Instance/node dropout #44
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WalkthroughThe recent updates enhance the handling of augmentations and missing data in datasets, introduce a new Changes
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Actionable comments posted: 13
Out of diff range and nitpick comments (1)
biogtr/datasets/sleap_dataset.py (1)
Line range hint
30-55: Improve the readability of the docstring for thehandle_missingparameter by formatting it as a list.- handle_missing: how to handle missing single nodes. one of `["drop", "ignore", "centroid"]`. - if "drop" then we dont include instances which are missing the `anchor`. - if "ignore" then we use a mask instead of a crop and nan centroids/bboxes. - if "centroid" then we default to the pose centroid as the node to crop around. + handle_missing: How to handle missing single nodes. One of: + - "drop": Do not include instances which are missing the `anchor`. + - "ignore": Use a mask instead of a crop and NaN centroids/bboxes. + - "centroid": Default to the pose centroid as the node to crop around.
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Files selected for processing (4)
- biogtr/data_structures.py (1 hunks)
- biogtr/datasets/base_dataset.py (1 hunks)
- biogtr/datasets/sleap_dataset.py (10 hunks)
- tests/test_models.py (1 hunks)
Additional Context Used
Ruff (8)
biogtr/datasets/sleap_dataset.py (1)
9-9:
warningsimported but unusedtests/test_models.py (7)
302-302: Local variable
timesis assigned to but never used
335-335: Redefinition of unused
test_multianchor_embeddingfrom line 292
338-338: Local variable
d_modelis assigned to but never used
345-345: Local variable
timesis assigned to but never used
368-368: Redefinition of unused
test_multianchor_embeddingfrom line 335
378-378: Local variable
timesis assigned to but never used
501-501: Local variable
img_shapeis assigned to but never used
Additional comments not posted (1)
biogtr/datasets/sleap_dataset.py (1)
87-87: Initialization of thehandle_missingattribute looks good.
biogtr/data_structures.py
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| if self._bbox.shape[0] and len(self._bbox.shape) == 1: | ||
| self._bbox = self._bbox.unsqueeze(0) # (n_anchors, 4) | ||
|
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Remove redundant code block.
The code block from lines 84 to 86 is redundant as it duplicates the logic already present earlier in the __init__ method. This can be safely removed to avoid unnecessary repetition.
- if self._bbox.shape[0] and len(self._bbox.shape) == 1:
- self._bbox = self._bbox.unsqueeze(0) # (n_anchors, 4)Committable suggestion
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| if self._bbox.shape[0] and len(self._bbox.shape) == 1: | |
| self._bbox = self._bbox.unsqueeze(0) # (n_anchors, 4) |
biogtr/data_structures.py
Outdated
| if self._bbox.shape[1] and len(self._bbox.shape) == 2: | ||
| self._bbox = self._bbox.unsqueeze(0) # (1, n_anchors, 4) | ||
|
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Remove redundant code block.
The code block from lines 87 to 89 is redundant as it duplicates the logic already present earlier in the __init__ method. This can be safely removed to avoid unnecessary repetition.
- if self._bbox.shape[1] and len(self._bbox.shape) == 2:
- self._bbox = self._bbox.unsqueeze(0) # (1, n_anchors, 4)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 self._bbox.shape[1] and len(self._bbox.shape) == 2: | |
| self._bbox = self._bbox.unsqueeze(0) # (1, n_anchors, 4) |
biogtr/data_structures.py
Outdated
| if centroid is not None: | ||
| self._centroid = centroid | ||
|
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Remove redundant code block.
The code block from lines 90 to 92 is redundant as it duplicates the logic already present earlier in the __init__ method. This can be safely removed to avoid unnecessary repetition.
- if centroid is not None:
- self._centroid = centroidCommittable 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 |
biogtr/data_structures.py
Outdated
| elif self.bbox.shape[1]: | ||
| y1, x1, y2, x2 = self.bbox.squeeze(dim=0).nanmean(dim=0) | ||
| self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])} | ||
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Remove redundant code block.
