From b00443758a7e3fa6f9a927f0cb6aa145bcf66344 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 9 Jan 2023 10:32:52 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 11 ++++++++---
1 file changed, 8 insertions(+), 3 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 57788cc9..027f7765 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -51,6 +51,7 @@ f.close()
mean_std_dct.update(mean_std_dct_l1b)
mean_std_dct.update(mean_std_dct_l2)
+IMG_DEPTH = 1
# label_param = 'cloud_fraction'
label_param = 'cld_opd_dcomp'
# label_param = 'cloud_probability'
@@ -92,6 +93,11 @@ elif KERNEL_SIZE == 5:
x_2 = np.arange(68)
y_2 = np.arange(68)
# ----------------------------------------
+# Exp for ESPCN version
+slc_x_2 = slice(0, 132, 2)
+slc_y_2 = slice(0, 132, 2)
+x_128 = slice(2, 130)
+y_128 = slice(2, 130)
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
@@ -411,7 +417,6 @@ class SRCNN:
conv = conv + conv_b
print(conv.shape)
-
# This is effectively a Dense layer
self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
@@ -747,8 +752,8 @@ 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_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)
h5f.close()
--
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