Support for different radiuses for the deconvolution input tensor

Also append radius to the checkpoint directory name.

main.py

@@ -13,6 +13,7 @@
 flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of the adam optimizer [1e-4]")
 flags.DEFINE_integer("c_dim", 1, "Dimension of image color [1]")
 flags.DEFINE_integer("scale", 2, "The size of scale factor for preprocessing input image [2]")
+flags.DEFINE_integer("radius", 1, "Max radius of the deconvolution input tensor [1]")
 flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")
 flags.DEFINE_string("output_dir", "result", "Name of test output directory [result]")
 flags.DEFINE_string("data_dir", "Train", "Name of data directory to train on [FastTrain]")

model.py

@@ -30,14 +30,16 @@ def __init__(self, sess, config):
     self.is_grayscale = (self.c_dim == 1)
     self.epoch = config.epoch
     self.scale = config.scale
+    self.radius = config.radius
     self.batch_size = config.batch_size
     self.learning_rate = config.learning_rate
     self.threads = config.threads
     self.distort = config.distort
     self.params = config.params
 
+    self.padding = 4
     # Different image/label sub-sizes for different scaling factors x2, x3, x4
-    scale_factors = [[24, 40], [18, 42], [16, 48]]
+    scale_factors = [[20 + self.padding, 40], [14 + self.padding, 42], [12 + self.padding, 48]]
     self.image_size, self.label_size = scale_factors[self.scale - 2]
     # Testing uses different strides to ensure sub-images line up correctly
     if not self.train:
@@ -48,8 +50,6 @@ def __init__(self, sess, config):
     # Different model layer counts and filter sizes for FSRCNN vs FSRCNN-s (fast), (d, s, m) in paper
     model_params = [[56, 12, 4], [32, 8, 1]]
     self.model_params = model_params[self.fast]
-
-    self.deconv_radius = [3, 5, 7][self.scale - 2]
 
     self.checkpoint_dir = config.checkpoint_dir
     self.output_dir = config.output_dir
@@ -169,7 +169,8 @@ def model(self):
     d, s, m = self.model_params
 
     # Feature Extraction
-    self.weights['w1'] = tf.get_variable('w1', initializer=tf.random_normal([5, 5, 1, d], stddev=0.0378, dtype=tf.float32))
+    size = self.radius * 2 + 1
+    self.weights['w1'] = tf.get_variable('w1', initializer=tf.random_normal([size, size, 1, d], stddev=0.0378, dtype=tf.float32))
     self.biases['b1'] = tf.get_variable('b1', initializer=tf.zeros([d]))
     conv = self.prelu(tf.nn.conv2d(self.images, self.weights['w1'], strides=[1,1,1,1], padding='VALID') + self.biases['b1'], 1)
 
@@ -196,7 +197,7 @@ def model(self):
       conv = self.prelu(tf.nn.conv2d(conv, expand_weights, strides=[1,1,1,1], padding='SAME') + expand_biases, m + 3)
 
     # Deconvolution
-    deconv_size = self.deconv_radius * 2 + 1
+    deconv_size = self.radius * self.scale * 2 + 1
     deconv_weights = tf.get_variable('w{}'.format(m + 4), initializer=tf.random_normal([deconv_size, deconv_size, 1, d], stddev=0.0001, dtype=tf.float32))
     deconv_biases = tf.get_variable('b{}'.format(m + 4), initializer=tf.zeros([1]))
     self.weights['w{}'.format(m + 4)], self.biases['b{}'.format(m + 4)] = deconv_weights, deconv_biases
@@ -220,7 +221,7 @@ def prelu(self, _x, i):
   def save(self, checkpoint_dir, step):
     model_name = "FSRCNN.model"
     d, s, m = self.model_params
-    model_dir = "%s_%s_%s-%s-%s" % ("fsrcnn", self.label_size, d, s, m)
+    model_dir = "%s_%s_%s-%s-%s_%s" % ("fsrcnn", self.label_size, d, s, m, "r"+str(self.radius))
     checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
 
     if not os.path.exists(checkpoint_dir):
@@ -233,7 +234,7 @@ def save(self, checkpoint_dir, step):
   def load(self, checkpoint_dir):
     print(" [*] Reading checkpoints...")
     d, s, m = self.model_params
-    model_dir = "%s_%s_%s-%s-%s" % ("fsrcnn", self.label_size, d, s, m)
+    model_dir = "%s_%s_%s-%s-%s_%s" % ("fsrcnn", self.label_size, d, s, m, "r"+str(self.radius))
     checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
 
     ckpt = tf.train.get_checkpoint_state(checkpoint_dir)

sort.py

@@ -2,26 +2,29 @@
 
 def main():
   scale = 2
-  radius = [3, 5, 7][scale-2]
+  radius = 2
+  size = radius * scale * 2 + 1
   d = 64        #size of the feature layer
+
   if len(sys.argv) == 2:
     fname=sys.argv[1]
     with open(fname) as f:
         content = f.readlines()
     content = [x.strip() for x in content]
 
