snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -261,7 +261,7 @@ class SRCNN:
         for param in data_params_half:
             idx = params.index(param)
             tmp = input_data[:, idx, :, :]
-            tmp = np.where(np.isnan(tmp), 0, tmp)
+            tmp = np.where(np.isnan(tmp), 0.0, tmp)
             tmp = tmp[:, self.slc_y_m, self.slc_x_m]
             tmp = self.upsample(tmp)
             tmp = normalize(tmp, param, mean_std_dct)
@@ -270,13 +270,13 @@ class SRCNN:
         for param in sub_fields:
             idx = params.index(param)
             tmp = input_data[:, idx, :, :]
-            tmp = np.where(np.isnan(tmp), 0, tmp)
+            tmp = np.where(np.isnan(tmp), 0.0, tmp)
             tmp = tmp[:, self.slc_y_m, self.slc_x_m]
             tmp = self.upsample(tmp)
             if param != 'refl_substddev_ch01':
                 tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
             else:
-                tmp = np.where(np.isnan(tmp), 0, tmp)
+                tmp = np.where(np.isnan(tmp), 0.0, tmp)
             data_norm.append(tmp)
 
         # for param in sub_fields:
@@ -292,7 +292,7 @@ class SRCNN:
         # ---------------------------------------------------
         tmp = input_label[:, label_idx_i, ::2, ::2]
         tmp = tmp.copy()
-        tmp = np.where(np.isnan(tmp), 0, tmp)
+        tmp = np.where(np.isnan(tmp), 0.0, tmp)
         tmp = tmp[:, self.slc_y_2, self.slc_x_2]
         tmp = self.upsample(tmp)
         tmp = normalize(tmp, label_param, mean_std_dct)
@@ -300,7 +300,6 @@ class SRCNN:
         # ---------
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
-        print(data.shape)
 
         # -----------------------------------------------------
         # -----------------------------------------------------
@@ -310,7 +309,7 @@ class SRCNN:
         label = scale(label, label_param, mean_std_dct)
         label = label[:, self.y_128, self.x_128]
 
-        label = np.where(np.isnan(label), 0, label)
+        label = np.where(np.isnan(label), 0.0, label)
         label = np.expand_dims(label, axis=3)
 
         data = data.astype(np.float32)