From 9cb85a626f8512aa6784aaae198c14cf8b016a3d Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 6 Jul 2023 13:28:35 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 14 +++++++++++---
1 file changed, 11 insertions(+), 3 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 0edffb96..acaec995 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -727,7 +727,7 @@ class SRCNN:
t1 = time.time()
print('read data time: ', (t1 - t0))
- cld_opd_sres = self.run_inference_(bt, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, 2*LEN_Y, 2*LEN_X)
+ cld_opd_sres = self.run_inference_(bt, refl, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, 2*LEN_Y, 2*LEN_X)
cld_opd_sres_out = np.zeros((LEN_Y, LEN_X), dtype=np.int8)
border = int((KERNEL_SIZE - 1) / 2)
@@ -740,7 +740,7 @@ class SRCNN:
else:
return cld_opd_sres
- def run_inference_(self, bt, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X):
+ def run_inference_(self, bt, refl, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd, LEN_Y, LEN_X):
self.slc_x_m = slice(1, int(LEN_X / 2) + 4)
self.slc_y_m = slice(1, int(LEN_Y / 2) + 4)
@@ -772,6 +772,14 @@ class SRCNN:
bt_us = smooth_2d(bt_us)
bt_us = normalize(bt_us, 'temp_11_0um_nom', mean_std_dct)
+ refl = np.where(np.isnan(refl), 0, refl)
+ refl = refl[self.slc_y_m, self.slc_x_m]
+ bt = np.expand_dims(refl, axis=0)
+ # bt_us = upsample_static(bt, x_2, y_2, t, s, None, None)
+ refl_us = self.upsample(refl)
+ refl_us = smooth_2d(refl_us)
+ refl_us = normalize(refl_us, 'refl_0_65um_nom', mean_std_dct)
+
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)
@@ -797,7 +805,7 @@ class SRCNN:
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 = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
cld_opd_sres = self.do_inference(data)
cld_opd_sres = denormalize(cld_opd_sres, label_param, mean_std_dct)
--
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