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VQ-VAE / app.py

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	import tensorflow as tf
	import gradio as gr
	import matplotlib.pyplot as plt
	import numpy as np

	model = tf.saved_model.load('VQ-VAE-Model')

	class VectorQuantizer(tf.keras.layers.Layer):
	def __init__(self, num_embeddings, embedding_dim, beta=0.25, **kwargs):
	super().__init__(**kwargs)
	self.embedding_dim = embedding_dim
	self.num_embeddings = num_embeddings
	self.beta = (
	beta # This parameter is best kept between [0.25, 2] as per the paper.
	)

	# Initialize the embeddings which we will quantize.
	w_init = tf.random_uniform_initializer()
	self.embeddings = tf.Variable(
	initial_value=w_init(
	shape=(self.embedding_dim, self.num_embeddings), dtype="float32"
	),
	trainable=True,
	name="embeddings_vqvae",
	)

	def call(self, x):
	# Calculate the input shape of the inputs and
	# then flatten the inputs keeping `embedding_dim` intact.
	input_shape = tf.shape(x)
	flattened = tf.reshape(x, [-1, self.embedding_dim])

	# Quantization.
	encoding_indices = self.get_code_indices(flattened)
	encodings = tf.one_hot(encoding_indices, self.num_embeddings)
	quantized = tf.matmul(encodings, self.embeddings, transpose_b=True)
	quantized = tf.reshape(quantized, input_shape)

	# Calculate vector quantization loss and add that to the layer. You can learn more
	# about adding losses to different layers here:
	# https://keras.io/guides/making_new_layers_and_models_via_subclassing/. Check
	# the original paper to get a handle on the formulation of the loss function.
	commitment_loss = self.beta * tf.reduce_mean(
	(tf.stop_gradient(quantized) - x) ** 2
	)
	codebook_loss = tf.reduce_mean((quantized - tf.stop_gradient(x)) ** 2)
	self.add_loss(commitment_loss + codebook_loss)

	# Straight-through estimator.
	quantized = x + tf.stop_gradient(quantized - x)
	return quantized

	def get_code_indices(self, flattened_inputs):
	# Calculate L2-normalized distance between the inputs and the codes.
	similarity = tf.matmul(flattened_inputs, self.embeddings)
	distances = (
	tf.reduce_sum(flattened_inputs ** 2, axis=1, keepdims=True)
	+ tf.reduce_sum(self.embeddings ** 2, axis=0)
	- 2 * similarity
	)

	# Derive the indices for minimum distances.
	encoding_indices = tf.argmin(distances, axis=1)
	return encoding_indices

	vq_object = VectorQuantizer(64, 16)
	embs = np.load('embeddings.npy')
	vq_object.embeddings = embs
	model = tf.keras.models.load_model('VQ-VAE', custom_objects={'vector_quantizer':vq_object})
	encoder = model.layers[1]

	#data load and preprocess
	_, (x_test, _) = tf.keras.datasets.mnist.load_data()
	x_test = np.expand_dims(x_test, -1)
	x_test_scaled = (x_test / 255.0) - 0.5

	def make_subplot_reconstruction(original, reconstructed):
	fig, axs = plt.subplots(3,2)
	for row_idx in range(3):
	axs[row_idx,0].imshow(original[row_idx].squeeze() + 0.5);
	axs[row_idx,0].axis('off')
	axs[row_idx,1].imshow(reconstructed[row_idx].squeeze() + 0.5);
	axs[row_idx,1].axis('off')

	axs[0,0].title.set_text("Original")
	axs[0,1].title.set_text("Reconstruction")
	plt.tight_layout()
	fig.set_size_inches(10, 10.5)
	return fig

	def make_subplot_latent(original, reconstructed):
	fig, axs = plt.subplots(3,2)
	for row_idx in range(3):
	axs[row_idx,0].matshow(original[row_idx].squeeze());
	axs[row_idx,0].axis('off')

	axs[row_idx,1].matshow(reconstructed[row_idx].squeeze());
	axs[row_idx,1].axis('off')
	for i in range(7):
	for j in range(7):
	c = reconstructed[row_idx][i,j]
	axs[row_idx,1].text(i, j, str(c), va='center', ha='center')

	axs[0,0].title.set_text("Original")
	axs[0,1].title.set_text("Discrete Latent Representation")
	plt.tight_layout()
	fig.set_size_inches(10, 10.5)
	return fig

	def plot_sample(mode):
	sample = np.random.choice(x_test.shape[0], 3)
	test_images = x_test_scaled[sample]
	if mode=='Reconstruction':
	reconstructions_test = model.predict(test_images)
	return make_subplot_reconstruction(test_images, reconstructions_test)
	encoded_out = encoder.predict(test_images)
	encoded = encoded_out.reshape(-1, encoded_out.shape[-1])
	quant = vq_object.get_code_indices(encoded)
	quant = quant.numpy().reshape(encoded_out.shape[:-1])

	return make_subplot_latent(test_images, quant)

	import gradio as gr
	radio = gr.Radio(choices=['Reconstruction','Latent Representation'])
	out = gr.Plot()

	gr.Interface(plot_sample, radio, out).launch()