Returns:
+            torch.Tensor with dummy inputs
+        """
+        return {"input_ids": torch.tensor(DUMMY_INPUTS)}
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__()
+        if not isinstance(config, PretrainedConfig):
+            raise ValueError(
+                "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
+                "To create a model from a pretrained model use "
+                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    self.__class__.__name__, self.__class__.__name__
+                )
+            )
+        # Save config in model
+        self.config = config
+
+    @property
+    def base_model(self):
+        return getattr(self, self.base_model_prefix, self)
+
+    def get_input_embeddings(self):
+        """
+        Returns the model's input embeddings.
+
+        Returns:
+            :obj:`nn.Module`:
+                A torch module mapping vocabulary to hidden states.
+        """
+        base_model = getattr(self, self.base_model_prefix, self)
+        if base_model is not self:
+            return base_model.get_input_embeddings()
+        else:
+            raise NotImplementedError
+
+    def set_input_embeddings(self, value: nn.Module):
+        """
+        Set model's input embeddings
+
+        Args:
+            value (:obj:`nn.Module`):
+                A module mapping vocabulary to hidden states.
+        """
+        base_model = getattr(self, self.base_model_prefix, self)
+        if base_model is not self:
+            base_model.set_input_embeddings(value)
+        else:
+            raise NotImplementedError
+
+    def get_output_embeddings(self):
+        """
+        Returns the model's output embeddings.
+
+        Returns:
+            :obj:`nn.Module`:
+                A torch module mapping hidden states to vocabulary.
+        """
+        return None  # Overwrite for models with output embeddings
+
+    def tie_weights(self):
+        """
+        Tie the weights between the input embeddings and the output embeddings.
+        If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning
+        the weights instead.
+        """
+        output_embeddings = self.get_output_embeddings()
+        if output_embeddings is not None:
+            self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
+
+    def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
+        """ Tie or clone module weights depending of whether we are using TorchScript or not
+        """
+        if self.config.torchscript:
+            output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
+        else:
+            output_embeddings.weight = input_embeddings.weight
+
+        if getattr(output_embeddings, "bias", None) is not None:
+            output_embeddings.bias.data = torch.nn.functional.pad(
+                output_embeddings.bias.data,
+                (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),
+                "constant",
+                0,
+            )
+        if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
+            output_embeddings.out_features = input_embeddings.num_embeddings
+
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None):
+        """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
+        Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
+
+        Arguments:
+
+            new_num_tokens: (`optional`) int:
+                New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
+                If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.
+
+        Return: ``torch.nn.Embeddings``
+            Pointer to the input tokens Embeddings Module of the model
+        """
+        base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
+        model_embeds = base_model._resize_token_embeddings(new_num_tokens)
+        if new_num_tokens is None:
+            return model_embeds
+
+        # Update base model and current model config
+        self.config.vocab_size = new_num_tokens
+        base_model.vocab_size = new_num_tokens
+
+        # Tie weights again if needed
+        self.tie_weights()
+
+        return model_embeds
+
+    def _resize_token_embeddings(self, new_num_tokens):
+        old_embeddings = self.get_input_embeddings()
+        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
+        self.set_input_embeddings(new_embeddings)
+        return self.get_input_embeddings()
+
+    def _get_resized_embeddings(
+        self, old_embeddings: torch.nn.Embedding, new_num_tokens: Optional[int] = None
+    ) -> torch.nn.Embedding:
+        """ Build a resized Embedding Module from a provided token Embedding Module.
+            Increasing the size will add newly initialized vectors at the end
+            Reducing the size will remove vectors from the end
+
+        Args:
+            old_embeddings: ``torch.nn.Embedding``
+                Old embeddings to be resized.
+            new_num_tokens: (`optional`) int
+                New number of tokens in the embedding matrix.
+                Increasing the size will add newly initialized vectors at the end
+                Reducing the size will remove vectors from the end
+                If not provided or None: return the provided token Embedding Module.
+        Return: ``torch.nn.Embedding``
+            Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
+        """
+        if new_num_tokens is None:
+            return old_embeddings
+
+        old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
+        if old_num_tokens == new_num_tokens:
+            return old_embeddings
+
+        # Build new embeddings
+        new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
+        new_embeddings.to(old_embeddings.weight.device)
+
+        # initialize all new embeddings (in particular added tokens)
+        self._init_weights(new_embeddings)
+
+        # Copy token embeddings from the previous weights
+        num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
+        new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]
+
+        return new_embeddings
+
+    def init_weights(self):
+        """ Initialize and prunes weights if needed. """
+        # Initialize weights
+        self.apply(self._init_weights)
+
+        # Prune heads if needed
+        if self.config.pruned_heads:
+            self.prune_heads(self.config.pruned_heads)
+
+        # Tie weights if needed
+        self.tie_weights()
+
+    def prune_heads(self, heads_to_prune: Dict):
+        """ Prunes heads of the base model.
+
+            Arguments:
+
+                heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
+                E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
+        """
+        # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
+        for layer, heads in heads_to_prune.items():
+            union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
+            self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON
+
+        self.base_model._prune_heads(heads_to_prune)
+
+    def save_pretrained(self, save_directory):
+        """ Save a model and its configuration file to a directory, so that it
+            can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
+
+            Arguments:
+                save_directory: directory to which to save.
+        """
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
+            return
+        os.makedirs(save_directory, exist_ok=True)
+
+        # Only save the model itself if we are using distributed training
+        model_to_save = self.module if hasattr(self, "module") else self
+
+        # Attach architecture to the config
+        model_to_save.config.architectures = [model_to_save.__class__.__name__]
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
+
+        if getattr(self.config, "xla_device", False):
+            import torch_xla.core.xla_model as xm
+
+            if xm.is_master_ordinal():
+                # Save configuration file
+                model_to_save.config.save_pretrained(save_directory)
+            # xm.save takes care of saving only from master
+            xm.save(model_to_save.state_dict(), output_model_file)
+        else:
+            model_to_save.config.save_pretrained(save_directory)
+            torch.save(model_to_save.state_dict(), output_model_file)
+
+        logger.info("Model weights saved in {}".format(output_model_file))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
+
+        The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
+        To train the model, you should first set it back in training mode with ``model.train()``
+
+        The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
+        It is up to you to train those weights with a downstream fine-tuning task.
+
+        The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.
+
+        Parameters:
+            pretrained_model_name_or_path: either:
+              - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
+              - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+              - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+              - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
+              - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)
+
+            model_args: (`optional`) Sequence of positional arguments:
+                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
+
+            config: (`optional`) one of:
+                - an instance of a class derived from :class:`~transformers.PretrainedConfig`, or
+                - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`
+
+                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
+                    - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
+                    - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                    - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
+
+            state_dict: (`optional`) dict:
+                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
+                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+
+            resume_download: (`optional`) boolean, default False:
+                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
+
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+
+            output_loading_info: (`optional`) boolean:
+                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
+
+            kwargs: (`optional`) Remaining dictionary of keyword arguments:
+                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
+
+                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+
+        Examples::
+
+            # For example purposes. Not runnable.
+            model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
+            assert model.config.output_attention == True
+            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
+            model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+        """
+        config = kwargs.pop("config", None)
+        state_dict = kwargs.pop("state_dict", None)
+        cache_dir = kwargs.pop("cache_dir", None)
+        from_tf = kwargs.pop("from_tf", False)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        output_loading_info = kwargs.pop("output_loading_info", False)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_cdn = kwargs.pop("use_cdn", True)
+
+        # Load config if we don't provide a configuration
+        if not isinstance(config, PretrainedConfig):
+            config_path = config if config is not None else pretrained_model_name_or_path
+            config, model_kwargs = cls.config_class.from_pretrained(
+                config_path,
+                *model_args,
+                cache_dir=cache_dir,
+                return_unused_kwargs=True,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                **kwargs,
+            )
+        else:
+            model_kwargs = kwargs
+
+        # Load model
+        if pretrained_model_name_or_path is not None:
+            if os.path.isdir(pretrained_model_name_or_path):
+                if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")):
+                    # Load from a TF 1.0 checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
+                elif from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
+                    # Load from a TF 2.0 checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
+                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
+                    # Load from a PyTorch checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+                else:
+                    raise EnvironmentError(
+                        "Error no file named {} found in directory {} or `from_tf` set to False".format(
+                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
+                            pretrained_model_name_or_path,
+                        )
+                    )
+            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+                archive_file = pretrained_model_name_or_path
+            elif os.path.isfile(pretrained_model_name_or_path + ".index"):
+                assert (
+                    from_tf
+                ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
+                    pretrained_model_name_or_path + ".index"
+                )
+                archive_file = pretrained_model_name_or_path + ".index"
+            else:
+                archive_file = hf_bucket_url(
+                    pretrained_model_name_or_path,
+                    filename=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
+                    use_cdn=use_cdn,
+                )
+
+            try:
+                # Load from URL or cache if already cached
+                resolved_archive_file = cached_path(
+                    archive_file,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                )
+                if resolved_archive_file is None:
+                    raise EnvironmentError
+            except EnvironmentError:
+                msg = (
+                    f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
+                    f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
+                    f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME}.\n\n"
+                )
+                raise EnvironmentError(msg)
+
+            if resolved_archive_file == archive_file:
+                logger.info("loading weights file {}".format(archive_file))
+            else:
+                logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
+        else:
+            resolved_archive_file = None
+
+        # Instantiate model.
+        model = cls(config, *model_args, **model_kwargs)
+
+        if state_dict is None and not from_tf:
+            try:
+                state_dict = torch.load(resolved_archive_file, map_location="cpu")
+            except Exception:
+                raise OSError(
+                    "Unable to load weights from pytorch checkpoint file. "
+                    "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
+                )
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+
+        if from_tf:
+            if resolved_archive_file.endswith(".index"):
+                # Load from a TensorFlow 1.X checkpoint - provided by original authors
+                model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
+            else:
+                # Load from our TensorFlow 2.0 checkpoints
+                try:
+                    from transformers import load_tf2_checkpoint_in_pytorch_model
+
+                    model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
+                except ImportError:
+                    logger.error(
+                        "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
+                        "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
+                    )
+                    raise
+        else:
+            # Convert old format to new format if needed from a PyTorch state_dict
+            old_keys = []
+            new_keys = []
+            for key in state_dict.keys():
+                new_key = None
+                if "gamma" in key:
+                    new_key = key.replace("gamma", "weight")
+                if "beta" in key:
+                    new_key = key.replace("beta", "bias")
+                if new_key:
+                    old_keys.append(key)
+                    new_keys.append(new_key)
+            for old_key, new_key in zip(old_keys, new_keys):
+                state_dict[new_key] = state_dict.pop(old_key)
+
+            # copy state_dict so _load_from_state_dict can modify it
+            metadata = getattr(state_dict, "_metadata", None)
+            state_dict = state_dict.copy()
+            if metadata is not None:
+                state_dict._metadata = metadata
+
+            ##############################################################################################
+            # Print out state_dict's contents: keys
+            '''
+            for key, _ in state_dict.items():
+                print(key)
+            '''
+            ##############################################################################################
+
+
+            # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
+            # so we need to apply the function recursively.
+            def load(module: nn.Module, prefix=""):
+                local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+                module._load_from_state_dict(
+                    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,
+                )
+                for name, child in module._modules.items():
+                    if child is not None:
+                        load(child, prefix + name + ".")
+
+            # Make sure we are able to load base models as well as derived models (with heads)
+            start_prefix = ""
+            model_to_load = model
+            has_prefix_module = any(s.startswith(cls.base_model_prefix) for s in state_dict.keys())
+            if not hasattr(model, cls.base_model_prefix) and has_prefix_module:
+                start_prefix = cls.base_model_prefix + "."
+            if hasattr(model, cls.base_model_prefix) and not has_prefix_module:
+                model_to_load = getattr(model, cls.base_model_prefix)
+
+            load(model_to_load, prefix=start_prefix)
+
+            if model.__class__.__name__ != model_to_load.__class__.__name__:
+                base_model_state_dict = model_to_load.state_dict().keys()
+                head_model_state_dict_without_base_prefix = [
+                    key.split(cls.base_model_prefix + ".")[-1] for key in model.state_dict().keys()
+                ]
+
+                missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)
+
+            if len(unexpected_keys) > 0:
+                logger.warning(
+                    f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
+                    f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
+                    f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
+                    f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n"
+                    f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
+                    f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
+                )
+            else:
+                logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+            if len(missing_keys) > 0:
+                logger.warning(
+                    f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
+                    f"and are newly initialized: {missing_keys}\n"
+                    f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
+                )
+            else:
+                logger.info(
+                    f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
+                    f"If your task is similar to the task the model of the ckeckpoint was trained on, "
+                    f"you can already use {model.__class__.__name__} for predictions without further training."
+                )
+            if len(error_msgs) > 0:
+                raise RuntimeError(
+                    "Error(s) in loading state_dict for {}:\n\t{}".format(
+                        model.__class__.__name__, "\n\t".join(error_msgs)
+                    )
+                )
+        model.tie_weights()  # make sure token embedding weights are still tied if needed
+
+        # Set model in evaluation mode to deactivate DropOut modules by default
+        model.eval()
+
+        if output_loading_info:
+            loading_info = {
+                "missing_keys": missing_keys,
+                "unexpected_keys": unexpected_keys,
+                "error_msgs": error_msgs,
+            }
+            return model, loading_info
+
+        if hasattr(config, "xla_device") and config.xla_device:
+            import torch_xla.core.xla_model as xm
+
+            model = xm.send_cpu_data_to_device(model, xm.xla_device())
+            model.to(xm.xla_device())
+
+        return model
+
+
+class Conv1D(nn.Module):
+    def __init__(self, nf, nx):
+        """ Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
+            Basically works like a Linear layer but the weights are transposed
+        """
+        super().__init__()
+        self.nf = nf
+        w = torch.empty(nx, nf)
+        nn.init.normal_(w, std=0.02)
+        self.weight = nn.Parameter(w)
+        self.bias = nn.Parameter(torch.zeros(nf))
+
+    def forward(self, x):
+        size_out = x.size()[:-1] + (self.nf,)
+        x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
+        x = x.view(*size_out)
+        return x
+
+
+class PoolerStartLogits(nn.Module):
+    """ Compute SQuAD start_logits from sequence hidden states. """
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, 1)
+
+    def forward(self, hidden_states, p_mask=None):
+        """ Args:
+            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
+                invalid position mask such as query and special symbols (PAD, SEP, CLS)
+                1.0 means token should be masked.
+        """
+        x = self.dense(hidden_states).squeeze(-1)
+
+        if p_mask is not None:
+            if next(self.parameters()).dtype == torch.float16:
+                x = x * (1 - p_mask) - 65500 * p_mask
+            else:
+                x = x * (1 - p_mask) - 1e30 * p_mask
+
+        return x
+
+
+class PoolerEndLogits(nn.Module):
+    """ Compute SQuAD end_logits from sequence hidden states and start token hidden state.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
+        self.activation = nn.Tanh()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dense_1 = nn.Linear(config.hidden_size, 1)
+
+    def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
+        """ Args:
+            One of ``start_states``, ``start_positions`` should be not None.
+            If both are set, ``start_positions`` overrides ``start_states``.
+
+            **start_states**: ``torch.LongTensor`` of shape identical to hidden_states
+                hidden states of the first tokens for the labeled span.
+            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+                position of the first token for the labeled span:
+            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
+                Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
+                1.0 means token should be masked.
+        """
+        assert (
+            start_states is not None or start_positions is not None
+        ), "One of start_states, start_positions should be not None"
+        if start_positions is not None:
+            slen, hsz = hidden_states.shape[-2:]
+            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
+            start_states = hidden_states.gather(-2, start_positions)  # shape (bsz, 1, hsz)
+            start_states = start_states.expand(-1, slen, -1)  # shape (bsz, slen, hsz)
+
+        x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
+        x = self.activation(x)
+        x = self.LayerNorm(x)
+        x = self.dense_1(x).squeeze(-1)
+
+        if p_mask is not None:
+            if next(self.parameters()).dtype == torch.float16:
+                x = x * (1 - p_mask) - 65500 * p_mask
+            else:
+                x = x * (1 - p_mask) - 1e30 * p_mask
+
+        return x
+
+
+class PoolerAnswerClass(nn.Module):
+    """ Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
+        self.activation = nn.Tanh()
+        self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)
+
+    def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
+        """
+        Args:
+            One of ``start_states``, ``start_positions`` should be not None.
+            If both are set, ``start_positions`` overrides ``start_states``.
+
+            **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
+                hidden states of the first tokens for the labeled span.
+            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+                position of the first token for the labeled span.
+            **cls_index**: torch.LongTensor of shape ``(batch_size,)``
+                position of the CLS token. If None, take the last token.
+
+            note(Original repo):
+                no dependency on end_feature so that we can obtain one single `cls_logits`
+                for each sample
+        """
+        hsz = hidden_states.shape[-1]
+        assert (
+            start_states is not None or start_positions is not None
+        ), "One of start_states, start_positions should be not None"
+        if start_positions is not None:
+            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
+            start_states = hidden_states.gather(-2, start_positions).squeeze(-2)  # shape (bsz, hsz)
+
+        if cls_index is not None:
+            cls_index = cls_index[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
+            cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, hsz)
+        else:
+            cls_token_state = hidden_states[:, -1, :]  # shape (bsz, hsz)
+
+        x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
+        x = self.activation(x)
+        x = self.dense_1(x).squeeze(-1)
+
+        return x
+
+
+class SQuADHead(nn.Module):
+    r""" A SQuAD head inspired by XLNet.
+
+    Parameters:
+        config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
+
+    Inputs:
+        **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
+            hidden states of sequence tokens
+        **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            position of the first token for the labeled span.
+        **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            position of the last token for the labeled span.
+        **cls_index**: torch.LongTensor of shape ``(batch_size,)``
+            position of the CLS token. If None, take the last token.
+        **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``
+            Whether the question has a possible answer in the paragraph or not.
+        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
+            Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
+            1.0 means token should be masked.
+
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
+        **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
+            Log probabilities for the top config.start_n_top start token possibilities (beam-search).
+        **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
+            Indices for the top config.start_n_top start token possibilities (beam-search).
+        **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size,)``
+            Log probabilities for the ``is_impossible`` label of the answers.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.start_n_top = config.start_n_top
+        self.end_n_top = config.end_n_top
+
+        self.start_logits = PoolerStartLogits(config)
+        self.end_logits = PoolerEndLogits(config)
+        self.answer_class = PoolerAnswerClass(config)
+
+    def forward(
+        self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
+    ):
+        outputs = ()
+
+        start_logits = self.start_logits(hidden_states, p_mask=p_mask)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, let's remove the dimension added by batch splitting
+            for x in (start_positions, end_positions, cls_index, is_impossible):
+                if x is not None and x.dim() > 1:
+                    x.squeeze_(-1)
+
+            # during training, compute the end logits based on the ground truth of the start position
+            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
+
+            loss_fct = CrossEntropyLoss()
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+            if cls_index is not None and is_impossible is not None:
+                # Predict answerability from the representation of CLS and START
+                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
+                loss_fct_cls = nn.BCEWithLogitsLoss()
+                cls_loss = loss_fct_cls(cls_logits, is_impossible)
+
+                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
+                total_loss += cls_loss * 0.5
+
+            outputs = (total_loss,) + outputs
+
+        else:
+            # during inference, compute the end logits based on beam search
+            bsz, slen, hsz = hidden_states.size()
+            start_log_probs = F.softmax(start_logits, dim=-1)  # shape (bsz, slen)
+
+            start_top_log_probs, start_top_index = torch.topk(
+                start_log_probs, self.start_n_top, dim=-1
+            )  # shape (bsz, start_n_top)
+            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz)  # shape (bsz, start_n_top, hsz)
+            start_states = torch.gather(hidden_states, -2, start_top_index_exp)  # shape (bsz, start_n_top, hsz)
+            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1)  # shape (bsz, slen, start_n_top, hsz)
+
+            hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(
+                start_states
+            )  # shape (bsz, slen, start_n_top, hsz)
+            p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
+            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
+            end_log_probs = F.softmax(end_logits, dim=1)  # shape (bsz, slen, start_n_top)
+
+            end_top_log_probs, end_top_index = torch.topk(
+                end_log_probs, self.end_n_top, dim=1
+            )  # shape (bsz, end_n_top, start_n_top)
+            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
+            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
+
+            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
+            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
+
+            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs
+
+        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
+        # or (if labels are provided) (total_loss,)
+        return outputs
+
+
+class SequenceSummary(nn.Module):
+    r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
+        Args of the config class:
+            summary_type:
+                - 'last' => [default] take the last token hidden state (like XLNet)
+                - 'first' => take the first token hidden state (like Bert)
+                - 'mean' => take the mean of all tokens hidden states
+                - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
+                - 'attn' => Not implemented now, use multi-head attention
+            summary_use_proj: Add a projection after the vector extraction
+            summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
+            summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default
+            summary_first_dropout: Add a dropout before the projection and activation
+            summary_last_dropout: Add a dropout after the projection and activation
+    """
+
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+
+        self.summary_type = getattr(config, "summary_type", "last")
+        if self.summary_type == "attn":
+            # We should use a standard multi-head attention module with absolute positional embedding for that.
+            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
+            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
+            raise NotImplementedError
+
+        self.summary = Identity()
+        if hasattr(config, "summary_use_proj") and config.summary_use_proj:
+            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
+                num_classes = config.num_labels
+            else:
+                num_classes = config.hidden_size
+            self.summary = nn.Linear(config.hidden_size, num_classes)
+
+        activation_string = getattr(config, "summary_activation", None)
+        self.activation: Callable = (get_activation(activation_string) if activation_string else Identity())
+
+        self.first_dropout = Identity()
+        if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
+            self.first_dropout = nn.Dropout(config.summary_first_dropout)
+
+        self.last_dropout = Identity()
+        if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
+            self.last_dropout = nn.Dropout(config.summary_last_dropout)
+
+    def forward(self, hidden_states, cls_index=None):
+        """ hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer.
+            cls_index: [optional] position of the classification token if summary_type == 'cls_index',
+                shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
+                if summary_type == 'cls_index' and cls_index is None:
+                    we take the last token of the sequence as classification token
+        """
+        if self.summary_type == "last":
+            output = hidden_states[:, -1]
+        elif self.summary_type == "first":
+            output = hidden_states[:, 0]
+        elif self.summary_type == "mean":
+            output = hidden_states.mean(dim=1)
+        elif self.summary_type == "cls_index":
+            if cls_index is None:
+                cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long,)
+            else:
+                cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
+                cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
+            # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
+            output = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, XX, hidden_size)
+        elif self.summary_type == "attn":
+            raise NotImplementedError
+
+        output = self.first_dropout(output)
+        output = self.summary(output)
+        output = self.activation(output)
+        output = self.last_dropout(output)
+
+        return output
+
+
+def prune_linear_layer(layer, index, dim=0):
+    """ Prune a linear layer (a model parameters) to keep only entries in index.
+        Return the pruned layer as a new layer with requires_grad=True.
+        Used to remove heads.
+    """
+    index = index.to(layer.weight.device)
+    W = layer.weight.index_select(dim, index).clone().detach()
+    if layer.bias is not None:
+        if dim == 1:
+            b = layer.bias.clone().detach()
+        else:
+            b = layer.bias[index].clone().detach()
+    new_size = list(layer.weight.size())
+    new_size[dim] = len(index)
+    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
+    new_layer.weight.requires_grad = False
+    new_layer.weight.copy_(W.contiguous())
+    new_layer.weight.requires_grad = True
+    if layer.bias is not None:
+        new_layer.bias.requires_grad = False
+        new_layer.bias.copy_(b.contiguous())
+        new_layer.bias.requires_grad = True
+    return new_layer
+
+
+def prune_conv1d_layer(layer, index, dim=1):
+    """ Prune a Conv1D layer (a model parameters) to keep only entries in index.
+        A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
+        Return the pruned layer as a new layer with requires_grad=True.
+        Used to remove heads.
+    """
+    index = index.to(layer.weight.device)
+    W = layer.weight.index_select(dim, index).clone().detach()
+    if dim == 0:
+        b = layer.bias.clone().detach()
+    else:
+        b = layer.bias[index].clone().detach()
+    new_size = list(layer.weight.size())
+    new_size[dim] = len(index)
+    new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
+    new_layer.weight.requires_grad = False
+    new_layer.weight.copy_(W.contiguous())
+    new_layer.weight.requires_grad = True
+    new_layer.bias.requires_grad = False
+    new_layer.bias.copy_(b.contiguous())
+    new_layer.bias.requires_grad = True
+    return new_layer
+
+
+def prune_layer(layer, index, dim=None):
+    """ Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.
+        Return the pruned layer as a new layer with requires_grad=True.
+        Used to remove heads.
+    """
+    if isinstance(layer, nn.Linear):
+        return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
+    elif isinstance(layer, Conv1D):
+        return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
+    else:
+        raise ValueError("Can't prune layer of class {}".format(layer.__class__))
+
+
+def apply_chunking_to_forward(
+    chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors
+) -> torch.Tensor:
+    """
+    This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`.
+    It then applies a layer `forward_fn` to each chunk independently to save memory.
+    If the `forward_fn` is independent across the `chunk_dim` this function will yield the
+    same result as not applying it.
+
+    Args:
+        chunk_size: int - the chunk size of a chunked tensor. `num_chunks` = `len(input_tensors[0]) / chunk_size`
+        chunk_dim: int - the dimension over which the input_tensors should be chunked
+        forward_fn: fn - the forward fn of the model
+        input_tensors: tuple(torch.Tensor) - the input tensors of `forward_fn` which are chunked
+    Returns:
+        a Tensor with the same shape the foward_fn would have given if applied
+
+
+    Examples::
+
+        # rename the usual forward() fn to forward_chunk()
+        def forward_chunk(self, hidden_states):
+            hidden_states = self.decoder(hidden_states)
+            return hidden_states
+
+        # implement a chunked forward function
+        def forward(self, hidden_states):
+            return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)
+    """
+
+    assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors)
+    tensor_shape = input_tensors[0].shape
+    assert all(
+        input_tensor.shape == tensor_shape for input_tensor in input_tensors
+    ), "All input tenors have to be of the same shape"
+
+    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability
+    num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
+    assert num_args_in_forward_chunk_fn == len(
+        input_tensors
+    ), "forward_chunk_fn expects {} arguments, but only {} input tensors are given".format(
+        num_args_in_forward_chunk_fn, len(input_tensors)
+    )
+
+    if chunk_size > 0:
+        assert (
+            input_tensors[0].shape[chunk_dim] % chunk_size == 0
+        ), "The dimension to be chunked {} has to be a multiple of the chunk size {}".format(
+            input_tensors[0].shape[chunk_dim], chunk_size
+        )
+
+        num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size
+
+        # chunk input tensor into tuples
+        input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
+        # apply forward fn to every tuple
+        output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
+        # concatenate output at same dimension
+        return torch.cat(output_chunks, dim=chunk_dim)
+
+    return forward_fn(*input_tensors)