The code block from lines 93 to 96 is redundant as it duplicates the logic already present earlier in the __init__ method. This can be safely removed to avoid unnecessary repetition.
- elif self.bbox.shape[1]:
- y1, x1, y2, x2 = self.bbox.squeeze(dim=0).nanmean(dim=0)
- self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])}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.
| elif self.bbox.shape[1]: | |
| y1, x1, y2, x2 = self.bbox.squeeze(dim=0).nanmean(dim=0) | |
| self._centroid = {"centroid": np.array([(x1 + x2) / 2, (y1 + y2) / 2])} |
biogtr/data_structures.py
Outdated
| else: | ||
| self._centroid = {} | ||
|
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Remove redundant code block.
The code block from lines 97 to 99 is redundant as it duplicates the logic already present earlier in the __init__ method. This can be safely removed to avoid unnecessary repetition.
- else:
- self._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.
| else: | |
| self._centroid = {} |
biogtr/datasets/sleap_dataset.py
Outdated
| if len(img.shape) == 2: | ||
| img = img.expand_dims(0) | ||
| h, w, c = img.shape |
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Use np.expand_dims instead of expand_dims to avoid potential errors.
- img = img.expand_dims(0)
+ img = np.expand_dims(img, 0)Committable suggestion
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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 len(img.shape) == 2: | |
| img = img.expand_dims(0) | |
| h, w, c = img.shape | |
| if len(img.shape) == 2: | |
| img = np.expand_dims(img, 0) | |
| h, w, c = img.shape |
tests/test_models.py
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| def test_multianchor_embedding(): | ||
| frames = 32 | ||
| objects = 10 | ||
| d_model = 256 | ||
| n_anchors = 10 | ||
| features = 256 | ||
|
|
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| N = frames * objects | ||
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| boxes = torch.rand(size=(N, n_anchors, 4)) | ||
| times = torch.rand(size=(N,)) | ||
|
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| fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=3) | ||
| learned_emb = Embedding( | ||
| "pos", "learned", features=features, n_mlp_layers=3, n_points=n_anchors | ||
| ) | ||
| assert not isinstance(fixed_emb.mlp, torch.nn.Identity) | ||
| assert not isinstance(learned_emb.mlp, torch.nn.Identity) | ||
|
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| emb = fixed_emb(boxes) | ||
| assert emb.size() == (N, features) | ||
|
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| emb = learned_emb(boxes) | ||
| assert emb.size() == (N, features) | ||
|
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| fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=0) | ||
| learned_emb = Embedding("pos", "learned", features=features, n_mlp_layers=0) | ||
| with pytest.raises(RuntimeError): | ||
| _ = fixed_emb(boxes) | ||
| with pytest.raises(RuntimeError): | ||
| _ = learned_emb(boxes) | ||
|
|
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Consolidate the redefinitions of the test_multianchor_embedding function to avoid redundancy.
