norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnet101_v1c',
    backbone=dict(
        type='ResNetV1c',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 2, 4),
        strides=(1, 2, 1, 1),
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    decode_head=dict(
        type='DAHead',
        in_channels=2048,
        in_index=3,
        channels=512,
        pam_channels=64,
        dropout_ratio=0.1,
        num_classes=5,# Need to change
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=5, # Need to change
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
dataset_type = 'CustomDataset'
data_root = 'data/my_dataset_v10'
img_norm_cfg = dict(
    mean=[129.24574653, 114.02886291, 100.29403737],
    std=[45.12454885, 45.51509298, 47.70796596],
    to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=(512, 512)),
    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[129.24574653, 114.02886291, 100.29403737],
        std=[45.12454885, 45.51509298, 47.70796596],
        to_rgb=True),
    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(512, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[129.24574653, 114.02886291, 100.29403737],
                std=[45.12454885, 45.51509298, 47.70796596],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='CustomDataset',
        data_root='data/my_dataset_v10',
        img_dir='img_dir/train',
        ann_dir='ann_dir/train',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
            dict(type='Resize', img_scale=(512, 512)),
            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(
                type='Normalize',
                mean=[129.24574653, 114.02886291, 100.29403737],
                std=[45.12454885, 45.51509298, 47.70796596],
                to_rgb=True),
            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
        ]),
    val=dict(
        type='CustomDataset',
        data_root='data/my_dataset_v10',
        img_dir='img_dir/val',
        ann_dir='ann_dir/val',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(512, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[129.24574653, 114.02886291, 100.29403737],
                        std=[45.12454885, 45.51509298, 47.70796596],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='CustomDataset',
        data_root='data/my_dataset_v10',
        img_dir='img_dir/test',
        ann_dir='ann_dir/test',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(512, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[129.24574653, 114.02886291, 100.29403737],
                        std=[45.12454885, 45.51509298, 47.70796596],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        split=None,
        img_suffix='.png',
        seg_map_suffix='.png',
        classes=('background', 'black', 'brown', 'rainbow', 'silver'),
        palette=[[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]))        
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1), ('val', 1)]
cudnn_benchmark = True
optimizer = dict(
    type='AdamW',
    lr=3e-05,
    betas=(0.9, 0.999),
    weight_decay=0.01,
    paramwise_cfg=dict(
        custom_keys=dict(
            pos_block=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0),
            head=dict(lr_mult=10.0))))
optimizer_config = dict()
lr_config = dict(
    policy='poly',
    warmup='linear',
    warmup_iters=1500,
    warmup_ratio=1e-06,
    power=1.0,
    min_lr=0.0,
    by_epoch=False)
runner = dict(type='EpochBasedRunner', max_epochs=200)
checkpoint_config = dict(by_epoch=True, interval=1)
evaluation = dict(by_epoch=True, interval=1, metric='mIoU')
log_config = dict(
    interval=1000,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='WandbLoggerHook',
            init_kwargs=dict(
                project='Oil_Spill',
                name='V10_USE_V7_val_test'))
    ])
auto_resume = False
work_dir = 'work_dirs/V10_USE_V7_val_test'
gpu_ids = [0]