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encoder-decoder_train_extended_data_4pyramids.py
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encoder-decoder_train_extended_data_4pyramids.py
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"""
Author: Tomasz Hachaj, 2021
Department of Signal Processing and Pattern Recognition
Institute of Computer Science in Pedagogical University of Krakow, Poland
https://sppr.up.krakow.pl/hachaj/
Data source:
https://drive.google.com/file/d/13VIyqFNzQ6zIGmWll9tEHjOdXp5R2GZt/view
https://drive.google.com/file/d/1U8bwYA8PgNuNYQnv5TNtR2az3AleyrEZ/view
https://drive.google.com/file/d/1h5udf2tB64q6-N3lEh0vDhvfIyDOD43N/view
"""
import numpy as np
from keras.applications.vgg16 import VGG16
from keras.applications.nasnet import preprocess_input
import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import Activation
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Concatenate
import os
from keras.callbacks import CSVLogger
from keras.callbacks import LearningRateScheduler
from tensorflow.python.keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import binary_crossentropy
#Run this code 14-teen times changing stimulus_id from 0 to 13
stimulus_id = 9
stimulus = ['ALG_1_v1_Page_1.jpg', 'ALG_1_v2_Page_1.jpg', 'ALG_2_v1_Page_1.jpg', 'ALG_2_v2_Page_1.jpg', 'BIO_Page_1.jpg',
'FIZ_WB1_Page_1.jpg', 'FIZ_WB2.jpg', 'FIZ_WB3_v1_Page_1.jpg', 'FIZ_WB3_v2_Page_1.jpg', 'FIZ_WB4_stereo_Page_1.jpg',
'FIZ_WZORY_Page_1.jpg', 'rz 1_Page_1.jpg', 'rz 2_Page_1.jpg', 'rz 3_Page_1.jpg']
def make_dir_with_check(my_path):
try:
os.mkdir(my_path)
except OSError:
print(my_path + ' exists')
else:
print(my_path + ' created')
make_dir_with_check('data')
make_dir_with_check('res_4pyramids')
make_dir_with_check('checkpoint_4pyramids')
for stim_name in stimulus:
make_dir_with_check('res_4pyramids/' + stim_name)
make_dir_with_check('checkpoint_4pyramids/' + stim_name)
def enable_tensorflow():
#Enables tensorflow on GPU
physical_devices = tf.config.experimental.list_physical_devices('GPU')
for physical_device in physical_devices:
tf.config.experimental.set_memory_growth(physical_device, True)
return physical_devices
physical_devices = enable_tensorflow()
tf.compat.v1.disable_eager_execution()
my_model = VGG16(weights='imagenet', include_top=False)
for layer in my_model.layers[:]:
layer.trainable = False
X = np.load('data/students.np.npy')
Y = np.load('data/students_map_gray.np.npy')
import csv
stimulus_X = []
with open('data/students_stimulus.txt', newline='') as f:
reader = csv.reader(f)
stimulus_X = list(reader)
#string_to_find = stimulus[stimulus_id]
print(my_model.summary())
extractor5 = Model(inputs=my_model.inputs,
outputs=my_model.get_layer("block5_pool").output)
extractor4 = Model(inputs=my_model.inputs,
outputs=my_model.get_layer("block4_pool").output)
extractor3 = Model(inputs=my_model.inputs,
outputs=my_model.get_layer("block3_pool").output)
xx = np.expand_dims(X[0], axis=0)
xx = preprocess_input(xx)
# build the encoder models
encoder5 = Model(extractor5.input, extractor5.output, name="encoder")
encoder4 = Model(extractor4.input, extractor4.output, name="encoder")
encoder3 = Model(extractor3.input, extractor3.output, name="encoder")
def decoder5():
filters = (64, 128, 256, 512, 512)
chanDim = -1
depth_out = 1
dec = encoder5.predict(xx)
print('decoder5')
print(dec.shape)
x = my_model.get_layer("block5_pool").output
for f in filters[::-1]:
# apply a CONV_TRANSPOSE => RELU => BN operation
x = Conv2DTranspose(f, (3, 3), strides=2,
padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2DTranspose(depth_out, (3, 3), padding="same", name='decoder51_out')(x)
outputs = Activation("sigmoid", name='decoder5_out')(x)
decoder = Model(my_model.inputs, outputs, name="decoder")
return decoder
def decoder4():
filters = (64, 128, 256, 512)
chanDim = -1
depth_out = 1
dec = encoder4.predict(xx)
print('decoder4')
print(dec.shape)
x = my_model.get_layer("block4_pool").output
for f in filters[::-1]:
# apply a CONV_TRANSPOSE => RELU => BN operation
x = Conv2DTranspose(f, (3, 3), strides=2,
padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2DTranspose(depth_out, (3, 3), padding="same", name='decoder41_out')(x)
outputs = Activation("sigmoid", name='decoder4_out')(x)
decoder = Model(my_model.inputs, outputs, name="decoder")
return decoder
def decoder3():
filters = (64, 128, 256)
chanDim = -1
depth_out = 1
dec = encoder3.predict(xx)
print('decoder3')
print(dec.shape)
x = my_model.get_layer("block3_pool").output
for f in filters[::-1]:
# apply a CONV_TRANSPOSE => RELU => BN operation
x = Conv2DTranspose(f, (3, 3), strides=2,
padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2DTranspose(depth_out, (3, 3), padding="same", name='decoder31_out')(x)
outputs = Activation("sigmoid", name='decoder3_out')(x)
decoder = Model(my_model.inputs, outputs, name="decoder")
return decoder
def decoder2():
filters = (64, 128)
chanDim = -1
depth_out = 1
dec = encoder3.predict(xx)
print('decoder2')
print(dec.shape)
x = my_model.get_layer("block2_pool").output
for f in filters[::-1]:
# apply a CONV_TRANSPOSE => RELU => BN operation
x = Conv2DTranspose(f, (3, 3), strides=2,
padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2DTranspose(depth_out, (3, 3), padding="same", name='decoder21_out')(x)
outputs = Activation("sigmoid", name='decoder2_out')(x)
decoder = Model(my_model.inputs, outputs, name="decoder")
return decoder
def decoder2345():
attention = Concatenate()([d2.output, d3.output, d4.output, d5.output])
x = Conv2D(1, (3, 3), strides=1, padding="same")(attention)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=-1)(x)
decoder = Model(my_model.inputs, x, name="decoder")
return decoder
d5 = decoder5()
d4 = decoder4()
d3 = decoder3()
d2 = decoder2()
d345 = decoder2345()
#print(my_model.summary())
#exit()
vv = d345.predict(xx)
print(vv.shape)
strings = sum(stimulus_X, [])
substring = stimulus[stimulus_id]
indices_train = [i for i, s in enumerate(strings) if substring not in s]
indices_test = [i for i, s in enumerate(strings) if substring in s]
print(len(indices_train))
print(len(indices_test))
print(len(strings))
X_train = X[indices_train, :, :, :]
X_test = X[indices_test, :, :, :]
Y_train = Y[indices_train, :, :, np.newaxis]
Y_test = Y[indices_test, :, :, np.newaxis]
X_train = X_train.astype("float32") / 255.0
Y_train = Y_train.astype("float32")
X_test = X_test.astype("float32") / 255.0
Y_test = Y_test.astype("float32")
X_2 = np.load('data/images.np.npy')
Y_2 = np.load('data/salMap_gray.np.npy')
X_2 = X_2[:, :, :, :]
Y_2 = Y_2[:, :, :, np.newaxis]
X_2 = X_2.astype("float32") / 255.0
Y_2 = Y_2.astype("float32")
print(X_train.shape)
print(X_2.shape)
X_train = np.concatenate((X_train, X_2), axis=0)
Y_train = np.concatenate((Y_train, Y_2), axis=0)
# checkpoint
latent_size = 512
EPOCHS = 10
BS = 8
path_to_checkpoints = "checkpoint_4pyramids/" + stimulus[stimulus_id]
DataFile = 'results.txt'
csv_logger = CSVLogger(path_to_checkpoints + "/" + DataFile + '.log')
# checkpoint
filepath= path_to_checkpoints + "/improvement-{epoch:02d}-{loss:.5f}-{val_loss:.5f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=False, mode='max', save_weights_only=True)
learning_rate_step = 10
def lr_scheduler(epoch, lr):
if epoch % learning_rate_step == 0 and epoch > 1:
lr = lr * 0.1
print(lr)
return lr
callbacks_list = [checkpoint, LearningRateScheduler(lr_scheduler, verbose=1), csv_logger]
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true - y_pred)
return tf.reduce_mean(squared_difference, axis=-1) # Note the `axis=-1`
def custom_loss(y_true, y_pred):
loss0 = binary_crossentropy(y_true, d2.output)
loss1 = binary_crossentropy(y_true, d3.output)
loss2 = binary_crossentropy(y_true, d4.output)
loss3 = binary_crossentropy(y_true, d5.output)
loss4 = binary_crossentropy(y_true, d345.output)
return (loss0 + loss1 + loss2 + loss3 + loss4) / 5.0
opt = Adam(lr=1e-2)
d345.compile(loss=custom_loss, optimizer=opt)
# train the model
H = d345.fit(
X_train, Y_train,
validation_data=(X_test, Y_test),
epochs=EPOCHS,
batch_size=BS,
callbacks=callbacks_list)