def summarize()

in reconstruction_model.py [0:0]

• 17 lines of code
• 2 McCabe index (conditional complexity)

	def summarize(self):

		# scalar and histogram stats
		stat = [
		tf.summary.scalar('reflect_error',self.reflect_loss),
		tf.summary.scalar('gamma_error',self.gamma_loss),
		tf.summary.scalar('id_sim_error',self.perceptual_loss),
		tf.summary.scalar('lm_error',tf.sqrt(self.landmark_loss)),
		tf.summary.scalar('photo_error',self.photo_loss),
		tf.summary.scalar('train_error',self.loss),
		tf.summary.histogram('id_coeff',self.Face3D.id_coeff),
		tf.summary.histogram('ex_coeff',self.Face3D.ex_coeff),
		tf.summary.histogram('tex_coeff',self.Face3D.tex_coeff)]

		self.summary_stat = tf.summary.merge(stat)
		# combine face region of reconstruction images with input images
		render_imgs = self.Face3D.render_imgs[:,:,:,::-1]*self.Face3D.img_mask + tf.cast(self.imgs[:,:,:,::-1],tf.float32)*(1-self.Face3D.img_mask)
		render_imgs = tf.clip_by_value(render_imgs,0,255)
		render_imgs = tf.cast(render_imgs,tf.uint8)
		# image stats
		img_stat = [tf.summary.image('imgs',tf.concat([tf.cast(self.imgs[:,:,:,::-1],tf.uint8),render_imgs],axis = 2), max_outputs = 8)]
		self.summary_img = tf.summary.merge(img_stat)