def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
batch_size: N
num_keypoints: K
heatmaps height: H
heatmaps weight: W
Args:
output (torch.Tensor[NxKxHxW]): Output heatmaps.
target (torch.Tensor[NxKxHxW]): Target heatmaps.
target_weight (torch.Tensor[NxKx1]):
Weights across different joint types.
"""
accuracy = dict()
if self.target_type == 'GaussianHeatmap':
_, avg_acc, _ = pose_pck_accuracy(
output.detach().cpu().numpy(),
target.detach().cpu().numpy(),
target_weight.detach().cpu().numpy().squeeze(-1) > 0)
accuracy['acc_pose'] = float(avg_acc)
return accuracy
def forward_train(self, img, target, target_weight, img_metas, **kwargs):
"""Defines the computation performed at every call when training."""
output = self.backbone(img)
if self.with_keypoint:
output = self.keypoint_head(output)
# if return loss
losses = dict()
if isinstance(output, list):
# multi-stage models
for output_i in output:
if 'mse_loss' not in losses:
losses['mse_loss'] = self.loss(output_i, target,
target_weight)
else:
losses['mse_loss'] += self.loss(output_i, target,
target_weight)
else:
losses['mse_loss'] = self.loss(output, target, target_weight)
if isinstance(output, list):
_, avg_acc, cnt = pose_pck_accuracy(
output[-1][target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy(),
target[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy())
else:
_, avg_acc, cnt = pose_pck_accuracy(
output[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy(),
target[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy())
losses['acc_pose'] = float(avg_acc)
return losses
def forward_train(self, img, target, target_weight, img_metas, **kwargs):
"""Defines the computation performed at every call when training."""
output = self.backbone(img)
if self.with_keypoint:
output = self.keypoint_head(output)
# if return loss
losses = dict()
if isinstance(output, list):
if target.dim() == 5 and target_weight.dim() == 4:
# target: [batch_size, num_outputs, num_joints, h, w]
# target_weight: [batch_size, num_outputs, num_joints, 1]
assert target.size(1) == len(output)
if isinstance(self.loss, nn.Sequential):
assert len(self.loss) == len(output)
if 'loss_weights' in self.train_cfg and self.train_cfg[
'loss_weights'] is not None:
assert len(self.train_cfg['loss_weights']) == len(output)
for i in range(len(output)):
if target.dim() == 5 and target_weight.dim() == 4:
target_i = target[:, i, :, :, :]
target_weight_i = target_weight[:, i, :, :]
else:
target_i = target
target_weight_i = target_weight
if isinstance(self.loss, nn.Sequential):
loss_func = self.loss[i]
else:
loss_func = self.loss
loss_i = loss_func(output[i], target_i, target_weight_i)
if 'loss_weights' in self.train_cfg and self.train_cfg[
'loss_weights']:
loss_i = loss_i * self.train_cfg['loss_weights'][i]
if 'mse_loss' not in losses:
losses['mse_loss'] = loss_i
else:
losses['mse_loss'] += loss_i
else:
assert not isinstance(self.loss, nn.Sequential)
assert target.dim() == 4 and target_weight.dim() == 3
# target: [batch_size, num_joints, h, w]
# target_weight: [batch_size, num_joints, 1]
losses['mse_loss'] = self.loss(output, target, target_weight)
if isinstance(output, list):
if target.dim() == 5 and target_weight.dim() == 4:
_, avg_acc, _ = pose_pck_accuracy(
output[-1][target_weight[:, -1, :, :].squeeze(-1) > 0].
unsqueeze(0).detach().cpu().numpy(),
target[:, -1, :, :, :][target_weight[:, -1, :, :].squeeze(
-1) > 0].unsqueeze(0).detach().cpu().numpy())
# Only use the last output for prediction
else:
_, avg_acc, _ = pose_pck_accuracy(
output[-1][target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy(),
target[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy())
else:
_, avg_acc, _ = pose_pck_accuracy(
output[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy(),
target[target_weight.squeeze(-1) > 0].unsqueeze(
0).detach().cpu().numpy())
losses['acc_pose'] = float(avg_acc)
return losses
