snapshot...

modules/deeplearning/cloud_opd_fcn_abi.py

@@ -3,7 +3,7 @@ import tensorflow as tf
 from util.plot_cm import confusion_matrix_values
 from util.augment import augment_image
 from util.setup_cloud_fraction import logdir, modeldir, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize, get_grid_values_all
+from util.util import EarlyStop, normalize, denormalize, scale, descale, get_grid_values_all
 import glob
 import os, datetime
 import numpy as np
@@ -261,7 +261,7 @@ 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 = 5
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -291,25 +291,32 @@ class SRCNN:
         input_label = np.concatenate(label_s)
 
         data_norm = []
-        for param in data_params_half:
-            idx = params.index(param)
-            tmp = input_data[:, idx, :, :]
-            tmp = tmp[:, slc_y, slc_x]
-            tmp = normalize(tmp, param, mean_std_dct)
-            data_norm.append(tmp)
+        # for param in data_params_half:
+        #     idx = params.index(param)
+        #     tmp = input_data[:, idx, :, :]
+        #     tmp = tmp[:, slc_y, slc_x]
+        #     tmp = normalize(tmp, param, mean_std_dct)
+        #     data_norm.append(tmp)
+
+        tmp = input_label[:, params_i.index('cloud_probability'), :, :]
+        tmp = get_grid_cell_mean(tmp)
+        tmp = tmp[:, slc_y, slc_x]
+        data_norm.append(tmp)
 
         for param in sub_fields:
             idx = params.index(param)
             tmp = input_data[:, idx, :, :]
             tmp = tmp[:, slc_y, slc_x]
             if param != 'refl_substddev_ch01':
-                tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+                # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+                tmp = scale(tmp, 'refl_0_65um_nom', mean_std_dct)
             else:
                 tmp = np.where(np.isnan(tmp), 0, tmp)
             data_norm.append(tmp)
 
         tmp = input_label[:, label_idx_i, :, :]
         tmp = get_grid_cell_mean(tmp)
+        tmp = scale(tmp, label_param, mean_std_dct)
         tmp = tmp[:, slc_y, slc_x]
         data_norm.append(tmp)
         # ---------
@@ -321,6 +328,7 @@ class SRCNN:
         label = input_label[:, label_idx_i, :, :]
         label = label[:, y_64, x_64]
         label = get_cldy_frac_opd(label)
+        label = scale(label, label_param, mean_std_dct)
 
         label = np.where(np.isnan(label), 0, label)
         label = np.expand_dims(label, axis=3)