From 8c1d2833f36e8a43971b199148b8250a26eb97e9 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 8 Aug 2022 11:58:34 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn.py | 19 ++++++++++---------
1 file changed, 10 insertions(+), 9 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index 6ddb3f14..d189b523 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -397,7 +397,7 @@ class ESPCN:
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
print(conv.shape)
if do_drop_out:
@@ -430,20 +430,21 @@ class ESPCN:
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
- conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- print(conv.shape)
-
- # conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ # conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
# print(conv.shape)
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
print(conv.shape)
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
+ conv = tf.keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
print(conv.shape)
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- print(conv.shape)
+ # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ # print(conv.shape)
+ #
+ # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ # print(conv.shape)
#self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability')(conv)
--
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