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alexnet_pytorch's Issues

Resuming from checkpoint

This is a TODO in the script.
Additional info:

model = model.AlexNet(cfg=cfg)
model.load_state_dict(torch.load('~/workspace/ton_618/checkpoint.pth'))
log.info(model)

Error:

RuntimeError: Error(s) in loading state_dict for AlexNet:
Missing key(s) in state_dict: "conv_feat.0.weight", "conv_feat.0.bias", "conv_feat.3.weight", "conv_feat.3.bias", "conv_feat.6.weight", "conv_feat.6.bias", "conv_feat.8.weight", "conv_feat.8.bias", "conv_feat.10.weight", "conv_feat.10.bias", "classifier.1.weight", "classifier.1.bias", "classifier.4.weight", "classifier.4.bias", "classifier.6.weight", "classifier.6.bias". 
Unexpected key(s) in state_dict: "epoch", "arch", "state_dict", "best_acc1", "optimzer", "lr".

Probably because currently the save_checkpoint() only adds particular 'unexpected' keys and not the actual state_dict

Fetching device details from yaml

Instead of having an arg for use-gpu, place the device under the yaml config entry.
Everywhere the cfg is used, appropriate device choice will be made between cuda and cpu.

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