Evaluating metric with S model

I'm trying to evaluate metric with different models, it works fine with the default l, but if I change it to s like this:
    @staticmethod
    def build(train_midas=False, fetch_features=False, freeze_bn=True, force_keep_ar=False, **kwargs):
        if "img_size" in kwargs:
            kwargs = DepthAnythingCore.parse_img_size(kwargs)
        img_size = kwargs.pop("img_size", [384, 384])

        # depth_anything = DPT_DINOv2(out_channels=[256, 512, 1024, 1024], use_clstoken=False)
        depth_anything = DPT_DINOv2(out_channels=[48, 96, 192, 384], use_clstoken=False)

        # state_dict = torch.load('./checkpoints/depth_anything_vitl14.pth', map_location='cpu')
        state_dict = torch.load('./checkpoints/depth_anything_vits14.pth', map_location='cpu')
        depth_anything.load_state_dict(state_dict)

        kwargs.update({'keep_aspect_ratio': force_keep_ar})

        depth_anything_core = DepthAnythingCore(depth_anything, trainable=train_midas, fetch_features=fetch_features,
                                                freeze_bn=freeze_bn, img_size=img_size, **kwargs)

        depth_anything_core.set_output_channels()
        return depth_anything_core

It fails:
python evaluate.py -m zoedepth --pretrained_resource="local::./checkpoints/depth_anything_metric_depth_indoor.pt" -d nyu
{'attractor_alpha': 1000,
 'attractor_gamma': 2,
 'attractor_kind': 'mean',
 'attractor_type': 'inv',
 'aug': True,
 'avoid_boundary': False,
 'bin_centers_type': 'softplus',
 'bin_embedding_dim': 128,
 'clip_grad': 0.1,
 'data_path': './data/nyu',
 'data_path_eval': './data/nyu',
 'dataset': 'nyu',
 'degree': 1.0,
 'distributed': True,
 'do_kb_crop': False,
 'do_random_rotate': True,
 'eigen_crop': True,
 'filenames_file': './train_test_inputs/nyudepthv2_train_files_with_gt.txt',
 'filenames_file_eval': './train_test_inputs/nyudepthv2_test_files_with_gt.txt',
 'garg_crop': False,
 'gpu': None,
 'gt_path': './data/nyu',
 'gt_path_eval': './data/nyu',
 'img_size': [392, 518],
 'input_height': 480,
 'input_width': 640,
 'inverse_midas': False,
 'log_images_every': 0.1,
 'max_depth': 10,
 'max_depth_diff': 10,
 'max_depth_eval': 10,
 'max_temp': 50.0,
 'max_translation': 100,
 'memory_efficient': True,
 'midas_model_type': 'DPT_BEiT_L_384',
 'min_depth': 0.001,
 'min_depth_diff': -10,
 'min_depth_eval': 0.001,
 'min_temp': 0.0212,
 'model': 'zoedepth',
 'n_attractors': [16, 8, 4, 1],
 'n_bins': 64,
 'name': 'ZoeDepth',
 'notes': '',
 'output_distribution': 'logbinomial',
 'prefetch': False,
 'pretrained_resource': 'local::./checkpoints/depth_anything_metric_depth_indoor.pt',
 'print_losses': False,
 'project': 'ZoeDepth',
 'random_crop': False,
 'random_translate': False,
 'root': '.',
 'save_dir': './depth_anything_finetune',
 'shared_dict': None,
 'tags': '',
 'train_midas': False,
 'translate_prob': 0.2,
 'uid': None,
 'use_amp': False,
 'use_pretrained_midas': False,
 'use_shared_dict': False,
 'validate_every': 0.25,
 'version_name': 'v1',
 'workers': 16}
Evaluating zoedepth on nyu...
Traceback (most recent call last):
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/evaluate.py", line 160, in <module>
    eval_model(args.model, pretrained_resource=args.pretrained_resource,
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/evaluate.py", line 132, in eval_model
    metrics = main(config)
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/evaluate.py", line 112, in main
    model = build_model(config)
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/zoedepth/models/builder.py", line 53, in build_model
    return get_version(config.version_name).build_from_config(config)
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/zoedepth/models/zoedepth/zoedepth_v1.py", line 264, in build_from_config
    return ZoeDepth.build(**config)
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/zoedepth/models/zoedepth/zoedepth_v1.py", line 253, in build
    core = DepthAnythingCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas,
  File "/src-misc/ml/3rd-party/Depth-Anything/metric_depth/zoedepth/models/base_models/depth_anything.py", line 349, in build
    depth_anything.load_state_dict(state_dict)
  File "/src-misc/ml/3rd-party/Depth-Anything/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2153, 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:
	Missing 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, 384]) from checkpoint, the shape in current model is torch.Size([1, 1, 1024]).
	size mismatch for pretrained.pos_embed: copying a param with shape torch.Size([1, 1370, 384]) from checkpoint, the shape in current model is torch.Size([1, 1370, 1024]).
	size mismatch for pretrained.mask_token: copying a param with shape torch.Size([1, 384]) from checkpoint, the shape in current model is torch.Size([1, 1024]).
	size mismatch for pretrained.patch_embed.proj.weight: copying a param with shape torch.Size([384, 3, 14, 14]) from checkpoint, the shape in current model is torch.Size([1024, 3, 14, 14]).
	size mismatch for pretrained.patch_embed.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.0.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.0.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.0.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.1.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.1.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.1.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.2.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.2.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.2.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.2.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.2.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.2.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.2.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.3.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.3.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.3.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.3.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.3.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.3.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.3.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.4.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.4.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.4.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.4.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.4.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.4.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.4.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.5.