snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -705,50 +705,6 @@ def run_restore_static(directory, ckpt_dir):
     nn.run_restore(directory, ckpt_dir)
 
 
-# def run_evaluate_static(in_file, out_file, ckpt_dir):
-#     N = 8
-#     sub_y, sub_x = (N+1) * 128, (N+1) * 128
-#     y_0, x_0, = 2500 - int(sub_y/2), 2500 - int(sub_x/2)
-#
-#     slc_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
-#     y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
-#     t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
-#
-#     h5f = h5py.File(in_file, 'r')
-#     grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
-#     grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
-#     grd_a = grd_a[slc_y_2, slc_x_2]
-#     bt = grd_a
-#     grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
-#     grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
-#
-#     grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
-#     grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
-#     grd_b = grd_b[slc_y_2, slc_x_2]
-#     refl = grd_b
-#     grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
-#     grd_b = resample_2d_linear_one(x_2, y_2, grd_b, t, s)
-#
-#     grd_c = get_grid_values_all(h5f, label_param)
-#     grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
-#     grd_c = grd_c[slc_y_2, slc_x_2]
-#     if label_param != 'cloud_probability':
-#         grd_c = normalize(grd_c, label_param, mean_std_dct)
-#     grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
-#
-#     data = np.stack([grd_a, grd_b, grd_c], axis=2)
-#     data = np.expand_dims(data, axis=0)
-#
-#     nn = SRCNN()
-#     out_sr = nn.run_evaluate(data, ckpt_dir)
-#     if label_param != 'cloud_probability':
-#         out_sr = denormalize(out_sr, label_param, mean_std_dct)
-#     if out_file is not None:
-#         np.save(out_file, out_sr)
-#     else:
-#         return out_sr, bt, refl
-
-
 def run_evaluate_static(in_file, out_file, ckpt_dir):
     N = 8