80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
_base_ = 'ssd300_coco.py'
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input_size = 512
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model = dict(
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neck=dict(
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out_channels=(512, 1024, 512, 256, 256, 256, 256),
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level_strides=(2, 2, 2, 2, 1),
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level_paddings=(1, 1, 1, 1, 1),
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last_kernel_size=4),
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bbox_head=dict(
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in_channels=(512, 1024, 512, 256, 256, 256, 256),
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anchor_generator=dict(
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type='SSDAnchorGenerator',
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scale_major=False,
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input_size=input_size,
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basesize_ratio_range=(0.1, 0.9),
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strides=[8, 16, 32, 64, 128, 256, 512],
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ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]])))
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# dataset settings
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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=[1, 1, 1], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='Expand',
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mean=img_norm_cfg['mean'],
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to_rgb=img_norm_cfg['to_rgb'],
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ratio_range=(1, 4)),
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dict(
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type='MinIoURandomCrop',
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min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
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min_crop_size=0.3),
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dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(
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type='PhotoMetricDistortion',
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brightness_delta=32,
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contrast_range=(0.5, 1.5),
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saturation_range=(0.5, 1.5),
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hue_delta=18),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
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]
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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=(512, 512),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=False),
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dict(type='Normalize', **img_norm_cfg),
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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=8,
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workers_per_gpu=3,
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train=dict(
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_delete_=True,
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type='RepeatDataset',
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times=5,
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dataset=dict(
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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)),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
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optimizer_config = dict(_delete_=True)
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custom_hooks = [
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dict(type='NumClassCheckHook'),
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dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
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]
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