- def test_multianchor_embedding():
- frames = 32
- objects = 10
- d_model = 256
- n_anchors = 10
- features = 256
-
- N = frames * objects
-
- boxes = torch.rand(size=(N, n_anchors, 4))
- times = torch.rand(size=(N,))
-
- fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=3)
- learned_emb = Embedding(
- "pos", "learned", features=features, n_mlp_layers=3, n_points=n_anchors
- )
- assert not isinstance(fixed_emb.mlp, torch.nn.Identity)
- assert not isinstance(learned_emb.mlp, torch.nn.Identity)
-
- emb = fixed_emb(boxes)
- assert emb.size() == (N, features)
-
- emb = learned_emb(boxes)
- assert emb.size() == (N, features)
-
- fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=0)
- learned_emb = Embedding("pos", "learned", features=features, n_mlp_layers=0)
- with pytest.raises(RuntimeError):
- _ = fixed_emb(boxes)
- with pytest.raises(RuntimeError):
- _ = learned_emb(boxes)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 test_multianchor_embedding(): | |
| frames = 32 | |
| objects = 10 | |
| d_model = 256 | |
| n_anchors = 10 | |
| features = 256 | |
| N = frames * objects | |
| boxes = torch.rand(size=(N, n_anchors, 4)) | |
| times = torch.rand(size=(N,)) | |
| fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=3) | |
| learned_emb = Embedding( | |
| "pos", "learned", features=features, n_mlp_layers=3, n_points=n_anchors | |
| ) | |
| assert not isinstance(fixed_emb.mlp, torch.nn.Identity) | |
| assert not isinstance(learned_emb.mlp, torch.nn.Identity) | |
| emb = fixed_emb(boxes) | |
| assert emb.size() == (N, features) | |
| emb = learned_emb(boxes) | |
| assert emb.size() == (N, features) | |
| fixed_emb = Embedding("pos", "fixed", features=features, n_mlp_layers=0) | |
| learned_emb = Embedding("pos", "learned", features=features, n_mlp_layers=0) | |
| with pytest.raises(RuntimeError): | |
| _ = fixed_emb(boxes) | |
| with pytest.raises(RuntimeError): | |
| _ = learned_emb(boxes) |
tests/test_models.py
Outdated
| def test_multianchor_embedding(): | ||
| frames = 32 | ||
| objects = 10 | ||
| d_model = 256 | ||
| n_anchors = 10 | ||
| features = 256 | ||
|
|
||
| N = frames * objects | ||
|
|
||
| boxes = torch.rand(size=(N, n_anchors, 4)) | ||
| times = torch.rand(size=(N,)) | ||
|
|
||
| fixed_emb = Embedding( | ||
| "pos", | ||
| "fixed", | ||
| features=features, | ||
| n_points=n_anchors, | ||
| mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model}, | ||
| ) | ||
| learned_emb = Embedding( | ||
| "pos", | ||
| "learned", | ||
| features=features, | ||
| n_points=n_anchors, | ||
| mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model}, | ||
| ) | ||
| assert not isinstance(fixed_emb.mlp, torch.nn.Identity) | ||
| assert not isinstance(learned_emb.mlp, torch.nn.Identity) | ||
|
|
||
| emb = fixed_emb(boxes) | ||
| assert emb.size() == (N, features) | ||
|
|
||
| emb = learned_emb(boxes) | ||
| assert emb.size() == (N, features) | ||
|
|
||
| fixed_emb = Embedding("pos", "fixed", features=features) | ||
| learned_emb = Embedding("pos", "learned", features=features) | ||
| with pytest.raises(RuntimeError): | ||
| _ = fixed_emb(boxes) | ||
| with pytest.raises(RuntimeError): | ||
| _ = learned_emb(boxes) | ||
|
|
There was a problem hiding this comment.
Remove the redundant redefinition of the test_multianchor_embedding function.
- def test_multianchor_embedding():
- frames = 32
- objects = 10
- d_model = 256
- n_anchors = 10
- features = 256
-
- N = frames * objects
-
- boxes = torch.rand(size=(N, n_anchors, 4))
- times = torch.rand(size=(N,))
-
- fixed_emb = Embedding(
- "pos",
- "fixed",
- features=features,
- n_points=n_anchors,
- mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model},
- )
- learned_emb = Embedding(
- "pos",
- "learned",
- features=features,
- n_points=n_anchors,
- mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model},
- )
- assert not isinstance(fixed_emb.mlp, torch.nn.Identity)
- assert not isinstance(learned_emb.mlp, torch.nn.Identity)
-
- emb = fixed_emb(boxes)
- assert emb.size() == (N, features)
-
- emb = learned_emb(boxes)
- assert emb.size() == (N, features)
-
- fixed_emb = Embedding("pos", "fixed", features=features)
- learned_emb = Embedding("pos", "learned", features=features)
- with pytest.raises(RuntimeError):
- _ = fixed_emb(boxes)
- with pytest.raises(RuntimeError):
