From 811850f0812fc900a66875fae9d53b23bccea0c8 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 29 Oct 2022 13:12:17 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 25 ++++++++++++++++---------
1 file changed, 16 insertions(+), 9 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 8a43aea3..aa2e49cd 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -67,11 +67,19 @@ 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 = x_134[2:133:2]
-# y_134_2 = y_134[2:133:2]
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]
+
+#----------- New
+# x_134_2 = x_134[1:134:2]
+# t = np.arange(1, 66, 0.5)
+#--------------------------
+
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
kernel_initializer='he_uniform', scale=None,
@@ -225,20 +233,19 @@ class SRCNN:
data_norm = []
for param in data_params:
idx = params.index(param)
- tmp = input_data[:, idx, 3:131:2, 3:131:2]
+ tmp = input_data[:, idx, y_128_2, x_128_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, 3:131:2, 3:131:2]
- # tmp = input_data[:, idx, 3:131, 3:131]
+ tmp = input_data[:, idx, y_128_2, x_128_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, 3:131:2, 3:131:2]
+ tmp = input_data[:, label_idx, y_128_2, x_128_2]
if label_param != 'cloud_fraction':
tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
else:
@@ -249,8 +256,8 @@ class SRCNN:
data = np.stack(data_norm, axis=3)
data = data.astype(np.float32)
# -----------------------------------------------------
- # label = input_data[:, label_idx, 3:131:2, 3:131:2]
- label = input_data[:, label_idx, 3:131, 3:131]
+ # -----------------------------------------------------
+ label = input_data[:, label_idx, y_128, x_128]
if label_param != 'cloud_fraction':
label = normalize(label, label_param, mean_std_dct)
else:
--
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