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.5.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.5.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.5.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.5.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.5.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.5.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.6.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.6.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.6.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.6.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.6.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.6.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.6.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.7.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.7.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.7.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.7.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.7.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.7.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.7.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.8.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.8.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.8.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.8.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.8.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.8.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.8.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.9.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.9.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.9.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.9.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.9.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.9.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.9.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.10.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.10.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.10.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.10.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.10.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.10.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.10.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.norm1.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.norm1.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.attn.qkv.weight: copying a param with shape torch.Size([1152, 384]) from checkpoint, the shape in current model is torch.Size([3072, 1024]).
	size mismatch for pretrained.blocks.11.attn.qkv.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([3072]).
	size mismatch for pretrained.blocks.11.attn.proj.weight: copying a param with shape torch.Size([384, 384]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
	size mismatch for pretrained.blocks.11.attn.proj.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.ls1.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.norm2.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.norm2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.mlp.fc1.weight: copying a param with shape torch.Size([1536, 384]) from checkpoint, the shape in current model is torch.Size([4096, 1024]).
	size mismatch for pretrained.blocks.11.mlp.fc1.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([4096]).
	size mismatch for pretrained.blocks.11.mlp.fc2.weight: copying a param with shape torch.Size([384, 1536]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
	size mismatch for pretrained.blocks.11.mlp.fc2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.blocks.11.ls2.gamma: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.norm.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for pretrained.norm.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for depth_head.projects.0.weight: copying a param with shape torch.Size([48, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([48, 1024, 1, 1]).
	size mismatch for depth_head.projects.1.weight: copying a param with shape torch.Size([96, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 1024, 1, 1]).
	size mismatch for depth_head.projects.2.weight: copying a param with shape torch.Size([192, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1024, 1, 1]).
	size mismatch for depth_head.projects.3.weight: copying a param with shape torch.Size([384, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 1024, 1, 1]).
	size mismatch for depth_head.scratch.layer1_rn.weight: copying a param with shape torch.Size([64, 48, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 48, 3, 3]).
	size mismatch for depth_head.scratch.layer2_rn.weight: copying a param with shape torch.Size([64, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 96, 3, 3]).
	size mismatch for depth_head.scratch.layer3_rn.weight: copying a param with shape torch.Size([64, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 192, 3, 3]).
	size mismatch for depth_head.scratch.layer4_rn.weight: copying a param with shape torch.Size([64, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 384, 3, 3]).
	size mismatch for depth_head.scratch.refinenet1.out_conv.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
	size mismatch for depth_head.scratch.refinenet1.out_conv.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet2.out_conv.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
	size mismatch for depth_head.scratch.refinenet2.out_conv.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet3.out_conv.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
	size mismatch for depth_head.scratch.refinenet3.out_conv.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet4.out_conv.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
	size mismatch for depth_head.scratch.refinenet4.out_conv.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for depth_head.scratch.output_conv1.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
	size mismatch for depth_head.scratch.output_conv1.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([128]).
	size mismatch for depth_head.scratch.output_conv2.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 128, 3, 3]).

What else I should change to use S model?

The error messages suggest that vit-s weights are being loaded into a vit-l model.
When loading the depth-anything model, you'll need to provide the encoder type as an argument:
depth_anything = DPT_DINOv2(encoder="vits", out_channels=[48, 96, 192, 384], use_clstoken=False)
Otherwise it defaults to vitl, which would explain the errors.

