Skip to content

Fix - setting cropping parameters in analyze_videos for PyTorch models #2849

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
maximpavliv merged 3 commits into from
Jan 27, 2025
Merged

Fix - setting cropping parameters in analyze_videos for PyTorch models #2849

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
6c5cb58
fix cropping in video analysis; bug fix for MPS prediction
n-poulsen Jan 27, 2025
295575c
remove debug prints
n-poulsen Jan 27, 2025
71d0c7e
improve cropping for video iterator
n-poulsen Jan 27, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
remove debug prints
  • Loading branch information
@n-poulsen
n-poulsen committed Jan 27, 2025
commit 295575c1df4c20fb8a5f79079d5f2d5078e56da7
14 changes: 0 additions & 14 deletions deeplabcut/pose_estimation_pytorch/models/predictors/single_predictor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -74,21 +74,13 @@ def forward(
>>> poses = predictor.forward(stride, output)
"""
heatmaps = outputs["heatmap"]
# print("---")
# print(f"HEATMAPS SHAPE: {heatmaps.shape}")
scale_factors = stride, stride

if self.apply_sigmoid:
heatmaps = self.sigmoid(heatmaps)

heatmaps = heatmaps.permute(0, 2, 3, 1)
batch_size, height, width, num_joints = heatmaps.shape
# print(f"HEATMAPS SHAPE: {heatmaps.shape}")
# print(f"STRIDE: {stride}")
# for i in range(heatmaps.shape[-1]):
# hm = heatmaps[0, :, :, i]
# y0, x0 = stride * (hm == torch.max(hm)).nonzero()[0]
# print(i, f"({x0}, {y0}):", torch.max(heatmaps[..., i]))

locrefs = None
if self.location_refinement:
Expand All @@ -99,16 +91,10 @@ def forward(
locrefs = locrefs * self.locref_std

poses = self.get_pose_prediction(heatmaps, locrefs, scale_factors)
# print(f"PREDICTION: {poses.shape}")
# for p in poses[0]:
# for kpt in p:
# print(kpt)
# print("---")

if self.clip_scores:
poses[..., 2] = torch.clip(poses[..., 2], min=0, max=1)

# raise ValueError("")
return {"poses": poses}

def get_top_values(
Expand Down