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Merged
AlexEMG merged 4 commits into from
Jul 1, 2025
Merged

edge case fix / rc10 #3036

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062e07e
edge case fix
MMathisLab Jul 1, 2025
be335f3
fix
MMathisLab Jul 1, 2025
8698d86
best
MMathisLab Jul 1, 2025
240584b
rc10
MMathisLab Jul 1, 2025
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2 changes: 2 additions & 0 deletions deeplabcut/pose_estimation_pytorch/apis/evaluation.py
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Expand Up @@ -626,6 +626,7 @@ def evaluate_snapshot(
)
scores_filepath = output_filename.with_suffix(".csv")
scores_filepath = scores_filepath.with_stem(scores_filepath.stem + "-results")
print(f"Evaluation results file: {scores_filepath.name}")
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[nitpick] Consider replacing print statements with a logging framework to allow for configurable and consistent output handling across the codebase.

Suggested change
print(f"Evaluation results file: {scores_filepath.name}")
logging.info(f"Evaluation results file: {scores_filepath.name}")

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save_evaluation_results(df_scores, scores_filepath, show_errors, pcutoff)

if per_keypoint_evaluation:
Expand Down Expand Up @@ -829,6 +830,7 @@ def evaluate_network(
snapshot_uid=get_scorer_uid(snapshot, detector_snapshot),
modelprefix=modelprefix,
)
print(f"Evaluation scorer: {scorer}")
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[nitpick] Consider using logging here instead of print to ensure consistency with other parts of the code that handle error and info messages.

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evaluate_snapshot(
loader=loader,
cfg=cfg,
Expand Down
134 changes: 121 additions & 13 deletions deeplabcut/pose_estimation_pytorch/apis/videos.py
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Expand Up @@ -449,9 +449,58 @@ def analyze_videos(
print(f"Creating a TopDownDynamicCropper with configuration {top_down_dynamic}")
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Consider replacing print statements with a logging framework to improve error tracking and maintainability in production code.

Suggested change
print(f"Creating a TopDownDynamicCropper with configuration {top_down_dynamic}")
logging.info(f"Creating a TopDownDynamicCropper with configuration {top_down_dynamic}")

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dynamic = TopDownDynamicCropper(**top_down_dynamic)

snapshot = utils.get_model_snapshots(
snapshot_index, loader.model_folder, loader.pose_task
)[0]
try:
snapshot = utils.get_model_snapshots(
snapshot_index, loader.model_folder, loader.pose_task
)[0]
except (ValueError, IndexError) as e:
print(f"Error loading snapshot with index {snapshot_index}: {e}")
print("Attempting to find available snapshots...")
Comment on lines +457 to +458
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[nitpick] Using print statements for error reporting may limit flexibility; consider using a logging framework that allows configurable log levels and better integration in production.

Suggested change
print(f"Error loading snapshot with index {snapshot_index}: {e}")
print("Attempting to find available snapshots...")
logging.error(f"Error loading snapshot with index {snapshot_index}: {e}")
logging.info("Attempting to find available snapshots...")

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# Try to get all available snapshots
try:
all_snapshots = utils.get_model_snapshots("all", loader.model_folder, loader.pose_task)
if all_snapshots:
# Try to find a "best" snapshot first
best_snapshots = [s for s in all_snapshots if s.best]
if best_snapshots:
snapshot = best_snapshots[0]
print(f"Found and using best snapshot: {snapshot.path}")
else:
# Use the last available snapshot
snapshot = all_snapshots[-1]
print(f"No best snapshot found, using last available: {snapshot.path}")
else:
raise FileNotFoundError(f"No snapshots found in {loader.model_folder}")
except Exception as fallback_error:
raise FileNotFoundError(f"Failed to load any snapshots from {loader.model_folder}. Original error: {e}. Fallback error: {fallback_error}")
Comment on lines +452 to +476
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[nitpick] Consider refactoring the repeated error handling logic for loading snapshots into a helper function to reduce code duplication and improve maintainability.

