snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -248,14 +248,15 @@ class SRCNN:
         data_norm = []
         for param in data_params:
             idx = params.index(param)
-            # tmp = input_data[:, idx, slc_y, slc_x]
             tmp = input_data[:, idx, :, :]
+            tmp = np.where(np.isnan(tmp), 0, tmp)
             tmp = smooth_2d(tmp, sigma=1.0)
             tmp = tmp[:, slc_y_2, slc_x_2]
+            tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+            tmp = tmp[:, y_k, x_k]
             tmp = normalize(tmp, param, mean_std_dct)
             if DO_ADD_NOISE:
                 tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
-            # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
             data_norm.append(tmp)
         # # --------------------------
         # param = 'refl_0_65um_nom'
@@ -268,10 +269,12 @@ class SRCNN:
         # # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
         # data_norm.append(tmp)
         # --------
-        #tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
         tmp = input_data[:, label_idx, :, :]
+        tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = smooth_2d(tmp, sigma=1.0)
         tmp = tmp[:, slc_y_2, slc_x_2]
+        tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+        tmp = tmp[:, y_k, x_k]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
             if DO_ADD_NOISE:
@@ -281,16 +284,12 @@ class SRCNN:
                 tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
                 tmp = np.where(tmp < 0.0, 0.0, tmp)
                 tmp = np.where(tmp > 1.0, 1.0, tmp)
-            tmp = np.where(np.isnan(tmp), 0, tmp)
-        tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-        tmp = tmp[:, y_k, x_k]
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
         # -----------------------------------------------------
         # -----------------------------------------------------
-        #label = input_data[:, label_idx, y_128, x_128]
         label = input_data[:, label_idx, :, :]
         # label = smooth_2d(label, sigma=1.0)
         label = label[:, y_128, x_128]