From 4dc27d7b45a3f2f47ab3f5e59796d7289d161576 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 9 Dec 2022 13:46:16 -0600
Subject: [PATCH] snapshot..
---
modules/deeplearning/srcnn_l1b_l2.py | 60 ++++++++++++++--------------
1 file changed, 30 insertions(+), 30 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 3da783c7..80fcbe4a 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -62,30 +62,30 @@ label_idx = params.index(label_param)
print('data_params: ', data_params)
print('label_param: ', label_param)
-# Kernel size: 3, target size: (128, 128)
-slc_x = slice(2, 132)
-slc_y = slice(2, 132)
-slc_x_2 = slice(1, 134, 2)
-slc_y_2 = slice(1, 134, 2)
-x_128 = slice(3, 131)
-y_128 = slice(3, 131)
-t = np.arange(1, 66, 0.5)
-s = np.arange(1, 66, 0.5)
-x_2 = np.arange(67)
-y_2 = np.arange(67)
-# ----------------------------------------
-
-# Kernel size: 5, target_size: (128, 128)
-# slc_x = slice(3, 135)
-# slc_y = slice(3, 135)
-# slc_x_2 = slice(2, 137, 2)
-# slc_y_2 = slice(2, 137, 2)
-# x_128 = slice(5, 133)
-# y_128 = slice(5, 133)
-# t = np.arange(1, 67, 0.5)
-# s = np.arange(1, 67, 0.5)
-# x_2 = np.arange(68)
-# y_2 = np.arange(68)
+KERNEL_SIZE = 3 # target size: (128, 128)
+
+if KERNEL_SIZE == 3:
+ slc_x = slice(2, 132)
+ slc_y = slice(2, 132)
+ slc_x_2 = slice(1, 134, 2)
+ slc_y_2 = slice(1, 134, 2)
+ x_128 = slice(3, 131)
+ y_128 = slice(3, 131)
+ t = np.arange(1, 66, 0.5)
+ s = np.arange(1, 66, 0.5)
+ x_2 = np.arange(67)
+ y_2 = np.arange(67)
+elif KERNEL_SIZE == 5:
+ slc_x = slice(3, 135)
+ slc_y = slice(3, 135)
+ slc_x_2 = slice(2, 137, 2)
+ slc_y_2 = slice(2, 137, 2)
+ x_128 = slice(5, 133)
+ y_128 = slice(5, 133)
+ t = np.arange(1, 67, 0.5)
+ s = np.arange(1, 67, 0.5)
+ x_2 = np.arange(68)
+ y_2 = np.arange(68)
# ----------------------------------------
@@ -412,7 +412,7 @@ class SRCNN:
input_2d = self.inputs[0]
print('input: ', input_2d.shape)
- conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d)
+ conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=KERNEL_SIZE, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d)
print(conv.shape)
# if NOISE_TRAINING:
@@ -420,15 +420,15 @@ class SRCNN:
scale = 0.2
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1', kernel_size=3, scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', kernel_size=3, scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=3, scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
- #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=3, scale=scale)
+ #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
- #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=3, scale=scale)
+ #conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b)
--
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