def __init__(self, data_format):
"""Creates a model for classifying a hand-written digit.
Args:
data_format: Either 'channels_first' or 'channels_last'.
"""
super(Mnist, self).__init__()
if data_format == "channels_first":
self._input_shape = [-1, 1, 28, 28]
else:
assert data_format == "channels_last"
self._input_shape = [-1, 28, 28, 1]
self.conv1 = tf_layers.Conv2D(32,
5,
padding="same",
data_format=data_format,
activation=nn.relu)
self.conv2 = tf_layers.Conv2D(64,
5,
padding="same",
data_format=data_format,
activation=nn.relu)
self.fc1 = tf_layers.Dense(1024, activation=nn.relu)
self.fc2 = tf_layers.Dense(10)
self.dropout = tf_layers.Dropout(0.4)
self.max_pool2d = tf_layers.MaxPooling2D((2, 2), (2, 2),
padding="same",
data_format=data_format)
def __init__(self, data_format):
"""Creates a model for classifying a hand-written digit.
Args:
data_format: Either "channels_first" or "channels_last".
"channels_first" is typically faster on GPUs while "channels_last" is
typically faster on CPUs. See
https://www.tensorflow.org/performance/performance_guide#data_formats
"""
super(Model, self).__init__()
self._input_shape = [-1, 28, 28, 1]
self.conv1 = layers.Conv2D(32,
5,
padding="same",
data_format=data_format,
activation=nn.relu)
self.conv2 = layers.Conv2D(64,
5,
padding="same",
data_format=data_format,
activation=nn.relu)
self.fc1 = layers.Dense(1024, activation=nn.relu)
self.fc2 = layers.Dense(10)
self.dropout = layers.Dropout(0.4)
self.max_pool2d = layers.MaxPooling2D((2, 2), (2, 2),
padding="same",
data_format=data_format)
def residual_block(x, num):
shortcut = x
x = layers.Conv2D(128, (3, 3),
strides=1,
padding='same',
name='resi_conv_%d_1' % num)(x)
x = layers.BatchNormalization(name='resi_normal_%d_1' % num)(x)
x = layers.Activation('relu', name='resi_relu_%d_1' % num)(x)
x = layers.Conv2D(128, (3, 3),
strides=1,
padding='same',
name='resi_conv_%d_2' % num)(x)
x = layers.BatchNormalization(name='resi_normal_%d_2' % num)(x)
m = layers.add([x, shortcut], name='resi_add_%d' % num)
return m
def sideBranch(input, factor):
Con1 = layers.Conv2D(1, (1, 1),
activation=None,
padding='SAME',
kernel_regularizer=l2(0.00001))(input)
kernelSize = (2 * factor, 2 * factor)
initWeight = upsampling_bilinear(factor, 1, 1)
# initWeight = tf.initializers.Constant(value=initWeight)
initWeight = initializers.constant(value=initWeight)
# initializer = tf.constant_initializer(value=initWeight)
# initWeight = tf.Variable(initial_value=initializer(shape=kernelSize, dtype=tf.float32))
# p = layers.Conv2DTranspose(1, kernelSize, strides=factor, padding='SAME', use_bias=False, activation=None, weights=initWeight)
# p.set_weights(initWeight)
# t = p.weights
DeCon = layers.Conv2DTranspose(1,
kernelSize,
strides=factor,
padding='SAME',
use_bias=False,
activation=None,
kernel_initializer=initWeight,
kernel_regularizer=l2(0.00001))(Con1)
# test = p.weights
return DeCon
def get_evaluate_model(width, height):
input_o = layers.Input(shape=(height, width, 3), dtype='float32', name='input_o')
c1 = layers.Conv2D(32, (9, 9), strides=1, padding='same', name='conv_1')(input_o)
c1 = layers.BatchNormalization(name='normal_1')(c1)
c1 = layers.Activation('relu', name='relu_1')(c1)
c2 = layers.Conv2D(64, (3, 3), strides=2, padding='same', name='conv_2')(c1)
c2 = layers.BatchNormalization(name='normal_2')(c2)
c2 = layers.Activation('relu', name='relu_2')(c2)
c3 = layers.Conv2D(128, (3, 3), strides=2, padding='same', name='conv_3')(c2)
c3 = layers.BatchNormalization(name='normal_3')(c3)
c3 = layers.Activation('relu', name='relu_3')(c3)
r1 = residual_block(c3, 1)
r2 = residual_block(r1, 2)
r3 = residual_block(r2, 3)
r4 = residual_block(r3, 4)
r5 = residual_block(r4, 5)
d1 = layers.Conv2DTranspose(64, (3, 3), strides=2, padding='same', name='conv_4')(r5)
d1 = layers.BatchNormalization(name='normal_4')(d1)
d1 = layers.Activation('relu', name='relu_4')(d1)
d2 = layers.Conv2DTranspose(32, (3, 3), strides=2, padding='same', name='conv_5')(d1)
d2 = layers.BatchNormalization(name='normal_5')(d2)
d2 = layers.Activation('relu', name='relu_5')(d2)
c4 = layers.Conv2D(3, (9, 9), strides=1, padding='same', name='conv_6')(d2)
c4 = layers.BatchNormalization(name='normal_6')(c4)
c4 = layers.Activation('tanh', name='tanh_1')(c4)
c4 = OutputScale(name='output')(c4)
model = Model([input_o], c4)
print("evaluate model built successfully!")
