mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py

_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    # use ResNeSt img_norm
    data_preprocessor=dict(
        mean=[123.68, 116.779, 103.939],
        std=[58.393, 57.12, 57.375],
        bgr_to_rgb=True),
    backbone=dict(
        type='ResNeSt',
        stem_channels=64,
        depth=50,
        radix=2,
        reduction_factor=4,
        avg_down_stride=True,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=norm_cfg,
        norm_eval=False,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
    roi_head=dict(
        bbox_head=dict(
            type='Shared4Conv1FCBBoxHead',
            conv_out_channels=256,
            norm_cfg=norm_cfg),
        mask_head=dict(norm_cfg=norm_cfg)))

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                (1333, 768), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))