From 71200f13553cc8bebf253b202a0427a8bce1b80f Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 6 Apr 2022 10:52:06 -0500
Subject: [PATCH] minor
---
modules/deeplearning/unet.py | 22 ++++++++++------------
1 file changed, 10 insertions(+), 12 deletions(-)
diff --git a/modules/deeplearning/unet.py b/modules/deeplearning/unet.py
index d0c23939..53cfff79 100644
--- a/modules/deeplearning/unet.py
+++ b/modules/deeplearning/unet.py
@@ -533,7 +533,8 @@ class UNET:
input_2d = self.inputs[0]
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=None)(input_2d)
- print(conv.shape)
+ print('Contracting Branch')
+ print('input: ', conv.shape)
skip = conv
if NOISE_TRAINING:
@@ -545,7 +546,6 @@ class UNET:
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
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.MaxPool2D(padding=padding)(skip)
@@ -553,7 +553,7 @@ class UNET:
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
- print(conv.shape)
+ print('1d: ', conv.shape)
# -----------------------------------------------------------------------------------------------------------
conv_2 = conv
@@ -562,7 +562,6 @@ class UNET:
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)
- print(conv.shape)
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)
@@ -570,7 +569,7 @@ class UNET:
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
- print(conv.shape)
+ print('2d: ', conv.shape)
# ----------------------------------------------------------------------------------------------------------
conv_3 = conv
@@ -579,7 +578,6 @@ class UNET:
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)
- print(conv.shape)
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)
@@ -587,6 +585,7 @@ class UNET:
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
+ print('3d: ', conv.shape)
# -----------------------------------------------------------------------------------------------------------
conv_4 = conv
@@ -595,7 +594,6 @@ class UNET:
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)
- print(conv.shape)
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)
@@ -603,6 +601,7 @@ class UNET:
conv = conv + skip
conv = tf.keras.layers.LeakyReLU()(conv)
+ print('4d: ', conv.shape)
# Expanding (Decoding) branch -------------------------------------------------------------------------------
print('expanding branch')
@@ -612,26 +611,25 @@ class UNET:
conv = tf.keras.layers.concatenate([conv, conv_4])
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
+ print('5: ', conv.shape)
num_filters /= 2
conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
conv = tf.keras.layers.concatenate([conv, conv_3])
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
+ print('6: ', conv.shape)
num_filters /= 2
conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
conv = tf.keras.layers.concatenate([conv, conv_2])
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
+ print('7: ', conv.shape)
num_filters /= 2
conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
- conv = tf.keras.layers.concatenate([conv, conv_1])
- print(conv.shape)
+ print('8: ', conv.shape)
if NumClasses == 2:
activation = tf.nn.sigmoid # For binary
--
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