_base_ = [
    '../_base_/models/mask-rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    data_preprocessor=dict(
        # The mean and std are used in PyCls when training RegNets
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False),
    backbone=dict(
        _delete_=True,
        type='RegNet',
        arch='regnetx_3.2gf',
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(
            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
    neck=dict(
        type='FPN',
        in_channels=[96, 192, 432, 1008],
        out_channels=256,
        num_outs=5))

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    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))

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
    clip_grad=dict(max_norm=35, norm_type=2))

# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[28, 34],
        gamma=0.1)
]

_base_ = [
    '../_base_/models/mask-rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    data_preprocessor=dict(
        # The mean and std are used in PyCls when training RegNets
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False),
    backbone=dict(
        _delete_=True,
        type='RegNet',
        arch='regnetx_3.2gf',
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(
            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
    neck=dict(
        type='FPN',
        in_channels=[96, 192, 432, 1008],
        out_channels=256,
        num_outs=5))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    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))

optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
    clip_grad=dict(max_norm=35, norm_type=2))

# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[28, 34],
        gamma=0.1)
]
