Randomize instance order and crop #38
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add functionality for randomizing crop anchor
WalkthroughThe updates across various modules in the BioGTR project focus on enhancing functionality related to data structure manipulation, especially concerning centroids and anchors. Key changes include the introduction of new properties and methods for handling centroids and anchors, conditional augmentation based on training mode, and improved bounding box calculations. These enhancements aim to streamline data handling and augment data processing capabilities in training scenarios. Changes
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Actionable comments posted: 5
Out of diff range and nitpick comments (3)
biogtr/datasets/microscopy_dataset.py (1)
Line range hint
86-86: Replace lambda expression with a function definition for better readability and maintainability.- parser = lambda x: data_utils.parse_synthetic(x, source=source) + def parser(x): + return data_utils.parse_synthetic(x, source=source)biogtr/datasets/data_utils.py (1)
Line range hint
334-334: Avoid using bareexceptto handle exceptions explicitly.- except: + except Exception as e: + print(f"Error encountered: {e}")biogtr/data_structures.py (1)
122-122: Validate the representation ofcentroidin the__repr__method.The representation of
centroidin the__repr__method might expose internal structure which could be sensitive. Consider overriding the__str__method in the dictionary to provide a more controlled output.
| @@ -16,6 +16,7 @@ def __init__( | |||
| pred_track_id: int = -1, | |||
| bbox: ArrayLike = torch.empty((0, 4)), | |||
| crop: ArrayLike = torch.tensor([]), | |||
| centroid: dict[str, ArrayLike] = None, | |||
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Ensure the default value for centroid is consistent with its usage.
The default value for centroid is set to None, which might lead to issues if methods assume it always contains a dictionary. Consider initializing it as an empty dictionary {} for safer handling.
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Bad! As is initializing with the torch.empty and torch.tensor above since these are all mutable!
Just change the type to centroid: Optional[dict[str, ArrayLike]] = None or centroid: dict[str, ArrayLike] | None = None (newer python syntax)
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@talmo, you're absolutely right. Using Optional[dict[str, ArrayLike]] = None or centroid: dict[str, ArrayLike] | None = None (for Python 3.10 and newer) in the type hint clearly indicates that the centroid can be None. This approach is safer and more explicit, especially in a dynamic setting where the centroid might not always be provided. This change also aligns with best practices in Python to avoid mutable default arguments which can lead to less predictable behaviors. Thank you for pointing this out!
New Learnings Added
User: talmo
PR: talmolab/biogtr#38
File: biogtr/data_structures.py:19-19
Timestamp: 2024-05-06T19:13:29.738Z
Learning: The `centroid` attribute in the `Instance` class should be initialized with `None` and explicitly allowed to be `None` through type hints using `Optional` or the union operator `|`. This avoids issues associated with mutable default arguments.
Note: Learnings are effective only in the context of specific code segments. To apply general coding guidelines, please configure review instructions.
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I opened an issue to go through and remove all the mutable defaults.
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
| if centroid is not None: | ||
| self._centroid = centroid | ||
| elif self.bbox.shape[0]: | ||
| y1, x1, y2, x2 = self.bbox.squeeze() | ||
| self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])} | ||
| else: | ||
| self._centroid = {} | ||
|
|
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Refactor the centroid calculation to a separate method.
The logic for calculating the centroid based on the bounding box is repeated in the constructor and the pose setter. Consider extracting this into a separate method to adhere to the DRY principle.
+ def calculate_centroid(self):
+ y1, x1, y2, x2 = self.bbox.squeeze()
+ return {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])}
- y1, x1, y2, x2 = self.bbox.squeeze()
- self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])}
+ self._centroid = self.calculate_centroid()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.
| if centroid is not None: | |
| self._centroid = centroid | |
| elif self.bbox.shape[0]: | |
| y1, x1, y2, x2 = self.bbox.squeeze() | |
| self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])} | |
| else: | |
| self._centroid = {} | |
| if centroid is not None: | |
| self._centroid = centroid | |
| elif self.bbox.shape[0]: | |
| self._centroid = self.calculate_centroid() | |
| else: | |
| self._centroid = {} | |
| def calculate_centroid(self): | |
| y1, x1, y2, x2 = self.bbox.squeeze() | |
| return {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])} |
| @@ -69,16 +69,16 @@ def __init__( | |||
| self.clip_length = clip_length | |||
| self.crop_size = crop_size | |||
| self.padding = padding | |||
| self.mode = mode | |||
| self.mode = mode.lower() | |||
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Ensure consistent use of string literals for mode comparison.
- if augmentations and self.mode == "train":
+ if augmentations and self.mode == 'train':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.
| self.mode = mode.lower() | |
| self.mode = mode.lower() |
| @@ -74,16 +74,17 @@ def __init__( | |||
| self.clip_length = clip_length | |||
| self.crop_size = crop_size | |||
| self.padding = padding | |||
| self.mode = mode | |||
| self.mode = mode.lower() | |||
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Ensure consistent use of string literals for mode comparison.
- if augmentations and self.mode == "train":
+ if augmentations and self.mode == 'train':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.
| self.mode = mode.lower() | |
| self.mode = mode.lower() |
| @@ -75,18 +75,18 @@ def __init__( | |||
| self.crop_size = crop_size | |||
| self.chunk = chunk | |||
| self.clip_length = clip_length | |||
| self.mode = mode | |||
| self.mode = mode.lower() | |||
There was a problem hiding this comment.
Ensure consistent use of string literals for mode comparison.
- if augmentations and self.mode == "train":
+ if augmentations and self.mode == 'train':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.
| self.mode = mode.lower() | |
| self.mode = mode.lower() |
As noted in #31, the model may be overfitting to the order in which the instance reid_features are fed into the model. Especially with our dataset logic in microscopy where we just iterate over the track ids this could be especially problematic. Thus in this PR we:
We also add a functionality for random anchor selection when we crop to test if we can generalize the model to missing anchors
Summary by CodeRabbit
New Features
Enhancements
Bug Fixes
modeattribute handling by converting it to lowercase across multiple dataset classes to ensure consistent behavior.