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Remove __target__ for Detection CollateFN (#1470)
* remove __target__ * undo * fix * add __init__ __all__ * fix __call__ in CrowdDetectionCollateFN + use main import --------- Co-authored-by: Eugene Khvedchenya <ekhvedchenya@gmail.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| from .dataset_exceptions import ( | ||
| EmptyDatasetException, | ||
| DatasetItemsException, | ||
| DatasetValidationException, | ||
| IllegalDatasetParameterException, | ||
| ParameterMismatchException, | ||
| UnsupportedBatchItemsFormat, | ||
| ) | ||
| from .loss_exceptions import RequiredLossComponentReductionException, IllegalRangeForLossAttributeException | ||
| from .factory_exceptions import UnknownTypeException | ||
| from .kd_trainer_exceptions import ( | ||
| KDModelException, | ||
| UnsupportedKDModelArgException, | ||
| UnsupportedKDArchitectureException, | ||
| ArchitectureKwargsException, | ||
| InconsistentParamsException, | ||
| TeacherKnowledgeException, | ||
| UndefinedNumClassesException, | ||
| ) | ||
| from .sg_trainer_exceptions import IllegalDataloaderInitialization, UnsupportedOptimizerFormat, UnsupportedTrainingParameterFormat, GPUModeNotSetupError | ||
|
|
||
| __all__ = [ | ||
| "EmptyDatasetException", | ||
| "DatasetItemsException", | ||
| "DatasetValidationException", | ||
| "IllegalDatasetParameterException", | ||
| "ParameterMismatchException", | ||
| "UnsupportedBatchItemsFormat", | ||
| "RequiredLossComponentReductionException", | ||
| "IllegalRangeForLossAttributeException", | ||
| "UnknownTypeException", | ||
| "KDModelException", | ||
| "UnsupportedKDModelArgException", | ||
| "UnsupportedKDArchitectureException", | ||
| "ArchitectureKwargsException", | ||
| "InconsistentParamsException", | ||
| "TeacherKnowledgeException", | ||
| "UndefinedNumClassesException", | ||
| "IllegalDataloaderInitialization", | ||
| "UnsupportedOptimizerFormat", | ||
| "UnsupportedTrainingParameterFormat", | ||
| "GPUModeNotSetupError", | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,6 @@ | ||
| from .detection_collate_fn import DetectionCollateFN | ||
| from .ppyoloe_collate_fn import PPYoloECollateFN | ||
| from .crowd_detection_collate_fn import CrowdDetectionCollateFN | ||
| from .crowd_detection_ppyoloe_collate_fn import CrowdDetectionPPYoloECollateFN | ||
|
|
||
| __all__ = ["DetectionCollateFN", "PPYoloECollateFN", "CrowdDetectionCollateFN", "CrowdDetectionPPYoloECollateFN"] |
26 changes: 26 additions & 0 deletions
26
src/super_gradients/training/utils/collate_fn/crowd_detection_collate_fn.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,26 @@ | ||
| from typing import Tuple, Dict | ||
|
|
||
| import torch | ||
|
|
||
| from super_gradients.common.registry import register_collate_function | ||
| from super_gradients.common.exceptions.dataset_exceptions import DatasetItemsException | ||
| from super_gradients.training.utils.collate_fn.detection_collate_fn import DetectionCollateFN | ||
|
|
||
|
|
||
| @register_collate_function() | ||
| class CrowdDetectionCollateFN(DetectionCollateFN): | ||
| """ | ||
| Collate function for Yolox training with additional_batch_items that includes crowd targets | ||
| """ | ||
|
|
||
| def __init__(self): | ||
| super().__init__() | ||
| self.expected_item_names = ("image", "targets", "crowd_targets") | ||
|
|
||
| def __call__(self, data) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]: | ||
| try: | ||
| images_batch, labels_batch, crowd_labels_batch = list(zip(*data)) | ||
| except (ValueError, TypeError): | ||
| raise DatasetItemsException(data_sample=data[0], collate_type=type(self), expected_item_names=self.expected_item_names) | ||
|
|
||
| return self._format_images(images_batch), self._format_targets(labels_batch), {"crowd_targets": self._format_targets(crowd_labels_batch)} |
30 changes: 30 additions & 0 deletions
30
