From b9ca1991d52fff5a3d3fcb4951b3f2be29b19cb0 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 1 Aug 2023 11:06:01 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 83 +++++++++++----------
1 file changed, 44 insertions(+), 39 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 146cd75d..67bbdbc0 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -212,7 +212,7 @@ class SRCNN:
self.test_label_files = None
# self.n_chans = len(data_params_half) + len(data_params_full) + 1
- self.n_chans = 3
+ self.n_chans = 1
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
@@ -264,23 +264,25 @@ class SRCNN:
input_label = np.concatenate(label_s)
data_norm = []
- for param in data_params_half:
- idx = params.index(param)
- tmp = input_data[:, idx, :, :]
- tmp = np.where(np.isnan(tmp), 0.0, tmp)
- tmp = tmp[:, self.slc_y_m, self.slc_x_m]
- tmp = self.upsample(tmp)
- if DO_SMOOTH:
- tmp = smooth_2d(tmp)
- tmp = normalize(tmp, param, mean_std_dct)
- data_norm.append(tmp)
+ # for param in data_params_half:
+ # idx = params.index(param)
+ # tmp = input_data[:, idx, :, :]
+ # tmp = np.where(np.isnan(tmp), 0.0, tmp)
+ # tmp = tmp[:, self.slc_y_m, self.slc_x_m]
+ # tmp = self.upsample(tmp)
+ # if DO_SMOOTH:
+ # tmp = smooth_2d(tmp)
+ # tmp = normalize(tmp, param, mean_std_dct)
+ # # tmp = scale(tmp, param, mean_std_dct)
+ # data_norm.append(tmp)
# High res refectance ----------
- idx = params_i.index('refl_0_65um_nom')
- tmp = input_label[:, idx, ::2, ::2]
- tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
- data_norm.append(tmp[:, self.slc_y, self.slc_x])
+ # idx = params_i.index('refl_0_65um_nom')
+ # tmp = input_label[:, idx, ::2, ::2]
+ # tmp = np.where(np.isnan(tmp), 0, tmp)
+ # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+ # # tmp = scale(tmp, 'refl_0_65um_nom', mean_std_dct)
+ # data_norm.append(tmp[:, self.slc_y, self.slc_x])
# High res reflectance down 2 ---------
# idx = params_i.index('refl_0_65um_nom')
@@ -301,7 +303,8 @@ class SRCNN:
tmp = self.upsample(tmp)
if DO_SMOOTH:
tmp = smooth_2d(tmp)
- tmp = normalize(tmp, label_param, mean_std_dct)
+ # tmp = normalize(tmp, label_param, mean_std_dct)
+ tmp = scale(tmp, label_param, mean_std_dct)
data_norm.append(tmp)
# for param in sub_fields:
@@ -336,8 +339,8 @@ class SRCNN:
# -----------------------------------------------------
label = input_label[:, label_idx_i, ::2, ::2]
label = label.copy()
- label = normalize(label, label_param, mean_std_dct)
- # label = scale(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), 0.0, label)
@@ -870,24 +873,24 @@ class SRCNN:
self.LEN_Y = LEN_Y
t0 = time.time()
- bt = np.where(np.isnan(bt), 0, bt)
- 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 = self.upsample(bt)
- if DO_SMOOTH:
- 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]
- refl = refl[self.slc_y, self.slc_x]
- refl = np.expand_dims(refl, axis=0)
- # refl_us = self.upsample(refl)
- refl_us = refl
- if DO_SMOOTH:
- refl_us = smooth_2d(refl)
- refl_us = normalize(refl_us, 'refl_0_65um_nom', mean_std_dct)
+ # bt = np.where(np.isnan(bt), 0, bt)
+ # 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 = self.upsample(bt)
+ # if DO_SMOOTH:
+ # 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]
+ # refl = refl[self.slc_y, self.slc_x]
+ # refl = np.expand_dims(refl, axis=0)
+ # # refl_us = self.upsample(refl)
+ # refl_us = refl
+ # if DO_SMOOTH:
+ # refl_us = smooth_2d(refl)
+ # 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]
@@ -896,7 +899,8 @@ class SRCNN:
cld_opd_us = self.upsample(cld_opd)
if DO_SMOOTH:
cld_opd_us = smooth_2d(cld_opd_us)
- cld_opd_us = normalize(cld_opd_us, label_param, mean_std_dct)
+ # cld_opd_us = normalize(cld_opd_us, label_param, mean_std_dct)
+ cld_opd_us = scale(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)
@@ -917,7 +921,8 @@ class SRCNN:
# data = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
# data = np.stack([bt_us, refl_us, cld_opd_us, refl_sub_std], axis=3)
- data = np.stack([bt_us, refl_us, cld_opd_us], axis=3)
+ # data = np.stack([bt_us, refl_us, cld_opd_us], axis=3)
+ data = np.stack([cld_opd_us], axis=3)
print('data in: ', data.shape)
cld_opd_sres = self.do_inference(data)
--
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