snapshot...

modules/deeplearning/cloud_fraction_fcn.py

@@ -8,6 +8,7 @@ import os, datetime
 import numpy as np
 import pickle
 import h5py
+import gc
 
 LOG_DEVICE_PLACEMENT = False
 
@@ -768,6 +769,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     h5f = h5py.File(in_file, 'r')
 
     bt = get_grid_values_all(h5f, 'orig/temp_11_0um')
+    y_len, x_len = bt.shape[0], bt.shape[1]
     lons = get_grid_values_all(h5f, 'longitude')
     lats = get_grid_values_all(h5f, 'latitude')
     bt = np.where(np.isnan(bt), 0, bt)
@@ -795,11 +797,17 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     nn = SRCNN()
     probs = nn.run_evaluate(data, ckpt_dir)
     cld_frac = probs.argmax(axis=3)
+    cld_frac = cld_frac.astype(np.int8)
+    cld_frac_out = np.zeros((y_len, x_len), dtype=np.int8)
+    cld_frac_out[1:y_len - 1, 1:x_len - 1] = cld_frac[0, :, :]
+
+    bt = denormalize(bt, 'temp_11_0um_nom', mean_std_dct)
+    refl_avg = denormalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
 
     if out_file is not None:
-        np.save(out_file, (cld_frac[0, :, :], denormalize(bt, 'temp_11_0um_nom', mean_std_dct), denormalize(refl_avg, 'refl_0_65um_nom', mean_std_dct), cp))
+        np.save(out_file, (cld_frac_out, bt, refl_avg, cp))
     else:
-        return cld_frac[0, :, :], denormalize(bt, 'temp_11_0um_nom', mean_std_dct), denormalize(refl_avg, 'refl_0_65um_nom', mean_std_dct), cp
+        return cld_frac_out, bt, refl_avg, cp
 
 
 def analyze2(nda_m, nda_i):