snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -31,7 +31,7 @@ NOISE_TRAINING = False
 NOISE_STDDEV = 0.01
 DO_AUGMENT = True
 
-DO_SMOOTH = False
+DO_SMOOTH = True
 SIGMA = 1.0
 DO_ZERO_OUT = False
 
@@ -268,7 +268,8 @@ class SRCNN:
             tmp = np.where(np.isnan(tmp), 0.0, tmp)
             tmp = tmp[:, self.slc_y_m, self.slc_x_m]
             tmp = self.upsample(tmp)
-            if DO_SMOOTH:
+            # if DO_SMOOTH:
+            if False:
                 tmp = smooth_2d(tmp)
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
@@ -456,17 +457,18 @@ class SRCNN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', kernel_size=KERNEL_SIZE, scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', kernel_size=KERNEL_SIZE, scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', kernel_size=KERNEL_SIZE, scale=scale)
 
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b)
 
-        conv = conv + conv_b
+        # conv = conv + conv_b
+        conv = conv_b
         print(conv.shape)
 
         # This is effectively a Dense layer