- _ = learned_emb(boxes)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 test_multianchor_embedding(): | |
| frames = 32 | |
| objects = 10 | |
| d_model = 256 | |
| n_anchors = 10 | |
| features = 256 | |
| N = frames * objects | |
| boxes = torch.rand(size=(N, n_anchors, 4)) | |
| times = torch.rand(size=(N,)) | |
| fixed_emb = Embedding( | |
| "pos", | |
| "fixed", | |
| features=features, | |
| n_points=n_anchors, | |
| mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model}, | |
| ) | |
| learned_emb = Embedding( | |
| "pos", | |
| "learned", | |
| features=features, | |
| n_points=n_anchors, | |
| mlp_cfg={"num_layers": 3, "hidden_dim": 2 * d_model}, | |
| ) | |
| assert not isinstance(fixed_emb.mlp, torch.nn.Identity) | |
| assert not isinstance(learned_emb.mlp, torch.nn.Identity) | |
| emb = fixed_emb(boxes) | |
| assert emb.size() == (N, features) | |
| emb = learned_emb(boxes) | |
| assert emb.size() == (N, features) | |
| fixed_emb = Embedding("pos", "fixed", features=features) | |
| learned_emb = Embedding("pos", "learned", features=features) | |
| with pytest.raises(RuntimeError): | |
| _ = fixed_emb(boxes) | |
| with pytest.raises(RuntimeError): | |
| _ = learned_emb(boxes) |
tests/test_models.py
Outdated
| N = frames * objects | ||
|
|
||
| boxes = torch.rand(size=(N, n_anchors, 4)) | ||
| times = torch.rand(size=(N,)) |
There was a problem hiding this comment.
Remove the unused times variable to clean up the code.
- times = torch.rand(size=(N,))Also applies to: 378-378
Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| times = torch.rand(size=(N,)) |
biogtr/datasets/sleap_dataset.py
Outdated
| @@ -202,7 +219,7 @@ def get_instances(self, label_idx: List[int], frame_idx: List[int]) -> list[dict | |||
| poses.append( | |||
| dict( | |||
| zip( | |||
| [n.name for n in instance.skeleton.nodes], | |||
| [n.name.lower() for n in instance.skeleton.nodes], | |||
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Why .lower() here? I think this one should probably be case sensitive TBH since it's a config-specifiable thing and we want to be precise.
biogtr/datasets/sleap_dataset.py
Outdated
| anchors_to_drop = np.random.permutation(anchors) | ||
| anchor_dropout_p = np.random.uniform(size=len(anchors_to_drop)) | ||
| dropped_anchor_inds = np.where( | ||
| anchor_dropout_p < self.node_dropout["p"] | ||
| ) | ||
| anchor_dropout_p = anchor_dropout_p[dropped_anchor_inds] | ||
| n_anchors_to_drop = min(self.node_dropout["n"], len(anchor_dropout_p)) | ||
| dropped_anchor_inds = np.argpartition( | ||
| anchor_dropout_p, -n_anchors_to_drop | ||
| )[-n_anchors_to_drop:] | ||
| dropped_anchors = anchors_to_drop[dropped_anchor_inds] |
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This is all a bit wild and should be in its own, very heavily tested function.
There are a lot of vectorized conditionals going on and I'm not super clear on the edge case behaviors.
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Actionable comments posted: 3
Out of diff range and nitpick comments (3)
tests/test_models.py (2)
Line range hint
378-378: Remove the unusedtimesvariable to clean up the code.- times = torch.rand(size=(N,))
Line range hint
424-424: The local variableimg_shapeis assigned but never used. Consider removing it to clean up the code.- img_shape = (1, 50, 50)biogtr/datasets/data_utils.py (1)
Line range hint
383-383: Avoid using bareexceptclauses. Replace it with a more specific exception to make debugging easier.- except Exception as e: + except (ValueError, TypeError) as e:
Review Details
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Review profile: CHILL
Files selected for processing (5)
- biogtr/datasets/base_dataset.py (1 hunks)
- biogtr/datasets/data_utils.py (1 hunks)
- biogtr/datasets/sleap_dataset.py (10 hunks)
- tests/test_datasets.py (2 hunks)
- tests/test_models.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- biogtr/datasets/base_dataset.py
Additional Context Used
Ruff (4)
biogtr/datasets/data_utils.py (1)
383-383: Do not use bare
exceptbiogtr/datasets/sleap_dataset.py (1)
9-9:
warningsimported but unusedtests/test_datasets.py (1)
448-449: SyntaxError: Expected ':', found newline
tests/test_models.py (1)
424-424: Local variable
img_shapeis assigned to but never used
Additional comments not posted (8)
tests/test_datasets.py (2)
4-4: The import statement forNodeDropoutlooks good.
