class ModelHandler:
def __init__(self, load_from=None, image_shape=(320, 480, 3)):
self.config = tf.ConfigProto()
self.config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
self.config.log_device_placement = True # to log device placement (on which device the operation ran)
self.sess = tf.Session(config=self.config)
set_session(self.sess) # set this TensorFlow session as the default session for Keras
self.num_classes = 36
self.sparse = False
self.image_handler = ImageHandler()
if load_from is None:
# initialize new model
self.model = Sequential()
self.model.add(Conv2D(64, 5, strides=(2, 2), padding='same', input_shape=image_shape, activation='relu'))
# self.model.add(ZeroPadding2D(padding=(0, 2)))
self.model.add(Conv2D(64, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
#TODO: removed last
self.model.add(Conv2D(64, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
#self.model.add(Dropout(0.5))
self.model.add(Conv2D(128, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(128, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
#self.model.add(Conv2D(128, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
#self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
#self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
#self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), padding='valid'))
#self.model.add(Dropout(0.5))
self.model.add(UpSampling2D(size=(2, 2)))
# Adding zero padding on one side to compensate for smaller X size at the output
# self.model.add(ZeroPadding2D(padding=(0, 1)))
self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
#self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
#self.model.add(Dropout(0.5))
self.model.add(Conv2D(512, 5, strides=(1, 1), padding='same', activation='relu'))
#self.model.add(Dropout(0.5))
self.model.add(UpSampling2D(size=(2, 2)))
self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(256, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(UpSampling2D(size=(2, 2)))
self.model.add(Conv2D(128, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(128, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(UpSampling2D(size=(2, 2)))
self.model.add(Dropout(0.5))
self.model.add(Conv2D(64, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(Conv2D(64, 5, strides=(1, 1), padding='same', activation='relu'))
self.model.add(UpSampling2D(size=(2, 2), name='to_out'))
# not required
#self.model.add(LocallyConnected2D(32, (3,3)))
#check amount of coonvolution filters to be 255. Older value =1
if self.sparse is not True:
self.model.add(
Conv2D(self.num_classes + 1, 1, strides=(1, 1), padding='same', activation='softmax', name='output'))
out_layer = self.model.get_layer(name='output')
newdim = tuple([x for x in out_layer.output_shape if x != 1 and x is not None])
self.model.add(Reshape(newdim))
optimizer = Adam(lr=0.00001, beta_1=0.995, beta_2=0.999, epsilon=1e-08, decay=0) # learning rate was 0.000025
#optimizer = Adam(lr=0.00001)
#optimizer = SGD(lr=0.01, momentum=0.0, decay=0.99, nesterov=False)
self.model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
else:
self.model.add(
Conv2D(self.num_classes + 1, 1, strides=(1, 1), padding='same', activation='softmax', name='output'))
out_layer = self.model.get_layer(name='output')
newdim = tuple([x for x in out_layer.output_shape if x != 1 and x is not None])
self.model.add(Reshape(newdim))
optimizer = Adam(lr=0.0001)
self.model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
#Reshape(newdim)(out_layer)
else:
# load model
self.model = keras.models.load_model(load_from)
#self.model = self.model.load_weights(load_from)
"""
prev_layer = self.model.get_layer(name='to_out')
last_layer = Conv2D(1, 1, strides=(1, 1), padding='same', activation='softmax')(prev_layer)
newdim = tuple([x for x in last_layer.shape.as_list() if x != 1 and x is not None])
last_layer = Reshape(newdim)(last_layer)
self.model.add(last_layer)
#self.model.add(Activation('softmax'))
"""
def get_model_parameters(self):
return self.model.summary()
def get_model_output(self, x):
return self.model.predict_classes(x)
def input_image_to_array(self, img):
return img_to_array(img)
# normalizes to 22 classes: 21 defined + 1 unidentified
def label_class_normalize(self, label_img, n_classes):
if 0 < n_classes <= 255:
label_img = label_img * 255 / (n_classes+1)
it = np.nditer(label_img, op_flags=['readwrite'])
for x in it:
if x > 1.0:
x[...] = 1.0
def group_bins(self, label_img, n_groups):
hist, bins = np.histogram(label_img, n_groups)
it = np.nditer(label_img, op_flags=['readwrite'])
