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ResNet, changed PixelShuffler to Conv2DTranspose

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iperov 2018-12-28 20:38:26 +04:00
parent f8824f9601
commit 1f86d7f1dd
1 changed files with 6 additions and 3 deletions
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7
nnlib/nnlib.py
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@ -515,6 +515,9 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
return keras.layers.convolutional.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint )
def Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
return keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint)
def func(input):
@ -548,8 +551,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
for i in range(n_blocks):
x = ResnetBlock(ngf*4)(x)
x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf*2 *4, 3, 1, 'same')(x))))
x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf *4, 3, 1, 'same')(x))))
x = ReLU()(XNormalization(Conv2DTranspose(ngf*2, 3, 2, 'same')(x)))
x = ReLU()(XNormalization(Conv2DTranspose(ngf , 3, 2, 'same')(x)))
x = ReflectionPadding2D((3,3))(x)
x = Conv2D(output_nc, 7, 1, 'valid')(x)