classes/model/pix2code2.py

__author__ = 'Ferdiand John Briones, attempt at pix2code2 through pretrained autoencoders'

from keras.layers import Input, Dense, Dropout, RepeatVector, LSTM, concatenate, Flatten
from keras.models import Sequential, Model
# from keras.optimizers import RMSprop
from tensorflow.keras.optimizers import RMSprop
from keras import *
from .Config import *
from .AModel import *
from .autoencoder_image import *

class pix2code2(AModel):
	def __init__(self, input_shape, output_size, output_path):
		AModel.__init__(self, input_shape, output_size, output_path)
		self.name = "pix2code2"

		visual_input = Input(shape=input_shape)

		#Load the pre-trained autoencoder model
		autoencoder_model = autoencoder_image(input_shape, input_shape, output_path)
		autoencoder_model.load('autoencoder')
		autoencoder_model.model.load_weights('../bin/autoencoder.h5')

		#Get only the model up to the encoded part
		hidden_layer_model_freeze = Model(inputs=autoencoder_model.model.input, outputs=autoencoder_model.model.get_layer('encoded_layer').output)
		hidden_layer_input = hidden_layer_model_freeze(visual_input)
		
		#Additional layers before concatenation
		hidden_layer_model = Flatten()(hidden_layer_input)
		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
		hidden_layer_result = RepeatVector(CONTEXT_LENGTH)(hidden_layer_model)

		#Make sure the loaded hidden_layer_model_freeze will no longer be updated
		for layer in hidden_layer_model_freeze.layers:
			layer.trainable = False

		#The same language model that of pix2code by Tony Beltramelli
		language_model = Sequential()
		language_model.add(LSTM(128, return_sequences=True, input_shape=(CONTEXT_LENGTH, output_size)))
		language_model.add(LSTM(128, return_sequences=True))

		textual_input = Input(shape=(CONTEXT_LENGTH, output_size))
		encoded_text = language_model(textual_input)

		decoder = concatenate([hidden_layer_result, encoded_text])

		decoder = LSTM(512, return_sequences=True)(decoder)
		decoder = LSTM(512, return_sequences=False)(decoder)
		decoder = Dense(output_size, activation='softmax')(decoder)

		self.model = Model(inputs=[visual_input, textual_input], outputs=decoder)

		optimizer = RMSprop(lr=0.0001, clipvalue=1.0)
		self.model.compile(loss='categorical_crossentropy', optimizer=optimizer)

	def fit_generator(self, generator, steps_per_epoch):
		self.model.summary()
		self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
		self.save()

	def predict(self, image, partial_caption):
		return self.model.predict([image, partial_caption], verbose=0)[0]

	def predict_batch(self, images, partial_captions):
		return self.model.predict([images, partial_captions], verbose=1)