Updated README, requirements, and code.

README.md

@@ -33,7 +33,7 @@ Both the source code and the datasets are provided to foster future research in
 ## Setup
 ### Prerequisites
 
-- Python 2 or 3
+- Python 3.11.x
 - pip
 
 ### Install dependencies

model/build_datasets.py

@@ -37,6 +37,10 @@
 evaluation_samples_number = len(paths) / (distribution + 1)
 training_samples_number = evaluation_samples_number * distribution
 
+evaluation_samples_number = int(evaluation_samples_number+0.5)
+training_samples_number = int(training_samples_number+0.5)
+
+
 assert training_samples_number + evaluation_samples_number == len(paths)
 
 print("Splitting datasets, training samples: {}, evaluation samples: {}".format(training_samples_number, evaluation_samples_number))

model/classes/dataset/Dataset.py

@@ -141,4 +141,4 @@ def sparsify_labels(next_words, voc):
         return temp
 
     def save_metadata(self, path):
-        np.save("{}/meta_dataset".format(path), np.array([self.input_shape, self.output_size, self.size]))
+        np.save("{}/meta_dataset".format(path), np.array([self.input_shape, self.output_size, self.size], dtype=object))

model/classes/dataset/Generator.py

@@ -65,7 +65,7 @@ def data_generator(voc, gui_paths, img_paths, batch_size, generate_binary_sequen
 
                         if verbose:
                             print("Yield batch")
-                        yield ([batch_input_images, batch_partial_sequences], batch_next_words)
+                        yield ((batch_input_images, batch_partial_sequences), batch_next_words)
 
                         batch_input_images = []
                         batch_partial_sequences = []

model/classes/model/pix2code.py

@@ -58,15 +58,15 @@ def __init__(self, input_shape, output_size, output_path):
 
         self.model = Model(inputs=[visual_input, textual_input], outputs=decoder)
 
-        optimizer = RMSprop(lr=0.0001, clipvalue=1.0)
+        optimizer = RMSprop(learning_rate=0.0001, clipvalue=1.0)
         self.model.compile(loss='categorical_crossentropy', optimizer=optimizer)
 
     def fit(self, images, partial_captions, next_words):
         self.model.fit([images, partial_captions], next_words, shuffle=False, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=1)
         self.save()
 
     def fit_generator(self, generator, steps_per_epoch):
-        self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
+        self.model.fit(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)
         self.save()
 
     def predict(self, image, partial_caption):

model/train.py

@@ -4,7 +4,7 @@
 __author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
 
 import tensorflow as tf
-sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
+sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))
 
 import sys
 
@@ -33,7 +33,7 @@ def run(input_path, output_path, is_memory_intensive=False, pretrained_model=Non
 
         input_shape = dataset.input_shape
         output_size = dataset.output_size
-        steps_per_epoch = dataset.size / BATCH_SIZE
+        steps_per_epoch = int(dataset.size / BATCH_SIZE)
 
         voc = Vocabulary()
         voc.retrieve(output_path)

requirements.txt

@@ -1,5 +1,5 @@
-Keras==2.1.2
-numpy==1.13.3
-opencv-python==3.3.0.10
-h5py==2.7.1
-tensorflow==1.4.0
+Keras==3.0.0
+numpy==1.23.5
+opencv-python==4.9.0.80
+h5py==3.10.0
+tensorflow==2.16.1