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- _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
- num_things_classes = 80
- num_stuff_classes = 0
- num_classes = num_things_classes + num_stuff_classes
- image_size = (1024, 1024)
- batch_augments = [
- dict(
- type='BatchFixedSizePad',
- size=image_size,
- img_pad_value=0,
- pad_mask=True,
- mask_pad_value=0,
- pad_seg=False)
- ]
- data_preprocessor = dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=32,
- pad_mask=True,
- mask_pad_value=0,
- pad_seg=False,
- batch_augments=batch_augments)
- model = dict(
- data_preprocessor=data_preprocessor,
- panoptic_head=dict(
- num_things_classes=num_things_classes,
- num_stuff_classes=num_stuff_classes,
- loss_cls=dict(class_weight=[1.0] * num_classes + [0.1])),
- panoptic_fusion_head=dict(
- num_things_classes=num_things_classes,
- num_stuff_classes=num_stuff_classes),
- test_cfg=dict(panoptic_on=False))
- # dataset settings
- train_pipeline = [
- dict(
- type='LoadImageFromFile',
- to_float32=True,
- backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='RandomFlip', prob=0.5),
- # large scale jittering
- dict(
- type='RandomResize',
- scale=image_size,
- ratio_range=(0.1, 2.0),
- resize_type='Resize',
- keep_ratio=True),
- dict(
- type='RandomCrop',
- crop_size=image_size,
- crop_type='absolute',
- recompute_bbox=True,
- allow_negative_crop=True),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-5, 1e-5), by_mask=True),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(
- type='LoadImageFromFile',
- to_float32=True,
- backend_args={{_base_.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, with_mask=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- train_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
- pipeline=train_pipeline))
- val_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- ann_file='annotations/instances_val2017.json',
- data_prefix=dict(img='val2017/'),
- pipeline=test_pipeline))
- test_dataloader = val_dataloader
- val_evaluator = dict(
- _delete_=True,
- type='CocoMetric',
- ann_file=data_root + 'annotations/instances_val2017.json',
- metric=['bbox', 'segm'],
- format_only=False,
- backend_args={{_base_.backend_args}})
- test_evaluator = val_evaluator
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