From aa4198531d43f24f44dc97a09feab3b15b82969a Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 9 Nov 2022 15:24:40 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn_l1b_l2.py | 15 +++++++--------
1 file changed, 7 insertions(+), 8 deletions(-)
diff --git a/modules/deeplearning/espcn_l1b_l2.py b/modules/deeplearning/espcn_l1b_l2.py
index 3c98ceba..4e987a46 100644
--- a/modules/deeplearning/espcn_l1b_l2.py
+++ b/modules/deeplearning/espcn_l1b_l2.py
@@ -382,25 +382,24 @@ class ESPCN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
- # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
- # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
- conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation, kernel_initializer=kernel_initializer)(conv_b)
+ conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', scale=scale)
- conv = conv + conv_b
- print(conv.shape)
+ conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, kernel_initializer=kernel_initializer)(conv_b)
- conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding=padding, activation=activation)(conv)
+ conv = conv + conv_b
print(conv.shape)
- conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding=padding, activation=activation)(conv)
print(conv.shape)
conv = tf.nn.depth_to_space(conv, factor)
print(conv.shape)
- self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
+ self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=3, strides=1, padding=padding, name='regression')(conv)
print(self.logits.shape)
--
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