From 66428b45a94a00bb017145ba1490ea82e42d27d5 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 23 Dec 2022 16:24:52 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 29 ++++++++++++++--------------
1 file changed, 15 insertions(+), 14 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 8fc7613e..e2dd4f4f 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -213,7 +213,7 @@ class SRCNN:
self.test_label_nda = None
# self.n_chans = len(data_params) + 2
- self.n_chans = 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))
@@ -246,18 +246,18 @@ class SRCNN:
DO_ADD_NOISE = True
data_norm = []
- for param in data_params:
- idx = params.index(param)
- tmp = input_data[:, idx, :, :]
- tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = smooth_2d(tmp, sigma=1.0)
- tmp = tmp[:, slc_y_2, slc_x_2]
- tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
- tmp = tmp[:, y_k, x_k]
- tmp = normalize(tmp, param, mean_std_dct)
- if DO_ADD_NOISE:
- tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
- data_norm.append(tmp)
+ # for param in data_params:
+ # idx = params.index(param)
+ # tmp = input_data[:, idx, :, :]
+ # tmp = np.where(np.isnan(tmp), 0, tmp)
+ # tmp = smooth_2d(tmp, sigma=1.0)
+ # tmp = tmp[:, slc_y_2, slc_x_2]
+ # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+ # tmp = tmp[:, y_k, x_k]
+ # tmp = normalize(tmp, param, mean_std_dct)
+ # if DO_ADD_NOISE:
+ # tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+ # data_norm.append(tmp)
# # --------------------------
# param = 'refl_0_65um_nom'
# idx = params.index(param)
@@ -777,7 +777,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
grd_c = normalize(grd_c, label_param, mean_std_dct)
# data = np.stack([grd_a, grd_b, grd_c], axis=2)
- data = np.stack([grd_a, grd_c], axis=2)
+ #data = np.stack([grd_a, grd_c], axis=2)
+ data = np.stack([grd_c], axis=2)
data = np.expand_dims(data, axis=0)
nn = SRCNN()
--
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