snapshot...

modules/deeplearning/cloud_opd_srcnn_viirs.py

@@ -682,37 +682,52 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     h5f = h5py.File(in_file, 'r')
 
+    refl = get_grid_values_all(h5f, 'super/refl_0_65um')
+    refl = np.where(np.isnan(refl), 0, refl)
+    refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
+    LEN_Y, LEN_X = refl.shape
+
+    nn = SRCNN(LEN_Y=LEN_Y, LEN_X=LEN_X)
+
+    refl = refl[nn.slc_y, nn.slc_x]
+
     bt = get_grid_values_all(h5f, 'orig/temp_11_0um')
     bt = np.where(np.isnan(bt), 0, bt)
+    bt = bt[nn.slc_y_m, nn.slc_x_m]
+    bt = nn.upsample(bt)
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
 
-    refl = get_grid_values_all(h5f, 'super/refl_0_65um')
-    refl = np.where(np.isnan(refl), 0, refl)
-    refl = np.expand_dims(refl, axis=0)
-    refl_lo, refl_hi, refl_std, refl_avg = get_min_max_std(refl)
-    refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
-    refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
-    refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
-    refl_lo = np.squeeze(refl_lo)
-    refl_hi = np.squeeze(refl_hi)
-    refl_avg = np.squeeze(refl_avg)
-
-    cp = get_grid_values_all(h5f, 'orig/'+label_param)
-    cp = np.where(np.isnan(cp), 0, cp)
-
-    data = np.stack([bt, refl_lo, refl_hi, refl_avg, cp], axis=2)
+    # refl = get_grid_values_all(h5f, 'super/refl_0_65um')
+    # refl = np.where(np.isnan(refl), 0, refl)
+    # refl = np.expand_dims(refl, axis=0)
+    # refl_lo, refl_hi, refl_std, refl_avg = get_min_max_std(refl)
+    # refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
+    # refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
+    # refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
+    # refl_lo = np.squeeze(refl_lo)
+    # refl_hi = np.squeeze(refl_hi)
+    # refl_avg = np.squeeze(refl_avg)
+
+    cld_opd = get_grid_values_all(h5f, 'orig/'+label_param)
+    cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
+    cld_opd = cld_opd[:, nn.slc_y_2, nn.slc_x_2]
+    cld_opd = nn.upsample(cld_opd)
+    cld_opd = normalize(cld_opd, label_param, mean_std_dct)
+
+    data = np.stack([bt, refl, cld_opd], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()
 
-    nn = SRCNN()
-    probs = nn.run_evaluate(data, ckpt_dir)
-    cld_frac = probs.argmax(axis=3)
+    cld_opd_sres = nn.run_evaluate(data, ckpt_dir)
+    cld_opd_sres_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
+    border = int((KERNEL_SIZE - 1) / 2)
+    cld_opd_sres_out[border:LEN_Y - border, border:LEN_X - border] = cld_opd_sres[0, :, :]
 
     if out_file is not None:
-        np.save(out_file, (cld_frac[0, :, :], bt, refl_avg, cp))
+        np.save(out_file, (cld_opd_sres_out, bt, refl))
     else:
-        return cld_frac[0, :, :], bt, refl_avg, cp
+        return cld_opd_sres_out, bt, refl
 
 
 if __name__ == "__main__":