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| # dataset settings | |
| from mmcv import LoadImageFromFile, RandomResize | |
| from mmdet.datasets import AspectRatioBatchSampler | |
| from mmdet.datasets.transforms import LoadAnnotations, RandomFlip, Resize, RandomCrop, PackDetInputs | |
| from mmdet.evaluation import CocoMetric | |
| from mmengine.dataset import DefaultSampler | |
| from seg.datasets.pipeliens.loading import FilterAnnotationsHB | |
| from seg.datasets.v3det import V3DetDataset | |
| dataset_type = V3DetDataset | |
| data_root = 'data/V3Det/' | |
| backend_args = None | |
| image_size = (1024, 1024) | |
| train_pipeline = [ | |
| dict( | |
| type=LoadImageFromFile, | |
| to_float32=True, | |
| backend_args=backend_args), | |
| dict( | |
| type=LoadAnnotations, | |
| with_bbox=True, | |
| with_mask=False, | |
| with_seg=False, | |
| backend_args=backend_args), | |
| dict(type=RandomFlip, prob=0.5), | |
| dict( | |
| type=RandomResize, | |
| resize_type=Resize, | |
| scale=image_size, | |
| ratio_range=(0.1, 2.0), | |
| keep_ratio=True, | |
| ), | |
| dict( | |
| type=RandomCrop, | |
| crop_size=image_size, | |
| crop_type='absolute', | |
| recompute_bbox=True, | |
| allow_negative_crop=True), | |
| dict( | |
| type=FilterAnnotationsHB, | |
| by_box=True, | |
| by_mask=False, | |
| min_gt_bbox_wh=(8, 8) | |
| ), | |
| dict(type=PackDetInputs) | |
| ] | |
| test_pipeline = [ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=Resize, scale=(1333, 800), keep_ratio=True), | |
| # If you don't have a gt annotation, delete the pipeline | |
| dict(type=LoadAnnotations, with_bbox=True), | |
| dict( | |
| type=PackDetInputs, | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type=DefaultSampler, shuffle=True), | |
| batch_sampler=dict(type=AspectRatioBatchSampler), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/v3det_2023_v1_train.json', | |
| data_prefix=dict(img=''), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=4), | |
| pipeline=train_pipeline, | |
| backend_args=backend_args | |
| ) | |
| ) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type=DefaultSampler, shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/v3det_2023_v1_val.json', | |
| data_prefix=dict(img=''), | |
| test_mode=True, | |
| pipeline=test_pipeline, | |
| backend_args=backend_args | |
| ) | |
| ) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type=CocoMetric, | |
| ann_file=data_root + 'annotations/v3det_2023_v1_val.json', | |
| metric='bbox', | |
| format_only=False, | |
| backend_args=backend_args, | |
| use_mp_eval=True, | |
| proposal_nums=[300] | |
| ) | |
| test_evaluator = val_evaluator | |