snapshot...

modules/deeplearning/cloud_opd_srcnn_viirs.py

@@ -747,30 +747,19 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     # cld_opd = normalize(cld_opd, label_param, mean_std_dct)
 
     data = np.stack([bt, refl, cld_opd], axis=3)
-    print('input data shape: ', data.shape)
 
     h5f.close()
 
     cld_opd_sres = nn.run_evaluate(data, ckpt_dir)
     cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
     # cld_opd_sres = descale(cld_opd_sres, label_param, mean_std_dct)
-    _, ylen, xlen, _ = cld_opd_sres.shape
-
-    # cld_opd_sres_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
-    # refl_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
-    # cld_opd_out = np.zeros((LEN_Y, LEN_X), dtype=np.float32)
-    #
-    # cld_opd_sres_out[border:(border+ylen), border:(border+xlen)] = cld_opd_sres[0, :, :, 0]
-    # refl_out[0:(ylen+2*border), 0:(xlen+2*border)] = refl[0, :, :]
-    # cld_opd_out[0:(ylen+2*border), 0:(xlen+2*border)] = cld_opd[0, :, :]
-    print(refl.shape, cld_opd.shape, cld_opd_hres.shape)
-    ylen_in, xlen_in = cld_opd_hres.shape
+
+    ylen, xlen = cld_opd_hres.shape
 
     cld_opd_sres_out = cld_opd_sres[0, :, :, 0]
-    refl_out = refl[0, 1:ylen_in-1, 1:xlen_in-1]
-    cld_opd_out = cld_opd[0, 1:ylen_in-1, 1:xlen_in-1]
-    cld_opd_hres = cld_opd_hres[1:ylen_in-1, 1:xlen_in-1]
-    print(cld_opd_sres_out.shape, refl_out.shape, cld_opd_out.shape, cld_opd_hres.shape)
+    refl_out = refl[0, border:ylen-border, border:xlen-border]
+    cld_opd_out = cld_opd[0, border:ylen-border, border:xlen-border]
+    cld_opd_hres = cld_opd_hres[border:ylen-border, border:xlen-border]
 
     refl_out = denormalize(refl_out, 'refl_0_65um_nom', mean_std_dct)
     cld_opd_out = denormalize(cld_opd_out, label_param, mean_std_dct)