import torch.nn as nn
from configs.base.loveda import train, test, data, optimizer, learning_rate

config = dict(
    model=dict(
        type='SemanticFPN',
        params=dict(
            backbone=dict(
                resnet_type='resnet50',
                weights='imagenet',
                in_channels=3
            ),
            fpn=dict(
                in_channels_list=(256, 512, 1024, 2048),
                out_channels=256,
                top_blocks=None,
            ),
            decoder=dict(
                in_channels=256,
                out_channels=128,
                in_feat_output_strides=(4, 8, 16, 32),
                out_feat_output_stride=4,
                norm_fn=nn.BatchNorm2d,
                num_groups_gn=None
            ),
            classes=7,
            loss=dict(
                ce=dict()
            )
        )
    ),
    data=data,
    optimizer=optimizer,
    learning_rate=learning_rate,
    train=train,
    test=test
)
