model = Transporter(encodings, DISTR, FOLDER, BATCH_SIZE_GEN)
elif MODEL == 'generator':
model = Generator(encodings, DISTR, FOLDER, BATCH_SIZE_GEN) # I Could try L2 Loss instead of L1?
else:
raise NotImplementedError
# Train the Latent Space Model
if GEN_LOAD:
model.load_weights(MODEL)
else:
model.train(STEPS, lr=0.001) # I should try adjusting the learning rate?
#model.train(STEPS//2, lr=0.0003)
#model.train(STEPS//2, lr=0.0001)
# Display Results
fake_distr = model.generate(batches=1)
fake_img = ae.decode(fake_distr)
fake_img = np.reshape(fake_img, ((BATCH_SIZE_GEN,) + shape))
save_image(torch.Tensor(fake_img[0:64]), os.path.join(FOLDER, "final.png"))
# Save Images in a file for later evaluation
eval_distr = model.generate(batches=10000//BATCH_SIZE_GEN)
eval_img = ae.decode(eval_distr)
np.save("{}/distribution.npy".format(FOLDER), eval_distr)
np.save("{}/images.npy".format(FOLDER), eval_img)
#channels_last = np.rollaxis(fake_img[0:16], 1, 4)
#display_img(channels_last, columns=4)
y = np.array(labels)
horse_indices = np.where(y == 7)[0]
horse_x = x[horse_indices]
print(np.shape(horse_x)) # (5000, 3072)
# Train the autoencoder on images of horses
input_dim = np.shape(horse_x)[1]
hidden_dim = 100
ae = Autoencoder(input_dim, hidden_dim)
ae.train(horse_x)
# Test the autoencoder on other images
test_data = unpickle('../cifar-10-batches-py/test_batch')
test_x = grayscale(test_data['data'])
test_labels = np.array(test_data['labels'])
encodings = ae.classify(test_x, test_labels)
# Decode images
numberOfImages = 4
plt.rcParams['figure.figsize'] = (100, 100)
plt.figure()
for i in range(numberOfImages):
plt.subplot(numberOfImages, 2, i * 2 + 1)
original_img = np.reshape(test_x[i, :], (32, 32))
plt.imshow(original_img, cmap='Greys_r')
plt.subplot(numberOfImages, 2, i * 2 + 2)
reconstructed_img = ae.decode([encodings[i]])
plt.imshow(reconstructed_img, cmap='Greys_r')
plt.show()
from tqdm import tqdm
from autoencoder import Autoencoder
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
AUTOENCODER_FILENAME = 'trained_models/autoencoder.to'
from image_loader import ImageDataset
dataset = ImageDataset(return_hashes=True)
data_loader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=4)
autoencoder = Autoencoder()
autoencoder.load_state_dict(torch.load(AUTOENCODER_FILENAME))
autoencoder.eval()
with torch.no_grad():
for sample in tqdm(data_loader):
image, hash = sample
hash = hash[0]
output = autoencoder.decode(
autoencoder.encode(image.to(device).unsqueeze(0)))
result = torch.zeros((3, 128, 256))
result[:, :, :128] = image.cpu()
result[:, :, 128:] = output
utils.save_image(result, 'data/test/{:s}.jpg'.format(hash))
plt.imshow(view_data[i].squeeze())
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# reconstructed image
ax = plt.subplot(2 * N_ROWS, N_COLS, 2 * r * N_COLS + c + N_COLS)
x = Variable(view_data[i])
_, y = encoder_net(x.view(1, -1))
plt.imshow(y.detach().squeeze().numpy().reshape(28, 28))
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
elif args.vis == "gmmn":
print("Images generated by GMMN")
gmm_net = GMMN(NOISE_SIZE, ENCODED_SIZE)
gmm_net.load_state_dict(torch.load(GMMN_SAVE_PATH))
for r in range(N_ROWS):
for c in range(N_COLS):
ax = plt.subplot(N_ROWS, N_COLS, r * N_COLS + c + 1)
noise = torch.rand((1, NOISE_SIZE)) * 2 - 1
encoded_x = gmm_net(Variable(noise))
y = encoder_net.decode(encoded_x)
plt.imshow(y.detach().squeeze().numpy().reshape(28, 28))
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
horse_x = x[horse_indices]
print(np.shape(horse_x)) # (5000, 3072)
input_dim = np.shape(horse_x)[1]
hidden_dim = 100
ae = Autoencoder(input_dim, hidden_dim)
ae.train(horse_x)
test_data = unpickle('./cifar-10-batches-py/test_batch')
test_x = grayscale(test_data['data'])
test_labels = np.array(test_data['labels'])
encoding = ae.classify(test_x, test_labels)
encoding = np.matrix(encoding)
from matplotlib import pyplot as plt
# encoding = np.matrix(np.random.choice([0, 1], size=(hidden_dim,)))
original_img = np.reshape(test_x[7, :], (32, 32))
plt.imshow(original_img, cmap='Greys_r')
plt.show()
print(np.size(encoding))
while (True):
img = ae.decode(encoding)
plt.imshow(img, cmap='Greys_r')
plt.show()
rand_idx = np.random.randint(np.size(encoding))
encoding[0, rand_idx] = np.random.randint(2)
SAMPLE_SIZE = 400
indices = [int(i / SAMPLE_SIZE * len(dataset)) for i in range(SAMPLE_SIZE)]
dataset.hashes = [dataset.hashes[i] for i in indices]
data_loader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=4)
autoencoder = Autoencoder(is_variational=USE_VARIATIONAL_AUTOENCODER)
autoencoder.load_state_dict(torch.load(AUTOENCODER_FILENAME))
autoencoder.eval()
STEPS = 5
with torch.no_grad():
for sample in tqdm(data_loader):
image, hash = sample
hash = hash[0]
latent_code = autoencoder.encode(image.to(device)).unsqueeze(0)
result = torch.zeros((3, 128, 128 * (STEPS + 1)))
result[:, :, :128] = image.cpu()
for i in range(STEPS):
output = autoencoder.decode(latent_code * (1.0 - i / (STEPS - 1)))
result[:, :, 128 * (i + 1):128 * (i + 2)] = output
result = (torch.clamp(result, 0, 1).numpy() * 255).astype(
np.uint8).transpose((1, 2, 0))
io.imsave('data/test/{:s}.jpg'.format(hash), result, quality=99)
