def inference(from_file_path, args):
with tf.Graph().as_default(), tf.Session() as sess:
alpha = args[0]
encoder = Encoder()
decoder = Decoder()
content_input = tf.placeholder(tf.float32,
shape=(1, None, None, 3),
name='content_input')
style_input = tf.placeholder(tf.float32,
shape=(1, None, None, 3),
name='style_input')
# switch RGB to BGR
content = tf.reverse(content_input, axis=[-1])
style = tf.reverse(style_input, axis=[-1])
# preprocess image
content = encoder.preprocess(content)
style = encoder.preprocess(style)
# encode image
# we should initial global variables before restore model
enc_c_net = encoder.encode(content, 'content/')
enc_s_net = encoder.encode(style, 'style/')
# pass the encoded images to AdaIN
target_features = AdaIN(enc_c_net.outputs,
enc_s_net.outputs,
alpha=alpha)
# decode target features back to image
dec_net = decoder.decode(target_features, prefix="decoder/")
generated_img = dec_net.outputs
# deprocess image
generated_img = encoder.deprocess(generated_img)
# switch BGR back to RGB
generated_img = tf.reverse(generated_img, axis=[-1])
# clip to 0..255
generated_img = tf.clip_by_value(generated_img, 0.0, 255.0)
sess.run(tf.global_variables_initializer())
encoder.restore_model(sess, ENCODER_PATH, enc_c_net)
encoder.restore_model(sess, ENCODER_PATH, enc_s_net)
decoder.restore_model(sess, DECODER_PATH, dec_net)
model_args = (sess, generated_img, content_input, style_input)
if from_file_path:
run_from_file_paths(model_args, args)
else:
return run_from_layers(model_args, args)
def init_session_handler(self):
self.sess = tf.Session()
encoder = Encoder()
decoder = Decoder()
self.content_input = tf.placeholder(tf.float32,
shape=(1, None, None, 3),
name='content_input')
self.style_input = tf.placeholder(tf.float32,
shape=(1, None, None, 3),
name='style_input')
# switch RGB to BGR
content = tf.reverse(self.content_input, axis=[-1])
style = tf.reverse(self.style_input, axis=[-1])
# preprocess image
content = encoder.preprocess(content)
style = encoder.preprocess(style)
# encode image
# we should initial global variables before restore model
enc_c_net = encoder.encode(content, 'content/')
enc_s_net = encoder.encode(style, 'style/')
# pass the encoded images to AdaIN
target_features = transfer_util.AdaIN(enc_c_net.outputs,
enc_s_net.outputs,
alpha=alpha)
# decode target features back to image
dec_net = decoder.decode(target_features, prefix="decoder/")
self.generated_img = dec_net.outputs
# deprocess image
self.generated_img = encoder.deprocess(self.generated_img)
# switch BGR back to RGB
self.generated_img = tf.reverse(self.generated_img, axis=[-1])
# clip to 0..255
self.generated_img = tf.clip_by_value(self.generated_img, 0.0, 255.0)
self.sess.run(tf.global_variables_initializer())
# sess.run(tf.global_variables_initializer())
encoder.restore_model(self.sess, self.encode_path, enc_c_net)
encoder.restore_model(self.sess, self.encode_path, enc_s_net)
decoder.restore_model(self.sess, self.decode_path, dec_net)
style_layer_loss.append(l2_mean + l2_sigma)
style_loss = tf.reduce_sum(style_layer_loss)
style_weight = 2.0
# compute the total loss
loss = content_loss + style_weight * style_loss
# Training step (Only train the decoder params)
train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(
loss, var_list=stylied_dec_net.all_params)
sess.run(tf.global_variables_initializer())
encoder.restore_model(sess, ENCODER_PATH, content_enc_net)
encoder.restore_model(sess, ENCODER_PATH, style_enc_net)
encoder.restore_model(sess, ENCODER_PATH, stylied_enc_net)
# """Start Training"""
step = 0
n_batches = int(num_imgs // BATCH_SIZE)
elapsed_time = datetime.now() - start_time
print(
'\nElapsed time for preprocessing before actually train the model: %s'
% elapsed_time)
print('Now begin to train the model...\n')
start_time = datetime.now()
dec_net = decoder.decode(target_features, prefix="decoder/")
generated_img = dec_net.outputs
# deprocess image
generated_img = encoder.deprocess(generated_img)
# switch BGR back to RGB
generated_img = tf.reverse(generated_img, axis=[-1])
# clip to 0..255
generated_img = tf.clip_by_value(generated_img, 0.0, 255.0)
sess.run(tf.global_variables_initializer())
encoder.restore_model(sess, ENCODER_PATH, enc_c_net)
encoder.restore_model(sess, ENCODER_PATH, enc_s_net)
decoder.restore_model(sess, DECODER_PATH, dec_net)
start_time = datetime.now()
image_count = 0
for s in style_images:
for c in content_images:
image_count = image_count + 1
# Load image from path and add one extra diamension to it.
content_image = imread(os.path.join(content_path, c),
mode='RGB')
style_image = imread(os.path.join(style_path, s), mode='RGB')
content_tensor = np.expand_dims(content_image, axis=0)
style_tensor = np.expand_dims(style_image, axis=0)