Hi @heyoeyo, I tried using the following model for inference:
depth_anything = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768], use_clstoken=False)
state_dict = torch.load('../include/depth_anything_vitb14.pth', map_location='cpu')

However, I get similar error to @RankoR, but for the core.core.depth_head, possibly has something to do with the pretrained model for metric depth. The similar occurs for the small model (vits), but there is no error when the large model (vitl) is used.
Params passed to Resize transform:
	width:  518
	height:  392
	resize_target:  True
	keep_aspect_ratio:  False
	ensure_multiple_of:  14
	resize_method:  minimal
Using pretrained resource local::../depth_anything_metric_depth_indoor.pt
Traceback (most recent call last):
  ...
    model = build_model(config).to(DEVICE)
  File "/src/depthanything/zoedepth/models/builder.py", line 51, in build_model
    return get_version(config.version_name).build_from_config(config)
  File /src/depthanything/zoedepth/models/zoedepth/zoedepth_v1.py", line 264, in build_from_config
    return ZoeDepth.build(**config)
  File "/src/depthanything/zoedepth/models/zoedepth/zoedepth_v1.py", line 259, in build
    model = load_state_from_resource(model, pretrained_resource)
  File "/src/depthanything/zoedepth/models/model_io.py", line 88, in load_state_from_resource
    return load_wts(model, path)
  File "/src/depthanything/zoedepth/models/model_io.py", line 56, in load_wts
    return load_state_dict(model, ckpt)
  File "/src/depthanything/zoedepth/models/model_io.py", line 49, in load_state_dict
    model.load_state_dict(state)
  File "/home/saad/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ZoeDepth:
	Unexpected key(s) in state_dict: "core.core.pretrained.blocks.12.norm1.weight", "core.core.pretrained.blocks.12.norm1.bias", "core.core.pretrained.blocks.12.attn.qkv.weight", "core.core.pretrained.blocks.12.attn.qkv.bias", "core.core.pretrained.blocks.12.attn.proj.weight", "core.core.pretrained.blocks.12.attn.proj.bias", "core.core.pretrained.blocks.12.ls1.gamma", "core.core.pretrained.blocks.12.norm2.weight", "core.core.pretrained.blocks.12.norm2.bias", "core.core.pretrained.blocks.12.mlp.fc1.weight", "core.core.pretrained.blocks.12.mlp.fc1.bias", "core.core.pretrained.blocks.12.mlp.fc2.weight", "core.core.pretrained.blocks.12.mlp.fc2.bias", "core.core.pretrained.blocks.12.ls2.gamma", "core.core.pretrained.blocks.13.norm1.weight", "core.core.pretrained.blocks.13.norm1.bias", "core.core.pretrained.blocks.13.attn.qkv.weight", "core.core.pretrained.blocks.13.attn.qkv.bias", "core.core.pretrained.blocks.13.attn.proj.weight", "core.core.pretrained.blocks.13.attn.proj.bias", "core.core.pretrained.blocks.13.ls1.gamma", "core.core.pretrained.blocks.13.norm2.weight", "core.core.pretrained.blocks.13.norm2.bias", "core.core.pretrained.blocks.13.mlp.fc1.weight", "core.core.pretrained.blocks.13.mlp.fc1.bias", "core.core.pretrained.blocks.13.mlp.fc2.weight", "core.core.pretrained.blocks.13.mlp.fc2.bias", "core.core.pretrained.blocks.13.ls2.gamma", "core.core.pretrained.blocks.14.norm1.weight", "core.core.pretrained.blocks.14.norm1.bias", "core.core.pretrained.blocks.14.attn.qkv.weight", "core.core.pretrained.blocks.14.attn.qkv.bias", "core.core.pretrained.blocks.14.attn.proj.weight", "core.core.pretrained.blocks.14.attn.proj.bias", "core.core.pretrained.blocks.14.ls1.gamma", "core.core.pretrained.blocks.14.norm2.weight", "core.core.pretrained.blocks.14.norm2.bias", "core.core.pretrained.blocks.14.mlp.fc1.weight", "core.core.pretrained.blocks.14.mlp.fc1.bias", "core.core.pretrained.blocks.14.mlp.fc2.weight", "core.core.pretrained.blocks.14.mlp.fc2.bias", "core