80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
_base_ = [
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'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
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'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
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]
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# model settings
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input_size = 300
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model = dict(
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bbox_head=dict(
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type='SSDHead',
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anchor_generator=dict(
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type='LegacySSDAnchorGenerator',
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scale_major=False,
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input_size=input_size,
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basesize_ratio_range=(0.15, 0.9),
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strides=[8, 16, 32, 64, 100, 300],
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ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
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bbox_coder=dict(
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type='LegacyDeltaXYWHBBoxCoder',
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target_means=[.0, .0, .0, .0],
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target_stds=[0.1, 0.1, 0.2, 0.2])))
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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', to_float32=True),
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dict(type='LoadAnnotations', with_bbox=True),
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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(
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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=(300, 300), keep_ratio=False),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomFlip', flip_ratio=0.5),
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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=(300, 300),
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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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dist_params = dict(backend='nccl', port=29555)
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