threestudio/utils/dpt.py [223:267]:
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    pretrained.act_postprocess1 = nn.Sequential(
        readout_oper[0],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[0],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.ConvTranspose2d(
            in_channels=features[0],
            out_channels=features[0],
            kernel_size=4,
            stride=4,
            padding=0,
            bias=True,
            dilation=1,
            groups=1,
        ),
    )

    pretrained.act_postprocess2 = nn.Sequential(
        readout_oper[1],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[1],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.ConvTranspose2d(
            in_channels=features[1],
            out_channels=features[1],
            kernel_size=2,
            stride=2,
            padding=0,
            bias=True,
            dilation=1,
            groups=1,
        ),
    )
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threestudio/utils/dpt.py [394:438]:
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        pretrained.act_postprocess1 = nn.Sequential(
            readout_oper[0],
            Transpose(1, 2),
            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
            nn.Conv2d(
                in_channels=vit_features,
                out_channels=features[0],
                kernel_size=1,
                stride=1,
                padding=0,
            ),
            nn.ConvTranspose2d(
                in_channels=features[0],
                out_channels=features[0],
                kernel_size=4,
                stride=4,
                padding=0,
                bias=True,
                dilation=1,
                groups=1,
            ),
        )

        pretrained.act_postprocess2 = nn.Sequential(
            readout_oper[1],
            Transpose(1, 2),
            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
            nn.Conv2d(
                in_channels=vit_features,
                out_channels=features[1],
                kernel_size=1,
                stride=1,
                padding=0,
            ),
            nn.ConvTranspose2d(
                in_channels=features[1],
                out_channels=features[1],
                kernel_size=2,
                stride=2,
                padding=0,
                bias=True,
                dilation=1,
                groups=1,
            ),
        )
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