From 6e9183ffbbbeecb38eaf26e84c87f3c1a320a1b9 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 1 Nov 2022 20:50:57 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 12 +++++++++---
1 file changed, 9 insertions(+), 3 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 3457cfa3..22e043e0 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -65,6 +65,8 @@ x_134 = np.arange(134)
y_134 = np.arange(134)
x_64 = np.arange(64)
y_64 = np.arange(64)
+x_67 = np.arange(67)
+y_67 = np.arange(67)
# x_134_2 = slice(3, 131, 2)
# y_134_2 = slice(3, 131, 2)
@@ -88,6 +90,8 @@ y_134_2 = slice(1, 134, 2)
# slc_y = y_128
# t = np.arange(0, 64, 0.5)
# s = np.arange(0, 64, 0.5)
+# x_2 = x_64
+# y_2 = y_64
slc_x_2 = x_134_2
slc_y_2 = y_134_2
@@ -95,6 +99,8 @@ slc_x = x_128
slc_y = y_128
t = np.arange(1, 66, 0.5)
s = np.arange(1, 66, 0.5)
+x_2 = x_67
+y_2 = y_67
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
@@ -251,14 +257,14 @@ class SRCNN:
idx = params.index(param)
tmp = input_data[:, idx, slc_y_2, slc_x_2]
tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
- tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
+ 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 = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
- tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
+ 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]
@@ -266,7 +272,7 @@ class SRCNN:
tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
else:
tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
+ tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
data_norm.append(tmp)
# ---------
data = np.stack(data_norm, axis=3)
--
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