return initial_epoch
def train_log(*args, **kwargs):
print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"), *args,
**kwargs)
if __name__ == '__main__':
print("Dataset folder: ", args.train_data)
# The only reason why I do this workaround (not necessary now) is because
# I am thinking to the situation where one MPI process has multiple gpus available
# In that case, the argument passed to get_gpu may be a numberID > 0
available_device = get_gpu(0)
# model selection
print('===> Building model')
model_classic = DnCNN()
model_anderson = deepcopy(model_classic)
# initial_epoch = findLastCheckpoint(save_dir=save_dir) # load the last model in matconvnet style
# criterion = nn.MSELoss(reduction = 'sum') # PyTorch 0.4.1
criterion = sum_squared_error()
if cuda:
print("Available device: ", available_device)
model_classic.to(available_device)
model_anderson.to(available_device)
optimizer_classic = optim.Adam(model_classic.parameters(), lr=args.lr)
for epoch in range(0, self.num_epochs):
self.train_epoch(epoch)
self.validation_epoch(epoch)
return self.training_loss_history, self.training_accuracy_history, self.validation_loss_history, self.validation_accuracy_history
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true',
help='resume from checkpoint')
args = parser.parse_args()
# The only reason why I do this workaround (not necessary now) is because
# I am thinking to the situation where one MPI process has multiple gpus available
# In that case, the argument passed to get_gpu may be a numberID > 0
device = get_gpu(0)
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),