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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import random | |
| from typing import Any, Sequence | |
| import torch | |
| from mmengine.dataset import COLLATE_FUNCTIONS | |
| from mmengine.logging import print_log | |
| from mmyolo.datasets.yolov5_coco import BatchShapePolicyDataset | |
| class RobustBatchShapePolicyDataset(BatchShapePolicyDataset): | |
| """Dataset with the batch shape policy that makes paddings with least | |
| pixels during batch inference process, which does not require the image | |
| scales of all batches to be the same throughout validation.""" | |
| def _prepare_data(self, idx: int) -> Any: | |
| if self.test_mode is False: | |
| data_info = self.get_data_info(idx) | |
| data_info['dataset'] = self | |
| return self.pipeline(data_info) | |
| else: | |
| return super().prepare_data(idx) | |
| def prepare_data(self, idx: int, timeout=10) -> Any: | |
| """Pass the dataset to the pipeline during training to support mixed | |
| data augmentation, such as Mosaic and MixUp.""" | |
| try: | |
| return self._prepare_data(idx) | |
| except Exception as e: | |
| if timeout <= 0: | |
| raise e | |
| print_log(f'Failed to prepare data, due to {e}.' | |
| f'Retrying {timeout} attempts.') | |
| if not self.test_mode: | |
| idx = random.randrange(len(self)) | |
| return self.prepare_data(idx, timeout=timeout - 1) | |
| def yolow_collate(data_batch: Sequence, | |
| use_ms_training: bool = False) -> dict: | |
| """Rewrite collate_fn to get faster training speed. | |
| Args: | |
| data_batch (Sequence): Batch of data. | |
| use_ms_training (bool): Whether to use multi-scale training. | |
| """ | |
| batch_imgs = [] | |
| batch_bboxes_labels = [] | |
| batch_masks = [] | |
| for i in range(len(data_batch)): | |
| datasamples = data_batch[i]['data_samples'] | |
| inputs = data_batch[i]['inputs'] | |
| batch_imgs.append(inputs) | |
| gt_bboxes = datasamples.gt_instances.bboxes.tensor | |
| gt_labels = datasamples.gt_instances.labels | |
| if 'masks' in datasamples.gt_instances: | |
| masks = datasamples.gt_instances.masks.to_tensor( | |
| dtype=torch.bool, device=gt_bboxes.device) | |
| batch_masks.append(masks) | |
| batch_idx = gt_labels.new_full((len(gt_labels), 1), i) | |
| bboxes_labels = torch.cat((batch_idx, gt_labels[:, None], gt_bboxes), | |
| dim=1) | |
| batch_bboxes_labels.append(bboxes_labels) | |
| collated_results = { | |
| 'data_samples': { | |
| 'bboxes_labels': torch.cat(batch_bboxes_labels, 0) | |
| } | |
| } | |
| if len(batch_masks) > 0: | |
| collated_results['data_samples']['masks'] = torch.cat(batch_masks, 0) | |
| if use_ms_training: | |
| collated_results['inputs'] = batch_imgs | |
| else: | |
| collated_results['inputs'] = torch.stack(batch_imgs, 0) | |
| if hasattr(data_batch[0]['data_samples'], 'texts'): | |
| batch_texts = [meta['data_samples'].texts for meta in data_batch] | |
| collated_results['data_samples']['texts'] = batch_texts | |
| if hasattr(data_batch[0]['data_samples'], 'is_detection'): | |
| # detection flag | |
| batch_detection = [meta['data_samples'].is_detection | |
| for meta in data_batch] | |
| collated_results['data_samples']['is_detection'] = torch.tensor( | |
| batch_detection) | |
| return collated_results | |