445-446: The list of nodes is correctly defined and looks good.biogtr/datasets/sleap_dataset.py (4)
30-30: Review the necessity of using.lower()for thehandle_missingparameter. It might be better to keep it case-sensitive for precision.
52-55: The docstring update to include thehandle_missingparameter looks good.
87-87: The addition of thehandle_missingattribute in the__init__method looks good.
304-308: The node dropout logic is correctly implemented and looks good.biogtr/datasets/data_utils.py (2)
183-191: The docstring for the__call__method is clear and well-documented. Ensure that theforwardmethod is consistent with this documentation.
195-203: The docstring for theforwardmethod is clear and well-documented. Ensure that the logic for dropping nodes is correctly implemented.
biogtr/datasets/data_utils.py
Outdated
| class NodeDropout: | ||
| def __init__(self, p: float, n: int) -> None: | ||
| """Initialize Node Dropout Augmentation | ||
|
|
||
| Args | ||
| p: the probability with which to drop the nodes | ||
| n: the maximum number of nodes to drop | ||
| """ |
There was a problem hiding this comment.
The docstring for the __init__ method is missing a colon after "Args". Add the colon for consistency and clarity.
- Args
+ Args: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.
| class NodeDropout: | |
| def __init__(self, p: float, n: int) -> None: | |
| """Initialize Node Dropout Augmentation | |
| Args | |
| p: the probability with which to drop the nodes | |
| n: the maximum number of nodes to drop | |
| """ | |
| class NodeDropout: | |
| def __init__(self, p: float, n: int) -> None: | |
| """Initialize Node Dropout Augmentation | |
| Args: | |
| p: the probability with which to drop the nodes | |
| n: the maximum number of nodes to drop | |
| """ |
tests/test_datasets.py
Outdated
| for p in [0, 1]: | ||
| for n in [0, 1, len(nodes)] | ||
|
|
There was a problem hiding this comment.
Fix the syntax error by adding a colon after the for statement.
- for n in [0, 1, len(nodes)]
+ for n in [0, 1, len(nodes)]: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.
| for p in [0, 1]: | |
| for n in [0, 1, len(nodes)] | |
| for p in [0, 1]: | |
| for n in [0, 1, len(nodes)]: | |
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Actionable comments posted: 1
Out of diff range and nitpick comments (1)
tests/test_datasets.py (1)
Line range hint
8-8: Remove unused importget_device.- from biogtr.models.model_utils import get_device
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Files selected for processing (1)
- tests/test_datasets.py (2 hunks)
Additional Context Used
Ruff (1)
tests/test_datasets.py (1)
8-8:
biogtr.models.model_utils.get_deviceimported but unused
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Actionable comments posted: 8
Out of diff range and nitpick comments (3)
biogtr/datasets/sleap_dataset.py (1)
Line range hint
9-9: Remove unused import.The
warningsmodule is imported but not used in this file. Removing it will clean up the code.- import warningsbiogtr/datasets/data_utils.py (2)
172-176: Add a more detailed class-level docstring.Consider adding a more detailed class-level docstring to explain the purpose and usage of the
NodeDropoutclass. This will help future developers understand the context and functionality of the class.
