mask_rcnn_r50_fpn_1x.py 5.21 KB
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# model settings
model = dict(
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    type='MaskRCNN',
    pretrained='modelzoo://resnet50',
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    backbone=dict(
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        type='ResNet',
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        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
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        style='pytorch'),
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    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[4, 8, 16, 32, 64],
        target_means=[.0, .0, .0, .0],
        target_stds=[1.0, 1.0, 1.0, 1.0],
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        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
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    bbox_roi_extractor=dict(
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        type='SingleRoIExtractor',
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        roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32]),
    bbox_head=dict(
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        type='SharedFCBBoxHead',
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        num_fcs=2,
        in_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=81,
        target_means=[0., 0., 0., 0.],
        target_stds=[0.1, 0.1, 0.2, 0.2],
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        reg_class_agnostic=False,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
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    mask_roi_extractor=dict(
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        type='SingleRoIExtractor',
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        roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32]),
    mask_head=dict(
        type='FCNMaskHead',
        num_convs=4,
        in_channels=256,
        conv_out_channels=256,
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        num_classes=81,
        loss_mask=dict(
            type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
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# model training and testing settings
train_cfg = dict(
    rpn=dict(
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        assigner=dict(
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            type='MaxIoUAssigner',
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            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            ignore_iof_thr=-1),
        sampler=dict(
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            type='RandomSampler',
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            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
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            add_gt_as_proposals=False),
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        allowed_border=0,
        pos_weight=-1,
        debug=False),
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    rpn_proposal=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=2000,
        max_num=2000,
        nms_thr=0.7,
        min_bbox_size=0),
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    rcnn=dict(
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        assigner=dict(
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            type='MaxIoUAssigner',
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            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.5,
            ignore_iof_thr=-1),
        sampler=dict(
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            type='RandomSampler',
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            num=512,
            pos_fraction=0.25,
            neg_pos_ub=-1,
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            add_gt_as_proposals=True),
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        mask_size=28,
        pos_weight=-1,
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        debug=False))
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
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        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
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        nms_thr=0.7,
        min_bbox_size=0),
    rcnn=dict(
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        score_thr=0.05,
        nms=dict(type='nms', iou_thr=0.5),
        max_per_img=100,
        mask_thr_binary=0.5))
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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=[58.395, 57.12, 57.375], to_rgb=True)
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data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        img_scale=(1333, 800),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
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        flip_ratio=0.5,
        with_mask=True,
        with_crowd=True,
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        with_label=True),
    val=dict(
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        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        img_scale=(1333, 800),
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        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
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        flip_ratio=0,
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        with_mask=True,
        with_crowd=True,
        with_label=True),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        img_scale=(1333, 800),
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        img_norm_cfg=img_norm_cfg,
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        size_divisor=32,
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        flip_ratio=0,
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        with_mask=False,
        with_label=False,
        test_mode=True))
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# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
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optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
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# learning policy
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lr_config = dict(
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    policy='step',
    warmup='linear',
    warmup_iters=500,
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    warmup_ratio=1.0 / 3,
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    step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
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    interval=50,
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    hooks=[
        dict(type='TextLoggerHook'),
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        # dict(type='TensorboardLoggerHook')
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    ])
# yapf:enable
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evaluation = dict(interval=1)
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# runtime settings
total_epochs = 12
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/mask_rcnn_r50_fpn_1x'
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load_from = None
resume_from = None
workflow = [('train', 1)]