elia/bert/multimodal_bert.py

@@ -0,0 +1,277 @@
+
+from .modeling_bert import BertModel
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+
+class MultiModalBert(BertModel):
+    def __init__(self, config, embed_dim, pwam_idx=[3,6,9,12], num_heads_fusion=[1,1,1,1], fusion_drop=0.0):
+        super().__init__(config)
+        self.pwam_idx = pwam_idx
+        self.num_heads_fusion = num_heads_fusion
+        self.fusion_drop = fusion_drop
+
+        pwam_dims=[embed_dim * 2** i for i in range(len(pwam_idx))] 
+        #print(pwam_dims)
+        self.pwams = nn.ModuleList()
+        self.res_gates = nn.ModuleList()
+        self.norms = nn.ModuleList()
+        for i in range(0, len(pwam_idx)):
+            dim = pwam_dims[i]
+            fusion = PWAM(768,  # both the visual input and for combining, num of channels
+                          dim,  # v_in
+                          768,  # l_in
+                          768,  # key
+                          768,  # value
+                          num_heads=num_heads_fusion[i],
+                          dropout=fusion_drop)
+            self.pwams.append(fusion)
+
+            res_gate = nn.Sequential(
+                nn.Linear(768, 768, bias=False),
+                nn.ReLU(),
+                nn.Linear(768, 768, bias=False),
+                nn.Tanh()
+            )
+            nn.init.zeros_(res_gate[0].weight)
+            nn.init.zeros_(res_gate[2].weight)
+            self.res_gates.append(res_gate)
+
+            self.norms.append(nn.LayerNorm(768))
+
+    def forward_stem(self, input_ids, attention_mask):
+        input_shape = input_ids.size()
+        token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=input_ids.device)
+
+        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, input_ids.device)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids, token_type_ids=token_type_ids
+        )
+        #print(embedding_output.shape, extended_attention_mask.shape, "?>>>")
+        return embedding_output, extended_attention_mask
+
+    def forward_stage1(self, hidden_states, attention_mask):
+        for i in range(0, self.pwam_idx[0]):
+            layer_module = self.encoder.layer[i]
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask,
+            )
+            hidden_states = layer_outputs[0]
+
+        return layer_outputs[0]
+
+    def forward_stage2(self, hidden_states, attention_mask):
+        for i in range(self.pwam_idx[0], self.pwam_idx[1]):
+            layer_module = self.encoder.layer[i]
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask,
+            )
+            hidden_states = layer_outputs[0]
+
+        return layer_outputs[0]
+
+    def forward_stage3(self, hidden_states, attention_mask):
+        for i in range(self.pwam_idx[1], self.pwam_idx[2]):
+            layer_module = self.encoder.layer[i]
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask,
+            )
+            hidden_states = layer_outputs[0]
+
+        return layer_outputs[0]
+
+    def forward_stage4(self, hidden_states, attention_mask):
+        for i in range(self.pwam_idx[2], self.pwam_idx[3]):
+            layer_module = self.encoder.layer[i]
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask,
+            )
+            hidden_states = layer_outputs[0]
+
+        return layer_outputs[0]
+
+    def forward_pwam1(self, x, l, l_mask):
+        l_residual = self.pwams[0](x, l, l_mask)
+        l = l + (self.res_gates[0](l_residual) * l_residual)
+        return self.norms[0](l_residual), l
+
+    def forward_pwam2(self, x, l, l_mask):
+        l_residual = self.pwams[1](x, l, l_mask)
+        l = l + (self.res_gates[1](l_residual) * l_residual)
+        return self.norms[1](l_residual), l
+
+    def forward_pwam3(self, x, l, l_mask):
+        l_residual = self.pwams[2](x, l, l_mask)
+        l = l + (self.res_gates[2](l_residual) * l_residual)
+        return self.norms[2](l_residual), l
+
+    def forward_pwam4(self, x, l, l_mask):
+        l_residual = self.pwams[3](x, l, l_mask)
+        l = l + (self.res_gates[3](l_residual) * l_residual)
+        return self.norms[3](l_residual), l
+
+class PWAM(nn.Module):
+    def __init__(self, dim, v_in_channels, l_in_channels, key_channels, value_channels, num_heads=0, dropout=0.0):
+        super(PWAM, self).__init__()
+        # input x shape: (B, H*W, dim)
+        #self.vis_project = nn.Sequential(nn.Conv1d(dim, dim, 1, 1),  # the init function sets bias to 0 if bias is True
+        #                                 nn.GELU(),
+        #                                 nn.Dropout(dropout)
+        #                                )
+        #self.vis_project = nn.Sequential(nn.Conv1d(dim, dim, 1, 1),  # the init function sets bias to 0 if bias is True
+        self.vis_project = nn.Sequential(nn.Linear(dim, dim),  # the init function sets bias to 0 if bias is True
+                                         nn.GELU(),
+                                         nn.Dropout(dropout)
+                                        )
+
+        self.image_lang_att = SpatialImageLanguageAttention(v_in_channels,  # v_in
+                                                            l_in_channels,  # l_in
+                                                            key_channels,  # key
+                                                            value_channels,  # value
+                                                            out_channels=value_channels,  # out
+                                                            num_heads=num_heads)
+
+        self.project_mm = nn.Sequential(nn.Conv1d(value_channels, value_channels, 1, 1),
+                                        nn.GELU(),
+                                        nn.Dropout(dropout)
+                                        )
+
+    def forward(self, x, l, l_mask):
+        # input x shape: (B, H*W, dim)
+        #print("???", x.shape, l.shape, l_mask.shape)
+        #print(self.vis_project)
+        #vis = self.vis_project(x.permute(0, 2, 1))  # (B, dim, H*W)
+        vis = self.vis_project(l)  # (B, dim, H*W)
+
+        lang = self.image_lang_att(x, l, l_mask)  # (B, H*W, dim)
+
+        lang = lang.permute(0, 2, 1)  # (B, dim, H*W)
+
+        #print("vis", vis.shape, "lang", lang.shape)
+        mm = torch.mul(vis.permute(0,2,1), lang)
+        #print(mm.shape)
+        mm = self.project_mm(mm)  # (B, dim, H*W)
+
+        mm = mm.permute(0, 2, 1)  # (B, H*W, dim)
+
+        return mm
+
+        #self.fusion = PWAM(dim,  # both the visual input and for combining, num of channels
+        #                   dim,  # v_in
+        #                   768,  # l_in
+        #                   dim,  # key
+        #                   dim,  # value
+        #                   num_heads=num_heads_fusion,
+        #                   dropout=fusion_drop)
+
+class SpatialImageLanguageAttention(nn.Module):
+    def __init__(self, v_in_channels, l_in_channels, key_channels, value_channels, out_channels=None, num_heads=1):
+        super(SpatialImageLanguageAttention, self).__init__()
+        # x shape: (B, H*W, v_in_channels)
+        # l input shape: (B, l_in_channels, N_l)
+        # l_mask shape: (B, N_l, 1)
+        self.v_in_channels = v_in_channels
+        self.l_in_channels = l_in_channels
+        self.out_channels = out_channels
+        self.key_channels = key_channels
+        self.value_channels = value_channels
+        self.num_heads = num_heads
+        if out_channels is None:
+            self.out_channels = self.value_channels
+
+        # Keys: language features: (B, l_in_channels, #words)
+        # avoid any form of spatial normalization because a sentence contains many padding 0s
+        self.f_query = nn.Sequential(
+            nn.Conv1d(self.l_in_channels, self.key_channels, kernel_size=1, stride=1),
+        )
+
+        # Queries: visual features: (B, H*W, v_in_channels)
+        self.f_key = nn.Sequential(
+            nn.Conv1d(self.v_in_channels, self.key_channels, kernel_size=1, stride=1),
+            nn.InstanceNorm1d(self.key_channels),
+        )
+
+        # Values: language features: (B, l_in_channels, #words)
+        #self.f_value = nn.Sequential(
+        #    nn.Conv1d(self.l_in_channels, self.value_channels, kernel_size=1, stride=1),
+        #)
+        self.f_value = nn.Sequential(
+            nn.Conv1d(self.v_in_channels, self.key_channels, kernel_size=1, stride=1),
+            nn.InstanceNorm1d(self.key_channels),
+        )
+
+        # Out projection
+        self.W = nn.Sequential(
+            nn.Conv1d(self.value_channels, self.out_channels, kernel_size=1, stride=1),
+            nn.InstanceNorm1d(self.out_channels),
+        )
+
+    def forward(self, x, l, l_mask):
+        #print('input shape', x.shape, l.shape, l_mask.shape)
+        l_mask = l_mask.squeeze(1)
+        # x shape: (B, H*W, v_in_channels)
+        # l input shape: (B, l_in_channels, N_l)
+        # l_mask shape: (B, N_l, 1)
+        B, HW = x.size(0), x.size(1)
+        x = x.permute(0, 2, 1)  # (B, key_channels, H*W)
+        l = l.permute(0,2,1)
+        #l_mask = l_mask.permute(0, 2, 1)  # (B, N_l, 1) -> (B, 1, N_l)
+        l_mask = l_mask  # (B, N_l, 1) -> (B, 1, N_l)
+
+        #query = self.f_query(x)  # (B, key_channels, H*W) if Conv1D
+        #query = query.permute(0, 2, 1)  # (B, H*W, key_channels)
+        #key = self.f_key(l)  # (B, key_channels, N_l)
+        #value = self.f_value(l)  # (B, self.value_channels, N_l)
+        #key = key * l_mask  # (B, key_channels, N_l)
+        #value = value * l_mask  # (B, self.value_channels, N_l)
+
+        #print(l.shape, self.f_query)
+        query = self.f_query(l)  # (B, key_channels, H*W) if Conv1D
+        query = query * l_mask  # (B, key_channels, N_l)
+        query = query.permute(0, 2, 1)  # (B, N_l, key_channels)
+
+        key = self.f_key(x)  # (B, key_channels, H*W) if Conv1D
+        value = self.f_value(x)  # (B, key_channels, H*W) if Conv1D
+
+        n_l = query.size(1)
+        #print(query.shape, key.shape, value.shape)
+
+        #query = query.reshape(B, HW, self.num_heads, self.key_channels//self.num_heads).permute(0, 2, 1, 3)
+        # (b, num_heads, H*W, self.key_channels//self.num_heads)
+        #key = key.reshape(B, self.num_heads, self.key_channels//self.num_heads, n_l)
+        # (b, num_heads, self.key_channels//self.num_heads, n_l)
+        #value = value.reshape(B, self.num_heads, self.value_channels//self.num_heads, n_l)
+        # # (b, num_heads, self.value_channels//self.num_heads, n_l)
+        key = key.reshape(B, self.num_heads, self.key_channels//self.num_heads, HW)
+        value = value.reshape(B, self.num_heads, self.key_channels//self.num_heads, HW)
+        # (b, num_heads, H*W, self.key_channels//self.num_heads)
+        #query = query.reshape(B, self.num_heads, self.key_channels//self.num_heads, n_l)
+        query = query.reshape(B, n_l, self.num_heads, self.key_channels//self.num_heads).permute(0, 2, 1, 3)
+        # (b, num_heads, self.key_channels//self.num_heads, n_l)
+        #value = value.reshape(B, self.num_heads, self.value_channels//self.num_heads, n_l)
+        #print('after reshape', query.shape, key.shape, value.shape)
+
+        l_mask = l_mask.unsqueeze(-1)  # (b, 1, 1, n_l)
+
+        #sim_map = torch.matmul(query, key)  # (B, self.num_heads, H*W, N_l)
+        sim_map = torch.matmul(query, key)  # (B, self.num_heads, N_l, H*W)
+        sim_map = (self.key_channels ** -.5) * sim_map  # scaled dot product
+
+        sim_map = sim_map + (1e4*l_mask - 1e4)  # assign a very small number to padding positions
+        sim_map = F.softmax(sim_map, dim=-1)  # (B, num_heads, h*w, N_l)
+        out = torch.matmul(sim_map, value.permute(0, 1, 3, 2))  # (B, num_heads, H*W, self.value_channels//num_heads)
+        #print('out', out.shape)
+        #out = out.permute(0, 2, 1, 3).contiguous().reshape(B, HW, self.value_channels)  # (B, H*W, value_channels)
+        out = out.permute(0, 2, 1, 3).contiguous().reshape(B, n_l, self.value_channels)  # (B, H*W, value_channels)
+        out = out.permute(0, 2, 1)  # (B, value_channels, HW)
+        out = self.W(out)  # (B, value_channels, HW)
+        out = out.permute(0, 2, 1)  # (B, HW, value_channels)
+
+        return out

