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| _base_ = [ | |
| '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', | |
| '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' | |
| ] | |
| # model settings | |
| input_size = 300 | |
| model = dict( | |
| bbox_head=dict( | |
| type='SSDHead', | |
| anchor_generator=dict( | |
| type='LegacySSDAnchorGenerator', | |
| scale_major=False, | |
| input_size=input_size, | |
| basesize_ratio_range=(0.15, 0.9), | |
| strides=[8, 16, 32, 64, 100, 300], | |
| ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), | |
| bbox_coder=dict( | |
| type='LegacyDeltaXYWHBBoxCoder', | |
| target_means=[.0, .0, .0, .0], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]))) | |
| # dataset settings | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PhotoMetricDistortion', | |
| brightness_delta=32, | |
| contrast_range=(0.5, 1.5), | |
| saturation_range=(0.5, 1.5), | |
| hue_delta=18), | |
| dict( | |
| type='Expand', | |
| mean=img_norm_cfg['mean'], | |
| to_rgb=img_norm_cfg['to_rgb'], | |
| ratio_range=(1, 4)), | |
| dict( | |
| type='MinIoURandomCrop', | |
| min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), | |
| min_crop_size=0.3), | |
| dict(type='Resize', img_scale=(300, 300), keep_ratio=False), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(300, 300), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=False), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']), | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=8, | |
| workers_per_gpu=3, | |
| train=dict( | |
| _delete_=True, | |
| type='RepeatDataset', | |
| times=5, | |
| dataset=dict( | |
| type=dataset_type, | |
| ann_file=data_root + 'annotations/instances_train2017.json', | |
| img_prefix=data_root + 'train2017/', | |
| pipeline=train_pipeline)), | |
| val=dict(pipeline=test_pipeline), | |
| test=dict(pipeline=test_pipeline)) | |
| # optimizer | |
| optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) | |
| optimizer_config = dict(_delete_=True) | |
| dist_params = dict(backend='nccl', port=29555) | |