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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple, Union
import torch
from torch import Tensor, nn
from mmpose.models.utils.tta import flip_visibility
from mmpose.registry import MODELS
from mmpose.utils.tensor_utils import to_numpy
from mmpose.utils.typing import (ConfigType, InstanceList, OptConfigType,
OptSampleList, Predictions)
from ..base_head import BaseHead
@MODELS.register_module()
class VisPredictHead(BaseHead):
"""VisPredictHead must be used together with other heads. It can predict
keypoints coordinates of and their visibility simultaneously. In the
current version, it only supports top-down approaches.
Args:
pose_cfg (Config): Config to construct keypoints prediction head
loss (Config): Config for visibility loss. Defaults to use
:class:`BCELoss`
use_sigmoid (bool): Whether to use sigmoid activation function
init_cfg (Config, optional): Config to control the initialization. See
:attr:`default_init_cfg` for default settings
"""
def __init__(self,
pose_cfg: ConfigType,
loss: ConfigType = dict(
type='BCELoss', use_target_weight=False,
use_sigmoid=True),
init_cfg: OptConfigType = None):
if init_cfg is None:
init_cfg = self.default_init_cfg
super().__init__(init_cfg)
self.in_channels = pose_cfg['in_channels']
if pose_cfg.get('num_joints', None) is not None:
self.out_channels = pose_cfg['num_joints']
elif pose_cfg.get('out_channels', None) is not None:
self.out_channels = pose_cfg['out_channels']
else:
raise ValueError('VisPredictHead requires \'num_joints\' or'
' \'out_channels\' in the pose_cfg.')
self.loss_module = MODELS.build(loss)
self.pose_head = MODELS.build(pose_cfg)
self.pose_cfg = pose_cfg
self.use_sigmoid = loss.get('use_sigmoid', False)
modules = [
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Linear(self.in_channels, self.out_channels)
]
if self.use_sigmoid:
modules.append(nn.Sigmoid())
self.vis_head = nn.Sequential(*modules)
def vis_forward(self, feats: Tuple[Tensor]):
"""Forward the vis_head. The input is multi scale feature maps and the
output is coordinates visibility.
Args:
feats (Tuple[Tensor]): Multi scale feature maps.
Returns:
Tensor: output coordinates visibility.
"""
x = feats[-1]
while len(x.shape) < 4:
x.unsqueeze_(-1)
x = self.vis_head(x)
return x.reshape(-1, self.out_channels)
def forward(self, feats: Tuple[Tensor]):
"""Forward the network. The input is multi scale feature maps and the
output is coordinates and coordinates visibility.
Args:
feats (Tuple[Tensor]): Multi scale feature maps.
Returns:
Tuple[Tensor]: output coordinates and coordinates visibility.
"""
x_pose = self.pose_head.forward(feats)
x_vis = self.vis_forward(feats)
return x_pose, x_vis
def integrate(self, batch_vis: Tensor,
pose_preds: Union[Tuple, Predictions]) -> InstanceList:
"""Add keypoints visibility prediction to pose prediction.
Overwrite the original keypoint_scores.
"""
if isinstance(pose_preds, tuple):
pose_pred_instances, pose_pred_fields = pose_preds
else:
pose_pred_instances = pose_preds
pose_pred_fields = None
batch_vis_np = to_numpy(batch_vis, unzip=True)
assert len(pose_pred_instances) == len(batch_vis_np)
for index, _ in enumerate(pose_pred_instances):
pose_pred_instances[index].keypoints_visible = batch_vis_np[index]
return pose_pred_instances, pose_pred_fields
def predict(self,
feats: Tuple[Tensor],
batch_data_samples: OptSampleList,
test_cfg: ConfigType = {}) -> Predictions:
"""Predict results from features.