elia/bert/tokenization_bert.py

@@ -0,0 +1,545 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# 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.
+"""Tokenization classes."""
+
+
+import collections
+import logging
+import os
+import unicodedata
+from typing import List, Optional
+
+from .tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+
+
+logger = logging.getLogger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
+        "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
+        "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
+        "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
+        "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
+        "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
+        "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
+        "bert-base-german-cased": "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
+        "bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
+        "bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
+        "bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
+        "bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
+        "bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
+        "bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt",
+        "bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt",
+        "TurkuNLP/bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/vocab.txt",
+        "TurkuNLP/bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/vocab.txt",
+        "wietsedv/bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/vocab.txt",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "bert-base-uncased": 512,
+    "bert-large-uncased": 512,
+    "bert-base-cased": 512,
+    "bert-large-cased": 512,
+    "bert-base-multilingual-uncased": 512,
+    "bert-base-multilingual-cased": 512,
+    "bert-base-chinese": 512,
+    "bert-base-german-cased": 512,
+    "bert-large-uncased-whole-word-masking": 512,
+    "bert-large-cased-whole-word-masking": 512,
+    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
+    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
+    "bert-base-cased-finetuned-mrpc": 512,
+    "bert-base-german-dbmdz-cased": 512,
+    "bert-base-german-dbmdz-uncased": 512,
+    "TurkuNLP/bert-base-finnish-cased-v1": 512,
+    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
+    "wietsedv/bert-base-dutch-cased": 512,
+}
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "bert-base-uncased": {"do_lower_case": True},
+    "bert-large-uncased": {"do_lower_case": True},
+    "bert-base-cased": {"do_lower_case": False},
+    "bert-large-cased": {"do_lower_case": False},
+    "bert-base-multilingual-uncased": {"do_lower_case": True},
+    "bert-base-multilingual-cased": {"do_lower_case": False},
+    "bert-base-chinese": {"do_lower_case": False},
+    "bert-base-german-cased": {"do_lower_case": False},
+    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
+    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
+    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
+    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
+    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
+    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
+    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
+    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
+    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
+    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
+}
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class BertTokenizer(PreTrainedTokenizer):
+    r"""
+    Constructs a BERT tokenizer. Based on WordPiece.
+
+    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users
+    should refer to the superclass for more information regarding methods.
+
+    Args:
+        vocab_file (:obj:`string`):
+            File containing the vocabulary.
+        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to lowercase the input when tokenizing.
+        do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to do basic tokenization before WordPiece.
+        never_split (:obj:`Iterable`, `optional`, defaults to :obj:`None`):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            :obj:`do_basic_tokenize=True`
+        unk_token (:obj:`string`, `optional`, defaults to "[UNK]"):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (:obj:`string`, `optional`, defaults to "[SEP]"):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
+            for sequence classification or for a text and a question for question answering.
+            It is also used as the last token of a sequence built with special tokens.
+        pad_token (:obj:`string`, `optional`, defaults to "[PAD]"):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (:obj:`string`, `optional`, defaults to "[CLS]"):
+            The classifier token which is used when doing sequence classification (classification of the whole
+            sequence instead of per-token classification). It is the first token of the sequence when built with
+            special tokens.
+        mask_token (:obj:`string`, `optional`, defaults to "[MASK]"):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to tokenize Chinese characters.
+            This should likely be deactivated for Japanese:
+            see: https://github.com/huggingface/transformers/issues/328
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=True,
+        do_basic_tokenize=True,
+        never_split=None,
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        tokenize_chinese_chars=True,
+        **kwargs
+    ):
+        super().__init__(
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            **kwargs,
+        )
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
+            )
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+            self.basic_tokenizer = BasicTokenizer(
+                do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars
+            )
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text):
+        split_tokens = []
+        if self.do_basic_tokenize:
+            for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
+
+                # If the token is part of the never_split set
+                if token in self.basic_tokenizer.never_split:
+                    split_tokens.append(token)
+                else:
+                    split_tokens += self.wordpiece_tokenizer.tokenize(token)
+        else:
+            split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = " ".join(tokens).replace(" ##", "").strip()
+        return out_string
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks
+        by concatenating and adding special tokens.
+        A BERT sequence has the following format:
+
+        - single sequence: ``[CLS] X [SEP]``
+        - pair of sequences: ``[CLS] A [SEP] B [SEP]``
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added
+            token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer ``prepare_for_model`` method.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of ids.
+            token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Set to True if the token list is already formatted with special tokens for the model
+
+        Returns:
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formated with special tokens for the model."
+                )
+            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
+        A BERT sequence pair mask has the following format:
+
+        ::
+
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence    | second sequence |
+
+        if token_ids_1 is None, only returns the first portion of the mask (0's).
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of ids.
+            token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, vocab_path):
+        """
+        Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory.
+
+        Args:
+            vocab_path (:obj:`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            :obj:`Tuple(str)`: Paths to the files saved.
+        """
+        index = 0
+        if os.path.isdir(vocab_path):
+            vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"])
+        else:
+            vocab_file = vocab_path
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        "Saving vocabulary to {}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!".format(vocab_file)
+                    )
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file,)
+
+
+class BasicTokenizer(object):
+    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+    def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True):
+        """ Constructs a BasicTokenizer.
+
+        Args:
+            **do_lower_case**: Whether to lower case the input.
+            **never_split**: (`optional`) list of str
+                Kept for backward compatibility purposes.
+                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
+                List of token not to split.
+            **tokenize_chinese_chars**: (`optional`) boolean (default True)
+                Whether to tokenize Chinese characters.
+                This should likely be deactivated for Japanese:
+                see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
+        """
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+
+    def tokenize(self, text, never_split=None):
+        """ Basic Tokenization of a piece of text.
+            Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
+
+        Args:
+            **never_split**: (`optional`) list of str
+                Kept for backward compatibility purposes.
+                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
+                List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if self.do_lower_case and token not in never_split:
+                token = token.lower()
+                token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if never_split is not None and text in never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if (
+            (cp >= 0x4E00 and cp <= 0x9FFF)
+            or (cp >= 0x3400 and cp <= 0x4DBF)  #
+            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
+        ):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text into its word pieces.
+
+        This uses a greedy longest-match-first algorithm to perform tokenization
+        using the given vocabulary.
+
+        For example:
+          input = "unaffable"
+          output = ["un", "##aff", "##able"]
+
+        Args:
+          text: A single token or whitespace separated tokens. This should have
+            already been passed through `BasicTokenizer`.
+
+        Returns:
+          A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+

