snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -56,7 +56,8 @@ label_param = 'cld_opd_dcomp'
 
 params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
 params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
-data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
+# data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
+data_params_half = ['temp_11_0um_nom']
 data_params_full = ['refl_0_65um_nom']
 sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
 # sub_fields = ['refl_stddev3x3_ch01']
@@ -209,7 +210,7 @@ class SRCNN:
         self.test_label_files = None
 
         # self.n_chans = len(data_params_half) + len(data_params_full) + 1
-        self.n_chans = 6
+        self.n_chans = 3
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -269,6 +270,11 @@ class SRCNN:
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
+        tmp = input_label[:, label_idx_i, :, :]
+        tmp = np.where(np.isnan(tmp), 0, tmp)
+        tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+        data_norm.append(tmp[:, self.slc_y, self.slc_x])
+
         tmp = input_label[:, label_idx_i, :, :]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0.0, tmp)
@@ -291,16 +297,16 @@ class SRCNN:
         #         tmp = np.where(np.isnan(tmp), 0.0, tmp)
         #     data_norm.append(tmp)
 
-        for param in sub_fields:
-            idx = params.index(param)
-            tmp = input_data[:, idx, :, :]
-            tmp = upsample_nearest(tmp)
-            tmp = tmp[:, self.slc_y, self.slc_x]
-            if param != 'refl_substddev_ch01':
-                tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
-            else:
-                tmp = np.where(np.isnan(tmp), 0, tmp)
-            data_norm.append(tmp)
+        # for param in sub_fields:
+        #     idx = params.index(param)
+        #     tmp = input_data[:, idx, :, :]
+        #     tmp = upsample_nearest(tmp)
+        #     tmp = tmp[:, self.slc_y, self.slc_x]
+        #     if param != 'refl_substddev_ch01':
+        #         tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+        #     else:
+        #         tmp = np.where(np.isnan(tmp), 0, tmp)
+        #     data_norm.append(tmp)
         # ---------------------------------------------------
 
         data = np.stack(data_norm, axis=3)
@@ -409,7 +415,7 @@ class SRCNN:
         activation = tf.nn.relu
         momentum = 0.99
 
-        num_filters = 64
+        num_filters = 32
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)