Line range hint
390-390: Avoid using bareexcept.Using a bare
exceptcan catch unexpected exceptions and make debugging difficult. Specify the exception type to catch.- except Exception as e: + except (SpecificExceptionType) as e:Replace
SpecificExceptionTypewith the appropriate exception type you expect.
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Files selected for processing (3)
- biogtr/datasets/data_utils.py (1 hunks)
- biogtr/datasets/sleap_dataset.py (10 hunks)
- tests/test_datasets.py (2 hunks)
Additional Context Used
Ruff (3)
biogtr/datasets/data_utils.py (1)
390-390: Do not use bare
exceptbiogtr/datasets/sleap_dataset.py (1)
9-9:
warningsimported but unusedtests/test_datasets.py (1)
8-8:
biogtr.models.model_utils.get_deviceimported but unused
| def __init__(self, p: float, n: int) -> None: | ||
| """Initialize Node Dropout Augmentation. | ||
|
|
||
| Args: | ||
| p: the probability with which to drop the nodes | ||
| n: the maximum number of nodes to drop | ||
| """ | ||
| self.n = n | ||
| self.p = p |
There was a problem hiding this comment.
Add type hints for p and n parameters.
The __init__ method is missing type hints for the p and n parameters. Adding type hints improves code readability and helps with static analysis.
- def __init__(self, p: float, n: int) -> None:
+ def __init__(self, p: float, n: int) -> None: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 __init__(self, p: float, n: int) -> None: | |
| """Initialize Node Dropout Augmentation. | |
| Args: | |
| p: the probability with which to drop the nodes | |
| n: the maximum number of nodes to drop | |
| """ | |
| self.n = n | |
| self.p = p | |
| def __init__(self, p: float, n: int) -> None: | |
| """Initialize Node Dropout Augmentation. | |
| Args: | |
| p: the probability with which to drop the nodes | |
| n: the maximum number of nodes to drop | |
| """ | |
| self.n = n | |
| self.p = p |
| def __call__(self, nodes: list[str]) -> list[str]: | ||
| """Wrap `drop_nodes` to enable class call. | ||
|
|
||
| Args: | ||
| nodes: A list of available node names to drop. | ||
|
|
||
| Returns: | ||
| dropped_nodes: A list of up to `self.n` nodes to drop. | ||
| """ | ||
| return self.forward(nodes) |
There was a problem hiding this comment.
Consider renaming forward method to drop_nodes.
The forward method name is not very descriptive. Consider renaming it to drop_nodes to better reflect its functionality.
- def forward(self, nodes: list[str]) -> list[str]:
+ def drop_nodes(self, nodes: list[str]) -> list[str]: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 __call__(self, nodes: list[str]) -> list[str]: | |
| """Wrap `drop_nodes` to enable class call. | |
| Args: | |
| nodes: A list of available node names to drop. | |
| Returns: | |
| dropped_nodes: A list of up to `self.n` nodes to drop. | |
| """ | |
| return self.forward(nodes) | |
| def __call__(self, nodes: list[str]) -> list[str]: | |
| """Wrap `drop_nodes` to enable class call. | |
| Args: | |
| nodes: A list of available node names to drop. | |
| Returns: | |
| dropped_nodes: A list of up to `self.n` nodes to drop. | |
| """ | |
| return self.drop_nodes(nodes) |
| def forward(self, nodes: list[str]) -> list[str]: | ||
| """Drop up to `n` random nodes with probability p. | ||
|
|
||
| Args: | ||
| nodes: A list of available node names to drop. | ||
|
|
||
| Returns: | ||
| dropped_nodes: A list of up to `self.n` nodes to drop. | ||
| """ | ||
| if self.n == 0 or self.p == 0: | ||
| return [] | ||
|
|
||
| nodes_to_drop = np.random.permutation(nodes) | ||
| node_dropout_p = np.random.uniform(size=len(nodes_to_drop)) | ||
|
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| dropped_node_inds = np.where(node_dropout_p < self.p) | ||
| node_dropout_p = node_dropout_p[dropped_node_inds] | ||
|
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| n_nodes_to_drop = min(self.n, len(node_dropout_p)) | ||
|
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| dropped_node_inds = np.argpartition(node_dropout_p, -n_nodes_to_drop)[ | ||
| -n_nodes_to_drop: | ||
| ] | ||
|
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| dropped_nodes = nodes_to_drop[dropped_node_inds] | ||
|
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| return dropped_nodes |
There was a problem hiding this comment.
