def train_ae(dataloaders_dict, device=0, num_epochs=5):
ae = AutoEncoder()
ae = ae.to(device)
distance = nn.MSELoss()
optimizer = optim.Adam(ae.parameters(), lr=0.001)
for epoch in range(num_epochs):
print('\nEpoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
for phase in ["train", "val", "test"]:
if phase == "train":
ae.train() # Set ae to training mode
else:
ae.eval()
for data in dataloaders_dict[phase]:
inputs, _ = data
inputs = inputs.to(device)
with torch.set_grad_enabled(phase == "train"):
output = ae(inputs)
loss = distance(output, inputs)
optimizer.zero_grad()
if phase == "train":
loss.backward()
optimizer.step()
print("{} Loss: {:.4f}".format(phase, loss.item()))
return ae
global args
args = parser.parse_args()
'''set the training gpu'''
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpuid)
'''load_data'''
Load_data = load_data()
test_data,test_gt = Load_data.test()
'''init model'''
autoencoder = AutoEncoder()
autoencoder.load_state_dict(torch.load(args.load_weight_dir)) #load pre-train
autoencoder.cuda()
autoencoder.eval()
loss_func = nn.L1Loss()
loss = 0
with torch.no_grad(): #it can save the memory,prevent it allocated,we dont need to keep the grad during the evualation
for index in range(0,test_data.size()[0],50):
x_in = torch.tensor(test_data[index:index+49,:,:,:], dtype=torch.float32).cuda()
decoded = autoencoder(x_in)
loss = loss+loss_func(decoded, x_in) # L1 loss
'''pick some sample to check vision performance'''
plt.title('autoencoder input')
plt.imshow(x_in[0,0,:,:].data.cpu().numpy(), cmap='gray')
plt.show()
plt.title('autoencoder outoput')
plt.imshow(decoded[0,0,:,:].data.cpu().numpy(), cmap='gray')
torch.save(model.state_dict(), 'conv_autoencoder_weight.pt')
torch.save(optimizer.state_dict(), 'conv_autoencoder_optimizer.pt')
data = []
print('Reading Images')
for i in tqdm(range(len(os.listdir(image_filepath)[0:3]))):
img_name = os.listdir(image_filepath)[i]
img_name = image_filepath + img_name
img = plt.imread(img_name)
data.append(img)
CUDA = torch.cuda.is_available()
model = AutoEncoder()
model.eval()
model.load_state_dict(
torch.load('conv_autoencoder_weight.pt', map_location='cpu'))
# optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay =1e-5)
# optimizer.load_state_dict(torch.load('conv_autoencoder_optimizer.pt'))
if CUDA:
model = AutoEncoder().cuda()
for epoch in (range(num_epochs)):
dataset = random.sample(data, batch_size)
for image in dataset:
image, orig_im, dim, w, h = prep_image(image, 256)
image = Variable(torch.tensor(image))
img = image.view(1, image.shape[2], image.shape[0], image.shape[1])