elia/bert/tokenization_utils.py

@@ -0,0 +1,723 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# 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.
+""" Tokenization classes for python tokenizers.
+    For fast tokenizers (provided by HuggingFace's tokenizers library) see tokenization_utils_fast.py
+"""
+
+import itertools
+import logging
+import re
+import unicodedata
+from typing import Dict, List, Optional, Tuple, Union
+
+from .file_utils import add_end_docstrings
+from .tokenization_utils_base import (
+    ENCODE_KWARGS_DOCSTRING,
+    ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
+    AddedToken,
+    BatchEncoding,
+    EncodedInput,
+    EncodedInputPair,
+    PaddingStrategy,
+    PreTokenizedInput,
+    PreTokenizedInputPair,
+    PreTrainedTokenizerBase,
+    TensorType,
+    TextInput,
+    TextInputPair,
+    TruncationStrategy,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+def _is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False
+
+
+def _is_end_of_word(text):
+    """Checks whether the last character in text is one of a punctuation, control or whitespace character."""
+    last_char = text[-1]
+    return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char))
+
+
+def _is_start_of_word(text):
+    """Checks whether the first character in text is one of a punctuation, control or whitespace character."""
+    first_char = text[0]
+    return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))
+
+
+class PreTrainedTokenizer(PreTrainedTokenizerBase):
+    """ Base class for all slow tokenizers.
+
+    Handle all the shared methods for tokenization and special tokens as well as methods
+    downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
+
+    This class also contain the added tokens in a unified way on top of all tokenizers so we don't
+    have to handle the specific vocabulary augmentation methods of the various underlying
+    dictionary structures (BPE, sentencepiece...).
+
+    Class attributes (overridden by derived classes):
+
+    - ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file
+      required by the model, and as associated values, the filename for saving the associated file (string).
+    - ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys
+      being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the
+      `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the
+      associated pretrained vocabulary file.
+    - ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained
+      models, and as associated values, the maximum length of the sequence inputs of this model, or None if the
+      model has no maximum input size.
+    - ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the
+      pretrained models, and as associated values, a dictionnary of specific arguments to pass to the
+      ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the
+      ``from_pretrained()`` method.
+
+    Args:
+        - ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.
+            When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated
+            model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).
+            no associated max_length can be found in ``max_model_input_sizes``.
+        - ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.
+            Should be selected between ['right', 'left']
+        - ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the
+            model ("token_type_ids", "attention_mask"...).
+        - ``bos_token``: (`Optional`) string: a beginning of sentence token.
+            Will be associated to ``self.bos_token`` and ``self.bos_token_id``
+        - ``eos_token``: (`Optional`) string: an end of sentence token.
+            Will be associated to ``self.eos_token`` and ``self.eos_token_id``
+        - ``unk_token``: (`Optional`) string: an unknown token.
+            Will be associated to ``self.unk_token`` and ``self.unk_token_id``
+        - ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).
+            Will be associated to ``self.sep_token`` and ``self.sep_token_id``
+        - ``pad_token``: (`Optional`) string: a padding token.
+            Will be associated to ``self.pad_token`` and ``self.pad_token_id``
+        - ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence
+            leveraging self-attention along the full depth of the model).
+            Will be associated to ``self.cls_token`` and ``self.cls_token_id``
+        - ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language
+            modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
+        - ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
+            Adding all special tokens here ensure they won't be split by the tokenization process.
+            Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
+
+
+    .. automethod:: __call__
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        # Added tokens - We store this for both slow and fast tokenizers
+        # until the serialization of Fast tokenizers is updated
+        self.added_tokens_encoder: Dict[str, int] = {}
+        self.added_tokens_decoder: Dict[int, str] = {}
+        self.unique_no_split_tokens: List[str] = []
+
+    @property
+    def is_fast(self) -> bool:
+        return False
+
+    @property
+    def vocab_size(self) -> int:
+        """ Size of the base vocabulary (without the added tokens) """
+        raise NotImplementedError
+
+    def get_vocab(self):
+        """ Returns the vocabulary as a dict of {token: index} pairs. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. """
+        raise NotImplementedError()
+
+    def get_added_vocab(self) -> Dict[str, int]:
+        return self.added_tokens_encoder
+
+    def __len__(self):
+        """ Size of the full vocabulary with the added tokens """
+        return self.vocab_size + len(self.added_tokens_encoder)
+
+    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens=False) -> int:
+        """
+        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
+        vocabulary, they are added to it with indices starting from length of the current vocabulary.
+
+        Args:
+            new_tokens: string or list of string. Each string is a token to add. Tokens are only added if they are not
+                already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
+
+        Returns:
+            Number of tokens added to the vocabulary.
+
+        Examples::
+
+            # Let's see how to increase the vocabulary of Bert model and tokenizer
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = BertModel.from_pretrained('bert-base-uncased')
+
+            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
+            print('We have added', num_added_toks, 'tokens')
+            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
+        """
+        new_tokens = [str(tok) for tok in new_tokens]
+
+        tokens_to_add = []
+        for token in new_tokens:
+            assert isinstance(token, str)
+            if not special_tokens and self.init_kwargs.get("do_lower_case", False):
+                token = token.lower()
+            if (
+                token != self.unk_token
+                and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
+                and token not in tokens_to_add
+            ):
+                tokens_to_add.append(token)
+                if self.verbose:
+                    logger.info("Adding %s to the vocabulary", token)
+
+        added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
+        added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
+        self.added_tokens_encoder.update(added_tok_encoder)
+        self.added_tokens_decoder.update(added_tok_decoder)
+
+        # Make sure we don't split on any special tokens (even they were already in the vocab before e.g. for Albert)
+        if special_tokens:
+            self.unique_no_split_tokens = list(set(self.unique_no_split_tokens).union(set(new_tokens)))
+        else:
+            # Or on the newly added tokens
+            self.unique_no_split_tokens = list(set(self.unique_no_split_tokens).union(set(tokens_to_add)))
+
+        return len(tokens_to_add)
+
+    def num_special_tokens_to_add(self, pair=False):
+        """
+        Returns the number of added tokens when encoding a sequence with special tokens.
+
+        Note:
+            This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
+            inside your training loop.
+
+        Args:
+            pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
+                number of added tokens in the case of a single sequence if set to False.
+
+        Returns:
+            Number of tokens added to sequences
+        """
+        token_ids_0 = []
+        token_ids_1 = []
+        return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
+
+    def tokenize(self, text: TextInput, **kwargs):
+        """ Converts a string in a sequence of tokens (string), using the tokenizer.
+            Split in words for word-based vocabulary or sub-words for sub-word-based
+            vocabularies (BPE/SentencePieces/WordPieces).
+
+            Take care of added tokens.
+
+            Args:
+                text (:obj:`string`): The sequence to be encoded.
+                **kwargs (:obj: `dict`): Arguments passed to the model-specific `prepare_for_tokenization` preprocessing method.
+        """
+        # Simple mapping string => AddedToken for special tokens with specific tokenization behaviors
+        all_special_tokens_extended = dict(
+            (str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)
+        )
+
+        text, kwargs = self.prepare_for_tokenization(text, **kwargs)
+
+        if kwargs:
+            logger.warning(f"Keyword arguments {kwargs} not recognized.")
+
+        # TODO: should this be in the base class?
+        if self.init_kwargs.get("do_lower_case", False):
+            # convert non-special tokens to lowercase
+            escaped_special_toks = [re.escape(s_tok) for s_tok in self.all_special_tokens]
+            pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
+            text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)
+
+        def split_on_token(tok, text):
+            result = []
+            tok_extended = all_special_tokens_extended.get(tok, None)
+            split_text = text.split(tok)
+            full_word = ""
+            for i, sub_text in enumerate(split_text):
+                # AddedToken can control whitespace stripping around them.
+                # We use them for GPT2 and Roberta to have different behavior depending on the special token
+                # Cf. https://github.com/huggingface/transformers/pull/2778
+                # and https://github.com/huggingface/transformers/issues/3788
+                if isinstance(tok_extended, AddedToken):
+                    if tok_extended.single_word:
+                        # Try to avoid splitting on token
+                        if (
+                            i < len(split_text) - 1
+                            and not _is_end_of_word(sub_text)
+                            and not _is_start_of_word(split_text[i + 1])
+                        ):
+                            # Don't extract the special token
+                            full_word += sub_text + tok
+                        elif full_word:
+                            full_word += sub_text
+                            result += [full_word]
+                            full_word = ""
+                            continue
+                    # Strip white spaces on the right
+                    if tok_extended.rstrip and i > 0:
+                        # A bit counter-intuitive but we strip the left of the string
+                        # since tok_extended.rstrip means the special token is eating all white spaces on its right
+                        sub_text = sub_text.lstrip()
+                    # Strip white spaces on the left
+                    if tok_extended.lstrip and i < len(split_text) - 1:
+                        sub_text = sub_text.rstrip()  # Opposite here
+                else:
+                    # We strip left and right by default
+                    if i < len(split_text) - 1:
+                        sub_text = sub_text.rstrip()
+                    if i > 0:
+                        sub_text = sub_text.lstrip()
+
+                if i == 0 and not sub_text:
+                    result += [tok]
+                elif i == len(split_text) - 1:
+                    if sub_text:
+                        result += [sub_text]
+                    else:
+                        pass
+                else:
+                    if sub_text:
+                        result += [sub_text]
+                    result += [tok]
+            return result
+
+        def split_on_tokens(tok_list, text):
+            if not text.strip():
+                return []
+            if not tok_list:
+                return self._tokenize(text)
+
+            tokenized_text = []
+            text_list = [text]
+            for tok in tok_list:
+                tokenized_text = []
+                for sub_text in text_list:
+                    if sub_text not in self.unique_no_split_tokens:
+                        tokenized_text += split_on_token(tok, sub_text)
+                    else:
+                        tokenized_text += [sub_text]
+                text_list = tokenized_text
+
+            return list(
+                itertools.chain.from_iterable(
+                    (
+                        self._tokenize(token) if token not in self.unique_no_split_tokens else [token]
+                        for token in tokenized_text
+                    )
+                )
+            )
+
+        no_split_token = self.unique_no_split_tokens
+        tokenized_text = split_on_tokens(no_split_token, text)
+        return tokenized_text
+
+    def _tokenize(self, text, **kwargs):
+        """ Converts a string in a sequence of tokens (string), using the tokenizer.
+            Split in words for word-based vocabulary or sub-words for sub-word-based
+            vocabularies (BPE/SentencePieces/WordPieces).
+
+            Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def convert_tokens_to_ids(self, tokens):
+        """ Converts a token string (or a sequence of tokens) in a single integer id
+            (or a sequence of ids), using the vocabulary.
+        """
+        if tokens is None:
+            return None
+
+        if isinstance(tokens, str):
+            return self._convert_token_to_id_with_added_voc(tokens)
+
+        ids = []
+        for token in tokens:
+            ids.append(self._convert_token_to_id_with_added_voc(token))
+        return ids
+
+    def _convert_token_to_id_with_added_voc(self, token):
+        if token is None:
+            return None
+
+        if token in self.added_tokens_encoder:
+            return self.added_tokens_encoder[token]
+        return self._convert_token_to_id(token)
+
+    def _convert_token_to_id(self, token):
+        raise NotImplementedError
+
+    def _encode_plus(
+        self,
+        text: Union[TextInput, PreTokenizedInput, EncodedInput],
+        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        is_pretokenized: bool = False,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        **kwargs
+    ) -> BatchEncoding:
+        def get_input_ids(text):
+            if isinstance(text, str):
+                tokens = self.tokenize(text, **kwargs)
+                return self.convert_tokens_to_ids(tokens)
+            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
+                if is_pretokenized:
+                    tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))
+                    return self.convert_tokens_to_ids(tokens)
+                else:
+                    return self.convert_tokens_to_ids(text)
+            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
+                return text
+            else:
+                if is_pretokenized:
+                    raise ValueError(
+                        f"Input {text} is not valid. Should be a string or a list/tuple of strings when `is_pretokenized=True`."
+                    )
+                else:
+                    raise ValueError(
+                        f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
+                    )
+
+        if return_offsets_mapping:
+            raise NotImplementedError(
+                "return_offset_mapping is not available when using Python tokenizers."
+                "To use this feature, change your tokenizer to one deriving from "
+                "transformers.PreTrainedTokenizerFast."
+                "More information on available tokenizers at "
+                "https://github.com/huggingface/transformers/pull/2674"
+            )
+
+        first_ids = get_input_ids(text)
+        second_ids = get_input_ids(text_pair) if text_pair is not None else None
+
+        return self.prepare_for_model(
+            first_ids,
+            pair_ids=second_ids,
+            add_special_tokens=add_special_tokens,
+            padding=padding_strategy.value,
+            truncation=truncation_strategy.value,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors=return_tensors,
+            prepend_batch_axis=True,
+            return_attention_mask=return_attention_mask,
+            return_token_type_ids=return_token_type_ids,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_length=return_length,
+            verbose=verbose,
+        )
+
+    def _batch_encode_plus(
+        self,
+        batch_text_or_text_pairs: Union[
+            List[TextInput],
+            List[TextInputPair],
+            List[PreTokenizedInput],
+            List[PreTokenizedInputPair],
+            List[EncodedInput],
+            List[EncodedInputPair],
+        ],
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        is_pretokenized: bool = False,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        **kwargs
+    ) -> BatchEncoding:
+        def get_input_ids(text):
+            if isinstance(text, str):
+                tokens = self.tokenize(text, **kwargs)
+                return self.convert_tokens_to_ids(tokens)
+            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
+                if is_pretokenized:
+                    tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))
+                    return self.convert_tokens_to_ids(tokens)
+                else:
+                    return self.convert_tokens_to_ids(text)
+            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
+                return text
+            else:
+                raise ValueError(
+                    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
+                )
+
+        if return_offsets_mapping:
+            raise NotImplementedError(
+                "return_offset_mapping is not available when using Python tokenizers."
+                "To use this feature, change your tokenizer to one deriving from "
+                "transformers.PreTrainedTokenizerFast."
+            )
+
+        input_ids = []
+        for ids_or_pair_ids in batch_text_or_text_pairs:
+            if not isinstance(ids_or_pair_ids, (list, tuple)):
+                ids, pair_ids = ids_or_pair_ids, None
+            elif is_pretokenized and not isinstance(ids_or_pair_ids[0], (list, tuple)):
+                ids, pair_ids = ids_or_pair_ids, None
+            else:
+                ids, pair_ids = ids_or_pair_ids
+
+            first_ids = get_input_ids(ids)
+            second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
+            input_ids.append((first_ids, second_ids))
+
+        batch_outputs = self._batch_prepare_for_model(
+            input_ids,
+            add_special_tokens=add_special_tokens,
+            padding_strategy=padding_strategy,
+            truncation_strategy=truncation_strategy,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=return_attention_mask,
+            return_token_type_ids=return_token_type_ids,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_length=return_length,
+            return_tensors=return_tensors,
+            verbose=verbose,
+        )
+
+        return BatchEncoding(batch_outputs)
+
+    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+    def _batch_prepare_for_model(
+        self,
+        batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[str] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+    ) -> BatchEncoding:
+        """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
+        It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+        manages a moving window (with user defined stride) for overflowing tokens
+
+        Args:
+            batch_ids_pairs: list of tokenized input ids or input ids pairs
+        """
+
+        batch_outputs = {}
+        for first_ids, second_ids in batch_ids_pairs:
+            outputs = self.prepare_for_model(
+                first_ids,
+                second_ids,
+                add_special_tokens=add_special_tokens,
+                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
+                truncation=truncation_strategy.value,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=None,  # we pad in batch afterward
+                return_attention_mask=False,  # we pad in batch afterward
+                return_token_type_ids=return_token_type_ids,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_length=return_length,
+                return_tensors=None,  # We convert the whole batch to tensors at the end
+                prepend_batch_axis=False,
+                verbose=verbose,
+            )
+
+            for key, value in outputs.items():
+                if key not in batch_outputs:
+                    batch_outputs[key] = []
+                batch_outputs[key].append(value)
+
+        batch_outputs = self.pad(
+            batch_outputs,
+            padding=padding_strategy.value,
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=return_attention_mask,
+        )
+
+        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+        return batch_outputs
+
+    def prepare_for_tokenization(self, text: str, is_pretokenized=False, **kwargs) -> (str, dict):
+        """ Performs any necessary transformations before tokenization.
+
+            This method should pop the arguments from kwargs and return kwargs as well.
+            We test kwargs at the end of the encoding process to be sure all the arguments have been used.
+        """
+        return (text, kwargs)
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer ``prepare_for_model`` method.
+
+        Args:
+            token_ids_0: list of ids (must not contain special tokens)
+            token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
+                for sequence pairs
+            already_has_special_tokens: (default False) Set to True if the token list is already formated with
+                special tokens for the model
+
+        Returns:
+            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
+
+    def convert_ids_to_tokens(
+        self, ids: Union[int, List[int]], skip_special_tokens: bool = False
+    ) -> Union[str, List[str]]:
+        """ Converts a single index or a sequence of indices (integers) in a token "
+            (resp.) a sequence of tokens (str), using the vocabulary and added tokens.
+
+            Args:
+                skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
+        """
+        if isinstance(ids, int):
+            if ids in self.added_tokens_decoder:
+                return self.added_tokens_decoder[ids]
+            else:
+                return self._convert_id_to_token(ids)
+        tokens = []
+        for index in ids:
+            index = int(index)
+            if skip_special_tokens and index in self.all_special_ids:
+                continue
+            if index in self.added_tokens_decoder:
+                tokens.append(self.added_tokens_decoder[index])
+            else:
+                tokens.append(self._convert_id_to_token(index))
+        return tokens
+
+    def _convert_id_to_token(self, index: int) -> str:
+        raise NotImplementedError
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """ Converts a sequence of tokens (string) in a single string.
+            The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))
+            but we often want to remove sub-word tokenization artifacts at the same time.
+        """
+        return " ".join(self.convert_ids_to_tokens(tokens))
+
+    def decode(
+        self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
+    ) -> str:
+        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+
+        # To avoid mixing byte-level and unicode for byte-level BPT
+        # we need to build string separatly for added tokens and byte-level tokens
+        # cf. https://github.com/huggingface/transformers/issues/1133
+        sub_texts = []
+        current_sub_text = []
+        for token in filtered_tokens:
+            if skip_special_tokens and token in self.all_special_ids:
+                continue
+            if token in self.added_tokens_encoder:
+                if current_sub_text:
+                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+                    current_sub_text = []
+                sub_texts.append(token)
+            else:
+                current_sub_text.append(token)
+        if current_sub_text:
+            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
+        text = " ".join(sub_texts)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
+
+    def save_vocabulary(self, save_directory) -> Tuple[str]:
+        """ Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
+            and special token mappings.
+
+            Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full
+            Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained`
+            class method.
+        """
+        raise NotImplementedError

elia/demo_inference.py

@@ -0,0 +1,295 @@
+image_path = './image001.png'
+sentence = 'spoon on the dish'
+weights = '/cluster/nvme4/cyx/lavt/vis/model_best_refcoco_0508.pth'
+device = 'cpu'
+
+# pre-process the input image
+from PIL import Image
+import torchvision.transforms as T
+import numpy as np
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from bert.multimodal_bert import MultiModalBert
+import torchvision
+
+from lib import multimodal_segmentation_ppm
+#import transforms as T
+import utils
+
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+from modeling.MaskFormerModel import MaskFormerHead
+from addict import Dict
+#from bert.modeling_bert import BertLMPredictionHead, BertEncoder
+import cv2
+import textwrap
+
+class WrapperModel(nn.Module):
+    def __init__(self, image_model, language_model, classifier) :
+        super(WrapperModel, self).__init__()
+        self.image_model = image_model
+        self.language_model = language_model
+        self.classifier = classifier
+
+        config = Dict({
+          "architectures": [
+           "BertForMaskedLM"
+          ],
+          "attention_probs_dropout_prob": 0.1,
+          "gradient_checkpointing": False,
+          "hidden_act": "gelu",
+          "hidden_dropout_prob": 0.1,
+          "hidden_size": 512,
+          "initializer_range": 0.02,
+          "intermediate_size": 3072,
+          "layer_norm_eps": 1e-12,
+          #"max_position_embeddings": 16+20,
+          "model_type": "bert",
+          "num_attention_heads": 8,
+          "num_hidden_layers": 8,
+         "pad_token_id": 0,
+          "position_embedding_type": "absolute",
+          "transformers_version": "4.6.0.dev0",
+          "type_vocab_size": 2,
+          "use_cache": True,
+          "vocab_size": 30522
+        })
+
+
+
+    def _get_binary_mask(self, target):
+        # 返回每类的binary mask
+        y, x = target.size()
+        target_onehot = torch.zeros(self.num_classes + 1, y, x)
+        target_onehot = target_onehot.scatter(dim=0, index=target.unsqueeze(0), value=1)
+        return target_onehot[1:]
+
+    def semantic_inference(self, mask_cls, mask_pred):       
+        mask_cls = F.softmax(mask_cls, dim=1)[...,1:]
+        mask_pred = mask_pred.sigmoid()      
+        semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred)        
+        return semseg
+
+    def forward(self, image, sentences, attentions): 
+        print(image.sum(), sentences.sum(), attentions.sum())
+        input_shape = image.shape[-2:]
+        l_mask = attentions.unsqueeze(dim=-1)
+
+        i0, Wh, Ww = self.image_model.forward_stem(image)
+        l0, extended_attention_mask = self.language_model.forward_stem(sentences, attentions)
+
+        i1 = self.image_model.forward_stage1(i0, Wh, Ww)
+        l1 = self.language_model.forward_stage1(l0, extended_attention_mask)
+        i1_residual, H, W, i1_temp, Wh, Ww  = self.image_model.forward_pwam1(i1, Wh, Ww, l1, l_mask)
+        l1_residual, l1 = self.language_model.forward_pwam1(i1, l1, extended_attention_mask) 
+        i1 = i1_temp
+
+        i2 = self.image_model.forward_stage2(i1, Wh, Ww)
+        l2 = self.language_model.forward_stage2(l1, extended_attention_mask)
+        i2_residual, H, W, i2_temp, Wh, Ww  = self.image_model.forward_pwam2(i2, Wh, Ww, l2, l_mask)
+        l2_residual, l2 = self.language_model.forward_pwam2(i2, l2, extended_attention_mask) 
+        i2 = i2_temp
+
+        i3 = self.image_model.forward_stage3(i2, Wh, Ww)
+        l3 = self.language_model.forward_stage3(l2, extended_attention_mask)
+        i3_residual, H, W, i3_temp, Wh, Ww  = self.image_model.forward_pwam3(i3, Wh, Ww, l3, l_mask)
+        l3_residual, l3 = self.language_model.forward_pwam3(i3, l3, extended_attention_mask) 
+        i3 = i3_temp
+
+        i4 = self.image_model.forward_stage4(i3, Wh, Ww)
+        l4 = self.language_model.forward_stage4(l3, extended_attention_mask)
+        i4_residual, H, W, i4_temp, Wh, Ww  = self.image_model.forward_pwam4(i4, Wh, Ww, l4, l_mask)
+        l4_residual, l4 = self.language_model.forward_pwam4(i4, l4, extended_attention_mask) 
+        i4 = i4_temp
+
+        #i1_residual, i2_residual, i3_residual, i4_residual = features
+        #x = self.classifier(i4_residual, i3_residual, i2_residual, i1_residual)
+        #x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)
+        outputs = {}
+        outputs['s1'] = i1_residual
+        outputs['s2'] = i2_residual
+        outputs['s3'] = i3_residual
+        outputs['s4'] = i4_residual
+
+        predictions = self.classifier(outputs)
+        return predictions
+
+#img = Image.open(image_path).convert("RGB")
+img = Image.open(image_path).convert("RGB")
+img_ndarray = np.array(img)  # (orig_h, orig_w, 3); for visualization
+original_w, original_h = img.size  # PIL .size returns width first and height second
+
+image_transforms = T.Compose(
+    [
+     T.Resize((480, 480)),
+     T.ToTensor(),
+     T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ]
+)
+
+img = image_transforms(img).unsqueeze(0)  # (1, 3, 480, 480)
+img = img.to(device)  # for inference (input)
+
+# pre-process the raw sentence
+from bert.tokenization_bert import BertTokenizer
+import torch
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+sentence_tokenized = tokenizer.encode(text=sentence, add_special_tokens=True)
+sentence_tokenized = sentence_tokenized[:20]  # if the sentence is longer than 20, then this truncates it to 20 words
+# pad the tokenized sentence
+padded_sent_toks = [0] * 20
+padded_sent_toks[:len(sentence_tokenized)] = sentence_tokenized
+# create a sentence token mask: 1 for real words; 0 for padded tokens
+attention_mask = [0] * 20
+attention_mask[:len(sentence_tokenized)] = [1]*len(sentence_tokenized)
+# convert lists to tensors
+padded_sent_toks = torch.tensor(padded_sent_toks).unsqueeze(0)  # (1, 20)
+attention_mask = torch.tensor(attention_mask).unsqueeze(0)  # (1, 20)
+padded_sent_toks = padded_sent_toks.to(device)  # for inference (input)
+attention_mask = attention_mask.to(device)  # for inference (input)
+
+# initialize model and load weights
+#from bert.modeling_bert import BertModel
+#from lib import segmentation
+
+# construct a mini args class; like from a config file
+
+
+class args:
+    swin_type = 'base'
+    window12 = True
+    mha = ''
+    fusion_drop = 0.0
+
+
+#single_model = segmentation.__dict__['lavt'](pretrained='', args=args)
+single_model = multimodal_segmentation_ppm.__dict__['lavt'](pretrained='',args=args)
+single_model.to(device)
+model_class = MultiModalBert
+single_bert_model = model_class.from_pretrained('bert-base-uncased', embed_dim=single_model.backbone.embed_dim)
+single_bert_model.pooler = None
+
+input_shape = dict()
+input_shape['s1'] = Dict({'channel': 128,  'stride': 4})
+input_shape['s2'] = Dict({'channel': 256,  'stride': 8})
+input_shape['s3'] = Dict({'channel': 512,  'stride': 16})
+input_shape['s4'] = Dict({'channel': 1024, 'stride': 32})
+
+
+
+cfg = Dict()
+cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 
+cfg.MODEL.MASK_FORMER.NHEADS = 8
+cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 4
+cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256
+cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"]
+
+cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1
+cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 1
+cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
+cfg.MODEL.MASK_FORMER.DEC_LAYERS = 10
+cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+
+maskformer_head = MaskFormerHead(cfg, input_shape)
+
+
+model = WrapperModel(single_model.backbone, single_bert_model, maskformer_head)
+
+
+
+checkpoint = torch.load(weights, map_location='cpu')
+
+model.load_state_dict(checkpoint['model'], strict=False)
+model.to(device)
+model.eval()
+#single_bert_model.load_state_dict(checkpoint['bert_model'])
+#single_model.load_state_dict(checkpoint['model'])
+#model = single_model.to(device)
+#bert_model = single_bert_model.to(device)
+
+
+# inference
+#import torch.nn.functional as F
+#last_hidden_states = bert_model(padded_sent_toks, attention_mask=attention_mask)[0]
+#embedding = last_hidden_states.permute(0, 2, 1)
+#output = model(img, embedding, l_mask=attention_mask.unsqueeze(-1))
+#output = output.argmax(1, keepdim=True)  # (1, 1, 480, 480)
+#output = F.interpolate(output.float(), (original_h, original_w))  # 'nearest'; resize to the original image size
+#output = output.squeeze()  # (orig_h, orig_w)
+#output = output.cpu().data.numpy()  # (orig_h, orig_w)
+
+output = model(img, padded_sent_toks, attention_mask)[0]
+#print(output[0].keys())
+#print(output[1].shape)
+mask_cls_results = output["pred_logits"]
+mask_pred_results = output["pred_masks"]
+
+target_shape = img_ndarray.shape[:2]
+#print(target_shape, mask_pred_results.shape)
+mask_pred_results = F.interpolate(mask_pred_results, size=(480,480), mode='bilinear', align_corners=True)
+
+pred_masks = model.semantic_inference(mask_cls_results, mask_pred_results)                
+#output = pred_masks[0]
+
+#output = output.cpu()
+
+
+
+#print(output.shape)
+#output_mask = output.argmax(1).data.numpy()
+#output = (output > 0.5).data.cpu().numpy()
+output = torch.nn.functional.interpolate(pred_masks, target_shape)
+output = (output > 0.5).data.cpu().numpy()
+
+
+# show/save results
+def overlay_davis(image, mask, colors=[[0, 0, 0], [255, 0, 0]], cscale=1, alpha=0.4):
+    from scipy.ndimage.morphology import binary_dilation
+
+    colors = np.reshape(colors, (-1, 3))
+    colors = np.atleast_2d(colors) * cscale
+
+    im_overlay = image.copy()
+    object_ids = np.unique(mask)
+
+    for object_id in object_ids[1:]:
+        # Overlay color on  binary mask
+        foreground = image*alpha + np.ones(image.shape)*(1-alpha) * np.array(colors[object_id])
+        binary_mask = mask == object_id
+
+        # Compose image
+        im_overlay[binary_mask] = foreground[binary_mask]
+
+        # countours = skimage.morphology.binary.binary_dilation(binary_mask) - binary_mask
+        countours = binary_dilation(binary_mask) ^ binary_mask
+        # countours = cv2.dilate(binary_mask, cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))) - binary_mask
+        im_overlay[countours, :] = 0
+
+    return im_overlay.astype(image.dtype)
+
+
+output = output.astype(np.uint8)  # (orig_h, orig_w), np.uint8
+# Overlay the mask on the image
+print(img_ndarray.shape, output.shape)
+visualization = overlay_davis(img_ndarray, output[0][0])  # red
+visualization = Image.fromarray(visualization)
+# show the visualization
+#visualization.show()
+# Save the visualization
+visualization.save('./demo/spoon_on_the_dish.jpg')
+
+
+
+

