diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py index 3c686fe481817538e1b088978fb1249cda0e1323..108921d76030e15ddae4a93118881acb88d348d3 100644 --- a/modules/deeplearning/cloud_opd_srcnn_abi.py +++ b/modules/deeplearning/cloud_opd_srcnn_abi.py @@ -2,7 +2,7 @@ import glob import tensorflow as tf from util.setup import logdir, modeldir, now, ancillary_path -from util.util import EarlyStop, normalize, denormalize, get_grid_values_all, resample_2d_linear +from util.util import EarlyStop, normalize, denormalize, scale, descale, get_grid_values_all, resample_2d_linear import os, datetime import numpy as np import pickle @@ -291,7 +291,8 @@ class SRCNN: # ----------------------------------------------------- # ----------------------------------------------------- label = input_label[:, label_idx_i, :, :] - label = normalize(label, label_param, mean_std_dct) + # label = normalize(label, label_param, mean_std_dct) + label = scale(label, label_param, mean_std_dct) label = label[:, self.y_128, self.x_128] label = np.where(np.isnan(label), 0, label) @@ -729,7 +730,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir): cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m] cld_opd = np.expand_dims(cld_opd, axis=0) cld_opd = nn.upsample(cld_opd) - cld_opd = normalize(cld_opd, label_param, mean_std_dct) + # cld_opd = normalize(cld_opd, label_param, mean_std_dct) + cld_opd = scale(cld_opd, label_param, mean_std_dct) print('OPD done') data = np.stack([bt, refl, cld_opd], axis=3) @@ -737,7 +739,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir): 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 = 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 print('OUT: ', ylen, xlen) @@ -751,7 +754,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir): cld_opd_out[0:(ylen+2*border), 0:(xlen+2*border)] = cld_opd[0, :, :] refl_out = denormalize(refl_out, 'refl_0_65um_nom', mean_std_dct) - cld_opd_out = denormalize(cld_opd_out, label_param, mean_std_dct) + # cld_opd_out = denormalize(cld_opd_out, label_param, mean_std_dct) + cld_opd_out = descale(cld_opd_out, label_param, mean_std_dct) if out_file is not None: np.save(out_file, (cld_opd_sres_out, refl_out, cld_opd_out, cld_opd_hres))