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V. D., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent + neural networks. arXiv preprint arXiv:1601.06759. + """ + + def __init__(self, h): + super(ResidualBlock, self).__init__(name='') + + self.conv2a = MaskedConv2D(mask_type='B', filters=h//2, kernel_size=1, strides=1) + self.conv2b = MaskedConv2D(mask_type='B', filters=h//2, kernel_size=7, strides=1) + self.conv2c = MaskedConv2D(mask_type='B', filters=h, kernel_size=1, strides=1) + + def call(self, input_tensor): + x = tf.nn.relu(input_tensor) + x = self.conv2a(x) + + x = tf.nn.relu(x) + x = self.conv2b(x) + + x = tf.nn.relu(x) + x = self.conv2c(x) + + x += input_tensor + return x + + +def quantise(images, q_levels): + """Quantise image into q levels.""" + return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32') + + +def main(): + # ------------------------------------------------------------------------------------ + # Defining random seeds + random_seed = 42 + tf.random.set_seed(random_seed) + np.random.seed(random_seed) + rn.seed(random_seed) + + # ------------------------------------------------------------------------------------ +# Loading data +(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() + + height = 32 + width = 32 + n_channel = 3 + + x_train = x_train.astype('float32') / 255. + x_test = x_test.astype('float32') / 255. + + x_train = x_train.reshape(x_train.shape[0], height, width, n_channel) + x_test = x_test.reshape(x_test.shape[0], height, width, n_channel) + + # -------------------------------------------------------------------------------------------------------------- + # Quantisize the input data in q levels + q_levels = 8 + x_train_quantised_of = quantisize(x_train_overfit, q_levels) + x_test_quantised_of = quantisize(x_test_overfit, q_levels) + + # ------------------------------------------------------------------------------------ + # Creating input stream using tf.data API + batch_size = 128 + train_buf = 60000 + + train_dataset = tf.data.Dataset.from_tensor_slices( + (x_train_quantised / (q_levels - 1), + x_train_quantised.astype('int32'))) + train_dataset = train_dataset.shuffle(buffer_size=train_buf) + train_dataset = train_dataset.batch(batch_size) + + test_dataset = tf.data.Dataset.from_tensor_slices((x_test_quantised / (q_levels - 1), + x_test_quantised.astype('int32'))) + test_dataset = test_dataset.batch(batch_size) + + # -------------------------------------------------------------------------------------------------------------- + # Create PixelCNN model + n_filters = 120 + inputs = keras.layers.Input(shape=(height, width, n_channel)) + x = MaskedConv2D(mask_type='A', filters=n_filters, kernel_size=7)(inputs) + + for i in range(15): + x = keras.layers.Activation(activation='relu')(x) + x = ResidualBlock(h=n_filters)(x) + + x = keras.layers.Activation(activation='relu')(x) + x = MaskedConv2D(mask_type='B', filters=n_filters, kernel_size=1)(x) + x = keras.layers.Activation(activation='relu')(x) + x = MaskedConv2D(mask_type='B', filters=n_channel * q_levels, kernel_size=1)(x) # shape [N,H,W,DC] + + pixelcnn = tf.keras.Model(inputs=inputs, outputs=x) + + # -------------------------------------------------------------------------------------------------------------- + # Prepare optimizer and loss function + lr_decay = 0.9999 + learning_rate = 5e-3 #5 + optimizer = tf.keras.optimizers.Adam(lr=learning_rate) + + compute_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) + + # -------------------------------------------------------------------------------------------------------------- + @tf.function + def train_step(batch_x, batch_y): + with tf.GradientTape() as ae_tape: + logits = pixelcnn(batch_x, training=True) + + logits = tf.reshape(logits, [-1, height, width, q_levels, n_channel]) + logits = tf.transpose(logits, perm=[0, 1, 2, 4, 3]) + + loss = compute_loss(tf.one_hot(batch_y, q_levels), logits) + + gradients = ae_tape.gradient(loss, pixelcnn.trainable_variables) + gradients, _ = tf.clip_by_global_norm(gradients, 1.0) + optimizer.apply_gradients(zip(gradients, pixelcnn.trainable_variables)) + + return loss + + # ------------------------------------------------------------------------------------ + # Training loop + n_epochs = 20 + n_iter = int(np.ceil(x_train_quantised.shape[0] / batch_size)) + for epoch in range(n_epochs): + progbar = Progbar(n_iter) + print('Epoch {:}/{:}'.format(epoch + 1, n_epochs)) + + for i_iter, (batch_x, batch_y) in enumerate(train_dataset): + optimizer.lr = optimizer.lr * lr_decay + loss = train_step(batch_x, batch_y) + + progbar.add(1, values=[('loss', loss)]) + + # ------------------------------------------------------------------------------------ + # Test set performance + test_loss = [] + for batch_x, batch_y in test_dataset: + logits = pixelcnn(batch_x, training=False) + + # Calculate cross-entropy (= negative log-likelihood) + loss = compute_loss(tf.squeeze(tf.one_hot(batch_y, q_levels)), logits) + + test_loss.append(loss) + print('nll : {:} nats'.format(np.array(test_loss).mean())) + print('bits/dim : {:}'.format(np.array(test_loss).mean() / np.log(2))) + + # ------------------------------------------------------------------------------------ + # Generating new images + samples = np.zeros((100, height, width, n_channel), dtype='float32') + for i in range(height): + for j in range(width): + logits = pixelcnn(samples) + next_sample = tf.random.categorical(logits[:, i, j, :], 1) + samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0] + + fig = plt.figure(figsize=(10, 10)) + for i in range(100): + ax = fig.add_subplot(10, 10, i + 1) + ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary) + plt.xticks(np.array([])) + plt.yticks(np.array([])) + plt.show() + + # ------------------------------------------------------------------------------------ + # Filling occluded images + occlude_start_row = 16 + num_generated_images = 10 + samples = np.copy(x_test_quantised[0:num_generated_images, :, :, :]) + samples = samples / (q_levels - 1) + samples[:, occlude_start_row:, :, :] = 0 + + fig = plt.figure(figsize=(10, 10)) + + for i in range(10): + ax = fig.add_subplot(1, 10, i + 1) + ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary) + plt.xticks(np.array([])) + plt.yticks(np.array([])) + + for i in range(occlude_start_row, height): + for j in range(width): + for k in range(n_channel): + logits = pixelcnn(samples) + logits = tf.reshape(logits, [-1, height, width, q_levels, n_channel]) + logits = tf.transpose(logits, perm=[0, 1, 2, 4, 3]) + next_sample = tf.random.categorical(logits[:, i, j, k, :], 1) + samples[:, i, j, k] = (next_sample.numpy() / (q_levels - 1))[:, 0] + fig = plt.figure(figsize=(10, 10)) + + for i in range(10): + ax = fig.add_subplot(1, 10, i + 1) + ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary) + plt.xticks(np.array([])) + plt.yticks(np.array([])) + plt.show() + + +if __name__ == '__main__': +main() +