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mnist: support --model-path and --load-model #1388

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mnist: support --model-path and --load-model #1388

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26 changes: 18 additions & 8 deletions mnist/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,8 +90,12 @@ def main():
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true',
parser.add_argument('--save-model', action='store_true',
help='For Saving the current Model')
parser.add_argument('--model-path', type=str, default='mnist_cnn.pt',
help='path to save the trained model')
parser.add_argument('--load-model', type=str, default=None,
help='Path to load a pre-trained model')
args = parser.parse_args()

use_accel = not args.no_accel and torch.accelerator.is_available()
Expand Down Expand Up @@ -125,16 +129,22 @@ def main():
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
if args.load_model:
print(f"Loading model from {args.load_model}")
model.load_state_dict(torch.load(args.load_model, map_location=device))
test(model, device, test_loader)
scheduler.step()
else:
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
scheduler.step()

if args.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
if args.save_model:
torch.save(model.state_dict(), args.model_path)


if __name__ == '__main__':
Expand Down