From b0fc613c4b9b9996f3182d76e1d29889bec8a079 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 1 Aug 2022 14:37:32 -0500
Subject: [PATCH] fix some cf issues
---
modules/deeplearning/cnn_l1b_l2_16.py | 60 +++++++++++++--------------
1 file changed, 28 insertions(+), 32 deletions(-)
diff --git a/modules/deeplearning/cnn_l1b_l2_16.py b/modules/deeplearning/cnn_l1b_l2_16.py
index 79cba6db..0531a21b 100644
--- a/modules/deeplearning/cnn_l1b_l2_16.py
+++ b/modules/deeplearning/cnn_l1b_l2_16.py
@@ -414,9 +414,9 @@ class UNET:
conv = input_layer
- # conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
+ conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
- # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
+ conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
# conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
@@ -467,8 +467,8 @@ class UNET:
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
- skip = tf.keras.layers.BatchNormalization()(skip)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+ conv = tf.keras.layers.BatchNormalization()(conv)
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
@@ -476,50 +476,46 @@ class UNET:
# -----------------------------------------------------------------------------------------------------------
skip = conv
- num_filters *= 2
+
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
- skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
- skip = tf.keras.layers.BatchNormalization()(skip)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+ conv = tf.keras.layers.BatchNormalization()(conv)
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
print('2d: ', conv.shape)
- # # ----------------------------------------------------------------------------------------------------------
- #
+ # ----------------------------------------------------------------------------------------------------------
+
skip = conv
- num_filters *= 2
+
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
- skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
- skip = tf.keras.layers.BatchNormalization()(skip)
+ conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+ conv = tf.keras.layers.BatchNormalization()(conv)
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
print('3d: ', conv.shape)
- #
- # return conv
- # -----------------------------------------------------------------------------------------------------------
- skip = conv
- num_filters *= 2
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- conv = tf.keras.layers.BatchNormalization()(conv)
-
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
- skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
- skip = tf.keras.layers.BatchNormalization()(skip)
-
- conv = conv + skip
- conv = tf.keras.layers.LeakyReLU()(conv)
- print('4d: ', conv.shape)
+ return conv
+
+ # -----------------------------------------------------------------------------------------------------------
+ # skip = conv
+ # num_filters *= 2
+ # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+ # conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
+ # conv = tf.keras.layers.BatchNormalization()(conv)
+ #
+ # skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
+ # skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
+ # skip = tf.keras.layers.BatchNormalization()(skip)
+ #
+ # conv = conv + skip
+ # conv = tf.keras.layers.LeakyReLU()(conv)
+ # print('4d: ', conv.shape)
return conv
--
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