src/super_gradients/training/utils/collate_fn/crowd_detection_ppyoloe_collate_fn.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| from typing import Union, List, Tuple, Dict | ||
|
|
||
| import torch | ||
|
|
||
| from super_gradients.common.registry import register_collate_function | ||
| from super_gradients.common.exceptions.dataset_exceptions import DatasetItemsException | ||
| from super_gradients.training.utils.collate_fn.ppyoloe_collate_fn import PPYoloECollateFN | ||
|
|
||
|
|
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| @register_collate_function() | ||
| class CrowdDetectionPPYoloECollateFN(PPYoloECollateFN): | ||
| """ | ||
| Collate function for Yolox training with additional_batch_items that includes crowd targets | ||
| """ | ||
|
|
||
| def __init__(self, random_resize_sizes: Union[List[int], None] = None, random_resize_modes: Union[List[int], None] = None): | ||
| super().__init__(random_resize_sizes, random_resize_modes) | ||
| self.expected_item_names = ("image", "targets", "crowd_targets") | ||
|
|
||
| def __call__(self, data) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]: | ||
|
|
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| if self.random_resize_sizes is not None: | ||
| data = self.random_resize(data) | ||
|
|
||
| try: | ||
| images_batch, labels_batch, crowd_labels_batch = list(zip(*data)) | ||
| except (ValueError, TypeError): | ||
| raise DatasetItemsException(data_sample=data[0], collate_type=type(self), expected_item_names=self.expected_item_names) | ||
|
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| return self._format_images(images_batch), self._format_targets(labels_batch), {"crowd_targets": self._format_targets(crowd_labels_batch)} |
47 changes: 47 additions & 0 deletions
47
src/super_gradients/training/utils/collate_fn/detection_collate_fn.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| from typing import Tuple, List, Union | ||
|
|
||
| import numpy as np | ||
| import torch | ||
|
|
||
| from super_gradients.common.registry import register_collate_function | ||
| from super_gradients.common.exceptions.dataset_exceptions import DatasetItemsException | ||
|
|
||
|
|
||
| @register_collate_function() | ||
| class DetectionCollateFN: | ||
| """ | ||
| Collate function for Yolox training | ||
| """ | ||
|
|
||
| def __init__(self): | ||
| self.expected_item_names = ("image", "targets") | ||
|
|
||
| def __call__(self, data) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| try: | ||
| images_batch, labels_batch = list(zip(*data)) | ||
| except (ValueError, TypeError): | ||
| raise DatasetItemsException(data_sample=data[0], collate_type=type(self), expected_item_names=self.expected_item_names) | ||
|
|
||
| return self._format_images(images_batch), self._format_targets(labels_batch) | ||
|
|
||
| def _format_images(self, images_batch: List[Union[torch.Tensor, np.array]]) -> torch.Tensor: | ||
| images_batch = [torch.tensor(img) for img in images_batch] | ||
| images_batch_stack = torch.stack(images_batch, 0) | ||
| if images_batch_stack.shape[3] == 3: | ||
| images_batch_stack = torch.moveaxis(images_batch_stack, -1, 1).float() | ||
| return images_batch_stack | ||
|
|
||
| def _format_targets(self, labels_batch: List[Union[torch.Tensor, np.array]]) -> torch.Tensor: | ||
| """ | ||
| Stack a batch id column to targets and concatenate | ||
| :param labels_batch: a list of targets per image (each of arbitrary length) | ||
| :return: one tensor of targets of all imahes of shape [N, 6], where N is the total number of targets in a batch | ||
| and the 1st column is batch item index | ||
| """ | ||
| labels_batch = [torch.tensor(labels) for labels in labels_batch] | ||
| labels_batch_indexed = [] | ||
| for i, labels in enumerate(labels_batch): | ||
| batch_column = labels.new_ones((labels.shape[0], 1)) * i | ||
| labels = torch.cat((batch_column, labels), dim=-1) | ||
| labels_batch_indexed.append(labels) | ||
| return torch.cat(labels_batch_indexed, 0) |
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