HighCWu commited on
Commit
e6bfa26
1 Parent(s): 33254bb

update tf version

Browse files
Files changed (3) hide show
  1. InstanceNorm.py +4 -3
  2. models.py +6 -6
  3. requirements.txt +6 -6
InstanceNorm.py CHANGED
@@ -1,7 +1,8 @@
1
- from keras.engine import Layer, InputSpec
 
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  from keras import initializers, regularizers, constraints
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  from keras import backend as K
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- from keras.utils.generic_utils import get_custom_objects
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6
  import tensorflow as tf
7
 
@@ -110,7 +111,7 @@ class InstanceNormalization(Layer):
110
 
111
  del reduction_axes[0]
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113
- mean, var = tf.nn.moments(inputs, reduction_axes, keep_dims=True)
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  stddev = tf.sqrt(var) + self.epsilon
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  normed = (inputs - mean) / stddev
116
 
 
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+ from keras.engine.base_layer import Layer
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+ from keras.engine.input_spec import InputSpec
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  from keras import initializers, regularizers, constraints
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  from keras import backend as K
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+ from keras.saving.object_registration import get_custom_objects
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  import tensorflow as tf
8
 
 
111
 
112
  del reduction_axes[0]
113
 
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+ mean, var = tf.nn.moments(inputs, reduction_axes, keepdims=True)
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  stddev = tf.sqrt(var) + self.epsilon
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  normed = (inputs - mean) / stddev
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models.py CHANGED
@@ -1,4 +1,4 @@
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- from keras.layers import Conv2D, Activation, Input, Concatenate, LeakyReLU, Lambda, AveragePooling2D, UpSampling2D, Convolution2D, BatchNormalization, Deconvolution2D, Add
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  from keras.models import Model
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  from InstanceNorm import InstanceNormalization
4
 
@@ -31,13 +31,13 @@ def make_standard_UNET(channels,outs):
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  bnc7 = BatchNormalization(axis=3, name='bnc7')
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  bnc8 = BatchNormalization(axis=3, name='bnc8')
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- dc8 = Deconvolution2D(filters=512, kernel_size=4, strides=2, padding='same', name='dc8_')
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  dc7 = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', name='dc7')
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- dc6 = Deconvolution2D(filters=256, kernel_size=4, strides=2, padding='same', name='dc6_')
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  dc5 = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', name='dc5')
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- dc4 = Deconvolution2D(filters=128, kernel_size=4, strides=2, padding='same', name='dc4_')
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  dc3 = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', name='dc3')
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- dc2 = Deconvolution2D(filters=64, kernel_size=4, strides=2, padding='same', name='dc2_')
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  dc1 = Convolution2D(filters=32, kernel_size=3, strides=1, padding='same', name='dc1')
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  dc0 = Convolution2D(filters=outs, kernel_size=3, strides=1, padding='same', name='dc0')
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@@ -212,7 +212,7 @@ def make_unet512():
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  return Conv2D(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
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  def donv(x, filters, strides=(2, 2), kernel_size=(4, 4)):
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- return Deconvolution2D(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
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217
  def relu(x):
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  return Activation('relu')(x)
 
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+ from keras.layers import Conv2D, Activation, Input, Concatenate, LeakyReLU, Lambda, AveragePooling2D, UpSampling2D, Convolution2D, BatchNormalization, Conv2DTranspose, Add
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  from keras.models import Model
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  from InstanceNorm import InstanceNormalization
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  bnc7 = BatchNormalization(axis=3, name='bnc7')
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  bnc8 = BatchNormalization(axis=3, name='bnc8')
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+ dc8 = Conv2DTranspose(filters=512, kernel_size=4, strides=2, padding='same', name='dc8_')
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  dc7 = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', name='dc7')
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+ dc6 = Conv2DTranspose(filters=256, kernel_size=4, strides=2, padding='same', name='dc6_')
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  dc5 = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', name='dc5')
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+ dc4 = Conv2DTranspose(filters=128, kernel_size=4, strides=2, padding='same', name='dc4_')
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  dc3 = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', name='dc3')
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+ dc2 = Conv2DTranspose(filters=64, kernel_size=4, strides=2, padding='same', name='dc2_')
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  dc1 = Convolution2D(filters=32, kernel_size=3, strides=1, padding='same', name='dc1')
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  dc0 = Convolution2D(filters=outs, kernel_size=3, strides=1, padding='same', name='dc0')
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212
  return Conv2D(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
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214
  def donv(x, filters, strides=(2, 2), kernel_size=(4, 4)):
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+ return Conv2DTranspose(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
216
 
217
  def relu(x):
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  return Activation('relu')(x)
requirements.txt CHANGED
@@ -1,11 +1,11 @@
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  opencv-contrib-python>=4.1.0.25
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- tensorflow_gpu==1.14.0
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  gradio>=3.20.1
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- keras==2.2.5
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- scikit-learn==0.23.1
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- scikit-image==0.14.5
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- llvmlite==0.36.0
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- numba==0.53.1
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  tqdm
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  paste
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  opencv-contrib-python>=4.1.0.25
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+ tensorflow>=2.12.0
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  gradio>=3.20.1
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+ keras>=2.2.5
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+ scikit-learn>=0.23.1
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+ scikit-image>=0.14.5
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+ llvmlite>=0.36.0
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+ numba>=0.53.1
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  tqdm
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  paste
11