return model
def get_training_model(width, height, bs=1, bi_style=False):
input_o = layers.Input(shape=(height, width, 3), dtype='float32', name='input_o')
c1 = layers.Conv2D(32, (9, 9), strides=1, padding='same', name='conv_1')(input_o)
c1 = layers.BatchNormalization(name='normal_1')(c1)
c1 = layers.Activation('relu', name='relu_1')(c1)
c2 = layers.Conv2D(64, (3, 3), strides=2, padding='same', name='conv_2')(c1)
c2 = layers.BatchNormalization(name='normal_2')(c2)
c2 = layers.Activation('relu', name='relu_2')(c2)
c3 = layers.Conv2D(128, (3, 3), strides=2, padding='same', name='conv_3')(c2)
c3 = layers.BatchNormalization(name='normal_3')(c3)
c3 = layers.Activation('relu', name='relu_3')(c3)
r1 = residual_block(c3, 1)
r2 = residual_block(r1, 2)
r3 = residual_block(r2, 3)
r4 = residual_block(r3, 4)
r5 = residual_block(r4, 5)
d1 = layers.Conv2DTranspose(64, (3, 3), strides=2, padding='same', name='conv_4')(r5)
d1 = layers.BatchNormalization(name='normal_4')(d1)
d1 = layers.Activation('relu', name='relu_4')(d1)
d2 = layers.Conv2DTranspose(32, (3, 3), strides=2, padding='same', name='conv_5')(d1)
d2 = layers.BatchNormalization(name='normal_5')(d2)
d2 = layers.Activation('relu', name='relu_5')(d2)
c4 = layers.Conv2D(3, (9, 9), strides=1, padding='same', name='conv_6')(d2)
c4 = layers.BatchNormalization(name='normal_6')(c4)
c4 = layers.Activation('tanh', name='tanh_1')(c4)
c4 = OutputScale(name='output')(c4)
content_activation = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation1 = layers.Input(shape=(height, width, 64), dtype='float32')
style_activation2 = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation3 = layers.Input(shape=(height // 4, width // 4, 256), dtype='float32')
style_activation4 = layers.Input(shape=(height // 8, width // 8, 512), dtype='float32')
if bi_style:
style_activation1_2 = layers.Input(shape=(height, width, 64), dtype='float32')
style_activation2_2 = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation3_2 = layers.Input(shape=(height // 4, width // 4, 256), dtype='float32')
style_activation4_2 = layers.Input(shape=(height // 8, width // 8, 512), dtype='float32')
total_variation_loss = layers.Lambda(get_tv_loss, output_shape=(1,), name='tv',
arguments={'width': width, 'height': height})([c4])
# Block 1
x = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(c4)
x = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
style_loss1 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style1', arguments={'batch_size': bs})([x, style_activation1])
if bi_style:
style_loss1_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style1_2', arguments={'batch_size': bs})([x, style_activation1_2])
style_loss1 = AverageAddTwo(name='style1_out')([style_loss1, style_loss1_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
content_loss = layers.Lambda(get_content_loss, output_shape=(1,), name='content')([x, content_activation])
style_loss2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style2', arguments={'batch_size': bs})([x, style_activation2])
if bi_style:
style_loss2_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style2_2', arguments={'batch_size': bs})([x, style_activation2_2])
style_loss2 = AverageAddTwo(name='style2_out')([style_loss2, style_loss2_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
style_loss3 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style3', arguments={'batch_size': bs})([x, style_activation3])
if bi_style:
style_loss3_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style3_2', arguments={'batch_size': bs})([x, style_activation3_2])
style_loss3 = AverageAddTwo(name='style3_out')([style_loss3, style_loss3_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
style_loss4 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style4', arguments={'batch_size': bs})([x, style_activation4])
if bi_style:
style_loss4_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style4_2', arguments={'batch_size': bs})([x, style_activation4_2])
style_loss4 = AverageAddTwo(name='style4_out')([style_loss4, style_loss4_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if bi_style:
model = Model(
[input_o, content_activation, style_activation1, style_activation2, style_activation3, style_activation4,
style_activation1_2, style_activation2_2, style_activation3_2, style_activation4_2],
[content_loss, style_loss1, style_loss2, style_loss3, style_loss4, total_variation_loss, c4])
else:
model = Model(
[input_o, content_activation, style_activation1, style_activation2, style_activation3, style_activation4],
[content_loss, style_loss1, style_loss2, style_loss3, style_loss4, total_variation_loss, c4])
model_layers = {layer.name: layer for layer in model.layers}
original_vgg = vgg16.VGG16(weights='imagenet', include_top=False)
original_vgg_layers = {layer.name: layer for layer in original_vgg.layers}
# load image_net weight
for layer in original_vgg.layers:
if layer.name in model_layers:
model_layers[layer.name].set_weights(original_vgg_layers[layer.name].get_weights())
model_layers[layer.name].trainable = False
print("training model built successfully!")
