######################################################################
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
######################################################################
-parser = argparse.ArgumentParser(description = 'Tiny LeNet-like auto-encoder.')
+parser = argparse.ArgumentParser(description="Tiny LeNet-like auto-encoder.")
-parser.add_argument('--nb_epochs',
- type = int, default = 25)
+parser.add_argument("--nb_epochs", type=int, default=25)
-parser.add_argument('--batch_size',
- type = int, default = 100)
+parser.add_argument("--batch_size", type=int, default=100)
-parser.add_argument('--data_dir',
- type = str, default = './data/')
+parser.add_argument("--data_dir", type=str, default="./data/")
-parser.add_argument('--log_filename',
- type = str, default = 'train.log')
+parser.add_argument("--log_filename", type=str, default="train.log")
-parser.add_argument('--embedding_dim',
- type = int, default = 8)
+parser.add_argument("--embedding_dim", type=int, default=8)
-parser.add_argument('--nb_channels',
- type = int, default = 32)
+parser.add_argument("--nb_channels", type=int, default=32)
args = parser.parse_args()
-log_file = open(args.log_filename, 'w')
+log_file = open(args.log_filename, "w")
######################################################################
+
def log_string(s):
t = time.strftime("%Y-%m-%d_%H:%M:%S - ", time.localtime())
if log_file is not None:
- log_file.write(t + s + '\n')
+ log_file.write(t + s + "\n")
log_file.flush()
print(t + s)
sys.stdout.flush()
+
######################################################################
+
class AutoEncoder(nn.Module):
def __init__(self, nb_channels, embedding_dim):
super(AutoEncoder, self).__init__()
self.encoder = nn.Sequential(
- nn.Conv2d(1, nb_channels, kernel_size = 5), # to 24x24
- nn.ReLU(inplace = True),
- nn.Conv2d(nb_channels, nb_channels, kernel_size = 5), # to 20x20
- nn.ReLU(inplace = True),
- nn.Conv2d(nb_channels, nb_channels, kernel_size = 4, stride = 2), # to 9x9
- nn.ReLU(inplace = True),
- nn.Conv2d(nb_channels, nb_channels, kernel_size = 3, stride = 2), # to 4x4
- nn.ReLU(inplace = True),
- nn.Conv2d(nb_channels, embedding_dim, kernel_size = 4)
+ nn.Conv2d(1, nb_channels, kernel_size=5), # to 24x24
+ nn.ReLU(inplace=True),
+ nn.Conv2d(nb_channels, nb_channels, kernel_size=5), # to 20x20
+ nn.ReLU(inplace=True),
+ nn.Conv2d(nb_channels, nb_channels, kernel_size=4, stride=2), # to 9x9
+ nn.ReLU(inplace=True),
+ nn.Conv2d(nb_channels, nb_channels, kernel_size=3, stride=2), # to 4x4
+ nn.ReLU(inplace=True),
+ nn.Conv2d(nb_channels, embedding_dim, kernel_size=4),
)
self.decoder = nn.Sequential(
- nn.ConvTranspose2d(embedding_dim, nb_channels, kernel_size = 4),
- nn.ReLU(inplace = True),
- nn.ConvTranspose2d(nb_channels, nb_channels, kernel_size = 3, stride = 2), # from 4x4
- nn.ReLU(inplace = True),
- nn.ConvTranspose2d(nb_channels, nb_channels, kernel_size = 4, stride = 2), # from 9x9
- nn.ReLU(inplace = True),
- nn.ConvTranspose2d(nb_channels, nb_channels, kernel_size = 5), # from 20x20
- nn.ReLU(inplace = True),
- nn.ConvTranspose2d(nb_channels, 1, kernel_size = 5), # from 24x24
+ nn.ConvTranspose2d(embedding_dim, nb_channels, kernel_size=4),
+ nn.ReLU(inplace=True),
+ nn.ConvTranspose2d(
+ nb_channels, nb_channels, kernel_size=3, stride=2
+ ), # from 4x4
+ nn.ReLU(inplace=True),
+ nn.ConvTranspose2d(
+ nb_channels, nb_channels, kernel_size=4, stride=2
+ ), # from 9x9
+ nn.ReLU(inplace=True),
+ nn.ConvTranspose2d(nb_channels, nb_channels, kernel_size=5), # from 20x20
+ nn.ReLU(inplace=True),
+ nn.ConvTranspose2d(nb_channels, 1, kernel_size=5), # from 24x24
)
def encode(self, x):
x = self.decoder(x)
return x
+
######################################################################
-train_set = torchvision.datasets.MNIST(args.data_dir + '/mnist/',
- train = True, download = True)
+train_set = torchvision.datasets.MNIST(
+ args.data_dir + "/mnist/", train=True, download=True
+)
train_input = train_set.data.view(-1, 1, 28, 28).float()
-test_set = torchvision.datasets.MNIST(args.data_dir + '/mnist/',
- train = False, download = True)
+test_set = torchvision.datasets.MNIST(
+ args.data_dir + "/mnist/", train=False, download=True
+)
test_input = test_set.data.view(-1, 1, 28, 28).float()
######################################################################
model = AutoEncoder(args.nb_channels, args.embedding_dim)
-optimizer = optim.Adam(model.parameters(), lr = 1e-3)
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
model.to(device)
######################################################################
for epoch in range(args.nb_epochs):
-
acc_loss = 0
for input in train_input.split(args.batch_size):
acc_loss += loss.item()
- log_string('acc_loss {:d} {:f}.'.format(epoch, acc_loss))
+ log_string("acc_loss {:d} {:f}.".format(epoch, acc_loss))
######################################################################
z = model.encode(input)
output = model.decode(z)
-torchvision.utils.save_image(1 - input, 'ae-input.png', nrow = 16, pad_value = 0.8)
-torchvision.utils.save_image(1 - output, 'ae-output.png', nrow = 16, pad_value = 0.8)
+torchvision.utils.save_image(1 - input, "ae-input.png", nrow=16, pad_value=0.8)
+torchvision.utils.save_image(1 - output, "ae-output.png", nrow=16, pad_value=0.8)
# Dumb synthesis
z = z.normal_() * std + mu
output = model.decode(z)
-torchvision.utils.save_image(1 - output, 'ae-synth.png', nrow = 16, pad_value = 0.8)
+torchvision.utils.save_image(1 - output, "ae-synth.png", nrow=16, pad_value=0.8)
######################################################################