norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='CascadeEncoderDecoder',
    num_stages=2,
    pretrained='open-mmlab://msra/hrnetv2_w18',
    backbone=dict(
        type='HRNet',
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        extra=dict(
            stage1=dict(
                num_modules=1,
                num_branches=1,
                block='BOTTLENECK',
                num_blocks=(4, ),
                num_channels=(64, )),
            stage2=dict(
                num_modules=1,
                num_branches=2,
                block='BASIC',
                num_blocks=(4, 4),
                num_channels=(18, 36)),
            stage3=dict(
                num_modules=4,
                num_branches=3,
                block='BASIC',
                num_blocks=(4, 4, 4),
                num_channels=(18, 36, 72)),
            stage4=dict(
                num_modules=3,
                num_branches=4,
                block='BASIC',
                num_blocks=(4, 4, 4, 4),
                num_channels=(18, 36, 72, 144)))),
    decode_head=[
        dict(
            type='FCNHead',
            in_channels=[18, 36, 72, 144],
            channels=270,
            in_index=(0, 1, 2, 3),
            input_transform='resize_concat',
            kernel_size=1,
            num_convs=1,
            concat_input=False,
            dropout_ratio=-1,
            num_classes=11,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
        dict(
            type='OCRHead',
            in_channels=[18, 36, 72, 144],
            in_index=(0, 1, 2, 3),
            input_transform='resize_concat',
            channels=512,
            ocr_channels=256,
            dropout_ratio=-1,
            num_classes=11,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
    ],
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
log_config = dict(
    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
dataset_type = 'TrashDataset'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='CustomLoadAnnotations', coco_json_path='../data/train.json'),
    dict(
        type='Resize',
        img_scale=[(256, 256), (512, 512), (1024, 1024)],
        multiscale_mode='value',
        keep_ratio=False),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=[(256, 256), (512, 512), (1024, 1024)],
        flip=[False, False, False],
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type='TrashDataset',
        coco_json_path='../data/train.json',
        is_valid=False,
        img_dir='../data',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='CustomLoadAnnotations',
                coco_json_path='../data/train.json'),
            dict(
                type='Resize',
                img_scale=[(256, 256), (512, 512), (1024, 1024)],
                multiscale_mode='value',
                keep_ratio=False),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
        ]),
    val=dict(
        type='TrashDataset',
        coco_json_path='../data/val.json',
        is_valid=True,
        img_dir='../data',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(256, 256), (512, 512), (1024, 1024)],
                flip=[False, False, False],
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='TrashDataset',
        coco_json_path='../data/test.json',
        is_valid=True,
        img_dir='../data',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=[(256, 256), (512, 512), (1024, 1024)],
                flip=[False, False, False],
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
optimizer = dict(type='SGD', momentum=0.9, lr=0.005, weight_decay=0.0005)
optimizer_config = dict()
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.1,
    min_lr_ratio=1e-06)
runner = dict(type='IterBasedRunner', max_iters=30000)
checkpoint_config = dict(by_epoch=False, interval=3000)
evaluation = dict(interval=3000, metric='mIoU', pre_eval=True)
work_dir = './result/test4'
gpu_ids = [0]
auto_resume = False