From 96a1d843319b086795ada98ca376a47c9bb099b8 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 18 Aug 2022 10:46:12 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn.py | 24 +++++++++++++-----------
1 file changed, 13 insertions(+), 11 deletions(-)
diff --git a/modules/deeplearning/srcnn.py b/modules/deeplearning/srcnn.py
index 51c96323..2153d54d 100644
--- a/modules/deeplearning/srcnn.py
+++ b/modules/deeplearning/srcnn.py
@@ -67,14 +67,15 @@ x_134_2 = x_134[2:133:2]
y_134_2 = y_134[2:133:2]
-def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.leaky_relu, padding='SAME'):
+def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME', kernel_initializer='he_uniform', scale=None):
with tf.name_scope(block_name):
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
-
+ skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding, kernel_initializer=kernel_initializer, activation=activation)(conv)
+ skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding, activation=None)(skip)
+ if scale is not None:
+ skip = tf.keras.layers.Lambda(lambda x: x * scale)(skip)
conv = conv + skip
- print(conv.shape)
+ print(block_name+':', conv.shape)
return conv
@@ -334,16 +335,17 @@ class SRCNN:
self.get_evaluate_dataset(idxs)
- def build_espcn(self, do_drop_out=False, do_batch_norm=False, drop_rate=0.5, factor=2):
+ def build_srcnn(self, do_drop_out=False, do_batch_norm=False, drop_rate=0.5, factor=2):
print('build_cnn')
padding = "SAME"
# activation = tf.nn.relu
# activation = tf.nn.elu
- activation = tf.nn.leaky_relu
+ activation = tf.nn.relu
momentum = 0.99
- num_filters = 32
+ num_filters = 256
+ num_res_blocks = 4
input_2d = self.inputs[0]
print('input: ', input_2d.shape)
@@ -352,7 +354,7 @@ class SRCNN:
print('input: ', conv.shape)
# conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding)(input_2d)
- conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding='VALID')(input_2d)
+ conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, kernel_initializer='he_uniform', activation=activation, padding='VALID')(input_2d)
print(conv.shape)
if NOISE_TRAINING:
@@ -366,7 +368,7 @@ class SRCNN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4')
- conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding)(conv_b)
+ conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding=padding)(conv_b)
conv = conv + conv_b
print(conv.shape)
@@ -649,7 +651,7 @@ class SRCNN:
# f.close()
def build_model(self):
- self.build_espcn()
+ self.build_srcnn()
self.model = tf.keras.Model(self.inputs, self.logits)
def restore(self, ckpt_dir):
--
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