.core.pretrained.blocks.14.ls2.gamma", "core.core.pretrained.blocks.15.norm1.weight", "core.core.pretrained.blocks.15.norm1.bias", "core.core.pretrained.blocks.15.attn.qkv.weight", "core.core.pretrained.blocks.15.attn.qkv.bias", "core.core.pretrained.blocks.15.attn.proj.weight", "core.core.pretrained.blocks.15.attn.proj.bias", "core.core.pretrained.blocks.15.ls1.gamma", "core.core.pretrained.blocks.15.norm2.weight", "core.core.pretrained.blocks.15.norm2.bias", "core.core.pretrained.blocks.15.mlp.fc1.weight", "core.core.pretrained.blocks.15.mlp.fc1.bias", "core.core.pretrained.blocks.15.mlp.fc2.weight", "core.core.pretrained.blocks.15.mlp.fc2.bias", "core.core.pretrained.blocks.15.ls2.gamma", "core.core.pretrained.blocks.16.norm1.weight", "core.core.pretrained.blocks.16.norm1.bias", "core.core.pretrained.blocks.16.attn.qkv.weight", "core.core.pretrained.blocks.16.attn.qkv.bias", "core.core.pretrained.blocks.16.attn.proj.weight", "core.core.pretrained.blocks.16.attn.proj.bias", "core.core.pretrained.blocks.16.ls1.gamma", "core.core.pretrained.blocks.16.norm2.weight", "core.core.pretrained.blocks.16.norm2.bias", "core.core.pretrained.blocks.16.mlp.fc1.weight", "core.core.pretrained.blocks.16.mlp.fc1.bias", "core.core.pretrained.blocks.16.mlp.fc2.weight", "core.core.pretrained.blocks.16.mlp.fc2.bias", "core.core.pretrained.blocks.16.ls2.gamma", "core.core.pretrained.blocks.17.norm1.weight", "core.core.pretrained.blocks.17.norm1.bias", "core.core.pretrained.blocks.17.attn.qkv.weight", "core.core.pretrained.blocks.17.attn.qkv.bias", "core.core.pretrained.blocks.17.attn.proj.weight", "core.core.pretrained.blocks.17.attn.proj.bias", "core.core.pretrained.blocks.17.ls1.gamma", "core.core.pretrained.blocks.17.norm2.weight", "core.core.pretrained.blocks.17.norm2.bias", "core.core.pretrained.blocks.17.mlp.fc1.weight", "core.core.pretrained.blocks.17.mlp.fc1.bias", "core.core.pretrained.blocks.17.mlp.fc2.weight", "core.core.pretrained.blocks.17.mlp.fc2.bias", "core.core.pretrained.blocks.17.ls2.gamma", "core.core.pretrained.blocks.18.norm1.weight", "core.core.pretrained.blocks.18.norm1.bias", "core.core.pretrained.blocks.18.attn.qkv.weight", "core.core.pretrained.blocks.18.attn.qkv.bias", "core.core.pretrained.blocks.18.attn.proj.weight", "core.core.pretrained.blocks.18.attn.proj.bias", "core.core.pretrained.blocks.18.ls1.gamma", "core.core.pretrained.blocks.18.norm2.weight", "core.core.pretrained.blocks.18.norm2.bias", "core.core.pretrained.blocks.18.mlp.fc1.weight", "core.core.pretrained.blocks.18.mlp.fc1.bias", "core.core.pretrained.blocks.18.mlp.fc2.weight", "core.core.pretrained.blocks.18.mlp.fc2.bias", "core.core.pretrained.blocks.18.ls2.gamma", "core.core.pretrained.blocks.19.norm1.weight", "core.core.pretrained.blocks.19.norm1.bias", "core.core.pretrained.blocks.19.attn.qkv.weight", "core.core.pretrained.blocks.19.attn.qkv.bias", "core.core.pretrained.blocks.19.attn.proj.weight", "core.core.pretrained.blocks.19.attn.proj.bias", "core.core.pretrained.blocks.19.ls1.gamma", "core.core.pretrained.blocks.19.norm2.weight", "core.core.pretrained.blocks.19.norm2.bias", "core.core.pretrained.blocks.19.mlp.fc1.weight", "core.core.pretrained.blocks.19.mlp.fc1.bias", "core.core.pretrained.blocks.19.mlp.fc2.weight", "core.core.pretrained.blocks.19.mlp.fc2.bias", "core.core.pretrained.blocks.19.ls2.gamma", "core.core.pretrained.blocks.20.norm1.weight", "core.core.pretrained.blocks.20.norm1.bias", "core.core.pretrained.blocks.20.attn.qkv.weight", "core.core.pretrained.blocks.20.attn.qkv.bias", "core.core.pretrained.blocks.20.attn.proj.weight", "core.core.pretrained.blocks.20.attn.proj.bias", "core.core.pretrained.blocks.20.ls1.gamma", "core.core.pretrained.blocks.20.norm2.weight", "core.core.pretrained.blocks.20.norm2.bias", "core.core.pretrained.blocks.20.mlp.fc1.weight", "core.core.pretrained.blocks.20.mlp.fc1.bias", "core.core.pretrained.blocks.20.mlp.fc2.weight", "core.core.pretrained.blocks.20.mlp.fc2.bias", "core.core.pretrained.blocks.20.ls2.gamma", "core.core.pretrained.blocks.21.norm1.weight", "core.core.pretrained.blocks.21.norm1.bias", "core.core.pretrained.blocks.21.attn.qkv.weight", "core.core.pretrained.blocks.21.attn.qkv.bias", "core.core.pretrained.blocks.21.attn.proj.weight", "core.core.pretrained.blocks.21.attn.proj.bias", "core.core.pretrained.blocks.21.ls1.gamma", "core.core.pretrained.blocks.21.norm2.weight", "core.core.pretrained.blocks.21.norm2.bias", "core.core.pretrained.blocks.21.mlp.fc1.weight", "core.core.pretrained.blocks.21.mlp.fc1.bias", "core.core.pretrained.blocks.21.mlp.fc2.weight", "core.core.pretrained.blocks.21.mlp.fc2.bias", "core.core.pretrained.blocks.21.ls2.gamma", "core.core.pretrained.blocks.22.norm1.weight", "core.core.pretrained.blocks.22.norm1.bias", "core.core.pretrained.blocks.22.attn.qkv.weight", "core.core.pretrained.blocks.22.attn.qkv.bias", "core.core.pretrained.blocks.22.attn.proj.weight", "core.core.pretrained.blocks.22.attn.proj.bias", "core.core.pretrained.blocks.22.ls1.gamma", "core.core.pretrained.blocks.22.norm2.weight", "core.core.pretrained.blocks.22.norm2.bias", "core.core.pretrained.blocks.22.mlp.fc1.weight", "core.core.pretrained.blocks.22.mlp.fc1.bias", "core.core.pretrained.blocks.22.mlp.fc2.weight", "core.core.pretrained.blocks.22.mlp.fc2.bias", "core.core.pretrained.blocks.22.ls2.gamma", "core.core.pretrained.blocks.23.norm1.weight", "core.core.pretrained.blocks.23.norm1.bias", "core.core.pretrained.blocks.23.attn.qkv.weight", "core.core.pretrained.blocks.23.attn.qkv.bias", "core.core.pretrained.blocks.23.attn.proj.weight", "core.core.pretrained.blocks.23.attn.proj.bias", "core.core.pretrained.blocks.23.ls1.gamma", "core.core.pretrained.blocks.23.norm2.weight", "core.core.pretrained.blocks.23.norm2.bias", "core.core.pretrained.blocks.23.mlp.fc1.weight", "core.core.pretrained.blocks.23.mlp.fc1.bias", "core.core.pretrained.blocks.23.mlp.fc2.weight", "core.core.pretrained.blocks.23.mlp.fc2.bias", "core.core.pretrained.blocks.23.ls2.gamma".
	size mismatch for core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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 core.core.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]).

I'm not familiar with the zoedepth stuff, but ya this error is similar (but reversed), where it looks like vit-l weights are being loaded into a vit-b model.
The error references a build function, which is where the weights are loading. That function is hard-coded to use the vit-l weights, so I'd guess that's where the error is coming from. I think either you'd need to modify that hard-coded link to the vit-l weights or avoid using that build function if possible (though I'm not sure how it's getting called, maybe it's hard to avoid it?)

Hi all, please check this thread: https://github.com/LiheYoung/Depth-Anything/issues/100.

Hi all, please check this thread: #100.

@LiheYoung It seems that #100 is only relevant if one wants to train. It seems that @Zhefan-Xu haven't replied yet, but it would be nice to also have public weights for the metric depth models fine-tuned using the small (and also the base) relative depth pretrained encoder.
Do you plan to do so? If not, could you give us more information about the training time / the hardware you used for training your models? I couldn't find that in the original paper, nor could I in this repo.