Optimize the node dropout logic.
The node dropout logic can be simplified and optimized. Here is a suggested refactor:
- def forward(self, nodes: list[str]) -> list[str]:
- if self.n == 0 or self.p == 0:
- return []
-
- nodes_to_drop = np.random.permutation(nodes)
- node_dropout_p = np.random.uniform(size=len(nodes_to_drop))
-
- dropped_node_inds = np.where(node_dropout_p < self.p)
- node_dropout_p = node_dropout_p[dropped_node_inds]
-
- n_nodes_to_drop = min(self.n, len(node_dropout_p))
-
- dropped_node_inds = np.argpartition(node_dropout_p, -n_nodes_to_drop)[
- -n_nodes_to_drop:
- ]
-
- dropped_nodes = nodes_to_drop[dropped_node_inds]
-
- return dropped_nodes
+ def drop_nodes(self, nodes: list[str]) -> list[str]:
+ if self.n == 0 or self.p == 0:
+ return []
+
+ nodes_to_drop = np.random.choice(nodes, size=self.n, replace=False, p=[self.p]*len(nodes))
+
+ return nodes_to_dropThis refactor simplifies the logic by using np.random.choice to directly select the nodes to drop.
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 forward(self, nodes: list[str]) -> list[str]: | |
| """Drop up to `n` random nodes with probability p. | |
| Args: | |
| nodes: A list of available node names to drop. | |
| Returns: | |
| dropped_nodes: A list of up to `self.n` nodes to drop. | |
| """ | |
| if self.n == 0 or self.p == 0: | |
| return [] | |
| nodes_to_drop = np.random.permutation(nodes) | |
| node_dropout_p = np.random.uniform(size=len(nodes_to_drop)) | |
| dropped_node_inds = np.where(node_dropout_p < self.p) | |
| node_dropout_p = node_dropout_p[dropped_node_inds] | |
| n_nodes_to_drop = min(self.n, len(node_dropout_p)) | |
| dropped_node_inds = np.argpartition(node_dropout_p, -n_nodes_to_drop)[ | |
| -n_nodes_to_drop: | |
| ] | |
| dropped_nodes = nodes_to_drop[dropped_node_inds] | |
| return dropped_nodes | |
| def drop_nodes(self, nodes: list[str]) -> list[str]: | |
| if self.n == 0 or self.p == 0: | |
| return [] | |
| nodes_to_drop = np.random.choice(nodes, size=self.n, replace=False, p=[self.p]*len(nodes)) | |
| return nodes_to_drop |
| @@ -1,7 +1,7 @@ | |||
| """Test dataset logic.""" | |||
|
|
|||
| from biogtr.datasets.base_dataset import BaseDataset | |||
| from biogtr.datasets.data_utils import get_max_padding | |||
| from biogtr.datasets.data_utils import get_max_padding, NodeDropout | |||
There was a problem hiding this comment.
Remove unused import.
The get_device function from biogtr.models.model_utils is imported but not used in this file. Removing it will clean up the code.
- from biogtr.models.model_utils import get_deviceCommittable 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.
| from biogtr.datasets.data_utils import get_max_padding, NodeDropout | |
| from biogtr.datasets.data_utils import get_max_padding, NodeDropout |
| nodes = ["head", "nose", "tti", "tail"] | ||
|
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||
| for p in [0, 1]: | ||
| for n in [0, 1, len(nodes)]: | ||
|
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| node_dropout = NodeDropout(p=p, n=n) | ||
| dropped_nodes = node_dropout(nodes) | ||
|
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||
| if p == 0: | ||
| assert len(dropped_nodes) == 0 | ||
| else: | ||
| assert ( | ||
| len(dropped_nodes) == n | ||
| ), f"p={node_dropout.p}, n={node_dropout.n},n_dropped={len(dropped_nodes)}" | ||
|
|
There was a problem hiding this comment.
