From edcdae6b99f61e0400f3e5102c4174ff063ae64c Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 10 Dec 2022 10:47:11 -0600
Subject: [PATCH] snapshot..
---
modules/deeplearning/srcnn_l1b_l2.py | 41 ++++++++++++++--------------
1 file changed, 21 insertions(+), 20 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index a460a0d3..bad986d9 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -208,7 +208,8 @@ class SRCNN:
self.test_data_nda = None
self.test_label_nda = None
- self.n_chans = len(data_params) + 2
+ # self.n_chans = len(data_params) + 2
+ self.n_chans = 1
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
@@ -241,25 +242,25 @@ class SRCNN:
DO_ADD_NOISE = True
data_norm = []
- for param in data_params:
- idx = params.index(param)
- # tmp = input_data[:, idx, slc_y_2, slc_x_2]
- tmp = input_data[:, idx, slc_y, slc_x]
- tmp = normalize(tmp, param, mean_std_dct)
- if DO_ADD_NOISE:
- tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
- # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
- data_norm.append(tmp)
- # --------------------------
- param = 'refl_0_65um_nom'
- idx = params.index(param)
- # tmp = input_data[:, idx, slc_y_2, slc_x_2]
- tmp = input_data[:, idx, slc_y, slc_x]
- tmp = normalize(tmp, param, mean_std_dct)
- if DO_ADD_NOISE:
- tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
- # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
- data_norm.append(tmp)
+ # for param in data_params:
+ # idx = params.index(param)
+ # # tmp = input_data[:, idx, slc_y_2, slc_x_2]
+ # tmp = input_data[:, idx, slc_y, slc_x]
+ # tmp = normalize(tmp, param, mean_std_dct)
+ # if DO_ADD_NOISE:
+ # tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+ # # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+ # data_norm.append(tmp)
+ # # --------------------------
+ # param = 'refl_0_65um_nom'
+ # idx = params.index(param)
+ # # tmp = input_data[:, idx, slc_y_2, slc_x_2]
+ # tmp = input_data[:, idx, slc_y, slc_x]
+ # tmp = normalize(tmp, param, mean_std_dct)
+ # if DO_ADD_NOISE:
+ # tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+ # # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+ # data_norm.append(tmp)
# --------
tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
if label_param != 'cloud_probability':
--
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