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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Sequence | |
| import torch | |
| from mmengine.dataset import COLLATE_FUNCTIONS | |
| 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( | |
| 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 | |