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RuntimeError: Error(s) in loading state_dict for DPTDepthModel:
Missing key(s) in state_dict: "pretrained.model.layers.3.downsample.reduction.weight", "pretrained.model.layers.3.downsample.norm.weight", > "pretrained.model.layers.3.downsample.norm.bias", "pretrained.model.head.fc.weight", "pretrained.model.head.fc.bias".
Unexpected key(s) in state_dict: "pretrained.model.layers.0.downsample.reduction.weight", "pretrained.model.layers.0.downsample.norm.weight", "pretrained.model.layers.0.downsample.norm.bias", "pretrained.model.layers.0.blocks.1.attn_mask", "pretrained.model.layers.1.blocks.1.attn_mask", "pretrained.model.head.weight", "pretrained.model.head.bias".
size mismatch for pretrained.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([384, 768]) from checkpoint, the shape in current model is torch.Size([192, 384]).
size mismatch for pretrained.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
size mismatch for pretrained.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
size mismatch for pretrained.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([768, 1536]) from checkpoint, the shape in current model is torch.Size([384, 768]).
size mismatch for pretrained.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for pretrained.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([384]).
The text was updated successfully, but these errors were encountered:
I am trying to use MiDaS (github.com/isl-org/MiDaS) from hub:
I am getting the following error:
The text was updated successfully, but these errors were encountered: