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