#241: Code Not Compatible with Latest Libraries (tensorflow 2 and scipy.misc)

evaluate.py

@@ -26,12 +26,12 @@ def ffwd_video(path_in, path_out, checkpoint_dir, device_t='/gpu:0', batch_size=
                                                     ffmpeg_params=None)
 
     g = tf.Graph()
-    soft_config = tf.ConfigProto(allow_soft_placement=True)
+    soft_config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
     soft_config.gpu_options.allow_growth = True
     with g.as_default(), g.device(device_t), \
-            tf.Session(config=soft_config) as sess:
+            tf.compat.v1.Session(config=soft_config) as sess:
         batch_shape = (batch_size, video_clip.size[1], video_clip.size[0], 3)
-        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
+        img_placeholder = tf.compat.v1.placeholder(tf.float32, shape=batch_shape,
                                          name='img_placeholder')
 
         preds = transform.net(img_placeholder)
@@ -82,16 +82,16 @@ def ffwd(data_in, paths_out, checkpoint_dir, device_t='/gpu:0', batch_size=4):
     g = tf.Graph()
     batch_size = min(len(paths_out), batch_size)
     curr_num = 0
-    soft_config = tf.ConfigProto(allow_soft_placement=True)
+    soft_config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
     soft_config.gpu_options.allow_growth = True
     with g.as_default(), g.device(device_t), \
-            tf.Session(config=soft_config) as sess:
+            tf.compat.v1.Session(config=soft_config) as sess:
         batch_shape = (batch_size,) + img_shape
-        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
+        img_placeholder = tf.compat.v1.placeholder(tf.float32, shape=batch_shape,
                                          name='img_placeholder')
 
         preds = transform.net(img_placeholder)
-        saver = tf.train.Saver()
+        saver = tf.compat.v1.train.Saver()
         if os.path.isdir(checkpoint_dir):
             ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
             if ckpt and ckpt.model_checkpoint_path:

src/transform.py

@@ -29,8 +29,7 @@ def _conv_layer(net, num_filters, filter_size, strides, relu=True):
 
 def _conv_tranpose_layer(net, num_filters, filter_size, strides):
     weights_init = _conv_init_vars(net, num_filters, filter_size, transpose=True)
-
-    batch_size, rows, cols, in_channels = [i.value for i in net.get_shape()]
+    batch_size, rows, cols, in_channels = [i for i in net.get_shape()]
     new_rows, new_cols = int(rows * strides), int(cols * strides)
     # new_shape = #tf.pack([tf.shape(net)[0], new_rows, new_cols, num_filters])
 
@@ -47,21 +46,21 @@ def _residual_block(net, filter_size=3):
     return net + _conv_layer(tmp, 128, filter_size, 1, relu=False)
 
 def _instance_norm(net, train=True):
-    batch, rows, cols, channels = [i.value for i in net.get_shape()]
+    batch, rows, cols, channels = [i for i in net.get_shape()]
     var_shape = [channels]
-    mu, sigma_sq = tf.nn.moments(net, [1,2], keep_dims=True)
+    mu, sigma_sq = tf.nn.moments(net, [1,2], keepdims=True)
     shift = tf.Variable(tf.zeros(var_shape))
     scale = tf.Variable(tf.ones(var_shape))
     epsilon = 1e-3
     normalized = (net-mu)/(sigma_sq + epsilon)**(.5)
     return scale * normalized + shift
 
 def _conv_init_vars(net, out_channels, filter_size, transpose=False):
-    _, rows, cols, in_channels = [i.value for i in net.get_shape()]
+    _, rows, cols, in_channels = [i for i in net.get_shape()]
     if not transpose:
         weights_shape = [filter_size, filter_size, in_channels, out_channels]
     else:
         weights_shape = [filter_size, filter_size, out_channels, in_channels]
 
-    weights_init = tf.Variable(tf.truncated_normal(weights_shape, stddev=WEIGHTS_INIT_STDEV, seed=1), dtype=tf.float32)
+    weights_init = tf.Variable(tf.compat.v1.truncated_normal(weights_shape, stddev=WEIGHTS_INIT_STDEV, seed=1), dtype=tf.float32)
     return weights_init

src/utils.py

@@ -1,23 +1,23 @@
-import scipy.misc, numpy as np, os, sys
+import imageio, skimage.transform, numpy as np, os, sys
 
 def save_img(out_path, img):
     img = np.clip(img, 0, 255).astype(np.uint8)
-    scipy.misc.imsave(out_path, img)
+    imageio.imsave(out_path, img)
 
 def scale_img(style_path, style_scale):
     scale = float(style_scale)
-    o0, o1, o2 = scipy.misc.imread(style_path, mode='RGB').shape
+    o0, o1, o2 = imageio.imread(style_path, pilmode='RGB').shape
     scale = float(style_scale)
     new_shape = (int(o0 * scale), int(o1 * scale), o2)
     style_target = _get_img(style_path, img_size=new_shape)
     return style_target
 
 def get_img(src, img_size=False):
-   img = scipy.misc.imread(src, mode='RGB') # misc.imresize(, (256, 256, 3))
+   img = imageio.imread(src, pilmode='RGB') # misc.imresize(, (256, 256, 3))
    if not (len(img.shape) == 3 and img.shape[2] == 3):
        img = np.dstack((img,img,img))
    if img_size != False:
-       img = scipy.misc.imresize(img, img_size)
+       img = skimage.transform.imresize(img, img_size)
    return img
 
 def exists(p, msg):