elia/requirements.txt

@@ -0,0 +1,14 @@
+requests
+filelock
+tqdm
+timm
+mmcv-full==1.3.12
+mmsegmentation==0.17.0
+ftfy
+regex
+scipy
+scikit-image
+pycocotools==2.0.2
+opencv-python==4.5.3.56
+tokenizers==0.8.1rc1
+h5py

elia/test_elia.py

@@ -0,0 +1,312 @@
+
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from bert.multimodal_bert import MultiModalBert
+import torchvision
+
+from lib import multimodal_segmentation_ppm
+import transforms as T
+import utils
+
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+from modeling.MaskFormerModel import MaskFormerHead
+from addict import Dict
+from bert.modeling_bert import BertLMPredictionHead, BertEncoder
+
+def get_dataset(image_set, transform, args):
+    from data.dataset_refer_bert import ReferDataset
+    ds = ReferDataset(args,
+                      split=image_set,
+                      image_transforms=transform,
+                      target_transforms=None,
+                      eval_mode=True
+                      )
+    num_classes = 2
+    return ds, num_classes
+
+
+def evaluate(model, data_loader, device):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+
+    # evaluation variables
+    cum_I, cum_U = 0, 0
+    eval_seg_iou_list = [.5, .6, .7, .8, .9]
+    seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
+    seg_total = 0
+    mean_IoU = []
+    header = 'Test:'
+
+    with torch.no_grad():
+        for data in metric_logger.log_every(data_loader, 100, header):
+            image, target, sentences, attentions = data
+            image, target, sentences, attentions = image.to(device), target.to(device), \
+                                                   sentences.to(device), attentions.to(device)
+            sentences = sentences.squeeze(1)
+            attentions = attentions.squeeze(1)
+            target = target.cpu().data.numpy()
+            for j in range(sentences.size(-1)):
+                #if bert_model is not None:
+                #    last_hidden_states = bert_model(sentences[:, :, j], attention_mask=attentions[:, :, j])[0]
+                #    embedding = last_hidden_states.permute(0, 2, 1)
+                #    output = model(image, embedding, l_mask=attentions[:, :, j].unsqueeze(-1))
+                #else:
+                output = model(image, sentences[:, :, j], attentions[:, :, j])
+                mask_cls_results = output["pred_logits"]
+                mask_pred_results = output["pred_masks"]
+
+                target_shape = target.shape[-2:]
+                mask_pred_results = F.interpolate(mask_pred_results, size=target_shape, mode='bilinear', align_corners=True)
+
+                pred_masks = model.semantic_inference(mask_cls_results, mask_pred_results)                
+                output = pred_masks[0]
+
+                output = output.cpu()
+                #print(output.shape)
+                #output_mask = output.argmax(1).data.numpy()
+                output_mask = (output > 0.5).data.numpy()
+                I, U = computeIoU(output_mask, target)
+                if U == 0:
+                    this_iou = 0.0
+                else:
+                    this_iou = I*1.0/U
+                mean_IoU.append(this_iou)
+                cum_I += I
+                cum_U += U
+                for n_eval_iou in range(len(eval_seg_iou_list)):
+                    eval_seg_iou = eval_seg_iou_list[n_eval_iou]
+                    seg_correct[n_eval_iou] += (this_iou >= eval_seg_iou)
+                seg_total += 1
+
+            #del image, target, sentences, attentions, output, output_mask
+            #if bert_model is not None:
+            #    del last_hidden_states, embedding
+
+    mean_IoU = np.array(mean_IoU)
+    mIoU = np.mean(mean_IoU)
+    print('Final results:')
+    print('Mean IoU is %.2f\n' % (mIoU*100.))
+    results_str = ''
+    for n_eval_iou in range(len(eval_seg_iou_list)):
+        results_str += '    precision@%s = %.2f\n' % \
+                       (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
+    results_str += '    overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
+    print(results_str)
+
+
+def get_transform(args):
+    transforms = [T.Resize(args.img_size, args.img_size),
+                  T.ToTensor(),
+                  T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                  ]
+
+    return T.Compose(transforms)
+
+
+def computeIoU(pred_seg, gd_seg):
+    I = np.sum(np.logical_and(pred_seg, gd_seg))
+    U = np.sum(np.logical_or(pred_seg, gd_seg))
+
+    return I, U
+
+class WrapperModel(nn.Module):
+    def __init__(self, image_model, language_model, classifier, args) :
+        super(WrapperModel, self).__init__()
+        self.image_model = image_model
+        self.language_model = language_model
+        self.classifier = classifier
+        self.lang_proj = nn.Linear(768,256)
+
+        config = Dict({
+          "architectures": [
+           "BertForMaskedLM"
+          ],
+          "attention_probs_dropout_prob": 0.1,
+          "gradient_checkpointing": False,
+          "hidden_act": "gelu",
+          "hidden_dropout_prob": 0.1,
+          "hidden_size": 512,
+          "initializer_range": 0.02,
+          "intermediate_size": 3072,
+          "layer_norm_eps": 1e-12,
+          #"max_position_embeddings": 16+20,
+          "model_type": "bert",
+          "num_attention_heads": 8,
+          "num_hidden_layers": 8,
+         "pad_token_id": 0,
+          "position_embedding_type": "absolute",
+          "transformers_version": "4.6.0.dev0",
+          "type_vocab_size": 2,
+          "use_cache": True,
+          "vocab_size": 30522
+        })
+        self.mlm_transformer = BertEncoder(config)
+
+        self.lang_proj = nn.Linear(768,256)
+        self.mlm_vis_proj = nn.Conv2d(1024,512,1)
+        self.mlm_lang_proj = nn.Linear(768,512)
+        #print(vis_proj)
+        self.mlm_head = BertLMPredictionHead(config)
+
+        assert args.img_size % 4 == 0
+        num_img_tokens = 20 + ((args.img_size // 4)//8) ** 2
+        print(num_img_tokens)
+        self.mlm_pos_embeds = nn.Embedding(num_img_tokens+1, 512)
+        self.mlm_modal_embeds = nn.Embedding(3, 512)
+
+        self.mlm_mask_embed = nn.Embedding(1, 512)
+        self.mlm_pos_mlp = nn.Sequential(
+            nn.Linear(2, 512),
+            nn.LayerNorm(512),
+            nn.Linear(512,512),
+            nn.GELU()
+        )
+
+    def _get_binary_mask(self, target):
+        # 返回每类的binary mask
+        y, x = target.size()
+        target_onehot = torch.zeros(self.num_classes + 1, y, x)
+        target_onehot = target_onehot.scatter(dim=0, index=target.unsqueeze(0), value=1)
+        return target_onehot[1:]
+
+    def semantic_inference(self, mask_cls, mask_pred):       
+        mask_cls = F.softmax(mask_cls, dim=1)[...,1:]
+        mask_pred = mask_pred.sigmoid()      
+        semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred)        
+        return semseg
+
+    def forward(self, image, sentences, attentions): 
+        input_shape = image.shape[-2:]
+        l_mask = attentions.unsqueeze(dim=-1)
+
+        i0, Wh, Ww = self.image_model.forward_stem(image)
+        l0, extended_attention_mask = self.language_model.forward_stem(sentences, attentions)
+
+        i1 = self.image_model.forward_stage1(i0, Wh, Ww)
+        l1 = self.language_model.forward_stage1(l0, extended_attention_mask)
+        i1_residual, H, W, i1_temp, Wh, Ww  = self.image_model.forward_pwam1(i1, Wh, Ww, l1, l_mask)
+        l1_residual, l1 = self.language_model.forward_pwam1(i1, l1, extended_attention_mask) 
+        i1 = i1_temp
+
+        i2 = self.image_model.forward_stage2(i1, Wh, Ww)
+        l2 = self.language_model.forward_stage2(l1, extended_attention_mask)
+        i2_residual, H, W, i2_temp, Wh, Ww  = self.image_model.forward_pwam2(i2, Wh, Ww, l2, l_mask)
+        l2_residual, l2 = self.language_model.forward_pwam2(i2, l2, extended_attention_mask) 
+        i2 = i2_temp
+
+        i3 = self.image_model.forward_stage3(i2, Wh, Ww)
+        l3 = self.language_model.forward_stage3(l2, extended_attention_mask)
+        i3_residual, H, W, i3_temp, Wh, Ww  = self.image_model.forward_pwam3(i3, Wh, Ww, l3, l_mask)
+        l3_residual, l3 = self.language_model.forward_pwam3(i3, l3, extended_attention_mask) 
+        i3 = i3_temp
+
+        i4 = self.image_model.forward_stage4(i3, Wh, Ww)
+        l4 = self.language_model.forward_stage4(l3, extended_attention_mask)
+        i4_residual, H, W, i4_temp, Wh, Ww  = self.image_model.forward_pwam4(i4, Wh, Ww, l4, l_mask)
+        l4_residual, l4 = self.language_model.forward_pwam4(i4, l4, extended_attention_mask) 
+        i4 = i4_temp
+
+        #i1_residual, i2_residual, i3_residual, i4_residual = features
+        #x = self.classifier(i4_residual, i3_residual, i2_residual, i1_residual)
+        #x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)
+        outputs = {}
+        outputs['s1'] = i1_residual
+        outputs['s2'] = i2_residual
+        outputs['s3'] = i3_residual
+        outputs['s4'] = i4_residual
+
+        predictions, _ = self.classifier(outputs)
+        return predictions
+
+def main(args):
+#def main(local_rank, args):
+
+    #device = torch.device(args.device)
+    device = 'cuda'
+    dataset_test, _ = get_dataset(args.split, get_transform(args=args), args)
+    test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+    data_loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=1,
+                                  sampler=test_sampler, num_workers=args.workers)
+    print(args.model)
+    single_model = multimodal_segmentation_ppm.__dict__[args.model](pretrained='',args=args)
+    #single_model = MultiModalFocal(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], focal_windows=[9,9,9,9], drop_path_rate=0.3)
+    #single_model.init_weights('./focalnet_base_lrf.pth')
+    checkpoint = torch.load(args.resume, map_location='cpu')
+    #single_model.load_state_dict(checkpoint['model'])
+    #model = single_model.to(device)
+
+    if args.model != 'lavt_one':
+        model_class = MultiModalBert
+        #single_bert_model = model_class.from_pretrained(args.ck_bert, embed_dim=128)
+        single_bert_model = model_class.from_pretrained(args.ck_bert, embed_dim=single_model.backbone.embed_dim)
+        # work-around for a transformers bug; need to update to a newer version of transformers to remove these two lines
+        if args.ddp_trained_weights:
+            single_bert_model.pooler = None
+        #single_bert_model.load_state_dict(checkpoint['bert_model'])
+        #bert_model = single_bert_model.to(device)
+    else:
+        bert_model = None
+
+    #model = WrapperModel(single_model.backbone, single_bert_model, single_model.classifier)
+    #model.load_state_dict(checkpoint['model'])
+    #model.to(device)
+    input_shape = dict()
+    input_shape['s1'] = Dict({'channel': 128,  'stride': 4})
+    input_shape['s2'] = Dict({'channel': 256,  'stride': 8})
+    input_shape['s3'] = Dict({'channel': 512,  'stride': 16})
+    input_shape['s4'] = Dict({'channel': 1024, 'stride': 32})
+
+
+
+    cfg = Dict()
+    cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+    cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 
+    cfg.MODEL.MASK_FORMER.NHEADS = 8
+    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 4
+    cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"]
+
+    cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1
+    cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+    cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 1
+    cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
+    cfg.MODEL.MASK_FORMER.DEC_LAYERS = 10
+    cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+
+    maskformer_head = MaskFormerHead(cfg, input_shape)
+    #maskformer_head = torch.nn.SyncBatchNorm.convert_sync_batchnorm(maskformer_head)
+    #maskformer_head.cuda()
+    #maskformer_head = torch.nn.parallel.DistributedDataParallel(maskformer_head, device_ids=[args.local_rank], find_unused_parameters=False)
+    #single_head = maskformer_head.module
+    #print(single_head)
+
+    model = WrapperModel(single_model.backbone, single_bert_model, maskformer_head, args)
+    model.load_state_dict(checkpoint['model'])
+    model.to(device)
+    #model.cuda()
+    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
+    #single_model = model.module
+    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
+    #single_model = model.module
+    evaluate(model, data_loader_test, device=device)
+
+
+if __name__ == "__main__":
+    from args import get_parser
+    parser = get_parser()
+    args = parser.parse_args()
+    print('Image size: {}'.format(str(args.img_size)))
+    print(args)
+    main(args)
+    #mp.spawn(main, args=(args,), nprocs=torch.cuda.device_count())

