snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -208,7 +208,7 @@ 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))
 
@@ -267,16 +267,16 @@ class SRCNN:
             tmp = normalize(tmp, param, mean_std_dct)
             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)
 
         # for param in data_params_full:
         #     idx = params_i.index(param)
@@ -287,6 +287,7 @@ class SRCNN:
         #     data_norm.append(tmp[:, self.slc_y, self.slc_x])
         # ---------------------------------------------------
         tmp = input_label[:, label_idx_i, ::2, ::2]
+        tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = tmp[:, self.slc_y_2, self.slc_x_2]
         tmp = self.upsample(tmp)
@@ -299,8 +300,9 @@ class SRCNN:
         # -----------------------------------------------------
         # -----------------------------------------------------
         label = input_label[:, label_idx_i, ::2, ::2]
-        label = normalize(label, label_param, mean_std_dct)
-        # label = scale(label, label_param, mean_std_dct)
+        label = label.copy()
+        # label = normalize(label, label_param, mean_std_dct)
+        label = scale(label, label_param, mean_std_dct)
         label = label[:, self.y_128, self.x_128]
 
         label = np.where(np.isnan(label), 0, label)
@@ -415,7 +417,7 @@ 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)
 
@@ -628,10 +630,10 @@ class SRCNN:
         preds = np.concatenate(self.test_preds)
         print(labels.shape, preds.shape)
 
-        labels_denorm = denormalize(labels, label_param, mean_std_dct)
-        preds_denorm = denormalize(preds, label_param, mean_std_dct)
-        # labels_denorm = descale(labels, label_param, mean_std_dct)
-        # preds_denorm = descale(preds, label_param, mean_std_dct)
+        # labels_denorm = denormalize(labels, label_param, mean_std_dct)
+        # preds_denorm = denormalize(preds, label_param, mean_std_dct)
+        labels_denorm = descale(labels, label_param, mean_std_dct)
+        preds_denorm = descale(preds, label_param, mean_std_dct)
 
         return labels_denorm, preds_denorm
 
@@ -761,8 +763,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     print('INPUT: ', data.shape)
 
     cld_opd_sres = nn.run_evaluate(data, ckpt_dir)
-    # cld_opd_sres = descale(cld_opd_sres, label_param, mean_std_dct)
-    cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
+    cld_opd_sres = descale(cld_opd_sres, label_param, mean_std_dct)
+    # cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
     _, ylen, xlen, _ = cld_opd_sres.shape
     print('OUT: ', ylen, xlen)