segmentation

norm_cfg = dict(type="BN", requires_grad=True)
model = dict(
    type="EncoderDecoder",
    pretrained="open-mmlab://resnet50_v1c",
    backbone=dict(
        type="ResNetV1c",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 1, 1),
        strides=(1, 2, 2, 2),
        norm_cfg=norm_cfg,
        norm_eval=False,
        style="pytorch",
        contract_dilation=True,
    ),
    neck=dict(
        type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=4
    ),
    decode_head=dict(
        type="FPNHead",
        in_channels=[256, 256, 256, 256],
        in_index=[0, 1, 2, 3],
        feature_strides=[4, 8, 16, 32],
        channels=128,
        dropout_ratio=0.1,
        num_classes={{cookiecutter.num_classes}},
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
    ),
)

train_cfg = dict()  # type: ignore
test_cfg = dict(mode="whole")

load_from = "/media/VA/pretrained_weights/mmseg/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth"