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| from mmcv import TransformBroadcaster, LoadImageFromFile, RandomResize | |
| from mmdet.datasets.transforms import Resize, RandomFlip, RandomCrop | |
| from mmengine.dataset import DefaultSampler | |
| from seg.datasets.pipeliens.loading import LoadVideoSegAnnotations, ResizeOri | |
| from seg.datasets.pipeliens.formatting import PackVidSegInputs | |
| from seg.datasets.pipeliens.frame_sampling import VideoClipSample | |
| from seg.datasets.samplers.batch_sampler import VideoSegAspectRatioBatchSampler | |
| from seg.datasets.vipseg import VIPSegDataset | |
| from seg.evaluation.metrics.vip_seg_metric import VIPSegMetric | |
| dataset_type = VIPSegDataset | |
| data_root = 'data/VIPSeg' | |
| backend_args = None | |
| image_size = (1280, 736) | |
| # dataset settings | |
| train_pipeline = [ | |
| dict( | |
| type=VideoClipSample, | |
| num_selected=2, | |
| interval=2), | |
| dict( | |
| type=TransformBroadcaster, | |
| share_random_params=True, | |
| transforms=[ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=LoadVideoSegAnnotations, with_bbox=True, with_label=True, with_mask=True, with_seg=True), | |
| dict( | |
| type=RandomResize, | |
| resize_type=Resize, | |
| scale=image_size, | |
| ratio_range=(.8, 2.), | |
| keep_ratio=True, | |
| ), | |
| dict( | |
| type=RandomCrop, | |
| crop_size=image_size, | |
| crop_type='absolute', | |
| recompute_bbox=True, | |
| allow_negative_crop=True), | |
| dict(type=RandomFlip, prob=0.5), | |
| ]), | |
| dict(type=PackVidSegInputs) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type=TransformBroadcaster, | |
| transforms=[ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=LoadVideoSegAnnotations, with_bbox=True, with_label=True, with_mask=True, with_seg=True), | |
| dict(type=Resize, scale=image_size, keep_ratio=True), | |
| dict(type=ResizeOri), | |
| ]), | |
| dict(type=PackVidSegInputs) | |
| ] | |
| # dataloader | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type=DefaultSampler, shuffle=True), | |
| batch_sampler=dict(type=VideoSegAspectRatioBatchSampler), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='train.txt', | |
| data_prefix=dict(img='imgs/', seg='panomasks/'), | |
| # check whether it is necessary. | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=train_pipeline)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type=DefaultSampler, shuffle=False, round_up=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='val.txt', | |
| data_prefix=dict(img='imgs/', seg='panomasks/'), | |
| test_mode=True, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type=VIPSegMetric, | |
| metric=['VPQ@1', 'VPQ@2', 'VPQ@4', 'VPQ@6'], | |
| ) | |
| test_evaluator = val_evaluator | |