try:
torch.nn.init.kaiming_uniform_(m.weight.data)
m.bias.data.zero_()
except:
pass
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if "residual" in experiment:
model = Model(num_features=num_features, num_residual=num_residual).to(device)
else:
model = Model(num_features=num_features).to(device)
criterion = nn.CrossEntropyLoss(ignore_index=8)
model.apply(init_weights)
print(type(model))
if os.path.exists(latest_model_path):
print("Model exists. Loading from {0}".format(latest_model_path))
model = torch.load(latest_model_path)
optimizer = optim.Adam(model.parameters(), lr=lr)
if os.path.exists(optim_path):
print("Optimizer state dict exists. Loading from {0}".format(optim_path))
optim = torch.load(optim_path)
optimizer.load_state_dict(optim['optimizer'])
model.to(device)
print("Model is using GPU: {0}".format(next(model.parameters()).is_cuda))