elia/test_lavt.py

@@ -0,0 +1,139 @@
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from bert.modeling_bert import BertModel
+import torchvision
+
+from lib import segmentation
+import transforms as T
+import utils
+
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+
+def get_dataset(image_set, transform, args):
+    from data.dataset_refer_bert import ReferDataset
+    ds = ReferDataset(args,
+                      split=image_set,
+                      image_transforms=transform,
+                      target_transforms=None,
+                      eval_mode=True
+                      )
+    num_classes = 2
+    return ds, num_classes
+
+
+def evaluate(model, data_loader, bert_model, device):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+
+    # evaluation variables
+    cum_I, cum_U = 0, 0
+    eval_seg_iou_list = [.5, .6, .7, .8, .9]
+    seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
+    seg_total = 0
+    mean_IoU = []
+    header = 'Test:'
+
+    with torch.no_grad():
+        for data in metric_logger.log_every(data_loader, 100, header):
+            image, target, sentences, attentions = data
+            image, target, sentences, attentions = image.to(device), target.to(device), \
+                                                   sentences.to(device), attentions.to(device)
+            sentences = sentences.squeeze(1)
+            attentions = attentions.squeeze(1)
+            target = target.cpu().data.numpy()
+            for j in range(sentences.size(-1)):
+                if bert_model is not None:
+                    last_hidden_states = bert_model(sentences[:, :, j], attention_mask=attentions[:, :, j])[0]
+                    embedding = last_hidden_states.permute(0, 2, 1)
+                    output = model(image, embedding, l_mask=attentions[:, :, j].unsqueeze(-1))
+                else:
+                    output = model(image, sentences[:, :, j], l_mask=attentions[:, :, j])
+
+                output = output.cpu()
+                output_mask = output.argmax(1).data.numpy()
+                I, U = computeIoU(output_mask, target)
+                if U == 0:
+                    this_iou = 0.0
+                else:
+                    this_iou = I*1.0/U
+                mean_IoU.append(this_iou)
+                cum_I += I
+                cum_U += U
+                for n_eval_iou in range(len(eval_seg_iou_list)):
+                    eval_seg_iou = eval_seg_iou_list[n_eval_iou]
+                    seg_correct[n_eval_iou] += (this_iou >= eval_seg_iou)
+                seg_total += 1
+
+            del image, target, sentences, attentions, output, output_mask
+            if bert_model is not None:
+                del last_hidden_states, embedding
+
+    mean_IoU = np.array(mean_IoU)
+    mIoU = np.mean(mean_IoU)
+    print('Final results:')
+    print('Mean IoU is %.2f\n' % (mIoU*100.))
+    results_str = ''
+    for n_eval_iou in range(len(eval_seg_iou_list)):
+        results_str += '    precision@%s = %.2f\n' % \
+                       (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
+    results_str += '    overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
+    print(results_str)
+
+
+def get_transform(args):
+    transforms = [T.Resize(args.img_size, args.img_size),
+                  T.ToTensor(),
+                  T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                  ]
+
+    return T.Compose(transforms)
+
+
+def computeIoU(pred_seg, gd_seg):
+    I = np.sum(np.logical_and(pred_seg, gd_seg))
+    U = np.sum(np.logical_or(pred_seg, gd_seg))
+
+    return I, U
+
+
+def main(args):
+    device = torch.device(args.device)
+    dataset_test, _ = get_dataset(args.split, get_transform(args=args), args)
+    test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+    data_loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=1,
+                                                   sampler=test_sampler, num_workers=args.workers)
+    print(args.model)
+    single_model = segmentation.__dict__[args.model](pretrained='',args=args)
+    checkpoint = torch.load(args.resume, map_location='cpu')
+    single_model.load_state_dict(checkpoint['model'])
+    model = single_model.to(device)
+
+    if args.model != 'lavt_one':
+        model_class = BertModel
+        single_bert_model = model_class.from_pretrained(args.ck_bert)
+        # work-around for a transformers bug; need to update to a newer version of transformers to remove these two lines
+        if args.ddp_trained_weights:
+            single_bert_model.pooler = None
+        single_bert_model.load_state_dict(checkpoint['bert_model'])
+        bert_model = single_bert_model.to(device)
+    else:
+        bert_model = None
+
+    evaluate(model, data_loader_test, bert_model, device=device)
+
+
+if __name__ == "__main__":
+    from args import get_parser
+    parser = get_parser()
+    args = parser.parse_args()
+    print('Image size: {}'.format(str(args.img_size)))
+    main(args)

