src/controlnet_aux/midas/midas/vit.py [427:463]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        )

    pretrained.act_postprocess3 = nn.Sequential(
        readout_oper[2],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[2],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
    )

    pretrained.act_postprocess4 = nn.Sequential(
        readout_oper[3],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[3],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.Conv2d(
            in_channels=features[3],
            out_channels=features[3],
            kernel_size=3,
            stride=2,
            padding=1,
        ),
    )

    pretrained.model.start_index = start_index
    pretrained.model.patch_size = [16, 16]
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src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/utils.py [226:262]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    )

    pretrained.act_postprocess3 = nn.Sequential(
        readout_oper[2],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[2],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
    )

    pretrained.act_postprocess4 = nn.Sequential(
        readout_oper[3],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[3],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.Conv2d(
            in_channels=features[3],
            out_channels=features[3],
            kernel_size=3,
            stride=2,
            padding=1,
        ),
    )

    pretrained.model.start_index = start_index
    pretrained.model.patch_size = [16, 16]
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