172 lines
5.0 KiB
Python
172 lines
5.0 KiB
Python
import argparse
|
|
|
|
from mnist_net import MnistNet
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torch.optim as optim
|
|
from torchvision import datasets, transforms
|
|
from torch.optim.lr_scheduler import StepLR
|
|
from tqdm import tqdm
|
|
|
|
|
|
def train(args, model, device, train_loader, optimizer, epoch):
|
|
model.train()
|
|
with tqdm(train_loader, desc=f"Train Epoch {epoch}", unit="batch") as t:
|
|
for batch_idx, (data, target) in enumerate(t):
|
|
data, target = data.to(device), target.to(device)
|
|
optimizer.zero_grad()
|
|
output = model(data)
|
|
loss = F.nll_loss(output, target)
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
if batch_idx % args.log_interval == 0:
|
|
t.set_postfix(loss=round(loss.item(), 3))
|
|
|
|
if args.dry_run:
|
|
break
|
|
|
|
|
|
def test(model, device, test_loader):
|
|
model.eval()
|
|
test_loss = 0
|
|
correct = 0
|
|
with torch.no_grad():
|
|
for data, target in test_loader:
|
|
data, target = data.to(device), target.to(device)
|
|
output = model(data)
|
|
test_loss += F.nll_loss(
|
|
output, target, reduction="sum"
|
|
).item() # sum up batch loss
|
|
pred = output.argmax(
|
|
dim=1, keepdim=True
|
|
) # get the index of the max log-probability
|
|
correct += pred.eq(target.view_as(pred)).sum().item()
|
|
|
|
test_loss /= len(test_loader.dataset)
|
|
|
|
print(
|
|
"Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
|
|
test_loss,
|
|
correct,
|
|
len(test_loader.dataset),
|
|
100.0 * correct / len(test_loader.dataset),
|
|
)
|
|
)
|
|
|
|
|
|
def main():
|
|
# Training settings
|
|
parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
|
|
parser.add_argument(
|
|
"--batch-size",
|
|
type=int,
|
|
default=64,
|
|
metavar="N",
|
|
help="input batch size for training (default: 64)",
|
|
)
|
|
parser.add_argument(
|
|
"--test-batch-size",
|
|
type=int,
|
|
default=1000,
|
|
metavar="N",
|
|
help="input batch size for testing (default: 1000)",
|
|
)
|
|
parser.add_argument(
|
|
"--epochs",
|
|
type=int,
|
|
default=14,
|
|
metavar="N",
|
|
help="number of epochs to train (default: 14)",
|
|
)
|
|
parser.add_argument(
|
|
"--lr",
|
|
type=float,
|
|
default=1.0,
|
|
metavar="LR",
|
|
help="learning rate (default: 1.0)",
|
|
)
|
|
parser.add_argument(
|
|
"--gamma",
|
|
type=float,
|
|
default=0.7,
|
|
metavar="M",
|
|
help="Learning rate step gamma (default: 0.7)",
|
|
)
|
|
parser.add_argument(
|
|
"--no-cuda", action="store_true", default=False, help="disables CUDA training"
|
|
)
|
|
parser.add_argument(
|
|
"--no-mps",
|
|
action="store_true",
|
|
default=False,
|
|
help="disables macOS GPU training",
|
|
)
|
|
parser.add_argument(
|
|
"--dry-run",
|
|
action="store_true",
|
|
default=False,
|
|
help="quickly check a single pass",
|
|
)
|
|
parser.add_argument(
|
|
"--seed", type=int, default=1, metavar="S", 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",
|
|
default=False,
|
|
help="For Saving the current Model",
|
|
)
|
|
args = parser.parse_args()
|
|
use_cuda = not args.no_cuda and torch.cuda.is_available()
|
|
use_mps = not args.no_mps and torch.backends.mps.is_available()
|
|
|
|
torch.manual_seed(args.seed)
|
|
|
|
if use_cuda:
|
|
device = torch.device("cuda")
|
|
elif use_mps:
|
|
device = torch.device("mps")
|
|
else:
|
|
device = torch.device("cpu")
|
|
|
|
print(f"Using device: {device}\n")
|
|
|
|
train_kwargs = {"batch_size": args.batch_size}
|
|
test_kwargs = {"batch_size": args.test_batch_size}
|
|
if use_cuda:
|
|
cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
|
|
train_kwargs.update(cuda_kwargs)
|
|
test_kwargs.update(cuda_kwargs)
|
|
|
|
transform = transforms.Compose(
|
|
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
|
|
)
|
|
dataset1 = datasets.MNIST("./data", train=True, download=True, transform=transform)
|
|
dataset2 = datasets.MNIST("./data", train=False, transform=transform)
|
|
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
|
|
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
|
|
|
|
model = MnistNet().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)
|
|
test(model, device, test_loader)
|
|
scheduler.step()
|
|
|
|
if args.save_model:
|
|
torch.save(model.state_dict(), "mnist_cnn.pt")
|
|
print("Model saved to mnist_cnn.pt")
|
|
|
|
if __name__ == "__main__":
|
|
main()
|