|
|
|
dataset_type = 'CocoDataset' |
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data_root = 'data/coco/' |
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img_norm_cfg = dict( |
|
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
|
train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', with_bbox=True), |
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), |
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] |
|
test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(1333, 800), |
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flip=False, |
|
transforms=[ |
|
dict(type='Resize', keep_ratio=True), |
|
dict(type='RandomFlip'), |
|
dict(type='Normalize', **img_norm_cfg), |
|
dict(type='Pad', size_divisor=32), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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data = dict( |
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samples_per_gpu=2, |
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workers_per_gpu=2, |
|
train=dict( |
|
type=dataset_type, |
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ann_file=data_root + 'annotations/instances_train2017.json', |
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img_prefix=data_root + 'train2017/', |
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pipeline=train_pipeline), |
|
val=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline)) |
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evaluation = dict(interval=1, metric='bbox') |
|
|