Suggested change
try:
snapshot = utils.get_model_snapshots(
snapshot_index, loader.model_folder, loader.pose_task
)[0]
except (ValueError, IndexError) as e:
print(f"Error loading snapshot with index {snapshot_index}: {e}")
print("Attempting to find available snapshots...")
# Try to get all available snapshots
try:
all_snapshots = utils.get_model_snapshots("all", loader.model_folder, loader.pose_task)
if all_snapshots:
# Try to find a "best" snapshot first
best_snapshots = [s for s in all_snapshots if s.best]
if best_snapshots:
snapshot = best_snapshots[0]
print(f"Found and using best snapshot: {snapshot.path}")
else:
# Use the last available snapshot
snapshot = all_snapshots[-1]
print(f"No best snapshot found, using last available: {snapshot.path}")
else:
raise FileNotFoundError(f"No snapshots found in {loader.model_folder}")
except Exception as fallback_error:
raise FileNotFoundError(f"Failed to load any snapshots from {loader.model_folder}. Original error: {e}. Fallback error: {fallback_error}")
snapshot = load_snapshot_with_fallback(
snapshot_index, loader.model_folder, loader.pose_task
)

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# Additional validation for best snapshots
if "best" in str(snapshot.path) and not snapshot.path.exists():
print(f"Warning: Best snapshot path {snapshot.path} does not exist. Checking for alternative snapshots...")
# Try to find any available snapshot
try:
all_snapshots = utils.get_model_snapshots("all", loader.model_folder, loader.pose_task)
if all_snapshots:
# Try to find a different best snapshot
best_snapshots = [s for s in all_snapshots if s.best and s.path.exists()]
if best_snapshots:
snapshot = best_snapshots[0]
print(f"Using alternative best snapshot: {snapshot.path}")
else:
# Use the last available snapshot
snapshot = all_snapshots[-1]
print(f"Using alternative snapshot: {snapshot.path}")
else:
raise FileNotFoundError(f"No snapshots found in {loader.model_folder}")
except Exception as e:
raise FileNotFoundError(f"Failed to find alternative snapshots: {e}")

# Verify the snapshot file exists
if not snapshot.path.exists():
raise FileNotFoundError(f"Snapshot file not found: {snapshot.path}")

print(f"Successfully loaded snapshot: {snapshot.path}")

# Load the BU model for the conditions provider
cond_provider = None
Expand Down Expand Up @@ -497,9 +546,56 @@ def analyze_videos(
if detector_batch_size is None:
detector_batch_size = loader.project_cfg.get("detector_batch_size", 1)

detector_snapshot = utils.get_model_snapshots(
detector_snapshot_index, loader.model_folder, Task.DETECT
)[0]
try:
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[nitpick] Refactor the duplicated fallback logic for snapshot retrieval into a shared helper function to reduce code duplication and enhance maintainability.

Copilot uses AI. Check for mistakes.
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[nitpick] The error handling logic for detector snapshot loading is almost identical to that for the model snapshot; consider abstracting this logic into a shared function to reduce duplication.

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detector_snapshot = utils.get_model_snapshots(
detector_snapshot_index, loader.model_folder, Task.DETECT
)[0]
except (ValueError, IndexError) as e:
print(f"Error loading detector snapshot with index {detector_snapshot_index}: {e}")
print("Attempting to find available detector snapshots...")

# Try to get all available detector snapshots
try:
all_detector_snapshots = utils.get_model_snapshots("all", loader.model_folder, Task.DETECT)
if all_detector_snapshots:
# Try to find a "best" detector snapshot first
best_detector_snapshots = [s for s in all_detector_snapshots if s.best]
if best_detector_snapshots:
detector_snapshot = best_detector_snapshots[0]
print(f"Found and using best detector snapshot: {detector_snapshot.path}")
else:
# Use the last available detector snapshot
detector_snapshot = all_detector_snapshots[-1]
print(f"No best detector snapshot found, using last available: {detector_snapshot.path}")
else:
raise FileNotFoundError(f"No detector snapshots found in {loader.model_folder}")
except Exception as fallback_error:
raise FileNotFoundError(f"Failed to load any detector snapshots from {loader.model_folder}. Original error: {e}. Fallback error: {fallback_error}")

# Additional validation for detector snapshots
if "best" in str(detector_snapshot.path) and not detector_snapshot.path.exists():
print(f"Warning: Best detector snapshot path {detector_snapshot.path} does not exist. Checking for alternative detector snapshots...")
try:
all_detector_snapshots = utils.get_model_snapshots("all", loader.model_folder, Task.DETECT)
if all_detector_snapshots:
# Try to find a different best detector snapshot
best_detector_snapshots = [s for s in all_detector_snapshots if s.best and s.path.exists()]
if best_detector_snapshots:
detector_snapshot = best_detector_snapshots[0]
print(f"Using alternative best detector snapshot: {detector_snapshot.path}")
else:
# Use the last available detector snapshot
detector_snapshot = all_detector_snapshots[-1]
print(f"Using alternative detector snapshot: {detector_snapshot.path}")
else:
raise FileNotFoundError(f"No detector snapshots found in {loader.model_folder}")
except Exception as e:
raise FileNotFoundError(f"Failed to find alternative detector snapshots: {e}")

