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# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Dict, List, Optional
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from torch.nn import functional as F
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
from detectron2.modeling.poolers import ROIPooler
from detectron2.modeling.roi_heads import select_foreground_proposals
from detectron2.structures import ImageList, Instances
from .. import (
build_densepose_data_filter,
build_densepose_embedder,
build_densepose_head,
build_densepose_losses,
build_densepose_predictor,
densepose_inference,
)
class Decoder(nn.Module):
"""
A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper
(https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from
all levels of the FPN into single output.
"""
def __init__(self, cfg, input_shape: Dict[str, ShapeSpec], in_features):
super(Decoder, self).__init__()
# fmt: off
self.in_features = in_features
feature_strides = {k: v.stride for k, v in input_shape.items()}
feature_channels = {k: v.channels for k, v in input_shape.items()}
num_classes = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES
conv_dims = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS
self.common_stride = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE
norm = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM
# fmt: on
self.scale_heads = []
for in_feature in self.in_features:
head_ops = []
head_length = max(
1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))
)
for k in range(head_length):
conv = Conv2d(
feature_channels[in_feature] if k == 0 else conv_dims,
conv_dims,
kernel_size=3,
stride=1,
padding=1,
bias=not norm,
norm=get_norm(norm, conv_dims),
activation=F.relu,
)
weight_init.c2_msra_fill(conv)
head_ops.append(conv)
if feature_strides[in_feature] != self.common_stride:
head_ops.append(
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
)
self.scale_heads.append(nn.Sequential(*head_ops))
self.add_module(in_feature, self.scale_heads[-1])
self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0)
weight_init.c2_msra_fill(self.predictor)
def forward(self, features: List[torch.Tensor]):
for i, _ in enumerate(self.in_features):
if i == 0:
x = self.scale_heads[i](features[i])
else:
x = x + self.scale_heads[i](features[i])
x = self.predictor(x)
return x
@ROI_HEADS_REGISTRY.register()
class DensePoseROIHeads(StandardROIHeads):
"""
A Standard ROIHeads which contains an addition of DensePose head.
"""
def __init__(self, cfg, input_shape):
super().__init__(cfg, input_shape)
self._init_densepose_head(cfg, input_shape)
def _init_densepose_head(self, cfg, input_shape):
# fmt: off
self.densepose_on = cfg.MODEL.DENSEPOSE_ON
if not self.densepose_on:
return
self.densepose_data_filter = build_densepose_data_filter(cfg)
dp_pooler_resolution = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION
dp_pooler_sampling_ratio = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO
dp_pooler_type = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE
self.use_decoder = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON
# fmt: on
if self.use_decoder:
dp_pooler_scales = (1.0 / input_shape[self.in_features[0]].stride,)
else:
dp_pooler_scales = tuple(1.0 / input_shape[k].stride for k in self.in_features)
in_channels = [input_shape[f].channels for f in self.in_features][0]
if self.use_decoder:
self.decoder = Decoder(cfg, input_shape, self.in_features)
self.densepose_pooler = ROIPooler(
output_size=dp_pooler_resolution,
scales=dp_pooler_scales,
sampling_ratio=dp_pooler_sampling_ratio,
pooler_type=dp_pooler_type,
)
self.densepose_head = build_densepose_head(cfg, in_channels)
self.densepose_predictor = build_densepose_predictor(
cfg, self.densepose_head.n_out_channels
)
self.densepose_losses = build_densepose_losses(cfg)
self.embedder = build_densepose_embedder(cfg)
def _forward_densepose(self, features: Dict[str, torch.Tensor], instances: List[Instances]):
"""
Forward logic of the densepose prediction branch.
Args:
features (dict[str, Tensor]): input data as a mapping from feature
map name to tensor. Axis 0 represents the number of images `N` in
the input data; axes 1-3 are channels, height, and width, which may
vary between feature maps (e.g., if a feature pyramid is used).
instances (list[Instances]): length `N` list of `Instances`. The i-th
`Instances` contains instances for the i-th input image,
In training, they can be the proposals.
In inference, they can be the predicted boxes.
Returns:
In training, a dict of losses.
In inference, update `instances` with new fields "densepose" and return it.
"""
if not self.densepose_on:
return {} if self.training else instances
features_list = [features[f] for f in self.in_features]
if self.training:
proposals, _ = select_foreground_proposals(instances, self.num_classes)
features_list, proposals = self.densepose_data_filter(features_list, proposals)
if len(proposals) > 0:
proposal_boxes = [x.proposal_boxes for x in proposals]
if self.use_decoder:
features_list = [self.decoder(features_list)]
features_dp = self.densepose_pooler(features_list, proposal_boxes)
densepose_head_outputs = self.densepose_head(features_dp)
densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs)
densepose_loss_dict = self.densepose_losses(
proposals, densepose_predictor_outputs, embedder=self.embedder
)
return densepose_loss_dict
else:
pred_boxes = [x.pred_boxes for x in instances]
if self.use_decoder:
features_list = [self.decoder(features_list)]
features_dp = self.densepose_pooler(features_list, pred_boxes)
if len(features_dp) > 0:
densepose_head_outputs = self.densepose_head(features_dp)
densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs)
else:
densepose_predictor_outputs = None
densepose_inference(densepose_predictor_outputs, instances)
return instances
def forward(
self,
images: ImageList,
features: Dict[str, torch.Tensor],
proposals: List[Instances],
targets: Optional[List[Instances]] = None,
):
instances, losses = super().forward(images, features, proposals, targets)
del targets, images
if self.training:
losses.update(self._forward_densepose(features, instances))
return instances, losses
def forward_with_given_boxes(
self, features: Dict[str, torch.Tensor], instances: List[Instances]
):
"""
Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
This is useful for downstream tasks where a box is known, but need to obtain
other attributes (outputs of other heads).
Test-time augmentation also uses this.
Args:
features: same as in `forward()`
instances (list[Instances]): instances to predict other outputs. Expect the keys
"pred_boxes" and "pred_classes" to exist.
Returns:
instances (list[Instances]):
the same `Instances` objects, with extra
fields such as `pred_masks` or `pred_keypoints`.
"""
instances = super().forward_with_given_boxes(features, instances)
instances = self._forward_densepose(features, instances)
return instances
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