update tf version

InstanceNorm.py

@@ -1,7 +1,8 @@
-from keras.engine import Layer, InputSpec
+from keras.engine.base_layer import Layer
+from keras.engine.input_spec import InputSpec
 from keras import initializers, regularizers, constraints
 from keras import backend as K
-from keras.utils.generic_utils import get_custom_objects
+from keras.saving.object_registration import get_custom_objects
 
 import tensorflow as tf
 
@@ -110,7 +111,7 @@ class InstanceNormalization(Layer):
 
  del reduction_axes[0]
 
- mean, var = tf.nn.moments(inputs, reduction_axes, keep_dims=True)
+ mean, var = tf.nn.moments(inputs, reduction_axes, keepdims=True)
  stddev = tf.sqrt(var) + self.epsilon
  normed = (inputs - mean) / stddev
 

models.py

@@ -1,4 +1,4 @@
-from keras.layers import Conv2D, Activation, Input, Concatenate, LeakyReLU, Lambda, AveragePooling2D, UpSampling2D, Convolution2D, BatchNormalization, Deconvolution2D, Add
+from keras.layers import Conv2D, Activation, Input, Concatenate, LeakyReLU, Lambda, AveragePooling2D, UpSampling2D, Convolution2D, BatchNormalization, Conv2DTranspose, Add
 from keras.models import Model
 from InstanceNorm import InstanceNormalization
 
@@ -31,13 +31,13 @@ def make_standard_UNET(channels,outs):
  bnc7 = BatchNormalization(axis=3, name='bnc7')
  bnc8 = BatchNormalization(axis=3, name='bnc8')
 
- dc8 = Deconvolution2D(filters=512, kernel_size=4, strides=2, padding='same', name='dc8_')
+ dc8 = Conv2DTranspose(filters=512, kernel_size=4, strides=2, padding='same', name='dc8_')
  dc7 = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', name='dc7')
- dc6 = Deconvolution2D(filters=256, kernel_size=4, strides=2, padding='same', name='dc6_')
+ dc6 = Conv2DTranspose(filters=256, kernel_size=4, strides=2, padding='same', name='dc6_')
  dc5 = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', name='dc5')
- dc4 = Deconvolution2D(filters=128, kernel_size=4, strides=2, padding='same', name='dc4_')
+ dc4 = Conv2DTranspose(filters=128, kernel_size=4, strides=2, padding='same', name='dc4_')
  dc3 = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', name='dc3')
- dc2 = Deconvolution2D(filters=64, kernel_size=4, strides=2, padding='same', name='dc2_')
+ dc2 = Conv2DTranspose(filters=64, kernel_size=4, strides=2, padding='same', name='dc2_')
  dc1 = Convolution2D(filters=32, kernel_size=3, strides=1, padding='same', name='dc1')
  dc0 = Convolution2D(filters=outs, kernel_size=3, strides=1, padding='same', name='dc0')
 
@@ -212,7 +212,7 @@ def make_unet512():
  return Conv2D(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
 
  def donv(x, filters, strides=(2, 2), kernel_size=(4, 4)):
- return Deconvolution2D(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
+ return Conv2DTranspose(filters=filters, strides=strides, kernel_size=kernel_size, padding='same')(x)
 
  def relu(x):
  return Activation('relu')(x)

requirements.txt

@@ -1,11 +1,11 @@
 opencv-contrib-python>=4.1.0.25
-tensorflow_gpu==1.14.0
+tensorflow>=2.12.0
 gradio>=3.20.1
-keras==2.2.5
-scikit-learn==0.23.1
-scikit-image==0.14.5
-llvmlite==0.36.0
-numba==0.53.1
+keras>=2.2.5
+scikit-learn>=0.23.1
+scikit-image>=0.14.5
+llvmlite>=0.36.0
+numba>=0.53.1
 tqdm
 paste