snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -249,7 +249,6 @@ class SRCNN:
         data_norm = []
         for param in data_params:
             idx = params.index(param)
-            # tmp = input_data[:, idx, y_128_2, x_128_2]
             tmp = input_data[:, idx, slc_y_2, slc_x_2]
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
@@ -257,13 +256,11 @@ class SRCNN:
         # --------------------------
         param = 'refl_0_65um_nom'
         idx = params.index(param)
-        # tmp = input_data[:, idx, y_128_2, x_128_2]
         tmp = input_data[:, idx, slc_y_2, slc_x_2]
         tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
         data_norm.append(tmp)
         # --------
-        # tmp = input_data[:, label_idx, y_128_2, x_128_2]
         tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
         if label_param != 'cloud_fraction':
             tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
@@ -276,7 +273,6 @@ class SRCNN:
         data = data.astype(np.float32)
         # -----------------------------------------------------
         # -----------------------------------------------------
-        # label = input_data[:, label_idx, y_128, x_128]
         label = input_data[:, label_idx, slc_y, slc_x]
         if label_param != 'cloud_fraction':
             label = normalize(label, label_param, mean_std_dct)