From c3ab67163efa8a63a43d5a2e8b1ee952c396853b Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 1 Nov 2022 15:59:55 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 40 +++++++++++++++++++++-------
1 file changed, 30 insertions(+), 10 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index aa2e49cd..894d05f7 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -65,21 +65,37 @@ x_134 = np.arange(134)
y_134 = np.arange(134)
x_64 = np.arange(64)
y_64 = np.arange(64)
-x_134_2 = x_134[3:131:2]
-y_134_2 = y_134[3:131:2]
+
+x_134_2 = slice(3, 131, 2)
+y_134_2 = slice(3, 131, 2)
+# x_134_2 = x_134[3:131:2]
+# y_134_2 = y_134[3:131:2]
+
t = np.arange(0, 64, 0.5)
s = np.arange(0, 64, 0.5)
-x_128_2 = x_134[3:131:2]
-y_128_2 = y_134[3:131:2]
-x_128 = x_134[3:131]
-y_128 = y_134[3:131]
+x_128_2 = slice(3, 131, 2)
+y_128_2 = slice(3, 131, 2)
+x_128 = slice(3, 131)
+y_128 = slice(3, 131)
+
+# x_128_2 = x_134[3:131:2]
+# y_128_2 = y_134[3:131:2]
+# x_128 = x_134[3:131]
+# y_128 = y_134[3:131]
+
#----------- New
# x_134_2 = x_134[1:134:2]
# t = np.arange(1, 66, 0.5)
+# s = np.arange(1, 66, 0.5)
#--------------------------
+slc_x_2 = x_128_2
+slc_y_2 = y_128_2
+slc_x = x_128
+slc_y = y_128
+
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
kernel_initializer='he_uniform', scale=None,
@@ -233,19 +249,22 @@ class SRCNN:
data_norm = []
for param in data_params:
idx = params.index(param)
- tmp = input_data[:, idx, y_128_2, x_128_2]
+ # tmp = input_data[:, idx, y_128_2, x_128_2]
+ 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)
data_norm.append(tmp)
# --------------------------
param = 'refl_0_65um_nom'
idx = params.index(param)
- tmp = input_data[:, idx, y_128_2, x_128_2]
+ # tmp = input_data[:, idx, y_128_2, x_128_2]
+ 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)
data_norm.append(tmp)
# --------
- tmp = input_data[:, label_idx, y_128_2, x_128_2]
+ # tmp = input_data[:, label_idx, y_128_2, x_128_2]
+ tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
if label_param != 'cloud_fraction':
tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
else:
@@ -257,7 +276,8 @@ class SRCNN:
data = data.astype(np.float32)
# -----------------------------------------------------
# -----------------------------------------------------
- label = input_data[:, label_idx, y_128, x_128]
+ # label = input_data[:, label_idx, y_128, x_128]
+ label = input_data[:, label_idx, slc_y, slc_x]
if label_param != 'cloud_fraction':
label = normalize(label, label_param, mean_std_dct)
else:
--
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