elia/train_elia.py

@@ -0,0 +1,812 @@
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from functools import reduce
+import operator
+from bert.multimodal_bert import MultiModalBert
+
+import torchvision
+from lib import multimodal_segmentation_ppm
+
+import transforms as T
+import utils
+import numpy as np
+
+import torch.nn.functional as F
+
+import gc
+from collections import OrderedDict
+
+import torch.backends.cudnn as cudnn
+
+#from ffrecord.torch import DataLoader,Dataset
+from modeling.MaskFormerModel import MaskFormerHead
+from addict import Dict
+
+from mask2former_utils.criterion import SetCriterion, Criterion
+from mask2former_utils.matcher import HungarianMatcher
+from bert.modeling_bert import BertLMPredictionHead, BertEncoder
+
+
+
+
+class WrapperModel(nn.Module):
+    def __init__(self, image_model, language_model, classifier, args) :
+        super(WrapperModel, self).__init__()
+        self.image_model = image_model
+        self.language_model = language_model
+        self.classifier = classifier
+
+        self.lang_proj = nn.Linear(768,256)
+
+        config = Dict({
+          "architectures": [
+           "BertForMaskedLM"
+          ],
+          "attention_probs_dropout_prob": 0.1,
+          "gradient_checkpointing": False,
+          "hidden_act": "gelu",
+          "hidden_dropout_prob": 0.1,
+          "hidden_size": 512,
+          "initializer_range": 0.02,
+          "intermediate_size": 3072,
+          "layer_norm_eps": 1e-12,
+          #"max_position_embeddings": 16+20,
+          "model_type": "bert",
+          "num_attention_heads": 8,
+          "num_hidden_layers": 8,
+         "pad_token_id": 0,
+          "position_embedding_type": "absolute",
+          "transformers_version": "4.6.0.dev0",
+          "type_vocab_size": 2,
+          "use_cache": True,
+          "vocab_size": 30522
+        })
+        self.mlm_transformer = BertEncoder(config)
+
+        self.lang_proj = nn.Linear(768,256)
+        self.mlm_vis_proj = nn.Conv2d(1024,512,1)
+        self.mlm_lang_proj = nn.Linear(768,512)
+        #print(vis_proj)
+        self.mlm_head = BertLMPredictionHead(config)
+
+        assert args.img_size % 4 == 0
+        num_img_tokens = 20 + ((args.img_size // 4)//8) ** 2
+        print(num_img_tokens)
+        self.mlm_pos_embeds = nn.Embedding(num_img_tokens+1, 512)
+        self.mlm_modal_embeds = nn.Embedding(3, 512)
+
+        self.mlm_mask_embed = nn.Embedding(1, 512)
+        self.mlm_pos_mlp = nn.Sequential(
+            nn.Linear(2, 512),
+            nn.LayerNorm(512),
+            nn.Linear(512,512),
+            nn.GELU()
+        )
+
+    def _get_binary_mask(self, target):
+        # 返回每类的binary mask
+        y, x = target.size()
+        target_onehot = torch.zeros(self.num_classes + 1, y, x)
+        target_onehot = target_onehot.scatter(dim=0, index=target.unsqueeze(0), value=1)
+        return target_onehot[1:]
+
+    def semantic_inference(self, mask_cls, mask_pred):       
+        mask_cls = F.softmax(mask_cls, dim=1)[...,1:]
+        mask_pred = mask_pred.sigmoid()      
+        semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred)        
+        return semseg
+
+    def forward(self, image, sentences, attentions, mlm_targets, mlm_masks, position): 
+        input_shape = image.shape[-2:]
+        l_mask = attentions.unsqueeze(dim=-1)
+
+        i0, Wh, Ww = self.image_model.forward_stem(image)
+        l0, extended_attention_mask = self.language_model.forward_stem(mlm_targets.squeeze(1), attentions)
+
+        i1 = self.image_model.forward_stage1(i0, Wh, Ww)
+        l1 = self.language_model.forward_stage1(l0, extended_attention_mask)
+        i1_residual, H, W, i1_temp, Wh, Ww  = self.image_model.forward_pwam1(i1, Wh, Ww, l1, l_mask)
+        l1_residual, l1 = self.language_model.forward_pwam1(i1, l1, extended_attention_mask) 
+        i1 = i1_temp
+
+        i2 = self.image_model.forward_stage2(i1, Wh, Ww)
+        l2 = self.language_model.forward_stage2(l1, extended_attention_mask)
+        i2_residual, H, W, i2_temp, Wh, Ww  = self.image_model.forward_pwam2(i2, Wh, Ww, l2, l_mask)
+        l2_residual, l2 = self.language_model.forward_pwam2(i2, l2, extended_attention_mask) 
+        i2 = i2_temp
+
+        i3 = self.image_model.forward_stage3(i2, Wh, Ww)
+        l3 = self.language_model.forward_stage3(l2, extended_attention_mask)
+        i3_residual, H, W, i3_temp, Wh, Ww  = self.image_model.forward_pwam3(i3, Wh, Ww, l3, l_mask)
+        l3_residual, l3 = self.language_model.forward_pwam3(i3, l3, extended_attention_mask) 
+        i3 = i3_temp
+
+        i4 = self.image_model.forward_stage4(i3, Wh, Ww)
+        l4 = self.language_model.forward_stage4(l3, extended_attention_mask)
+        i4_residual, H, W, i4_temp, Wh, Ww  = self.image_model.forward_pwam4(i4, Wh, Ww, l4, l_mask)
+        l4_residual, l4 = self.language_model.forward_pwam4(i4, l4, extended_attention_mask) 
+        i4 = i4_temp
+
+        #i1_residual, i2_residual, i3_residual, i4_residual = features
+        #x = self.classifier(i4_residual, i3_residual, i2_residual, i1_residual)
+        #x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)
+        outputs = {}
+        outputs['s1'] = i1_residual
+        outputs['s2'] = i2_residual
+        outputs['s3'] = i3_residual
+        outputs['s4'] = i4_residual
+
+        predictions, mask_features = self.classifier(outputs)
+
+        #print(target_reshape.shape)
+        #tmp = np.argwhere(target_reshape[:, 0].detach().cpu().numpy()).reshape(-1, target_reshape.shape[2]*target_reshape[3], 3)
+        #centroid = tmp.mean(1)
+        #print(centroid)
+        #centroid_x, centroid_y = int(centroid[1]), int(centroid[0])
+        #last_hidden_states = brt_model(sentences, attention_mask=attentions)[0]  # (6, 10, 768)
+        #embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
+
+
+        l0, extended_attention_mask = self.language_model.forward_stem(sentences, attentions)
+        l1 = self.language_model.forward_stage1(l0, extended_attention_mask)
+        l2 = self.language_model.forward_stage2(l1, extended_attention_mask)
+        l3 = self.language_model.forward_stage3(l2, extended_attention_mask)
+        l4 = self.language_model.forward_stage4(l3, extended_attention_mask)
+
+
+        mlp_embed = self.mlm_pos_mlp(position)
+        #print(centroid_x, centroid_y)
+
+        mlm_targets = torch.where(
+            mlm_masks > 0,
+            mlm_targets,
+            torch.ones_like(mlm_targets) * (-1)
+        )
+
+        #print(x_c4[target_reshape[:, [0]].bool()].shape)
+        vis_features = self.mlm_vis_proj(i4_residual).flatten(2).permute(0,2,1)
+        #print(l4.shape)
+        lang_features = self.mlm_lang_proj(l4)
+        
+        #print(lang_features.shape, vis_features.shape, mlp_embed.shape)
+        mm_features = torch.cat([lang_features, vis_features, mlp_embed.unsqueeze(1)], dim=1)
+        #print(mm_features.shape)
+
+        #print(mlm_modal_embeds.weight.shape)
+        modal_embeds = torch.cat([self.mlm_modal_embeds.weight[0].unsqueeze(0).repeat(1, lang_features.shape[1], 1), self.mlm_modal_embeds.weight[1].unsqueeze(0).repeat(1, vis_features.shape[1], 1), self.mlm_modal_embeds.weight[2].unsqueeze(0).repeat(1,1,1)], dim=1)
+        #print(modal_embeds.shape)
+
+        #print(mlm_transformer)
+
+
+        #print(attentions.shape)
+        mixed_attention_mask = torch.cat([attentions.unsqueeze(-1), torch.ones(attentions.shape[0], vis_features.shape[1]+1, 1).to(attentions.device)], dim=1)
+        mixed_attention_mask = mixed_attention_mask.permute(0,2,1).unsqueeze(1)
+        mixed_attention_mask = (1-mixed_attention_mask)* -10000.0
+        head_mask = [None] * 8
+        #extended_attention_mask = get_extended_attention_mask(mixed_attention_mask, mm_features.shape, mm_features.device)
+        #print(mm_features.shape, mixed_attention_mask.shape, head_mask)
+        #print(mm_features.shape, self.mlm_pos_embeds.weight.shape, self.mlm_modal_embeds.weight.shape)
+        head_features = self.mlm_transformer(mm_features + self.mlm_pos_embeds.weight.unsqueeze(0) + modal_embeds, mixed_attention_mask, head_mask)[0]
+        #print(head_features.shape, attentions.shape)
+        head_features = head_features[:, :20][attentions.bool()]
+        
+        #print(embedding.shape, mask_features.shape)
+        mlm_predictions = self.mlm_head(head_features)
+        mlm_predictions = mlm_predictions.reshape(-1, self.language_model.config.vocab_size)
+        mlm_targets = mlm_targets.squeeze(1)[attentions.bool()]
+        #mlm_loss = mlm_weight * nn.CrossEntropyLoss(ignore_index=-1)(mlm_predictions, mlm_targets)
+        #loss += mlm_loss
+        #mlm_loss_print=mlm_loss.item()
+
+        return predictions, mask_features, self.lang_proj((l4_residual * l_mask).sum(1)/l_mask.sum(1)), mlm_predictions, mlm_targets
+# IoU calculation for validation
+def IoU(pred, gt):
+    #pred = pred.argmax(1)
+    pred = (pred > 0.5)
+
+    intersection = torch.sum(torch.mul(pred, gt))
+    union = torch.sum(torch.add(pred, gt)) - intersection
+
+    if intersection == 0 or union == 0:
+        iou = 0
+    else:
+        iou = float(intersection) / float(union)
+
+    return iou, intersection, union
+
+def get_dataset(image_set, transform, args):
+    from data.dataset_refer_bert_mlm import ReferDataset
+    ds = ReferDataset(args,
+                      split=image_set,
+                      image_transforms=transform,
+                      target_transforms=None
+                      )
+    num_classes = 2
+
+    return ds, num_classes
+
+
+
+def get_transform(args):
+    transforms = [T.Resize(args.img_size, args.img_size),
+                  T.ToTensor(),
+                  T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                  ]
+
+    return T.Compose(transforms)
+
+
+#def criterion(input, target):
+#    weight = torch.FloatTensor([0.9, 1.1]).cuda()
+#    return nn.functional.cross_entropy(input, target, weight=weight)
+
+
+def evaluate(model, data_loader):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+    total_its = 0
+    acc_ious = 0
+
+    # evaluation variables
+    cum_I, cum_U = 0, 0
+    eval_seg_iou_list = [.5, .6, .7, .8, .9]
+    seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
+    seg_total = 0
+    mean_IoU = []
+
+    with torch.no_grad():
+        for data in metric_logger.log_every(data_loader, 100, header):
+            total_its += 1
+            #image, target, sentences, attentions = data
+            #image, target, sentences, attentions = image.cuda(non_blocking=True),\
+            #                                       target.cuda(non_blocking=True),\
+            #                                       sentences.cuda(non_blocking=True),\
+            #                                       attentions.cuda(non_blocking=True)
+
+            image, target, sentences, attentions, mlm_targets, mlm_masks, position = data
+            image, target, sentences, attentions, mlm_targets, mlm_masks, position = image.cuda(non_blocking=True),\
+                                                   target.cuda(non_blocking=True),\
+                                                   sentences.cuda(non_blocking=True),\
+                                                   attentions.cuda(non_blocking=True), \
+                                                   mlm_targets.cuda(non_blocking=True), \
+                                                   mlm_masks.cuda(non_blocking=True), \
+                                                   position.cuda(non_blocking=True)
+
+            sentences = sentences.squeeze(1)
+            attentions = attentions.squeeze(1)
+            #print("sentences", sentences.shape)
+            #print("attentions", attentions.shape)
+
+
+            output, mask_features, avg_lang_feature, mlm_predictions, mlm_targets = model(image, sentences, attentions, mlm_targets, mlm_masks, position)
+            mask_cls_results = output["pred_logits"]
+            mask_pred_results = output["pred_masks"]
+
+            target_shape = target.shape[-2:]
+            mask_pred_results = F.interpolate(mask_pred_results, size=target_shape, mode='bilinear', align_corners=True)
+
+            pred_masks = model.module.semantic_inference(mask_cls_results, mask_pred_results)                
+            output = pred_masks[0]
+
+
+            iou, I, U = IoU(output, target)
+            acc_ious += iou
+            mean_IoU.append(iou)
+            cum_I += I
+            cum_U += U
+            for n_eval_iou in range(len(eval_seg_iou_list)):
+                eval_seg_iou = eval_seg_iou_list[n_eval_iou]
+                seg_correct[n_eval_iou] += (iou >= eval_seg_iou)
+            seg_total += 1
+        iou = acc_ious / total_its
+
+    mean_IoU = np.array(mean_IoU)
+    mIoU = np.mean(mean_IoU)
+    print('Final results:')
+    print('Mean IoU is %.2f\n' % (mIoU * 100.))
+    results_str = ''
+    for n_eval_iou in range(len(eval_seg_iou_list)):
+        results_str += '    precision@%s = %.2f\n' % \
+                       (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
+    results_str += '    overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
+    print(results_str)
+
+    return 100 * iou, 100 * cum_I / cum_U
+
+
+def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq,
+                    iterations, args):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    train_loss = 0
+    total_its = 0
+
+    for data in metric_logger.log_every(data_loader, print_freq, header):
+        total_its += 1
+        #image, target, sentences, attentions = data
+        #image, target, sentences, attentions = image.cuda(non_blocking=True),\
+        #                                       target.cuda(non_blocking=True),\
+        #                                       sentences.cuda(non_blocking=True),\
+        #                                       attentions.cuda(non_blocking=True)
+        image, target, sentences, attentions, mlm_targets, mlm_masks, position = data
+        image, target, sentences, attentions, mlm_targets, mlm_masks, position = image.cuda(non_blocking=True),\
+                                               target.cuda(non_blocking=True),\
+                                               sentences.cuda(non_blocking=True),\
+                                               attentions.cuda(non_blocking=True), \
+                                               mlm_targets.cuda(non_blocking=True), \
+                                               mlm_masks.cuda(non_blocking=True), \
+                                               position.cuda(non_blocking=True)
+
+        sentences = sentences.squeeze(1)
+        attentions = attentions.squeeze(1)
+        #l_mask = attentions.unsqueeze(dim=-1)
+
+        output, mask_features, avg_lang_feature, mlm_predictions, mlm_targets = model(image, sentences, attentions, mlm_targets, mlm_masks, position)
+        #print(avg_lang_feature.shape)
+        avg_lang_feature = torch.nn.functional.normalize(avg_lang_feature, dim=1)
+        #print("----")
+        #print(output.shape)
+        #print(mask_features.shape)
+        #print(avg_lang_feature.shape)
+        #print( mlm_predictions.shape)
+        #print(mlm_targets.shape)
+        #print("----")
+
+        target_shape = target.shape[-2:]
+        output['pred_masks'] = F.interpolate(output['pred_masks'], size=target_shape, mode='bilinear', align_corners=True)
+
+        if "aux_outputs" in output:
+            for i, aux_outputs in enumerate(output["aux_outputs"]):
+                output['aux_outputs'][i]['pred_masks'] = F.interpolate(output['aux_outputs'][i]['pred_masks'], size=target_shape, mode='bilinear', align_corners=True)
+
+        # pixel region
+        B, C, H, W = mask_features.shape
+
+        target_reshape = F.interpolate(target.unsqueeze(1).float(), size=mask_features.shape[-2:], mode='nearest').long()
+
+        target_reshape = target_reshape.repeat(1, mask_features.shape[1], 1, 1)
+        #print(avg_pos_feature.shape, avg_lang_feature.shape, avg_neg_feature.shape)
+
+        #cl_loss = 0.0
+        plic_lang_loss = 0.0
+        plic_pos_loss = 0.0
+        plic_neg_loss = 0.0
+        for i in range(B):
+            if ((target_reshape[[i]] == 0).sum() != 0 and (target_reshape[[i]] == 1).sum() != 0):
+
+                avg_pos_feature = (mask_features[[i]] * target_reshape[[i]]).sum(-1).sum(-1) / target_reshape[[i]].sum(-1).sum(-1)
+                avg_neg_feature = (mask_features[[i]] * (1.0-target_reshape[[i]])).sum(-1).sum(-1) / (1.0-target_reshape[[i]]).sum(-1).sum(-1)
+                avg_pos_feature = torch.nn.functional.normalize(avg_pos_feature, dim=1)
+                avg_neg_feature = torch.nn.functional.normalize(avg_neg_feature, dim=1)
+
+                #avg lang feature no normalize???
+
+
+
+                pos_features = mask_features[[i]][target_reshape[[i]]==1].view(1, C, -1)
+                neg_features = mask_features[[i]][target_reshape[[i]]==0].view(1, C, -1)
+                #inter_neg_features = mask_features[[B-i-1]][target_reshape[[B-i-1]]==1].view(1, C, -1)
+                #neg_features = torch.cat([intra_neg_features, inter_neg_features], dim=2)
+
+                pos_features = torch.nn.functional.normalize(pos_features, dim=1)
+                neg_features = torch.nn.functional.normalize(neg_features, dim=1)
+
+                #print(avg_lang_feature.shape, avg_lang_feature[[i]].shape, pos_features.shape) 
+                lang_pos_scores = torch.einsum("bq,bqn->bn", avg_lang_feature[[i]], pos_features)
+                lang_neg_scores = torch.einsum("bq,bqn->bn", avg_lang_feature[[i]], neg_features)
+
+                lang_matrix = torch.cat([lang_pos_scores.unsqueeze(-1), lang_neg_scores.unsqueeze(1).repeat(1, lang_pos_scores.shape[1], 1)], dim=2)
+                lang_labels = torch.zeros(lang_matrix.shape[1], dtype=torch.long).cuda()
+                lang_labels = lang_labels.unsqueeze(0).repeat(lang_matrix.shape[0], 1)
+
+                lang_score = torch.softmax(lang_matrix, -1)
+                lang_score = 1.0 - lang_score[:, :, 0]
+
+                pos_pos_scores = torch.einsum("bq,bqn->bn", avg_pos_feature, pos_features)
+                pos_neg_scores = torch.einsum("bqn,bqm->bnm", pos_features, neg_features)
+
+                pos_matrix = torch.cat([pos_pos_scores.unsqueeze(-1), pos_neg_scores], dim=2)
+                pos_labels = torch.zeros(pos_matrix.shape[1], dtype=torch.long).cuda()
+                pos_labels = pos_labels.unsqueeze(0).repeat(pos_matrix.shape[0], 1)
+
+                pos_score = torch.softmax(pos_matrix, -1)
+                pos_score = 1.0 - pos_score[:, :, 0]
+                #pos_weight = pos_weight.view(-1, pos_weight.shape[-1])
+
+                #intra_neg_features = torch.nn.functional.normalize(intra_neg_features, dim=1)
+                neg_neg_scores = torch.einsum("bq,bqn->bn", avg_neg_feature, neg_features)
+                neg_pos_scores = torch.einsum("bqn,bqm->bnm", neg_features, pos_features)
+
+                neg_matrix = torch.cat([neg_neg_scores.unsqueeze(-1), neg_pos_scores], dim=2)
+                neg_labels = torch.zeros(neg_matrix.shape[1], dtype=torch.long).cuda()
+                neg_labels = neg_labels.unsqueeze(0).repeat(neg_matrix.shape[0], 1)
+
+                neg_score = torch.softmax(neg_matrix, -1)
+                neg_score = 1.0 - neg_score[:, :, 0]
+                #neg_weight = neg_weight.view(-1, neg_weight.shape[-1])
+
+                pos_loss = (torch.pow(pos_score, args.plic_pos_alpha) * torch.nn.functional.cross_entropy(pos_matrix.view(-1, pos_matrix.shape[-1])/args.plic_pos_temp, pos_labels.view(-1), reduction='none')).mean()
+                neg_loss = (torch.pow(neg_score, args.plic_neg_alpha) * torch.nn.functional.cross_entropy(neg_matrix.view(-1, neg_matrix.shape[-1])/args.plic_neg_temp, neg_labels.view(-1), reduction='none')).mean()
+
+                lang_loss = (torch.pow(lang_score, args.plic_lang_alpha) * torch.nn.functional.cross_entropy(lang_matrix.view(-1, lang_matrix.shape[-1])/args.plic_lang_temp, lang_labels.view(-1), reduction='none')).mean()
+
+                plic_pos_loss += pos_loss 
+                plic_neg_loss += neg_loss 
+                plic_lang_loss += lang_loss 
+            #cl_loss += 0.5 * (torch.nn.functional.cross_entropy(pos_matrix.view(-1, pos_matrix.shape[-1])/cl_temp, pos_labels.view(-1))+torch.nn.functional.cross_entropy(neg_matrix.view(-1, neg_matrix.shape[-1])/cl_temp, neg_labels.view(-1)))
+        plic_pos_loss = (args.plic_pos_weight * plic_pos_loss) / B
+        plic_neg_loss = (args.plic_neg_weight * plic_neg_loss) / B
+        plic_lang_loss = (args.plic_lang_weight * plic_lang_loss) / B
+        plic_loss = plic_pos_loss + plic_neg_loss +plic_lang_loss
+
+
+        #print(output.device, target.device)
+        losses = criterion(output, target)
+        weight_dict = criterion.weight_dict
+                    
+        loss_ce = 0.0
+        loss_dice = 0.0
+        loss_mask = 0.0
+        for k in list(losses.keys()):
+            if k in weight_dict:
+                losses[k] *= criterion.weight_dict[k]
+                if '_ce' in k:
+                    loss_ce += losses[k]
+                elif '_dice' in k:
+                    loss_dice += losses[k]
+                else:
+                    loss_mask += losses[k]
+            else:
+                # remove this loss if not specified in `weight_dict`
+                losses.pop(k)
+        #loss = 0.3 * loss_ce + 0.3 * loss_dice + 0.4 * loss_mask
+        smlm_loss = args.smlm_weight * nn.CrossEntropyLoss(ignore_index=-1)(mlm_predictions, mlm_targets)
+        loss = loss_ce + loss_dice + loss_mask + plic_loss + smlm_loss
+
+
+        #loss = criterion(output.squeeze(1), target.float())
+        optimizer.zero_grad()  # set_to_none=True is only available in pytorch 1.6+
+        loss.backward()
+        optimizer.step()
+        lr_scheduler.step()
+
+        torch.cuda.synchronize()
+        train_loss += loss.item()
+        iterations += 1
+        #metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"], loss_ce=loss_ce.item(), loss_dice=loss_dice.item(), loss_mask=loss_mask.item(), plic_loss=plic_loss.item(), plic_lang_loss=plic_lang_loss.item(), plic_pos_loss=plic_pos_loss.item(), plic_neg_loss=plic_neg_loss.item(), smlm_loss=smlm_loss.item())
+        #metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"], loss_ce=loss_ce.item(), loss_dice=loss_dice.item(), loss_mask=loss_mask.item(), cl_loss=cl_loss.item(), cl_lang_loss=cl_lang_loss_print, cl_pos_loss=cl_pos_loss_print, cl_neg_loss=cl_neg_loss_print)
+
+        #del image, target, sentences, attentions, loss, output, data
+        #if bert_model is not None:
+        #    del last_hidden_states, embedding
+
+        #gc.collect()
+        #torch.cuda.empty_cache()
+        #del loss
+        #del cl_loss
+        #del cl_lang_loss
+        #del loss_ce
+        #del loss_dice
+        #del loss_mask
+        torch.cuda.synchronize()
+
+
+def main(args):
+#def main(local_rank, args):
+    #ip = os.environ['MASTER_IP']
+    #port = os.environ['MASTER_PORT']
+    #hosts = int(os.environ['WORLD_SIZE'])  # 机器个数 1
+    #rank = int(os.environ['RANK'])  # 当前机器编号
+    #gpus = torch.cuda.device_count()  # 每台机器的GPU个数
+    #print(local_rank, rank, gpus) #3 0 8
+    #dist.init_process_group(backend='nccl', init_method=f'tcp://{ip}:{port}', world_size=hosts*gpus, rank=rank*gpus+local_rank)
+    #torch.cuda.set_device(local_rank)
+    #dist.barrier()
+
+    ##utils.init_distributed_mode(args)
+    #args.distributed=True
+    #args.gpu = local_rank
+    #print(args)
+    ##misc.init_distributed_mode(args)
+
+    #print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
+    #print("{}".format(args).replace(', ', ',\n'))
+
+    #device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    print('seed', seed)
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+    #cudnn.benchmark = True
+
+    dataset, num_classes = get_dataset("train",
+                                       get_transform(args=args),
+                                       args=args)
+    dataset_test, _ = get_dataset("val",
+                                  get_transform(args=args),
+                                  args=args)
+
+    # batch sampler
+    print(f"local rank {args.local_rank} / global rank {utils.get_rank()} successfully built train dataset.")
+    num_tasks = utils.get_world_size()
+    global_rank = utils.get_rank()
+    #num_tasks = hosts*gpus
+    #global_rank = rank*gpus+local_rank
+    train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank,
+                                                                    shuffle=True)
+    test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    # data loader
+    data_loader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.batch_size,
+        sampler=train_sampler, num_workers=args.workers, pin_memory=True, drop_last=True)
+
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=1, sampler=test_sampler, pin_memory=True, num_workers=args.workers)
+
+    # model initialization
+    print(args.model)
+    model = multimodal_segmentation_ppm.__dict__[args.model](pretrained=args.pretrained_swin_weights,
+                                              args=args)
+    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+    #model.cuda()
+    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], find_unused_parameters=True)
+    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=False)
+    #single_model = model.module
+
+    if args.model != 'lavt_one':
+        model_class = MultiModalBert
+        bert_model = model_class.from_pretrained(args.ck_bert, embed_dim=model.backbone.embed_dim)
+        bert_model.pooler = None  # a work-around for a bug in Transformers = 3.0.2 that appears for DistributedDataParallel
+        #bert_model.cuda()
+        bert_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(bert_model)
+        #bert_model = torch.nn.parallel.DistributedDataParallel(bert_model, device_ids=[local_rank])
+        #single_bert_model = bert_model.module
+    else:
+        bert_model = None
+        single_bert_model = None
+
+    input_shape = dict()
+    input_shape['s1'] = Dict({'channel': 128,  'stride': 4})
+    input_shape['s2'] = Dict({'channel': 256,  'stride': 8})
+    input_shape['s3'] = Dict({'channel': 512,  'stride': 16})
+    input_shape['s4'] = Dict({'channel': 1024, 'stride': 32})
+
+
+
+    cfg = Dict()
+    cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+    cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 
+    cfg.MODEL.MASK_FORMER.NHEADS = 8
+    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = args.transformer_enc_layers
+    cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"]
+
+    cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1
+    cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+    cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = args.num_object_queries
+    cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = args.dim_feedforward
+    cfg.MODEL.MASK_FORMER.DEC_LAYERS = args.dec_layers
+    cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+    cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True
+    cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = args.no_object_weight
+    cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = args.class_weight
+    cfg.MODEL.MASK_FORMER.DICE_WEIGHT = args.dice_weight
+    cfg.MODEL.MASK_FORMER.MASK_WEIGHT = args.mask_weight
+
+    cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = args.train_num_points
+    cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0
+    cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
+    print(cfg)
+
+    maskformer_head = MaskFormerHead(cfg, input_shape)
+    maskformer_head = torch.nn.SyncBatchNorm.convert_sync_batchnorm(maskformer_head)
+    #maskformer_head.cuda()
+    #maskformer_head = torch.nn.parallel.DistributedDataParallel(maskformer_head, device_ids=[args.local_rank], find_unused_parameters=False)
+    #single_head = maskformer_head.module
+    #print(single_head)
+
+    model = WrapperModel(model.backbone, bert_model, maskformer_head, args)
+    model.cuda()
+    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
+    single_model = model.module
+
+    # mask2former loss
+    deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
+    no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
+
+    # loss weights
+    class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT
+    dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
+    mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT
+    # self.criterion = Criterion(self.num_classes)
+
+    # building criterion
+
+    matcher = HungarianMatcher(
+        cost_class=class_weight,
+        cost_mask=mask_weight,
+        cost_dice=dice_weight,
+        num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+    )
+
+    weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight}
+    if deep_supervision:
+        dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
+        aux_weight_dict = {}
+        for i in range(dec_layers - 1):
+            aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
+        weight_dict.update(aux_weight_dict)
+
+    losses = ["labels", "masks"]
+    criterion = SetCriterion(
+        cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
+        matcher=matcher,
+        weight_dict=weight_dict,
+        eos_coef=no_object_weight,
+        losses=losses,
+        num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+        oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO,
+        importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO,
+        device='cuda'
+    )
+    
+    if args.resume == "auto":
+        last_ckpt = ""
+        for e in range(args.epochs):
+            ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth')
+            if os.path.exists(ckpt_path):
+                last_ckpt = ckpt_path
+        args.resume = last_ckpt
+
+    # resume training
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu')
+        single_model.load_state_dict(checkpoint['model'])
+        #if args.model != 'lavt_one':
+        #    single_bert_model.load_state_dict(checkpoint['bert_model'])
+
+    # parameters to optimize
+    backbone_no_decay = list()
+    backbone_decay = list()
+    for name, m in single_model.image_model.named_parameters():
+        if 'norm' in name or 'absolute_pos_embed' in name or 'relative_position_bias_table' in name:
+            backbone_no_decay.append(m)
+        else:
+            backbone_decay.append(m)
+
+    params_to_optimize = [
+        {'params': backbone_no_decay, 'weight_decay': 0.0},
+        {'params': backbone_decay},
+        {"params": [p for p in single_model.classifier.parameters() if p.requires_grad]},
+        # the following are the parameters of bert
+        {"params": reduce(operator.concat,
+                          [[p for p in single_model.language_model.encoder.layer[i].parameters()
+                            if p.requires_grad] for i in range(10)])},
+        {"params": single_model.language_model.pwams.parameters()},
+        {"params": single_model.language_model.res_gates.parameters()},
+        {"params": single_model.language_model.norms.parameters()},
+        {"params": single_model.lang_proj.parameters()},
+        #{"params": single_model.language_model.parameters()},
+        {'params': single_model.mlm_head.parameters()},
+        {'params': single_model.mlm_vis_proj.parameters()},
+        {'params': single_model.mlm_lang_proj.parameters()},
+        {'params': single_model.mlm_transformer.parameters()},
+        {'params': single_model.mlm_pos_embeds.parameters()},
+        {'params': single_model.mlm_modal_embeds.parameters()},
+        {'params': single_model.mlm_mask_embed.parameters()},
+        {'params': single_model.mlm_pos_mlp.parameters()},
+        #{'params': mlm_head.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_vis_proj.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_lang_proj.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_transformer.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_pos_embeds.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_modal_embeds.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_mask_embed.parameters(), 'weight_decay': 0.0},
+        #{'params': mlm_pos_mlp.parameters(), 'weight_decay': 0.0},
+    ]
+
+
+    # optimizer
+    optimizer = torch.optim.AdamW(params_to_optimize,
+                                  lr=args.lr,
+                                  weight_decay=args.weight_decay,
+                                  amsgrad=args.amsgrad
+                                  )
+
+    # learning rate scheduler
+    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
+                                                     lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)
+
+    # housekeeping
+    start_time = time.time()
+    iterations = 0
+    best_oIoU = -0.1
+
+    # resume training (optimizer, lr scheduler, and the epoch)
+    if args.resume:
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+        resume_epoch = checkpoint['epoch']
+    else:
+        resume_epoch = -999
+
+    # training loops
+    for epoch in range(max(0, resume_epoch+1), args.epochs):
+        data_loader.sampler.set_epoch(epoch)
+        train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq,
+                        iterations, args)
+        iou, overallIoU = evaluate(model, data_loader_test)
+
+        print('Average object IoU {}'.format(iou))
+        print('Overall IoU {}'.format(overallIoU))
+
+
+        dict_to_save = {'model': single_model.state_dict(),
+                        'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                        'lr_scheduler': lr_scheduler.state_dict()}
+
+        checkpoint_path = os.path.join(args.output_dir, 'checkpoint-{}.pth'.format(epoch))
+        utils.save_on_master(dict_to_save, str(checkpoint_path) + '_TEMP')
+        if utils.is_main_process():
+            os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path))
+
+        if utils.is_main_process():
+            ckpt_paths = []
+            for e in range(args.epochs):
+                ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth')
+                print(ckpt_path)
+                if os.path.exists(ckpt_path):
+                    ckpt_paths.append(ckpt_path)
+            print(ckpt_paths)
+            for ckpt_path in ckpt_paths[:-args.max_ckpt]:
+                os.remove(ckpt_path)
+                print("remove {:s}".format(ckpt_path))
+
+
+        save_checkpoint = (best_oIoU < overallIoU)
+        if save_checkpoint:
+            print('Better epoch: {}\n'.format(epoch))
+            dict_to_save = {'model': single_model.state_dict(),
+                            'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                            'lr_scheduler': lr_scheduler.state_dict()}
+
+            checkpoint_path = os.path.join(args.output_dir, 'model_best_{}.pth'.format(args.model_id))
+            utils.save_on_master(dict_to_save, checkpoint_path + '_TEMP')
+            if utils.is_main_process():
+                os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path))
+            best_oIoU = overallIoU
+        torch.cuda.empty_cache()
+
+    # summarize
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+if __name__ == "__main__":
+    from args import get_parser
+    parser = get_parser()
+    args = parser.parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+    # set up distributed learning
+    utils.init_distributed_mode(args)
+    print('Image size: {}'.format(str(args.img_size)))
+    main(args)
+    #mp.spawn(main, args=(args,), nprocs=torch.cuda.device_count())

