src/controlnet_aux/midas/midas/vit.py [61:90]:
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    layer_1 = pretrained.activations["1"]
    layer_2 = pretrained.activations["2"]
    layer_3 = pretrained.activations["3"]
    layer_4 = pretrained.activations["4"]

    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
    layer_4 = pretrained.act_postprocess4[0:2](layer_4)

    unflatten = nn.Sequential(
        nn.Unflatten(
            2,
            torch.Size(
                [
                    h // pretrained.model.patch_size[1],
                    w // pretrained.model.patch_size[0],
                ]
            ),
        )
    )

    if layer_1.ndim == 3:
        layer_1 = unflatten(layer_1)
    if layer_2.ndim == 3:
        layer_2 = unflatten(layer_2)
    if layer_3.ndim == 3:
        layer_3 = unflatten(layer_3)
    if layer_4.ndim == 3:
        layer_4 = unflatten(layer_4)
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src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/utils.py [103:132]:
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    layer_1 = pretrained.activations["1"]
    layer_2 = pretrained.activations["2"]
    layer_3 = pretrained.activations["3"]
    layer_4 = pretrained.activations["4"]

    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
    layer_4 = pretrained.act_postprocess4[0:2](layer_4)

    unflatten = nn.Sequential(
        nn.Unflatten(
            2,
            torch.Size(
                [
                    h // pretrained.model.patch_size[1],
                    w // pretrained.model.patch_size[0],
                ]
            ),
        )
    )

    if layer_1.ndim == 3:
        layer_1 = unflatten(layer_1)
    if layer_2.ndim == 3:
        layer_2 = unflatten(layer_2)
    if layer_3.ndim == 3:
        layer_3 = unflatten(layer_3)
    if layer_4.ndim == 3:
        layer_4 = unflatten(layer_4)
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