How to export the large model?

Thank you for your work on this repo, This is amazing and I was able to do inference on my AGX Orin with TensorRT model, however the base model didn't fit my needs and I want to see if the larger model is more capable.

I have downloaded the large Pytorch model and ran this

python export.py --encoder vitb --load_from depth_anything_vitb14.pth --image_shape 3 518 518 It appears like there is a size mismatch? Does the export also work for the large weights? It results in raceback (most recent call last): File "/home/ubuntu/Depth-Anything/export.py", line 63, in <module> main() File "/home/ubuntu/Depth-Anything/export.py", line 60, in main export_model(args.encoder, args.load_from, tuple(args.image_shape)) File "/home/ubuntu/Depth-Anything/export.py", line 35, in export_model depth_anything.load_state_dict(torch.load(load_from, map_location='cpu'), strict=True) File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2152, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for DPT_DINOv2: Unexpected key(s) in state_dict: "pretrained.blocks.12.norm1.weight", "pretrained.blocks.12.norm1.bias", "pretrained.blocks.12.attn.qkv.weight", "pretrained.blocks.12.attn.qkv.bias", "pretrained.blocks.12.attn.proj.weight", "pretrained.blocks.12.attn.proj.bias", "pretrained.blocks.12.ls1.gamma", "pretrained.blocks.12.norm2.weight", "pretrained.blocks.12.norm2.bias", "pretrained.blocks.12.mlp.fc1.weight", "pretrained.blocks.12.mlp.fc1.bias", "pretrained.blocks.12.mlp.fc2.weight", "pretrained.blocks.12.mlp.fc2.bias", "pretrained.blocks.12.ls2.gamma", "pretrained.blocks.13.norm1.weight", "pretrained.blocks.13.norm1.bias", "pretrained.blocks.13.attn.qkv.weight", "pretrained.blocks.13.attn.qkv.bias", "pretrained.blocks.13.attn.proj.weight", "pretrained.blocks.13.attn.proj.bias", "pretrained.blocks.13.ls1.gamma", "pretrained.blocks.13.norm2.weight", "pretrained.blocks.13.norm2.bias", "pretrained.blocks.13.mlp.fc1.weight", "pretrained.blocks.13.mlp.fc1.bias", "pretrained.blocks.13.mlp.fc2.weight", "pretrained.blocks.13.mlp.fc2.bias", "pretrained.blocks.13.ls2.gamma", "pretrained.blocks.14.norm1.weight", "pretrained.blocks.14.norm1.bias", "pretrained.blocks.14.attn.qkv.weight", "pretrained.blocks.14.attn.qkv.bias", "pretrained.blocks.14.attn.proj.weight", "pretrained.blocks.14.attn.proj.bias", "pretrained.blocks.14.ls1.gamma", "pretrained.blocks.14.norm2.weight", "pretrained.blocks.14.norm2.bias", "pretrained.blocks.14.mlp.fc1.weight", "pretrained.blocks.14.mlp.fc1.bias", "pretrained.blocks.14.mlp.fc2.weight", "pretrained.blocks.14.mlp.fc2.bias", "pretrained.blocks.14.ls2.gamma", "pretrained.blocks.15.norm1.weight", "pretrained.blocks.15.norm1.bias", "pretrained.blocks.15.attn.qkv.weight", "pretrained.blocks.15.attn.qkv.bias", "pretrained.blocks.15.attn.proj.weight", "pretrained.blocks.15.attn.proj.bias", "pretrained.blocks.15.ls1.gamma", "pretrained.blocks.15.norm2.weight", "pretrained.blocks.15.norm2.bias", "pretrained.blocks.15.mlp.fc1.weight", "pretrained.blocks.15.mlp.fc1.bias", "pretrained.blocks.15.mlp.fc2.weight", "pretrained.blocks.15.mlp.fc2.bias", "pretrained.blocks.15.ls2.gamma", "pretrained.blocks.16.norm1.weight", "pretrained.blocks.16.norm1.bias", "pretrained.blocks.16.attn.qkv.weight", "pretrained.blocks.16.attn.qkv.bias", "pretrained.blocks.16.attn.proj.weight", "pretrained.blocks.16.attn.proj.bias", "pretrained.blocks.16.ls1.gamma", "pretrained.blocks.16.norm2.weight", "pretrained.blocks.16.norm2.bias", "pretrained.blocks.16.mlp.fc1.weight", "pretrained.blocks.16.mlp.fc1.bias", "pretrained.blocks.16.mlp.fc2.weight", "pretrained.blocks.16.mlp.fc2.bias", "pretrained.blocks.16.ls2.gamma", "pretrained.blocks.17.norm1.weight", "pretrained.blocks.17.norm1.bias", "pretrained.blocks.17.attn.qkv.weight", "pretrained.blocks.17.attn.qkv.bias", "pretrained.blocks.17.attn.proj.weight", "pretrained.blocks.17.attn.proj.bias", "pretrained.blocks.17.ls1.gamma", "pretrained.blocks.17.norm2.weight", "pretrained.blocks.17.norm2.bias", "pretrained.blocks.17.mlp.fc1.weight", "pretrained.blocks.17.mlp.fc1.bias", "pretrained.blocks.17.mlp.fc2.weight", "pretrained.blocks.17.mlp.fc2.bias", "pretrained.blocks.17.ls2.gamma", "pretrained.blocks.18.norm1.weight", "pretrained.blocks.18.norm1.bias", "pretrained.blocks.18.attn.qkv.weight", "pretrained.blocks.18.attn.qkv.bias", "pretrained.blocks.18.attn.proj.weight", "pretrained.blocks.18.attn.proj.bias", "pretrained.blocks.18.ls1.gamma", "pretrained.blocks.18.norm2.weight", "pretrained.blocks.18.norm2.bias", "pretrained.blocks.18.mlp.fc1.weight", "pretrained.blocks.18.mlp.fc1.bias", "pretrained.blocks.18.mlp.fc2.weight", "pretrained.blocks.18.mlp.fc2.bias", "pretrained.blocks.18.ls2.gamma", "pretrained.blocks.19.norm1.weight", "pretrained.blocks.19.norm1.bias", "pretrained.blocks.19.attn.qkv.weight", "pretrained.blocks.19.attn.qkv.bias", "pretrained.blocks.19.attn.proj.weight", "pretrained.blocks.19.attn.proj.bias", "pretrained.blocks.19.ls1.gamma", "pretrained.blocks.19.norm2.weight", "pretrained.blocks.19.norm2.bias", "pretrained.blocks.19.mlp.fc1.weight", "pretrained.blocks.19.mlp.fc1.bias", "pretrained.blocks.19.mlp.fc2.weight", "pretrained.blocks.19.mlp.fc2.bias", "pretrained.blocks.19.ls2.gamma", "pretrained.blocks.20.norm1.weight", "pretrained.blocks.20.norm1.bias", "pretrained.blocks.20.attn.qkv.weight", "pretrained.blocks.20.attn.qkv.bias", "pretrained.blocks.20.attn.proj.weight", "pretrained.blocks.20.attn.proj.bias", "pretrained.blocks.20.ls1.gamma", "pretrained.blocks.20.norm2.weight", "pretrained.blocks.20.norm2.bias", "pretrained.blocks.20.mlp.fc1.weight", "pretrained.blocks.20.mlp.fc1.bias", "pretrained.blocks.20.mlp.fc2.weight", "pretrained.blocks.20.mlp.fc2.bias", "pretrained.blocks.20.ls2.gamma", "pretrained.blocks.21.norm1.weight", "pretrained.blocks.21.norm1.bias", "pretrained.blocks.21.attn.qkv.weight", "pretrained.blocks.21.attn.qkv.bias", "pretrained.blocks.21.attn.proj.weight", "pretrained.blocks.21.attn.proj.bias", "pretrained.blocks.21.ls1.gamma", "pretrained.blocks.21.norm2.weight", "pretrained.blocks.21.norm2.bias", "pretrained.blocks.21.mlp.fc1.weight", "pretrained.blocks.21.mlp.fc1.bias", "pretrained.blocks.21.mlp.fc2.weight", "pretrained.blocks.21.mlp.fc2.bias", "pretrained.blocks.21.ls2.gamma", "pretrained.blocks.22.norm1.weight", "pretrained.blocks.22.norm1.bias", "pretrained.blocks.22.attn.qkv.weight", "pretrained.blocks.22.attn.qkv.bias", "pretrained.blocks.22.attn.proj.weight", "pretrained.blocks.22.attn.proj.bias", "pretrained.blocks.22.ls1.gamma", "pretrained.blocks.22.norm2.weight", "pretrained.blocks.22.norm2.bias", "pretrained.blocks.22.mlp.fc1.weight", "pretrained.blocks.22.mlp.fc1.bias", "pretrained.blocks.22.mlp.fc2.weight", "pretrained.blocks.22.mlp.fc2.bias", "pretrained.blocks.22.ls2.gamma", "pretrained.blocks.23.norm1.weight", "pretrained.blocks.23.norm1.bias", "pretrained.blocks.23.attn.qkv.weight", "pretrained.blocks.23.attn.qkv.bias", "pretrained.blocks.23.attn.proj.weight", "pretrained.blocks.23.attn.proj.bias", "pretrained.blocks.23.ls1.gamma", "pretrained.blocks.23.norm2.weight", "pretrained.blocks.23.norm2.bias", "pretrained.blocks.23.mlp.fc1.weight", "pretrained.blocks.23.mlp.fc1.bias", "pretrained.blocks.23.mlp.fc2.weight", "pretrained.blocks.23.mlp.fc2.bias", "pretrained.blocks.23.ls2.gamma". size mismatch for pretrained.cls_token: copying a param with shape torch.Size([1, 1, 1024]) from checkpoint, the shape in current model is torch.Size([1, 1, 768]). size mismatch for pretrained.pos_embed: copying a param with shape torch.Size([1, 1370, 1024]) from checkpoint, the shape in current model is torch.Size([1, 1370, 768]). size mismatch for pretrained.mask_token: copying a param with shape torch.Size([1, 1024]) from checkpoint, the shape in current model is torch.Size([1, 768]). size mismatch for pretrained.patch_embed.proj.weight: copying a param with shape torch.Size([1024, 3, 14, 14]) from checkpoint, the shape in current model is torch.Size([768, 3, 14, 14]). size mismatch for pretrained.patch_embed.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.0.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.0.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.0.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.1.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.1.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.1.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.2.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.2.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.2.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.2.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.2.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.2.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.2.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.3.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.3.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.3.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.3.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.3.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.3.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.3.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.4.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.4.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.4.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.4.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.4.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.4.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.4.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.5.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.5.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.5.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.5.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.5.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.5.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.5.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.6.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.6.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.6.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.6.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.6.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.6.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.6.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.7.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.7.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.7.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.7.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.7.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.7.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.7.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.8.