elia/train_lavt.py

@@ -0,0 +1,444 @@
+import haienv
+haienv.set_env('lavt2')
+import torch.multiprocessing as mp
+import torch.distributed as dist
+
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+
+from functools import reduce
+import operator
+from bert.modeling_bert import BertModel
+
+import torchvision
+from lib import segmentation
+
+import transforms as T
+import utils
+import numpy as np
+
+import torch.nn.functional as F
+
+import gc
+from collections import OrderedDict
+
+import torch.backends.cudnn as cudnn
+
+from ffrecord.torch import DataLoader,Dataset
+def get_dataset(image_set, transform, args):
+    from data.dataset_refer_bert import ReferDataset
+    ds = ReferDataset(args,
+                      split=image_set,
+                      image_transforms=transform,
+                      target_transforms=None
+                      )
+    num_classes = 2
+
+    return ds, num_classes
+
+
+# IoU calculation for validation
+def IoU(pred, gt):
+    pred = pred.argmax(1)
+
+    intersection = torch.sum(torch.mul(pred, gt))
+    union = torch.sum(torch.add(pred, gt)) - intersection
+
+    if intersection == 0 or union == 0:
+        iou = 0
+    else:
+        iou = float(intersection) / float(union)
+
+    return iou, intersection, union
+
+
+def get_transform(args):
+    transforms = [T.Resize(args.img_size, args.img_size),
+                  T.ToTensor(),
+                  T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                  ]
+
+    return T.Compose(transforms)
+
+
+def criterion(input, target):
+    weight = torch.FloatTensor([0.9, 1.1]).cuda()
+    return nn.functional.cross_entropy(input, target, weight=weight)
+
+
+def evaluate(model, data_loader, bert_model):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+    total_its = 0
+    acc_ious = 0
+
+    # evaluation variables
+    cum_I, cum_U = 0, 0
+    eval_seg_iou_list = [.5, .6, .7, .8, .9]
+    seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
+    seg_total = 0
+    mean_IoU = []
+
+    with torch.no_grad():
+        for data in metric_logger.log_every(data_loader, 100, header):
+            total_its += 1
+            image, target, sentences, attentions = data
+            image, target, sentences, attentions = image.cuda(non_blocking=True),\
+                                                   target.cuda(non_blocking=True),\
+                                                   sentences.cuda(non_blocking=True),\
+                                                   attentions.cuda(non_blocking=True)
+
+            sentences = sentences.squeeze(1)
+            attentions = attentions.squeeze(1)
+            #print("sentences", sentences.shape)
+            #print("attentions", attentions.shape)
+
+            if bert_model is not None:
+                last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]
+                #print("last hidden states", last_hidden_states.shape)
+                embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
+                attentions = attentions.unsqueeze(dim=-1)  # (B, N_l, 1)
+                output = model(image, embedding, l_mask=attentions)
+            else:
+                output = model(image, sentences, l_mask=attentions)
+
+            iou, I, U = IoU(output, target)
+            acc_ious += iou
+            mean_IoU.append(iou)
+            cum_I += I
+            cum_U += U
+            for n_eval_iou in range(len(eval_seg_iou_list)):
+                eval_seg_iou = eval_seg_iou_list[n_eval_iou]
+                seg_correct[n_eval_iou] += (iou >= eval_seg_iou)
+            seg_total += 1
+        iou = acc_ious / total_its
+
+    mean_IoU = np.array(mean_IoU)
+    mIoU = np.mean(mean_IoU)
+    print('Final results:')
+    print('Mean IoU is %.2f\n' % (mIoU * 100.))
+    results_str = ''
+    for n_eval_iou in range(len(eval_seg_iou_list)):
+        results_str += '    precision@%s = %.2f\n' % \
+                       (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
+    results_str += '    overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
+    print(results_str)
+
+    return 100 * iou, 100 * cum_I / cum_U
+
+
+def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq,
+                    iterations, bert_model):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    train_loss = 0
+    total_its = 0
+
+    for data in metric_logger.log_every(data_loader, print_freq, header):
+        total_its += 1
+        image, target, sentences, attentions = data
+        image, target, sentences, attentions = image.cuda(non_blocking=True),\
+                                               target.cuda(non_blocking=True),\
+                                               sentences.cuda(non_blocking=True),\
+                                               attentions.cuda(non_blocking=True)
+
+        sentences = sentences.squeeze(1)
+        attentions = attentions.squeeze(1)
+        #print(sentences.shape, attentions.shape, target.shape)
+        #print(sentences)
+        #print('a', sentences.shape)
+        #print('b', attentions.shape)
+
+        if bert_model is not None:
+            last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]  # (6, 10, 768)
+            #print('c', last_hidden_states.shape)
+
+            embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
+            #print('e', embedding.shape)
+            attentions = attentions.unsqueeze(dim=-1)  # (batch, N_l, 1)
+            #print('f', attentions.shape)
+            output = model(image, embedding, l_mask=attentions)
+        else:
+            output = model(image, sentences, l_mask=attentions)
+
+        loss = criterion(output, target)
+        optimizer.zero_grad()  # set_to_none=True is only available in pytorch 1.6+
+        loss.backward()
+        optimizer.step()
+        lr_scheduler.step()
+
+        torch.cuda.synchronize()
+        train_loss += loss.item()
+        iterations += 1
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+
+        del image, target, sentences, attentions, loss, output, data
+        if bert_model is not None:
+            del last_hidden_states, embedding
+
+        #gc.collect()
+        #torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+
+#def main(args):
+def main(local_rank, args):
+    ip = os.environ['MASTER_IP']
+    port = os.environ['MASTER_PORT']
+    hosts = int(os.environ['WORLD_SIZE'])  # 机器个数 1
+    rank = int(os.environ['RANK'])  # 当前机器编号
+    gpus = torch.cuda.device_count()  # 每台机器的GPU个数
+    print(local_rank, rank, gpus) #3 0 8
+    dist.init_process_group(backend='nccl', init_method=f'tcp://{ip}:{port}', world_size=hosts*gpus, rank=rank*gpus+local_rank)
+    torch.cuda.set_device(local_rank)
+    dist.barrier()
+
+    #utils.init_distributed_mode(args)
+    args.distributed=True
+    args.gpu = local_rank
+    print(args)
+    #misc.init_distributed_mode(args)
+
+    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
+    print("{}".format(args).replace(', ', ',\n'))
+
+    device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+    #cudnn.benchmark = True
+
+    dataset, num_classes = get_dataset("train",
+                                       get_transform(args=args),
+                                       args=args)
+    dataset_test, _ = get_dataset("val",
+                                  get_transform(args=args),
+                                  args=args)
+
+    # batch sampler
+    print(f"local rank {args.local_rank} / global rank {utils.get_rank()} successfully built train dataset.")
+    #num_tasks = utils.get_world_size()
+    #global_rank = utils.get_rank()
+    num_tasks = hosts*gpus
+    global_rank = rank*gpus+local_rank
+    train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank,
+                                                                    shuffle=True)
+    test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    # data loader
+    data_loader = DataLoader(
+        dataset, batch_size=args.batch_size,
+        sampler=train_sampler, num_workers=args.workers, pin_memory=True, drop_last=True)
+
+    data_loader_test = DataLoader(
+        dataset_test, batch_size=1, sampler=test_sampler, pin_memory=True, num_workers=args.workers)
+
+    # model initialization
+    print(args.model)
+    model = segmentation.__dict__[args.model](pretrained=args.pretrained_swin_weights,
+                                              args=args)
+    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+    model.cuda()
+    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
+    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=False)
+    single_model = model.module
+
+    if args.model != 'lavt_one':
+        model_class = BertModel
+        bert_model = model_class.from_pretrained(args.ck_bert)
+        bert_model.pooler = None  # a work-around for a bug in Transformers = 3.0.2 that appears for DistributedDataParallel
+        bert_model.cuda()
+        bert_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(bert_model)
+        bert_model = torch.nn.parallel.DistributedDataParallel(bert_model, device_ids=[args.local_rank])
+        single_bert_model = bert_model.module
+    else:
+        bert_model = None
+        single_bert_model = None
+
+    input_shape = dict()
+    input_shape['s1'] = Dict({'channel': 128,  'stride': 4})
+    input_shape['s2'] = Dict({'channel': 256,  'stride': 8})
+    input_shape['s3'] = Dict({'channel': 512,  'stride': 16})
+    input_shape['s4'] = Dict({'channel': 1024, 'stride': 32})
+
+
+
+    cfg = Dict()
+    cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+    cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 
+    cfg.MODEL.MASK_FORMER.NHEADS = 8
+    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 4
+    cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"]
+
+    cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1
+    cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+    cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 1
+    cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
+    cfg.MODEL.MASK_FORMER.DEC_LAYERS = 10
+    cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+
+    maskformer_head = MaskFormerHead(cfg, input_shape)
+    maskformer_head = torch.nn.SyncBatchNorm.convert_sync_batchnorm(maskformer_head)
+    maskformer_head.cuda()
+    maskformer_head = torch.nn.parallel.DistributedDataParallel(maskformer_head, device_ids=[args.local_rank], find_unused_parameters=False)
+    single_head = maskformer_head.module
+    print(single_head)
+    
+    
+    if args.resume == "auto":
+        last_ckpt = ""
+        for e in range(args.epochs):
+            ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth')
+            if os.path.exists(ckpt_path):
+                last_ckpt = ckpt_path
+        args.resume = last_ckpt
+
+    # resume training
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu')
+        single_model.load_state_dict(checkpoint['model'])
+        single_head.load_state_dict(checkpoint['head_model'])
+        if args.model != 'lavt_one':
+            single_bert_model.load_state_dict(checkpoint['bert_model'])
+
+    # parameters to optimize
+    backbone_no_decay = list()
+    backbone_decay = list()
+    for name, m in single_model.backbone.named_parameters():
+        if 'norm' in name or 'absolute_pos_embed' in name or 'relative_position_bias_table' in name:
+            backbone_no_decay.append(m)
+        else:
+            backbone_decay.append(m)
+
+    if args.model != 'lavt_one':
+        params_to_optimize = [
+            {'params': backbone_no_decay, 'weight_decay': 0.0},
+            {'params': backbone_decay},
+            {"params": [p for p in single_model.classifier.parameters() if p.requires_grad]},
+            # the following are the parameters of bert
+            {"params": reduce(operator.concat,
+                              [[p for p in single_bert_model.encoder.layer[i].parameters()
+                                if p.requires_grad] for i in range(10)])},
+            {"params": single_head.parameters()}
+        ]
+    else:
+        params_to_optimize = [
+            {'params': backbone_no_decay, 'weight_decay': 0.0},
+            {'params': backbone_decay},
+            {"params": [p for p in single_model.classifier.parameters() if p.requires_grad]},
+            # the following are the parameters of bert
+            {"params": reduce(operator.concat,
+                              [[p for p in single_model.text_encoder.encoder.layer[i].parameters()
+                                if p.requires_grad] for i in range(10)])},
+        ]
+
+    # optimizer
+    optimizer = torch.optim.AdamW(params_to_optimize,
+                                  lr=args.lr,
+                                  weight_decay=args.weight_decay,
+                                  amsgrad=args.amsgrad
+                                  )
+
+    # learning rate scheduler
+    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
+                                                     lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)
+
+    # housekeeping
+    start_time = time.time()
+    iterations = 0
+    best_oIoU = -0.1
+
+    # resume training (optimizer, lr scheduler, and the epoch)
+    if args.resume:
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+        resume_epoch = checkpoint['epoch']
+    else:
+        resume_epoch = -999
+
+    # training loops
+    for epoch in range(max(0, resume_epoch+1), args.epochs):
+        data_loader.sampler.set_epoch(epoch)
+        train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq,
+                        iterations, bert_model, single_head)
+        iou, overallIoU = evaluate(model, data_loader_test, bert_model, single_head)
+
+        print('Average object IoU {}'.format(iou))
+        print('Overall IoU {}'.format(overallIoU))
+
+
+        if single_bert_model is not None:
+            dict_to_save = {'model': single_model.state_dict(), 'bert_model': single_bert_model.state_dict(),
+                            'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                            'lr_scheduler': lr_scheduler.state_dict(), 'head_model': single_head.state_dict()}
+        else:
+            dict_to_save = {'model': single_model.state_dict(),
+                            'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                            'lr_scheduler': lr_scheduler.state_dict()}
+
+        checkpoint_path = os.path.join(args.output_dir, 'checkpoint-{}.pth'.format(epoch))
+        utils.save_on_master(dict_to_save, str(checkpoint_path) + '_TEMP')
+        if utils.is_main_process():
+            os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path))
+
+        if utils.is_main_process():
+            ckpt_paths = []
+            for e in range(args.epochs):
+                ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth')
+                print(ckpt_path)
+                if os.path.exists(ckpt_path):
+                    ckpt_paths.append(ckpt_path)
+            print(ckpt_paths)
+            for ckpt_path in ckpt_paths[:-args.max_ckpt]:
+                os.remove(ckpt_path)
+                print("remove {:s}".format(ckpt_path))
+
+
+        save_checkpoint = (best_oIoU < overallIoU)
+        if save_checkpoint:
+            print('Better epoch: {}\n'.format(epoch))
+            if single_bert_model is not None:
+                dict_to_save = {'model': single_model.state_dict(), 'bert_model': single_bert_model.state_dict(),
+                                'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                                'lr_scheduler': lr_scheduler.state_dict()}
+            else:
+                dict_to_save = {'model': single_model.state_dict(),
+                                'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
+                                'lr_scheduler': lr_scheduler.state_dict()}
+
+            checkpoint_path = os.path.join(args.output_dir, 'model_best_{}.pth'.format(args.model_id))
+            utils.save_on_master(dict_to_save, checkpoint_path + '_TEMP')
+            if utils.is_main_process():
+                os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path))
+            best_oIoU = overallIoU
+
+    # summarize
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+if __name__ == "__main__":
+    from args import get_parser
+    parser = get_parser()
+    args = parser.parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+    # set up distributed learning
+    #utils.init_distributed_mode(args)
+    print('Image size: {}'.format(str(args.img_size)))
+    #main(args)
+    mp.spawn(main, args=(args,), nprocs=torch.cuda.device_count())