torch.hub example is broken

[NicolasHug]

Hi all,

We're the maintainers of torch.hub and it looks like executing the code in the MiDaS example fails with the following error:

QObject::moveToThread: Current thread (0x5f956b80) is not the object's thread (0x601f7590).
E           Cannot move to target thread (0x5f956b80)
E           
E           qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/home/circleci/miniconda3/lib/python3.8/site-packages/cv2/qt/plugins" even though it was found.
E           This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
E           
E           Available platform plugins are: xcb, eglfs, minimal, minimalegl, offscreen, vnc, webgl.

You can find more details about the execution here

Would you mind submitting a fix either in your own repo if relevant, or directly to the MiDaS torchhub file? Thank you!

(I suspect that the problem may be that Qt is required to run the model somehow, while our CI job is headless so the X server isn't available - I could be completely wrong though).

[thias15]

Hi @NicolasHug. We will look into it.

[thias15]

Both the Colab notebook and hugging face demo seem to be working. How can we reproduce the error above? It might be worthwhile to update the colab notebook and documentation to show how all the new models (MiDaS v3.1) can be used.

[shaibagon]

Another error on hub:

I am trying to use MiDaS (github.com/isl-org/MiDaS) from hub:

midas = torch.hub.load("intel-isl/MiDaS", "DPT_SwinV2_T_256")

I am getting the following error:

RuntimeError: Error(s) in loading state_dict for DPTDepthModel:
        Missing key(s) in state_dict: "pretrained.model.layers.3.downsample.reduction.weight", "pretrained.model.layers.3.downsample.norm.weight", >     "pretrained.model.layers.3.downsample.norm.bias", "pretrained.model.head.fc.weight", "pretrained.model.head.fc.bias".
        Unexpected key(s) in state_dict: "pretrained.model.layers.0.downsample.reduction.weight", "pretrained.model.layers.0.downsample.norm.weight", "pretrained.model.layers.0.downsample.norm.bias", "pretrained.model.layers.0.blocks.1.attn_mask", "pretrained.model.layers.1.blocks.1.attn_mask", "pretrained.model.head.weight", "pretrained.model.head.bias".
        size mismatch for pretrained.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([384, 768]) from checkpoint, the shape in current model is torch.Size([192, 384]).
        size mismatch for pretrained.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for pretrained.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for pretrained.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([768, 1536]) from checkpoint, the shape in current model is torch.Size([384, 768]).
        size mismatch for pretrained.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for pretrained.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([384]).

opened an issue there as well: pytorch/hub#357 (comment)