optimizer = optim.Adam(net.parameters(), lr=lr)
scheduler = optim.lr_scheduler.StepLR(optimizer, 5)
net.to(device)
criterion.to(device)
train_loss = []
viz = Visdom()
for epoch in range(epochs):
train_loss.clear()
net.train()
for step, (x, _) in enumerate(train_loader):
x = x.to(device)
x_hat = net(x)
loss = criterion(x_hat, x)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss.append(loss.item())
if step%50==0:
print('epoch:{} batch:{} loss:{:.6f}'.format(epoch, step, loss.item()))
scheduler.step()
net.eval()
x, _= next(iter(test_loader))
x = x.to(device)
x_hat = net(x)
viz.images(x, nrow=6, win='x', opts=dict(title='x'))
viz.images(x_hat, nrow=6, win='x_hat', opts=dict(title='x_hat'))
if (epoch+1)%10==0:
torch.save(net, 'encoder_decoder/ae_{}.pkl'.format(epoch))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
test_transform = transforms.Compose([
transforms.ToTensor(),
])
testset = torchvision.datasets.ImageFolder(val_data_folder,
transform=test_transform)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=20)
model = AE(K=K).to(device)
model = nn.DataParallel(model, device_ids=[0])
model.load_state_dict(
torch.load(saved_model_name, map_location={'cuda:1': 'cuda:0'}))
if not os.path.exists(save_folder_name):
os.makedirs(save_folder_name)
with tqdm(total=len(test_loader), desc="Batches") as pbar:
for i, (data) in enumerate(test_loader):
model.eval()
img, labels = data
encoded, out, hashed = model(img)
torch.save(out, save_folder_name + "/out/out_{}.pt".format(i))
torch.save(labels, save_folder_name + "/lab/lab_{}.pt".format(i))
torch.save(hashed, save_folder_name + "/hash/hash_{}.pt".format(i))
pbar.update(1)
