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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)
@COLLATE_FUNCTIONS.register_module()
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
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