return model
def get_temp_view_model(width, height, bs=1, bi_style=False):
input_o = layers.Input(shape=(height, width, 3), dtype='float32')
y = InputReflect(width, height, name='output')(input_o)
total_variation_loss = layers.Lambda(get_tv_loss, output_shape=(1,), name='tv',
arguments={'width': width, 'height': height})([y])
content_activation = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation1 = layers.Input(shape=(height, width, 64), dtype='float32')
style_activation2 = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation3 = layers.Input(shape=(height // 4, width // 4, 256), dtype='float32')
style_activation4 = layers.Input(shape=(height // 8, width // 8, 512), dtype='float32')
if bi_style:
style_activation1_2 = layers.Input(shape=(height, width, 64), dtype='float32')
style_activation2_2 = layers.Input(shape=(height // 2, width // 2, 128), dtype='float32')
style_activation3_2 = layers.Input(shape=(height // 4, width // 4, 256), dtype='float32')
style_activation4_2 = layers.Input(shape=(height // 8, width // 8, 512), dtype='float32')
# Block 1
x = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(y)
x = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
style_loss1 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style1', arguments={'batch_size': bs})([x, style_activation1])
if bi_style:
style_loss1_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style1_2', arguments={'batch_size': bs})([x, style_activation1_2])
style_loss1 = AverageAddTwo(name='style1_out')([style_loss1, style_loss1_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
content_loss = layers.Lambda(get_content_loss, output_shape=(1,), name='content')([x, content_activation])
style_loss2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style2', arguments={'batch_size': bs})([x, style_activation2])
if bi_style:
style_loss2_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style2_2', arguments={'batch_size': bs})([x, style_activation2_2])
style_loss2 = AverageAddTwo(name='style2_out')([style_loss2, style_loss2_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
style_loss3 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style3', arguments={'batch_size': bs})([x, style_activation3])
if bi_style:
style_loss3_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style3_2', arguments={'batch_size': bs})([x, style_activation3_2])
style_loss3 = AverageAddTwo(name='style3_out')([style_loss3, style_loss3_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
style_loss4 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style4', arguments={'batch_size': bs})([x, style_activation4])
if bi_style:
style_loss4_2 = layers.Lambda(get_style_loss, output_shape=(1,),
name='style4_2', arguments={'batch_size': bs})([x, style_activation4_2])
style_loss4 = AverageAddTwo(name='style4_out')([style_loss4, style_loss4_2])
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if bi_style:
model = Model(
[input_o, content_activation, style_activation1, style_activation2, style_activation3,
style_activation4,
style_activation1_2, style_activation2_2, style_activation3_2, style_activation4_2],
[content_loss, style_loss1, style_loss2, style_loss3, style_loss4, total_variation_loss, y])
else:
model = Model(
[input_o, content_activation, style_activation1, style_activation2, style_activation3,
style_activation4],
[content_loss, style_loss1, style_loss2, style_loss3, style_loss4, total_variation_loss, y])
model_layers = {layer.name: layer for layer in model.layers}
original_vgg = vgg16.VGG16(weights='imagenet', include_top=False)
original_vgg_layers = {layer.name: layer for layer in original_vgg.layers}
# load image_net weight
for layer in original_vgg.layers:
if layer.name in model_layers:
model_layers[layer.name].set_weights(original_vgg_layers[layer.name].get_weights())
model_layers[layer.name].trainable = False
print("temp_view model built successfully!")
return model
def test_weight_norm_tflayers(self):
images = random_ops.random_uniform((2, 4, 4, 3))
wn_wrapper = wrappers.WeightNorm(layers.Conv2D(32, [2, 2]),
input_shape=(4, 4, 3))
wn_wrapper.apply(images)
self.assertTrue(hasattr(wn_wrapper.layer, 'g'))
