From e673694be5899f86184f759c2d9d0bdb1890e1e6 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 6 Jul 2023 12:39:45 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 52 ++++++++++++---------
1 file changed, 30 insertions(+), 22 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 0fb761bd..40d2020a 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -729,16 +729,22 @@ class SRCNN:
t1 = time.time()
print('read data time: ', (t1 - t0))
- self.run_inference_(bt, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X)
+ self.run_inference_(bt, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, 2*LEN_Y, 2*LEN_X)
def run_inference_(self, bt, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X):
- slc_x = slice(0, (LEN_X - 16) + 4)
- slc_y = slice(0, (LEN_Y - 16) + 4)
- x_2 = np.arange((LEN_X - 16) + 4)
- y_2 = np.arange((LEN_Y - 16) + 4)
- t = np.arange(0, (LEN_X - 16) + 4, 0.5)
- s = np.arange(0, (LEN_Y - 16) + 4, 0.5)
+ self.slc_x_m = slice(1, int(LEN_X / 2) + 4)
+ self.slc_y_m = slice(1, int(LEN_Y / 2) + 4)
+ self.slc_x = slice(3, LEN_X + 5)
+ self.slc_y = slice(3, LEN_Y + 5)
+ self.slc_x_2 = slice(2, LEN_X + 7, 2)
+ self.slc_y_2 = slice(2, LEN_Y + 7, 2)
+ self.x_2 = np.arange(int(LEN_X / 2) + 3)
+ self.y_2 = np.arange(int(LEN_Y / 2) + 3)
+ self.t = np.arange(0, int(LEN_X / 2) + 3, 0.5)
+ self.s = np.arange(0, int(LEN_Y / 2) + 3, 0.5)
+ self.x_k = slice(1, LEN_X + 3)
+ self.y_k = slice(1, LEN_Y + 3)
# refl = np.where(np.isnan(refl), 0, refl)
# refl = refl[slc_y, slc_x]
@@ -750,42 +756,44 @@ class SRCNN:
t0 = time.time()
bt = np.where(np.isnan(bt), 0, bt)
- bt = bt[slc_y, slc_x]
+ bt = bt[self.slc_y_m, self.slc_x_m]
bt = np.expand_dims(bt, axis=0)
- bt_us = upsample_static(bt, x_2, y_2, t, s, None, None)
+ # bt_us = upsample_static(bt, x_2, y_2, t, s, None, None)
+ bt_us = self.upsample(bt)
bt_us = smooth_2d(bt_us)
bt_us = normalize(bt_us, 'temp_11_0um_nom', mean_std_dct)
- print('BT done')
- refl_sub_lo = refl_sub_lo[slc_y, slc_x]
+ cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
+ cld_opd = cld_opd[self.slc_y_m, self.slc_x_m]
+ cld_opd = np.expand_dims(cld_opd, axis=0)
+ # cld_opd_us = upsample_static(cld_opd, x_2, y_2, t, s, None, None)
+ cld_opd_us = self.upsample(cld_opd)
+ cld_opd_us = smooth_2d(cld_opd_us)
+ cld_opd_us = normalize(cld_opd_us, label_param, mean_std_dct)
+
refl_sub_lo = np.expand_dims(refl_sub_lo, axis=0)
refl_sub_lo = upsample_nearest(refl_sub_lo)
+ refl_sub_lo = refl_sub_lo[self.slc_y, self.slc_x]
refl_sub_lo = normalize(refl_sub_lo, 'refl_0_65um_nom', mean_std_dct)
- refl_sub_hi = refl_sub_hi[slc_y, slc_x]
refl_sub_hi = np.expand_dims(refl_sub_hi, axis=0)
refl_sub_hi = upsample_nearest(refl_sub_hi)
+ refl_sub_hi = refl_sub_hi[self.slc_y, self.slc_x]
refl_sub_hi = normalize(refl_sub_hi, 'refl_0_65um_nom', mean_std_dct)
- refl_sub_std = refl_sub_std[slc_y, slc_x]
refl_sub_std = np.expand_dims(refl_sub_std, axis=0)
refl_sub_std = upsample_nearest(refl_sub_std)
+ refl_sub_std = refl_sub_std[self.slc_y, self.slc_x]
- cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
- cld_opd = cld_opd[slc_y, slc_x]
- cld_opd = np.expand_dims(cld_opd, axis=0)
- cld_opd_us = upsample_static(cld_opd, x_2, y_2, t, s, None, None)
- cld_opd_us = smooth_2d(cld_opd_us)
- cld_opd_us = normalize(cld_opd_us, label_param, mean_std_dct)
- print('OPD done')
t1 = time.time()
print('upsample/normalize time: ', (t1 - t0))
data = np.stack([bt_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
- # data = self.do_inference(data)
+ cld_opd_sres = self.do_inference(data)
+ cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
- return None
+ return cld_opd_sres
def run_restore_static(directory, ckpt_dir, out_file=None):
--
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