Hi, everyone! I’m stuck with silly problem. Check out my code for toy example:
def train(model, optim, criterion, train_dataloader, val_dataloader, device, epochs):
train_acc_ = []
val_acc_ = []
for ep in range(1, epochs + 1):
model.train(True)
train_acc = 0
for x, y in tqdm(train_dataloader):
optim.zero_grad()
x = x.to(device)
y = y.to(device)
out = model(x)
loss = criterion(out, y)
loss.backward()
optim.step()
train_acc += torch.mean((y == torch.argmax(out, 1)).type(torch.float))
train_acc /= len(train_dataloader)
val_acc = 0
model.train(False)
with torch.no_grad():
for x, y in tqdm(val_dataloader):
x = x.to(device)
y = y.to(device)
out = model(x)
val_acc += torch.mean((y == torch.argmax(out, 1)).type(torch.float))
val_acc /= len(val_dataloader)
train_acc = train_acc.detach().cpu().item()
val_acc = val_acc.detach().cpu().item()
train_acc_.append(train_acc)
val_acc_.append(val_acc)
clear_output()
plt.plot(np.arange(ep), train_acc_, label=f"Train acc (last: {round(train_acc, 3)})")
plt.plot(np.arange(ep), val_acc_, label=f"Val acc (last: {round(val_acc, 3)})")
plt.legend()
plt.show()
return train_acc_, val_acc_
tr = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(train_mean, train_std)
])
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = torchvision.models.resnet18()
model.conv1 = torch.nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
model.fc = torch.nn.Linear(512, 10)
model = model.to(device)
train_data = datasets.CIFAR10("./cifar10_data", train=True, transform=tr)
val_data = datasets.CIFAR10("./cifar10_data", train=False, transform=tr)
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=128)
val_dataloader = torch.utils.data.DataLoader(val_data, batch_size=256)
criterion = torch.nn.CrossEntropyLoss()
optim = torch.optim.Adam(model.parameters())
when I run this code my accuracy plot looks like this
But when I removed model.train(False) in my train function everything is ok.
Where did I go wrong?
