snapshot...

modules/deeplearning/espcn.py

@@ -344,7 +344,7 @@ class ESPCN:
         activation = tf.nn.leaky_relu
         momentum = 0.99
 
-        num_filters = 128
+        num_filters = 64
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
@@ -359,15 +359,17 @@ class ESPCN:
         if NOISE_TRAINING:
             conv = conv_b = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1')
+        scale = None
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2')
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3')
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4')
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5')
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
 
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding)(conv_b)