snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -10,6 +10,7 @@ import pickle
 import h5py
 import xarray as xr
 import gc
+import time
 
 AUTOTUNE = tf.data.AUTOTUNE
 
@@ -825,6 +826,7 @@ class SRCNN:
     def run_inference_full_disk(self, in_file, out_file):
         gc.collect()
 
+        t0 = time.time()
         h5f = h5py.File(in_file, 'r')
 
         bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
@@ -835,6 +837,8 @@ class SRCNN:
         refl_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
         refl_std = get_grid_values_all(h5f, 'refl_0_65um_nom_stddev_sub')
         cp = get_grid_values_all(h5f, label_param)
+        t1 = time.time()
+        print('   read time:', (t1-t0))
         # lons = get_grid_values_all(h5f, 'longitude')
         # lats = get_grid_values_all(h5f, 'latitude')
 
@@ -852,9 +856,11 @@ class SRCNN:
         refl_std_sh = refl_std[h_y_len - 1:y_len, :]
         cp_sh = cp[h_y_len - 1:y_len, :]
 
+        t0 = time.time()
         cld_frac_nh = self.run_inference_(bt_nh, refl_nh, refl_lo_nh, refl_hi_nh, refl_std_nh, cp_nh)
-
         cld_frac_sh = self.run_inference_(bt_sh, refl_sh, refl_lo_sh, refl_hi_sh, refl_std_sh, cp_sh)
+        t1 = time.time()
+        print('   inference time: ', (t1-t0))
 
         cld_frac_out = np.zeros((y_len, x_len), dtype=np.int8)
         border = int((KERNEL_SIZE - 1) / 2)