Cleaned up print statements

1 files changed, 29 insertions, 31 deletions

diff --git a/neural_style.py b/neural_style.py
index 565dbc0..985ad85 100644
--- a/neural_style.py
+++ b/neural_style.py
@@ -4,8 +4,7 @@ import scipy.io
 import argparse 
 import struct
 import time                       
-import cv2 
-import csv
+import cv2
 import os
 
 '''
@@ -88,9 +87,7 @@ def parse_args():
   parser.add_argument('--style_layer_weights', nargs='+', type=float, 
     default=[0.2, 0.2, 0.2, 0.2, 0.2],
     help='Contributions (weights) of each style layer to loss. (default: %(default)s)')
-  
-  parser.add_argument('--style_scale', type=float, default=1.0)
-  
+    
   parser.add_argument('--original_colors', action='store_true',
     help='Transfer the style but not the colors.')
 
@@ -345,17 +342,12 @@ def content_layer_loss(p, x):
   if args.content_loss_function   == 1:
   	K = 1. / (2 * N**0.5 * M**0.5)
   elif args.content_loss_function == 2:
-  	K = 1. / 2.
+    K = 1. / (N * M)
   elif args.content_loss_function == 3:  
-  	K = 1. / (N * M)
+  	K = 1. / 2.
   loss = K * tf.reduce_sum(tf.pow((x - p), 2))
   return loss
 
-def gram_matrix(x, area, depth):
-  F = tf.reshape(x[0], (area, depth))
-  G = tf.matmul(tf.transpose(F), F)
-  return G
-
 def style_layer_loss(a, x):
   _, h, w, d = a.get_shape()
   M = h.value * w.value
@@ -365,6 +357,11 @@ def style_layer_loss(a, x):
   loss = (1./(4 * N**2 * M**2)) * tf.reduce_sum(tf.pow((G - A), 2))
   return loss
 
+def gram_matrix(x, area, depth):
+  F = tf.reshape(x[0], (area, depth))
+  G = tf.matmul(tf.transpose(F), F)
+  return G
+
 def mask_style_layer(a, x, mask_img):
   _, h, w, d = a.get_shape()
   mask = get_mask_image(mask_img, w.value, h.value)
@@ -462,7 +459,7 @@ def sum_shortterm_temporal_losses(net, frame, x):
 '''
   denoising loss function
 
-  remark: not convinced this does anything significant.
+  remark: not sure this does anything significant.
 '''
 def sum_total_variation_losses(x):
   b, h, w, d = x.shape
@@ -480,7 +477,7 @@ def sum_total_variation_losses(x):
   utilities and i/o
 '''
 def read_image(path):
-  # BGR image
+  # bgr image
   img = cv2.imread(path, cv2.IMREAD_COLOR).astype('float')
   img = preprocess(img, vgg19_mean)
   return img
@@ -490,21 +487,19 @@ def write_image(path, img):
   cv2.imwrite(path, img)
 
 def preprocess(img, mean):
-  # BGR to RGB
+  # bgr to rgb
   img = img[...,::-1]
   # shape (h, w, d) to (1, h, w, d)
   img = img[np.newaxis,:,:,:]
-  # subtract mean
   img -= mean
   return img
 
 def postprocess(img, mean):
-  # add mean
   img += mean
   # shape (1, h, w, d) to (h, w, d)
   img = img[0]
   img = np.clip(img, 0, 255).astype('uint8')
-  # RGB to BGR
+  # rgb to bgr
   img = img[...,::-1]
   return img
 
@@ -597,14 +592,14 @@ def stylize(content_img, style_imgs, init_img, frame=None):
       write_image_output(output_img, content_img, style_imgs, init_img)
 
 def minimize_with_lbfgs(sess, net, optimizer, init_img):
-  if args.verbose: print('MINIMIZING LOSS USING: L-BFGS OPTIMIZER')
+  if args.verbose: print('\nMINIMIZING LOSS USING: L-BFGS OPTIMIZER')
   init_op = tf.initialize_all_variables()
   sess.run(init_op)
   sess.run(net['input'].assign(init_img))
   optimizer.minimize(sess)
 
