From df612ef570f5e7940ba41c09a2e96869bacd3067 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sun, 14 Aug 2022 22:05:22 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn.py | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index 8e41c165..362f4483 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -61,8 +61,10 @@ label_param = label_params[label_idx]
x_70 = np.arange(70)
y_70 = np.arange(70)
-x_70_2 = x_70[3:67:2]
-y_70_2 = y_70[3:67:2]
+#x_70_2 = x_70[3:67:2]
+#y_70_2 = y_70[3:67:2]
+x_70_2 = x_70[2:68:2]
+y_70_2 = y_70[2:68:2]
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.leaky_relu, padding='SAME'):
@@ -177,7 +179,7 @@ class ESPCN:
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
- self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
+ # self.X_img = tf.keras.Input(shape=(34, 34, self.n_chans))
# self.X_img = tf.keras.Input(shape=(66, 66, self.n_chans))
self.inputs.append(self.X_img)
@@ -204,8 +206,8 @@ class ESPCN:
data = np.expand_dims(data, axis=3)
# label = label[:, label_idx, :, :]
- # label = label[:, label_idx, 3:67:2, 3:67:2]
- label = label[:, label_idx, 3:67, 3:67]
+ label = label[:, label_idx, 3:67:2, 3:67:2]
+ # label = label[:, label_idx, 3:67, 3:67]
label = np.expand_dims(label, axis=3)
data = data.astype(np.float32)
@@ -349,8 +351,8 @@ class ESPCN:
conv = input_2d
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, padding=padding)(input_2d)
+ conv = conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding='VALID')(input_2d)
print(conv.shape)
if NOISE_TRAINING:
@@ -369,11 +371,10 @@ class ESPCN:
conv = conv + conv_b
print(conv.shape)
- conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding='same')(conv)
- # conv = tf.keras.layers.Conv2D((factor ** 2), 3, padding='same')(conv)
+ # conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding='same')(conv)
print(conv.shape)
- conv = tf.nn.depth_to_space(conv, factor)
+ #conv = tf.nn.depth_to_space(conv, factor)
print(conv.shape)
self.logits = tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding=padding, name='regression')(conv)
--
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