snapshot...

modules/deeplearning/cnn_cld_frac_mod_res.py

@@ -40,7 +40,6 @@ DO_AUGMENT = True
 DO_SMOOTH = False
 SIGMA = 1.0
 DO_ZERO_OUT = False
-DO_ESPCN = False  # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
 
 # setup scaling parameters dictionary
 mean_std_dct = {}
@@ -103,12 +102,6 @@ elif KERNEL_SIZE == 5:
     x_2 = np.arange(68)
     y_2 = np.arange(68)
 # ----------------------------------------
-# Exp for ESPCN version
-if DO_ESPCN:
-    slc_x_2 = slice(0, 132, 2)
-    slc_y_2 = slice(0, 132, 2)
-    x_128 = slice(2, 130)
-    y_128 = slice(2, 130)
 
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
@@ -340,10 +333,7 @@ class SRCNN:
             idx = params.index(param)
             tmp = input_data[:, idx, :, :]
             tmp = tmp.copy()
-            if DO_ESPCN:
-                tmp = tmp[:, slc_y_2, slc_x_2]
-            else:
-                tmp = tmp[:, slc_y, slc_x]
+            tmp = tmp[:, slc_y, slc_x]
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
@@ -365,16 +355,12 @@ class SRCNN:
         # ---------------------------------------------------
         tmp = input_data[:, label_idx, :, :]
         tmp = tmp.copy()
-        if DO_ESPCN:
-            tmp = tmp[:, slc_y_2, slc_x_2]
-        else:
-            tmp = tmp[:, slc_y, slc_x]
-        if label_param != 'cloud_probability':
-            tmp = normalize(tmp, label_param, mean_std_dct)
+        tmp = tmp[:, slc_y, slc_x]
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
+
         # -----------------------------------------------------
         # -----------------------------------------------------
         label = input_label[:, label_idx_i, :, :]
@@ -385,10 +371,7 @@ class SRCNN:
         else:
             label = get_label_data(label)
 
-        if label_param != 'cloud_probability':
-            label = normalize(label, label_param, mean_std_dct)
-        else:
-            label = np.where(np.isnan(label), 0, label)
+        label = np.where(np.isnan(label), 0, label)
         label = np.expand_dims(label, axis=3)
 
         data = data.astype(np.float32)
@@ -519,15 +502,8 @@ class SRCNN:
         else:
             final_activation = tf.nn.softmax  # For multi-class
 
-        if not DO_ESPCN:
-            # This is effectively a Dense layer
-            self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, activation=final_activation)(conv)
-        else:
-            conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding=padding, activation=activation)(conv)
-            print(conv.shape)
-            conv = tf.nn.depth_to_space(conv, factor)
-            print(conv.shape)
-            self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=3, strides=1, padding=padding, activation=final_activation)(conv)
+        # This is effectively a Dense layer
+        self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, activation=final_activation)(conv)
         print(self.logits.shape)
 
     def build_training(self):