_base_ = [
    '../_base_/models/mask-rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]

model = dict(
    backbone=dict(
        frozen_stages=0,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        init_cfg=dict(
            type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomResize', scale=[(1333, 640), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

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

_base_ = [
    '../_base_/models/mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]

model = dict(
    backbone=dict(
        frozen_stages=0,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        init_cfg=dict(
            type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomResize', scale=[(1333, 640), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

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