Args:
feats (Tuple[Tensor] | List[Tuple[Tensor]]): The multi-stage
features (or multiple multi-stage features in TTA)
batch_data_samples (List[:obj:`PoseDataSample`]): The batch
data samples
test_cfg (dict): The runtime config for testing process. Defaults
to {}
Returns:
Union[InstanceList | Tuple[InstanceList | PixelDataList]]: If
posehead's ``test_cfg['output_heatmap']==True``, return both
pose and heatmap prediction; otherwise only return the pose
prediction.
The pose prediction is a list of ``InstanceData``, each contains
the following fields:
- keypoints (np.ndarray): predicted keypoint coordinates in
shape (num_instances, K, D) where K is the keypoint number
and D is the keypoint dimension
- keypoint_scores (np.ndarray): predicted keypoint scores in
shape (num_instances, K)
- keypoint_visibility (np.ndarray): predicted keypoints
visibility in shape (num_instances, K)
The heatmap prediction is a list of ``PixelData``, each contains
the following fields:
- heatmaps (Tensor): The predicted heatmaps in shape (K, h, w)
"""
if test_cfg.get('flip_test', False):
# TTA: flip test -> feats = [orig, flipped]
assert isinstance(feats, list) and len(feats) == 2
flip_indices = batch_data_samples[0].metainfo['flip_indices']
_feats, _feats_flip = feats
_batch_vis = self.vis_forward(_feats)
_batch_vis_flip = flip_visibility(
self.vis_forward(_feats_flip), flip_indices=flip_indices)
batch_vis = (_batch_vis + _batch_vis_flip) * 0.5
else:
batch_vis = self.vis_forward(feats) # (B, K, D)
batch_vis.unsqueeze_(dim=1) # (B, N, K, D)
if not self.use_sigmoid:
batch_vis = torch.sigmoid(batch_vis)
batch_pose = self.pose_head.predict(feats, batch_data_samples,
test_cfg)
return self.integrate(batch_vis, batch_pose)
@torch.no_grad()
def vis_accuracy(self, vis_pred_outputs, vis_labels, vis_weights=None):
"""Calculate visibility prediction accuracy."""
if not self.use_sigmoid:
vis_pred_outputs = torch.sigmoid(vis_pred_outputs)
threshold = 0.5
predictions = (vis_pred_outputs >= threshold).float()
correct = (predictions == vis_labels).float()
if vis_weights is not None:
accuracy = (correct * vis_weights).sum(dim=1) / (
vis_weights.sum(dim=1) + 1e-6)
else:
accuracy = correct.mean(dim=1)
return accuracy.mean()
def loss(self,
feats: Tuple[Tensor],
batch_data_samples: OptSampleList,
train_cfg: OptConfigType = {}) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
feats (Tuple[Tensor]): The multi-stage features
batch_data_samples (List[:obj:`PoseDataSample`]): The batch
data samples
train_cfg (dict): The runtime config for training process.
Defaults to {}
Returns:
dict: A dictionary of losses.
"""
vis_pred_outputs = self.vis_forward(feats)
vis_labels = []
vis_weights = [] if self.loss_module.use_target_weight else None
for d in batch_data_samples:
vis_label = d.gt_instance_labels.keypoint_weights.float()
vis_labels.append(vis_label)
if vis_weights is not None:
vis_weights.append(
getattr(d.gt_instance_labels, 'keypoints_visible_weights',
vis_label.new_ones(vis_label.shape)))
vis_labels = torch.cat(vis_labels)
vis_weights = torch.cat(vis_weights) if vis_weights else None
# calculate vis losses
losses = dict()
loss_vis = self.loss_module(vis_pred_outputs, vis_labels, vis_weights)
losses.update(loss_vis=loss_vis)
# calculate vis accuracy
acc_vis = self.vis_accuracy(vis_pred_outputs, vis_labels, vis_weights)
losses.update(acc_vis=acc_vis)
# calculate keypoints losses
loss_kpt = self.pose_head.loss(feats, batch_data_samples)
losses.update(loss_kpt)
return losses
@property
def default_init_cfg(self):
init_cfg = [dict(type='Normal', layer=['Linear'], std=0.01, bias=0)]
return init_cfg
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