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| _base_ = [ | |
| '../_base_/models/mask_rcnn_r50_fpn.py', | |
| '../_base_/datasets/coco_instance.py', | |
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | |
| ] | |
| norm_cfg = dict(type='BN', requires_grad=True) | |
| model = dict( | |
| backbone=dict(norm_cfg=norm_cfg, norm_eval=False), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| norm_cfg=norm_cfg, | |
| num_outs=5), | |
| roi_head=dict( | |
| bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | |
| dict( | |
| type='Resize', | |
| img_scale=(640, 640), | |
| ratio_range=(0.8, 1.2), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=(640, 640)), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size=(640, 640)), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(640, 640), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=64), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']), | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=8, | |
| workers_per_gpu=4, | |
| train=dict(pipeline=train_pipeline), | |
| val=dict(pipeline=test_pipeline), | |
| test=dict(pipeline=test_pipeline)) | |
| # learning policy | |
| optimizer = dict( | |
| type='SGD', | |
| lr=0.08, | |
| momentum=0.9, | |
| weight_decay=0.0001, | |
| paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) | |
| optimizer_config = dict(grad_clip=None) | |
| # learning policy | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=1000, | |
| warmup_ratio=0.1, | |
| step=[30, 40]) | |
| # runtime settings | |
| runner = dict(max_epochs=50) | |
| evaluation = dict(interval=2) | |