Adjust deconvolution filter size for different scaling factors

model.py

@@ -48,6 +48,8 @@ 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
@@ -65,11 +67,12 @@ def build_model(self):
     d, s, m = self.model_params
 
     expand_weight, deconv_weight = 'w{}'.format(m + 3), 'w{}'.format(m + 4)
+    deconv_size = self.deconv_radius * 2 + 1
     self.weights = {
       'w1': tf.Variable(tf.random_normal([5, 5, 1, d], stddev=0.0378, dtype=tf.float32), name='w1'),
       'w2': tf.Variable(tf.random_normal([1, 1, d, s], stddev=0.3536, dtype=tf.float32), name='w2'),
       expand_weight: tf.Variable(tf.random_normal([1, 1, s, d], stddev=0.189, dtype=tf.float32), name=expand_weight),
-      deconv_weight: tf.Variable(tf.random_normal([9, 9, 1, d], stddev=0.0001, dtype=tf.float32), name=deconv_weight)
+      deconv_weight: tf.Variable(tf.random_normal([deconv_size, deconv_size, 1, d], stddev=0.0001, dtype=tf.float32), name=deconv_weight)
     }
 
     expand_bias, deconv_bias = 'b{}'.format(m + 3), 'b{}'.format(m + 4)