Add osx-arm64 env #13
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #13 +/- ##
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- Coverage 75.28% 75.00% -0.29%
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Files 24 24
Lines 1513 1532 +19
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+ Hits 1139 1149 +10
- Misses 374 383 +9 ☔ View full report in Codecov by Sentry. |
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Got it working on mps/cpu, but now device errors on local ubuntu tests with cuda. I'll need to debug before merging. |
Co-authored-by: aaprasad <aaprasad.ucsd.edu> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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| def pose_bbox(points: np.ndarray, bbox_size: Union[tuple[int], int]) -> torch.Tensor: | ||
| """Calculate bbox around instance pose. | ||
|
|
||
| Args: | ||
| instance: a labeled instance in a frame, | ||
| padding: the amount to pad around the pose crop | ||
| im_shape: the size of the original image in (w,h) | ||
| bbox_size: size of bbox either an int indicating square bbox or in (x,y) | ||
|
|
||
| Returns: | ||
| Bounding box in [y1, x1, y2, x2] format. | ||
| """ | ||
| w, h = im_shape | ||
| if isinstance(bbox_size, int): | ||
| bbox_size = (bbox_size, bbox_size) | ||
| # print(points) | ||
| minx = np.nanmin(points[:, 0], axis=-1) | ||
| miny = np.nanmin(points[:, -1], axis=-1) | ||
| minpoints = np.array([minx, miny]).T | ||
|
|
||
| points = torch.Tensor([[p.x, p.y] for p in instance.points]) | ||
| maxx = np.nanmax(points[:, 0], axis=-1) | ||
| maxy = np.nanmax(points[:, -1], axis=-1) | ||
| maxpoints = np.array([maxx, maxy]).T | ||
|
|
||
| min_x = max(torch.nanmin(points[:, 0]) - padding, 0) | ||
| min_y = max(torch.nanmin(points[:, 1]) - padding, 0) | ||
| max_x = min(torch.nanmax(points[:, 0]) + padding, w) | ||
| max_y = min(torch.nanmax(points[:, 1]) + padding, h) | ||
| c = (minpoints + maxpoints) / 2 | ||
|
|
||
| bbox = torch.Tensor([min_y, min_x, max_y, max_x]) | ||
| bbox = torch.Tensor( | ||
| [ | ||
| c[-1] - bbox_size[-1] / 2, | ||
| c[0] - bbox_size[0] / 2, | ||
| c[-1] + bbox_size[-1] / 2, | ||
| c[0] + bbox_size[0] / 2, | ||
| ] | ||
| ) |
There was a problem hiding this comment.
Refactor the pose_bbox function to improve readability and maintainability.
- if isinstance(bbox_size, int):
- bbox_size = (bbox_size, bbox_size)
- minx = np.nanmin(points[:, 0], axis=-1)
- miny = np.nanmin(points[:, -1], axis=-1)
- minpoints = np.array([minx, miny]).T
-
- maxx = np.nanmax(points[:, 0], axis=-1)
- maxy = np.nanmax(points[:, -1], axis=-1)
- maxpoints = np.array([maxx, maxy]).T
-
- c = (minpoints + maxpoints) / 2
-
- bbox = torch.Tensor(
- [
- c[-1] - bbox_size[-1] / 2,
- c[0] - bbox_size[0] / 2,
- c[-1] + bbox_size[-1] / 2,
- c[0] + bbox_size[0] / 2,
- ]
- )
+ half_height, half_width = (bbox_size if isinstance(bbox_size, tuple) else (bbox_size, bbox_size))
+ min_coords = np.nanmin(points, axis=0)
+ max_coords = np.nanmax(points, axis=0)
+ center = (min_coords + max_coords) / 2
+ y1, x1 = center[1] - half_height // 2, center[0] - half_width // 2
+ y2, x2 = center[1] + half_height // 2, center[0] + half_width // 2
+ bbox = torch.tensor([y1, x1, y2, x2])Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def pose_bbox(points: np.ndarray, bbox_size: Union[tuple[int], int]) -> torch.Tensor: | |
| """Calculate bbox around instance pose. | |
| Args: | |
| instance: a labeled instance in a frame, | |
| padding: the amount to pad around the pose crop | |
| im_shape: the size of the original image in (w,h) | |
