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In my pytorch model, I'm initializing my model and optimizer like this.
model = MyModelClass(config, shape, x_tr_mean, x_tr,std)
optimizer = optim.SGD(model.parameters(), lr=config.learning_rate)
And here is the path to my checkpoint file.
checkpoint_file = os.path.join(config.save_dir, "checkpoint.pth")
To load this checkpoint file, I check and see if the checkpoint file exists and then I load it as well as the model and optimizer.
if os.path.exists(checkpoint_file):
if config.resume:
torch.load(checkpoint_file)
model.load_state_dict(torch.load(checkpoint_file))
optimizer.load_state_dict(torch.load(checkpoint_file))
Also, here's how I'm saving my model and optimizer.
torch.save({'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'iter_idx': iter_idx, 'best_va_acc': best_va_acc}, checkpoint_file)
For some reason I keep getting a strange error whenever I run this code.
model.load_state_dict(torch.load(checkpoint_file))
File "/home/Josh/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 769, in load_state_dict
self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for MyModelClass:
Missing key(s) in state_dict: "mean", "std", "attribute.weight", "attribute.bias".
Unexpected key(s) in state_dict: "model", "optimizer", "iter_idx", "best_va_acc"
Does anyone know why I'm getting this error?
478
pt file and load it into your model, you could do the following. file = "model.pt" model = your_model() model. load_state_dict(torch. load(file)) # this will automatically load the file and load the parameters into the model.
A common PyTorch convention is to save these checkpoints using the .tar file extension. To load the items, first initialize the model and optimizer, then load the dictionary locally using torch.load(). From here, you can easily access the saved items by simply querying the dictionary as you would expect.
To save multiple checkpoints, you must organize them in a dictionary and use torch.save () to serialize the dictionary. A common PyTorch convention is to save these checkpoints using the .tar file extension. To load the items, first initialize the model and optimizer, then load the dictionary locally using torch.load ().
A common PyTorch convention is to save these checkpoints using the .tar file extension. To load the models, first initialize the models and optimizers, then load the dictionary locally using torch.load(). From here, you can easily
A common PyTorch convention is to save these checkpoints using the .tar file extension. To load the items, first initialize the model and optimizer, then load the dictionary locally using torch.load (). From here, you can easily access the saved items by simply querying the dictionary as you would expect.
You saved the model parameters in a dictionary. You're supposed to use the keys, that you used while saving earlier, to load the model checkpoint and state_dicts like this:
if os.path.exists(checkpoint_file):
if config.resume:
checkpoint = torch.load(checkpoint_file)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
You can check the official tutorial on PyTorch website for more info.
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