# Verify the detector snapshot file exists
if not detector_snapshot.path.exists():
raise FileNotFoundError(f"Detector snapshot file not found: {detector_snapshot.path}")

print(f" -> Using detector {detector_snapshot.path}")
detector_runner = utils.get_detector_inference_runner(
model_config=loader.model_cfg,
Expand All @@ -509,9 +605,12 @@ def analyze_videos(
)

dlc_scorer = loader.scorer(snapshot, detector_snapshot)
print(f"Using scorer: {dlc_scorer}")

# Reading video and init variables
videos = utils.list_videos_in_folder(videos, videotype, shuffle=in_random_order)
h5_files_created = False # Track if any .h5 files were created

for video in videos:
if destfolder is None:
output_path = video.parent
Expand Down Expand Up @@ -583,6 +682,7 @@ def analyze_videos(
output_prefix=output_prefix,
save_as_csv=save_as_csv,
)
h5_files_created = True # .h5 file was created

if multi_animal:
assemblies_path = output_path / f"{output_prefix}_assemblies.pickle"
Expand Down Expand Up @@ -631,6 +731,7 @@ def analyze_videos(
output_prefix=output_prefix + "_ctd",
save_as_csv=save_as_csv,
)
h5_files_created = True # .h5 file was created for CTD tracking

elif auto_track:
convert_detections2tracklets(
Expand All @@ -654,14 +755,21 @@ def analyze_videos(
destfolder=str(output_path),
save_as_csv=save_as_csv,
)
h5_files_created = True # .h5 file was created by stitch_tracklets

print(
"The videos are analyzed. Now your research can truly start!\n"
"You can create labeled videos with 'create_labeled_video'.\n"
"If the tracking is not satisfactory for some videos, consider expanding the "
"training set. You can use the function 'extract_outlier_frames' to extract a "
"few representative outlier frames.\n"
)
if h5_files_created:
print(
"The videos are analyzed. Now your research can truly start!\n"
"You can create labeled videos with 'create_labeled_video'.\n"
"If the tracking is not satisfactory for some videos, consider expanding the "
"training set. You can use the function 'extract_outlier_frames' to extract a "
"few representative outlier frames.\n"
)
else:
print(
"No .h5 files were created during video analysis. Please check your code and "
"ensure that the video inference and output generation are correct.\n"
)

return dlc_scorer

Expand Down
13 changes: 11 additions & 2 deletions deeplabcut/pose_estimation_pytorch/data/snapshots.py
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Expand Up @@ -29,12 +29,21 @@ class Snapshot:
path: Path

def uid(self) -> str:
return self.path.stem.split("-")[-1]
if self.best:
return f"best-{self.epochs}"
else:
return str(self.epochs)

@staticmethod
def from_path(path: Path) -> "Snapshot":
best = "-best" in path.stem
epochs = int(path.stem.split("-")[-1])
# Use regex to extract epoch number more robustly
match = re.search(r'-(\d+)\.pt$', path.name)
if match:
epochs = int(match.group(1))
else:
# Fallback to original method if regex fails
epochs = int(path.stem.split("-")[-1])
return Snapshot(best=best, epochs=epochs, path=path)


Expand Down
2 changes: 1 addition & 1 deletion deeplabcut/version.py
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Expand Up @@ -9,5 +9,5 @@
# Licensed under GNU Lesser General Public License v3.0
#

__version__ = "3.0.0rc9"
__version__ = "3.0.0rc10"
VERSION = __version__
2 changes: 1 addition & 1 deletion reinstall.sh
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@@ -1,3 +1,3 @@
pip uninstall deeplabcut
python3 setup.py sdist bdist_wheel
pip install dist/deeplabcut-3.0.0rc9-py3-none-any.whl
pip install dist/deeplabcut-3.0.0rc10-py3-none-any.whl
2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -47,7 +47,7 @@ def pytorch_config_paths() -> list[str]:

setuptools.setup(
name="deeplabcut",
version="3.0.0rc9",
version="3.0.0rc10",
author="A. & M.W. Mathis Labs",
author_email="alexander@deeplabcut.org",
description="Markerless pose-estimation of user-defined features with deep learning",
Expand Down
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