| bbox_size: size of bbox either an int indicating square bbox or in (x,y) | |
| Returns: | |
| Bounding box in [y1, x1, y2, x2] format. | |
| """ | |
| w, h = im_shape | |
| if isinstance(bbox_size, int): | |
| bbox_size = (bbox_size, bbox_size) | |
| # print(points) | |
| minx = np.nanmin(points[:, 0], axis=-1) | |
| miny = np.nanmin(points[:, -1], axis=-1) | |
| minpoints = np.array([minx, miny]).T | |
| points = torch.Tensor([[p.x, p.y] for p in instance.points]) | |
| maxx = np.nanmax(points[:, 0], axis=-1) | |
| maxy = np.nanmax(points[:, -1], axis=-1) | |
| maxpoints = np.array([maxx, maxy]).T | |
| min_x = max(torch.nanmin(points[:, 0]) - padding, 0) | |
| min_y = max(torch.nanmin(points[:, 1]) - padding, 0) | |
| max_x = min(torch.nanmax(points[:, 0]) + padding, w) | |
| max_y = min(torch.nanmax(points[:, 1]) + padding, h) | |
| c = (minpoints + maxpoints) / 2 | |
| bbox = torch.Tensor([min_y, min_x, max_y, max_x]) | |
| bbox = torch.Tensor( | |
| [ | |
| c[-1] - bbox_size[-1] / 2, | |
| c[0] - bbox_size[0] / 2, | |
| c[-1] + bbox_size[-1] / 2, | |
| c[0] + bbox_size[0] / 2, | |
| ] | |
| ) | |
| def pose_bbox(points: np.ndarray, bbox_size: Union[tuple[int], int]) -> torch.Tensor: | |
| """Calculate bbox around instance pose. | |
| Args: | |
| instance: a labeled instance in a frame, | |
| bbox_size: size of bbox either an int indicating square bbox or in (x,y) | |
| Returns: | |
| Bounding box in [y1, x1, y2, x2] format. | |
| """ | |
| half_height, half_width = (bbox_size if isinstance(bbox_size, tuple) else (bbox_size, bbox_size)) | |
| min_coords = np.nanmin(points, axis=0) | |
| max_coords = np.nanmax(points, axis=0) | |
| center = (min_coords + max_coords) / 2 | |
| y1, x1 = center[1] - half_height // 2, center[0] - half_width // 2 | |
| y2, x2 = center[1] + half_height // 2, center[0] + half_width // 2 | |
| bbox = torch.tensor([y1, x1, y2, x2]) |
| try: | ||
| reid_features = torch.cat( | ||
| [frame.get_features() for frame in frames], dim=0 | ||
| ).unsqueeze(0) | ||
| except Exception as e: | ||
| print([[f.device for f in frame.get_features()] for frame in frames]) | ||
| raise (e) |
There was a problem hiding this comment.
Refactor error handling to provide more informative error messages and avoid generic exception catching.
- except Exception as e:
- print([[f.device for f in frame.get_features()] for frame in frames])
- raise (e)
+ except RuntimeError as e:
+ print("Error during tensor concatenation:", e)
+ raiseCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| try: | |
| reid_features = torch.cat( | |
| [frame.get_features() for frame in frames], dim=0 | |
| ).unsqueeze(0) | |
| except Exception as e: | |
| print([[f.device for f in frame.get_features()] for frame in frames]) | |
| raise (e) | |
| try: | |
| reid_features = torch.cat( | |
| [frame.get_features() for frame in frames], dim=0 | |
| ).unsqueeze(0) | |
| except RuntimeError as e: | |
| print("Error during tensor concatenation:", e) | |
| raise |
|
|
||
| if not self.persistent_tracking: | ||
| if self.verbose: | ||
| warnings.warn(f"Clearing Queue after tracking") |
There was a problem hiding this comment.
Remove unnecessary warning message or add placeholders to the f-string.
- warnings.warn(f"Clearing Queue after tracking")
+ warnings.warn("Clearing Queue after tracking")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| warnings.warn(f"Clearing Queue after tracking") | |
| warnings.warn("Clearing Queue after tracking") |
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