+    x=list(reversed(range(scale)))
+    x=x[-1:]+x[:-1]
     xy = []
-    for i in range(0, scale):
-        for j in range(0, scale):
+    for i in x:
+        for j in x:
             xy.append([j, i])
-    xy = list(reversed(xy))
 
     m = []
     for i in range(0, len(xy)):
         xi, yi = xy[i]
-        for x in range(xi, radius*2+1, scale):
-            for y in range(yi, radius*2+1, scale):
-                m.append(y + x*(radius*2+1))
+        for y in range(yi, size, scale):
+            for x in range(xi, size, scale):
+                m.append(y + x * size)
     #print(m)
     content = list(reversed(content))
     sort = [content[m[l]].strip(",") for l in range(0, len(m))]

utils.py

@@ -120,11 +120,9 @@ def modcrop(image, scale=3):
 
 def train_input_worker(args):
   image_data, config = args
-  image_size, label_size, stride, scale, distort = config
+  image_size, label_size, stride, scale, padding, distort = config
 
   single_input_sequence, single_label_sequence = [], []
-  padding = abs(image_size - label_size) // 2 # eg. for 3x: (21 - 11) / 2 = 5
-  label_padding = abs((image_size - 4) - label_size) // 2 # eg. for 3x: (21 - (11 - 4)) / 2 = 7
 
   input_, label_ = preprocess(image_data, scale, distort=distort)
 
@@ -133,10 +131,10 @@ def train_input_worker(args):
   else:
     h, w = input_.shape
 
-  for x in range(0, h - image_size - padding + 1, stride):
-    for y in range(0, w - image_size - padding + 1, stride):
-      sub_input = input_[x + padding : x + padding + image_size, y + padding : y + padding + image_size]
-      x_loc, y_loc = x + label_padding, y + label_padding
+  for x in range(0, h - image_size + 1, stride):
+    for y in range(0, w - image_size + 1, stride):
+      sub_input = input_[x : x + image_size, y : y + image_size]
+      x_loc, y_loc = x + padding, y + padding
       sub_label = label_[x_loc * scale : x_loc * scale + label_size, y_loc * scale : y_loc * scale + label_size]
 
       sub_input = sub_input.reshape([image_size, image_size, 1])
@@ -165,7 +163,7 @@ def thread_train_setup(config):
   pool = Pool(config.threads)
 
   # Distribute |images_per_thread| images across each worker process
-  config_values = [config.image_size, config.label_size, config.stride, config.scale, config.distort]
+  config_values = [config.image_size, config.label_size, config.stride, config.scale, config.padding // 2, config.distort]
   images_per_thread = len(data) // config.threads
   workers = []
   for thread in range(config.threads):
@@ -202,14 +200,12 @@ def train_input_setup(config):
   Read image files, make their sub-images, and save them as a h5 file format.
   """
   sess = config.sess
-  image_size, label_size, stride, scale = config.image_size, config.label_size, config.stride, config.scale
+  image_size, label_size, stride, scale, padding = config.image_size, config.label_size, config.stride, config.scale, config.padding // 2
 
   # Load data path
   data = prepare_data(sess, dataset=config.data_dir)
 
   sub_input_sequence, sub_label_sequence = [], []
-  padding = abs(image_size - label_size) // 2 # eg. for 3x: (21 - 11) / 2 = 5
-  label_padding = abs((image_size - 4) - label_size) // 2 # eg. for 3x: (21 - (11 - 4)) / 2 = 7
 
   for i in range(len(data)):
     input_, label_ = preprocess(data[i], scale, distort=config.distort)
@@ -219,10 +215,10 @@ def train_input_setup(config):
     else:
       h, w = input_.shape
 
-    for x in range(0, h - image_size - padding + 1, stride):
-      for y in range(0, w - image_size - padding + 1, stride):
-        sub_input = input_[x + padding : x + padding + image_size, y + padding : y + padding + image_size]
-        x_loc, y_loc = x + label_padding, y + label_padding
+    for x in range(0, h - image_size + 1, stride):
+      for y in range(0, w - image_size + 1, stride):
+        sub_input = input_[x : x + image_size, y : y + image_size]
+        x_loc, y_loc = x + padding, y + padding
         sub_label = label_[x_loc * scale : x_loc * scale + label_size, y_loc * scale : y_loc * scale + label_size]
 
         sub_input = sub_input.reshape([image_size, image_size, 1])
@@ -242,14 +238,12 @@ def test_input_setup(config):
   Read image files, make their sub-images, and save them as a h5 file format.
   """
   sess = config.sess
-  image_size, label_size, stride, scale = config.image_size, config.label_size, config.stride, config.scale
+  image_size, label_size, stride, scale, padding = config.image_size, config.label_size, config.stride, config.scale, config.padding // 2
 
   # Load data path
   data = prepare_data(sess, dataset="Test")
 
   sub_input_sequence, sub_label_sequence = [], []
-  padding = abs(image_size - label_size) // 2 # eg. (21 - 11) / 2 = 5
-  label_padding = abs((image_size - 4) - label_size) // 2 # eg. for 3x: (21 - (11 - 4)) / 2 = 7
 
   pic_index = 2 # Index of image based on lexicographic order in data folder
   input_, label_ = preprocess(data[pic_index], config.scale)
@@ -260,13 +254,13 @@ def test_input_setup(config):
     h, w = input_.shape
 
   nx, ny = 0, 0
-  for x in range(0, h - image_size - padding + 1, stride):
+  for x in range(0, h - image_size + 1, stride):
     nx += 1
     ny = 0
-    for y in range(0, w - image_size - padding + 1, stride):
+    for y in range(0, w - image_size + 1, stride):
       ny += 1
-      sub_input = input_[x + padding : x + padding + image_size, y + padding : y + padding + image_size]
-      x_loc, y_loc = x + label_padding, y + label_padding
+      sub_input = input_[x : x + image_size, y : y + image_size]
+      x_loc, y_loc = x + padding, y + padding
       sub_label = label_[x_loc * scale : x_loc * scale + label_size, y_loc * scale : y_loc * scale + label_size]
 
       sub_input = sub_input.reshape([image_size, image_size, 1])