label_img_reg = label_img
for j in range(n_groups):
for x in range(label_img.shape(0)):
for y in range(label_img.shape(1)):
if label_img[x][y] >= bins[j] and label_img[x][y] < bins[j+1]:
label_img_reg[x][y] = bins[j]
return label_img
def check_exceed_one(self, label_img):
it = np.nditer(label_img, op_flags=['readwrite'])
for x in it:
if x > 1.0:
print("BUG: further check required; X= ", x)
def batch_generator_lrc(self, img_paths_l, img_paths_r, label_paths_l, label_paths_r, batch_size, is_training):
while True:
batch_img = []
batch_label = []
for i in range(batch_size):
random_i = random.randint(0, len(img_paths_l) - 1)
random_b = random.randint(0, 1)
if is_training:
# add augmentation
if random_b:
img, label = self.image_handler.random_augmentation(img_paths_l[random_i],
label_paths_l[random_i])
else:
img, label = self.image_handler.random_augmentation(img_paths_r[random_i],
label_paths_r[random_i])
else:
if random_b:
img, label = self.image_handler.load_resized(img_paths_l[random_i],
label_paths_l[random_i])
else:
img, label = self.image_handler.load_resized(img_paths_r[random_i],
label_paths_r[random_i])
#TODO: insert here visual image check
'''
fig_img, axs = plt.subplots(1, 2, figsize=(15, 50))
fig_img.tight_layout()
axs[0].imshow(img)
axs[0].set_title('Original input image')
axs[1].imshow(label)
axs[1].set_title('Augmented input image')
plt.show(fig_img)
'''
label = to_categorical(label * self.num_classes, num_classes=self.num_classes + 1, dtype='float')
batch_img.append(img)
batch_label.append(label)
yield (np.asarray(batch_img), np.asarray(batch_label))
def batch_generator_lrc_sparse(self, img_paths_l, img_paths_r, label_paths_l, label_paths_r, batch_size, is_training):
while True:
batch_img = []
batch_label = []
for i in range(batch_size):
random_i = random.randint(0, len(img_paths_l) - 1)
if is_training:
# add augmentation
img_l, label_l = self.image_handler.random_augmentation(img_paths_l[random_i],
label_paths_l[random_i])
# img_r, label_r = self.image_handler.random_augmentation(img_paths_r[random_i], label_paths_r[random_i])
else:
# take original image pairs
img_l, label_l = self.image_handler.load_resized(img_paths_l[random_i], label_paths_l[random_i])
# label_l = to_categorical(label_l, num_classes=25)
# img_r, label_r = self.image_handler.load_preprocessed(img_paths_r[random_i], label_paths_r[random_i])
batch_img.append(img_l)
# batch_img.append(img_r)
batch_label.append(label_l)
# batch_label.append(label_r)
yield (np.asarray(batch_img), np.asarray(batch_label))
def model_fit(self, img_paths_l, img_paths_r, label_paths_l, label_paths_r,
img_paths_l_v, img_paths_r_v, label_paths_l_v, label_paths_r_v):
if self.sparse is not True:
model_history = self.model.fit_generator(self.batch_generator_lrc(img_paths_l, img_paths_r, label_paths_l,
label_paths_r, batch_size=1, is_training=1),
steps_per_epoch=200, epochs=30,
validation_data=self.batch_generator_lrc(img_paths_l_v, img_paths_r_v, label_paths_l_v,
label_paths_r_v, batch_size=1, is_training=0),
validation_steps=10, verbose=1, shuffle=1)
else:
model_history = self.model.fit_generator(
self.batch_generator_lrc_sparse(img_paths_l, img_paths_r, label_paths_l,
label_paths_r, batch_size=1, is_training=1),
steps_per_epoch=100, epochs=50,
validation_data=self.batch_generator_lrc_sparse(img_paths_l_v, img_paths_r_v, label_paths_l_v,
label_paths_r_v, batch_size=1, is_training=0),
validation_steps=30, verbose=1, shuffle=1)
return model_history
def model_save(self):
self.model.save('model_x.h5')
def model_plot_result(self, history):
plt.clf()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['training', 'validation'])
plt.title('Loss')
plt.xlabel('Epoch')
plt.show()
plt.clf()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.legend(['training', 'validation'])
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.show()
model_handler.model_save()
model_handler.model_plot_result(history)
else:
model_handler = ModelHandler(load_from='model_x.h5')
model_handler.get_model_parameters()
i_batch = []
i_batch_size = 2
i_batch_start_ind = 20
for ind in range(i_batch_size):
path_i = image_paths_r[i_batch_start_ind + ind]
path_o = output_paths_r[i_batch_start_ind + ind]
i, o = im_handler.load_resized(path_i, path_o)
i = model_handler.input_image_to_array(i)
i_batch.append(i)
i_batch = np.array(i_batch)
#i = np.reshape(i, (None, np.shape(i)))
y_batch = model_handler.get_model_output(i_batch)
y_batch = np.array(y_batch)
fig_img, axs = plt.subplots(i_batch_size, 2, figsize=(25, 50))
fig_img.tight_layout()
# create overlay