Fix the syntax error by adding a colon after the for statement.
There is a missing colon after the for statement on line 448, which will cause a syntax error.
- for n in [0, 1, len(nodes)]
+ for n in [0, 1, len(nodes)]: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.
| nodes = ["head", "nose", "tti", "tail"] | |
| for p in [0, 1]: | |
| for n in [0, 1, len(nodes)]: | |
| node_dropout = NodeDropout(p=p, n=n) | |
| dropped_nodes = node_dropout(nodes) | |
| if p == 0: | |
| assert len(dropped_nodes) == 0 | |
| else: | |
| assert ( | |
| len(dropped_nodes) == n | |
| ), f"p={node_dropout.p}, n={node_dropout.n},n_dropped={len(dropped_nodes)}" | |
| nodes = ["head", "nose", "tti", "tail"] | |
| for p in [0, 1]: | |
| for n in [0, 1, len(nodes)]: | |
| node_dropout = NodeDropout(p=p, n=n) | |
| dropped_nodes = node_dropout(nodes) | |
| if p == 0: | |
| assert len(dropped_nodes) == 0 | |
| else: | |
| assert ( | |
| len(dropped_nodes) == n | |
| ), f"p={node_dropout.p}, n={node_dropout.n},n_dropped={len(dropped_nodes)}" | |
| @@ -27,6 +27,7 @@ def __init__( | |||
| chunk: bool = True, | |||
| clip_length: int = 500, | |||
| mode: str = "train", | |||
| handle_missing: str = "centroid", | |||
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Ensure case sensitivity for handle_missing.
The handle_missing parameter is converted to lowercase, which might not be necessary and could lead to unexpected behavior if case sensitivity is required.
- self.handle_missing = handle_missing.lower()
+ self.handle_missing = handle_missingCommittable 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.
| handle_missing: str = "centroid", | |
| handle_missing: str = "centroid", | |
| self.handle_missing = handle_missing |
| if self.handle_missing == "drop" and boxes.isnan().any(): | ||
| continue |
There was a problem hiding this comment.
Add a log message when instances are dropped due to missing anchors.
Adding a log message when instances are dropped due to missing anchors can help with debugging and understanding the dataset processing.
- if self.handle_missing == "drop" and boxes.isnan().any():
+ if self.handle_missing == "drop" and boxes.isnan().any():
+ if self.verbose:
+ print(f"Dropping instance {j} due to missing anchors.")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 self.handle_missing == "drop" and boxes.isnan().any(): | |
| continue | |
| if self.handle_missing == "drop" and boxes.isnan().any(): | |
| if self.verbose: | |
| print(f"Dropping instance {j} due to missing anchors.") | |
| continue |
| n_instances_dropped = 0 | ||
|
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| gt_instances = lf.instances | ||
| if self.mode == "train": | ||
| np.random.shuffle(gt_instances) | ||
|
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||
| for instance in gt_instances: | ||
| if ( | ||
| np.random.uniform() < self.instance_dropout["p"] | ||
| and n_instances_dropped < self.instance_dropout["n"] | ||
| ): | ||
| n_instances_dropped += 1 | ||
| continue |
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Combine conditions for instance dropout.
Combining the conditions for instance dropout can improve readability and maintainability.
if np.random.uniform() < self.instance_dropout["p"] and n_instances_dropped < self.instance_dropout["n"]:
n_instances_dropped += 1
continue
As per #42, there is some correlation between the instance and nodes disappearing and model failures. Here we add a Node/Instance dropout augmentation as well as the ability to handle missing nodes when only using single node crops in different ways such as using a zeros mask, dropping the instance, or defaulting to the centroid
Summary by CodeRabbit
New Features
Enhancements
Bug Fixes
Tests