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.8.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.8.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.8.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.8.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.8.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.8.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.9.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.9.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.9.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.9.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.9.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.9.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.9.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.10.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.10.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.10.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.10.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.10.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.10.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.10.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]). size mismatch for pretrained.blocks.11.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]). size mismatch for pretrained.blocks.11.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]). size mismatch for pretrained.blocks.11.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.ls1.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]). size mismatch for pretrained.blocks.11.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]). size mismatch for pretrained.blocks.11.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]). size mismatch for pretrained.blocks.11.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.blocks.11.ls2.gamma: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for pretrained.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for depth_head.projects.0.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 768, 1, 1]). size mismatch for depth_head.projects.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for depth_head.projects.1.weight: copying a param with shape torch.Size([512, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 768, 1, 1]). size mismatch for depth_head.projects.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([192]). size mismatch for depth_head.projects.2.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 768, 1, 1]). size mismatch for depth_head.projects.2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([384]). size mismatch for depth_head.projects.3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([768, 768, 1, 1]). size mismatch for depth_head.projects.3.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for depth_head.resize_layers.0.weight: copying a param with shape torch.Size([256, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([96, 96, 4, 4]). size mismatch for depth_head.resize_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for depth_head.resize_layers.1.weight: copying a param with shape torch.Size([512, 512, 2, 2]) from checkpoint, the shape in current model is torch.Size([192, 192, 2, 2]). size mismatch for depth_head.resize_layers.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([192]). size mismatch for depth_head.resize_layers.3.weight: copying a param with shape torch.Size([1024, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([768, 768, 3, 3]). size mismatch for depth_head.resize_layers.3.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]). size mismatch for depth_head.scratch.layer1_rn.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 96, 3, 3]). size mismatch for depth_head.scratch.layer2_rn.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 192, 3, 3]). size mismatch for depth_head.scratch.layer3_rn.weight: copying a param with shape torch.Size([256, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 384, 3, 3]). size mismatch for depth_head.scratch.layer4_rn.weight: copying a param with shape torch.Size([256, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 768, 3, 3]). size mismatch for depth_head.scratch.refinenet1.out_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). size mismatch for depth_head.scratch.refinenet1.out_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet2.out_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). size mismatch for depth_head.scratch.refinenet2.out_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet3.out_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). size mismatch for depth_head.scratch.refinenet3.out_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet4.out_conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 128, 1, 1]). size mismatch for depth_head.scratch.refinenet4.out_conv.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]). size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for depth_head.scratch.output_conv1.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 128, 3, 3]). size mismatch for depth_head.scratch.output_conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]). size mismatch for depth_head.scratch.output_conv2.0.weight: copying a param with shape torch.Size([32, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 64, 3, 3]).

spacewalk01 / depth-anything-tensorrt

How to export the large model? #24