 def minimize_with_adam(sess, net, optimizer, init_img, loss):
-  if args.verbose: print('MINIMIZING LOSS USING: ADAM OPTIMIZER')
+  if args.verbose: print('\nMINIMIZING LOSS USING: ADAM OPTIMIZER')
   train_op = optimizer.minimize(loss)
   init_op = tf.initialize_all_variables()
   sess.run(init_op)
@@ -612,6 +607,9 @@ def minimize_with_adam(sess, net, optimizer, init_img, loss):
   iterations = 0
   while (iterations < args.max_iterations):
     sess.run(train_op)
+    if iterations % args.print_iterations == 0 and args.verbose:
+      curr_loss = loss.eval()
+      print("At iterate {}\tf=  {:.2E}".format(iterations, curr_loss))
     iterations += 1
 
 def get_optimizer(loss):
@@ -650,16 +648,16 @@ def write_image_output(output_img, content_img, style_imgs, init_img):
   # save the configuration settings
   out_file = os.path.join(out_dir, 'meta_data.txt')
   f = open(out_file, 'w')
-  f.write('image name: {}\n'.format(args.img_name))
+  f.write('image_name: {}\n'.format(args.img_name))
   f.write('content: {}\n'.format(args.content_img))
   index = 0
   for style_img, weight in zip(args.style_imgs, args.style_imgs_weights):
-    f.write('styles ['+str(index)+']: {} * {}\n'.format(weight, style_img))
+    f.write('styles['+str(index)+']: {} * {}\n'.format(weight, style_img))
     index += 1
   index = 0
   if args.style_mask_imgs is not None:
     for mask in args.style_mask_imgs:
-      f.write('style masks ['+str(index)+']: {}\n'.format(mask))
+      f.write('style_masks['+str(index)+']: {}\n'.format(mask))
       index += 1
   f.write('init_type: {}\n'.format(args.init_img_type))
   f.write('content_weight: {}\n'.format(args.content_weight))
@@ -698,8 +696,8 @@ def get_content_frame(frame):
   return img
 
 def get_content_image(content_img):
-  # BGR image
   path = os.path.join(args.content_img_dir, content_img)
+   # bgr image
   img = cv2.imread(path, cv2.IMREAD_COLOR).astype('float')
   h, w, d = img.shape
   mx = args.max_size
@@ -713,14 +711,14 @@ def get_content_image(content_img):
   img = preprocess(img, vgg19_mean)
   return img
 
-def get_style_images(content_img, scale):
+def get_style_images(content_img):
+  _, ch, cw, cd = content_img.shape
   style_imgs = []
   for style_fn in args.style_imgs:
     path = os.path.join(args.style_imgs_dir, style_fn)
-    # BGR image
+    # bgr image
     img = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32)
-    _, h, w, d = content_img.shape
-    img = cv2.resize(img, dsize=(int(w*scale), int(h*scale)))
+    img = cv2.resize(img, dsize=(cw, ch))
     img = preprocess(img, vgg19_mean)
     style_imgs.append(img)
   return style_imgs
@@ -796,7 +794,7 @@ def convert_to_original_colors(content_img, stylized_img):
 
 def render_single_image():
   content_img = get_content_image(args.content_img)
-  style_imgs = get_style_images(content_img, args.style_scale)
+  style_imgs = get_style_images(content_img)
   with tf.Graph().as_default():
     print('\n---- RENDERING SINGLE IMAGE ----\n')
     init_img = get_init_image(args.init_img_type, content_img, style_imgs)
@@ -811,7 +809,7 @@ def render_video():
       print('\n---- RENDERING VIDEO FRAME: {}/{} ----\n'.format(frame, args.end_frame))
       if frame == 1:
         content_frame = get_content_frame(frame)
-        style_imgs = get_style_images(content_frame, args.style_scale)
+        style_imgs = get_style_images(content_frame)
         init_img = get_init_image(args.first_frame_type, content_frame, style_imgs, frame)
         args.max_iterations = args.first_frame_iterations
         tick = time.time()
@@ -820,7 +818,7 @@ def render_video():
         print('Frame {} elapsed time: {}'.format(frame, tock - tick))
       else:
         content_frame = get_content_frame(frame)
-        style_imgs = get_style_images(content_frame, args.style_scale)
+        style_imgs = get_style_images(content_frame)
         init_img = get_init_image(args.init_frame_type, content_frame, style_imgs, frame)
         args.max_iterations = args.frame_iterations
         tick = time.time()