How do I load a local model with torch.hub.load?

Question

I need to avoid downloading the model from the web (due to restrictions on the machine installed).

This works, but it downloads the model from the Internet

model = torch.hub.load('pytorch/vision:v0.9.0', 'deeplabv3_resnet101', pretrained=True)

I have placed the .pth file and the hubconf.py file in the /tmp/ folder and changed my code to

model = torch.hub.load('/tmp/', 'deeplabv3_resnet101', pretrained=True, source='local')

but to my surprise, it still downloads the model from the Internet. What am I doing wrong? How can I load the model locally?

Just to give you a bit more details, I’m doing all this in a Docker container that has a read-only volume at runtime, so that’s why the download of new files fails.

Asked By: coding-dude.com

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Source

Answer 1

There are two approaches you can take to get a shippable model on a machine without an Internet connection.

Load DeepLab with a pretrained model on a normal machine, use a JIT compiler to export it as a graph, and put it into the machine. The Script is easy to follow:

 # To export
 model = torch.hub.load('pytorch/vision:v0.9.0', 'deeplabv3_resnet101', pretrained=True).eval()
 traced_graph = torch.jit.trace(model, torch.randn(1, 3, H, W))
 traced_graph.save('DeepLab.pth')

 # To load
 model = torch.jit.load('DeepLab.pth').eval().to(device)

In this case, the weights and network structure is saved as computational graph, so you won’t need any extra files.

Take a look at torchvision’s GitHub repository.

There’s a download URL for DeepLabV3 with Resnet101 backbone weights.

You can download those weights once, and then use deeplab from torchvision with pretrained=False flag and load weights manually.
```
 model = torch.hub.load('pytorch/vision:v0.9.0', 'deeplabv3_resnet101', pretrained=False)
 model.load_state_dict(torch.load('downloaded weights path'))
```
Take in consideration, there might be a [‘state_dict’] or some similar parent key in state dict, where you would use:
```
 model.load_state_dict(torch.load('downloaded weights path')['state_dict'])
```

Answered By: deepconsc

Answer 2

model_name='best.pt'
model = torch.hub.load(os.getcwd(), 'custom', source='local', path = model_name, force_reload = True)

This worked for me. Default source is github.

Answered By: Samvandha Pathak

Answer 3

model = torch.hub.load(‘path/to/yolov5’, ‘custom’, path=’path/to/best.pt’, source=’local’) # local repo
‘path/to/yolov5’ where could find hubconf.py

Answered By: xinying li

How do I load a local model with torch.hub.load?

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