From 3d4711546e518fc3083ad6a4f8c4861065bfacef Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 9 Aug 2022 12:42:13 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn.py | 69 +++++++----------------------------
1 file changed, 13 insertions(+), 56 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index 2756f0bf..d3e326b7 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -361,9 +361,8 @@ class ESPCN:
self.get_evaluate_dataset(idxs)
- def build_espcn(self, do_drop_out=False, do_batch_norm=False, drop_rate=0.5):
+ def build_espcn(self, do_drop_out=False, do_batch_norm=False, drop_rate=0.5, factor=2):
print('build_cnn')
- # padding = "VALID"
padding = "SAME"
# activation = tf.nn.relu
@@ -371,78 +370,36 @@ class ESPCN:
activation = tf.nn.leaky_relu
momentum = 0.99
- # num_filters = len(self.train_params) * 4
- num_filters = self.n_chans * 64
+ num_filters = 64
input_2d = self.inputs[0]
print('input: ', input_2d.shape)
+
# conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding='VALID', activation=None)(input_2d)
conv = input_2d
print('input: ', conv.shape)
skip = conv
+ conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding)(input_2d)
+
if NOISE_TRAINING:
conv = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv)
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(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:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- 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)
- print(conv.shape)
-
- # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
- # conv = tf.keras.layers.BatchNormalization()(conv)
- # print(conv.shape)
- #
- # conv = conv + skip
- # conv = tf.keras.layers.LeakyReLU()(conv)
- # print(conv.shape)
-
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- 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)
- conv = tf.keras.layers.BatchNormalization()(conv)
- print(conv.shape)
+ conv_b = build_conv2d_block(conv_b, num_filters, 'Residual_Block_1')
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(conv)
+ conv_b = build_conv2d_block(conv_b, num_filters, 'Residual_Block_2')
- # conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- # print(conv.shape)
+ conv_b = build_conv2d_block(conv_b, num_filters, 'Residual_Block_3')
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=4, strides=2, padding=padding, activation=activation)(conv)
- print(conv.shape)
+ conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding)(conv_b)
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
- print(conv.shape)
+ conv = conv + conv_b
- conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
- print(conv.shape)
+ conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding='same')(conv)
- #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)
+ conv = tf.nn.depth_to_space(conv, factor)
- # conv = tf.nn.depth_to_space(conv, block_size=2)
- # conv = tf.keras.layers.Activation(activation=activation)(conv)
- # print(conv.shape)
- #
- # # Called logits, but these are actually probabilities, see activation
- # self.logits = tf.keras.layers.Activation(activation=activation)(conv)
+ self.logits = tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding=padding, name='regression')(conv)
print(self.logits.shape)
--
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