def double_conv_layer(x, filter_size, size, dropout, batch_norm=False):
'''
construction of a double convolutional layer using
SAME padding
RELU nonlinear activation function
:param x: input
:param filter_size: size of convolutional filter
:param size: number of filters
:param dropout: FLAG & RATE of dropout.
if < 0 dropout cancelled, if > 0 set as the rate
:param batch_norm: flag of if batch_norm used,
if True batch normalization
:return: output of a double convolutional layer
'''
axis = 3
conv = layers.Conv2D(size, (filter_size, filter_size), padding='same')(x)
if batch_norm is True:
conv = layers.BatchNormalization(axis=axis)(conv)
conv = layers.Activation('relu')(conv)
conv = layers.Conv2D(size, (filter_size, filter_size),
padding='same')(conv)
if batch_norm is True:
conv = layers.BatchNormalization(axis=axis)(conv)
conv = layers.Activation('relu')(conv)
if dropout > 0:
conv = layers.Dropout(dropout)(conv)
shortcut = layers.Conv2D(size, kernel_size=(1, 1), padding='same')(x)
if batch_norm is True:
shortcut = layers.BatchNormalization(axis=axis)(shortcut)
res_path = layers.add([shortcut, conv])
return res_path
def feature_fusion_module_new(self, input, name, num_features):
input_big = input[0]
input_small = input[1]
b_shape = input_big.get_shape()
s_shape = input_small.get_shape()
if(b_shape[1].value > s_shape[1].value):
up_sampled_input = keras_ly.UpSampling2D(size=(2, 2), name=name+'_upsample')(input_small)
else:
up_sampled_input = input_small
concat_1 = tf.concat(axis=3, values=[input_big, up_sampled_input], name=name+'_concat')
conv_1 = keras_ly.Conv2D(num_features, [3, 3], padding='SAME', name=name+'_conv1')(concat_1)
conv_1_bn_relu = tf.nn.relu(slim.batch_norm(conv_1, fused=True))
global_pool = tf.reduce_mean(conv_1_bn_relu, [1, 2], keep_dims=True)
conv_2 = keras_ly.Conv2D(num_features, [1, 1], padding='SAME', name=name+'_conv2')(global_pool)
conv_3 = keras_ly.Conv2D(num_features, [1, 1], padding='SAME', name=name+'_conv3')(conv_2)
sigmoid = tf.sigmoid(conv_3, name=name+'_sigmoid')
mul = tf.multiply(sigmoid, conv_1_bn_relu, name=name+'_multiply') #sigmoid * conv_1
add_out = tf.add(conv_1_bn_relu, mul, name=name+'_add_out') # conv_1 + mul
return add_out
def VGG6(inputs, n_class=10):
# Block 1
x = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(inputs)
x = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(512, activation='relu', name='fc1')(x)
features = layers.Dense(512, activation='relu', name='fc2')(x)
outputs = layers.Dense(n_class, activation='softmax',
name='predictions')(features)
return outputs
def feature_fusion_module(self, input, name):
input_big = input[0]
input_small = input[1]
up_sampled_input = keras_ly.UpSampling2D(size=(2, 2),
name=name +
'_upsample')(input_small)
concat_1 = tf.concat(axis=3,
values=[input_big, up_sampled_input],
name=name + '_concat')
conv_1 = keras_ly.Conv2D(1024, [3, 3],
padding='SAME',
name=name + '_conv1')(concat_1)
global_pool = tf.reduce_mean(conv_1, [1, 2], keep_dims=True)
conv_2 = keras_ly.Conv2D(1024, [1, 1],
padding='SAME',
name=name + '_conv2')(global_pool)
conv_3 = keras_ly.Conv2D(1024, [1, 1],
padding='SAME',
name=name + '_conv3')(conv_2)
sigmoid = tf.sigmoid(conv_3, name=name + '_sigmoid')
mul = tf.multiply(sigmoid, conv_1, name=name + '_multiply')
add_out = tf.add(conv_1, mul, name=name + '_add_out')
return add_out
def grouped_convolution(y, nb_channels, _strides):
# when `cardinality` == 1 this is just a standard convolution
if cardinality == 1:
return layers.Conv2D(nb_channels,
kernel_size=(3, 3),
strides=_strides,
padding='same')(y)
assert not nb_channels % cardinality
_d = nb_channels // cardinality
# in a grouped convolution layer, input and output channels are divided into `cardinality` groups,
# and convolutions are separately performed within each group
groups = []
for j in range(cardinality):
group = layers.Lambda(lambda z: z[:, :, :, j * _d:j * _d + _d])(y)
groups.append(
layers.Conv2D(_d,
kernel_size=(3, 3),
strides=_strides,
padding='same')(group))
# the grouped convolutional layer concatenates them as the outputs of the layer
y = layers.concatenate(groups)
return y
def classifier_model():
model = models.Sequential()
model.add(
layers.Conv2D(NUM_FILTERS_1, [3, 3],
strides=(2, 2),
padding='same',
activation='relu',
input_shape=(28, 28, 1),
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
model.add(
layers.Conv2D(NUM_FILTERS_2, [3, 3],
strides=(2, 2),
padding='same',
activation='relu',
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
model.add(layers.Flatten())
model.add(
layers.Dense(NUM_CLASSES,
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
return model
def olliNetwork(self):
self.model = models.Sequential()
self.model.add(
layers.Conv2D(64, (5, 5),
activation='relu',
input_shape=(48, 48, 1)))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Conv2D(128, (4, 4), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Flatten())
self.model.add(layers.Dense(3072, activation='relu'))
self.model.add(layers.Dropout(0.5))
self.model.add(layers.Dense(128, activation='relu'))
self.model.add(layers.Dropout(0.5))
self.model.add(layers.Dense(3, activation='softmax'))
def attention_block(x, gating, inter_shape):
shape_x = K.int_shape(x)
shape_g = K.int_shape(gating)
theta_x = layers.Conv2D(inter_shape, (2, 2),
strides=(2, 2),
padding='same')(x) # 16
shape_theta_x = K.int_shape(theta_x)
phi_g = layers.Conv2D(inter_shape, (1, 1), padding='same')(gating)
upsample_g = layers.Conv2DTranspose(
inter_shape, (3, 3),
strides=(shape_theta_x[1] // shape_g[1],
shape_theta_x[2] // shape_g[2]),
padding='same')(phi_g) # 16
concat_xg = layers.add([upsample_g, theta_x])
act_xg = layers.Activation('relu')(concat_xg)
psi = layers.Conv2D(1, (1, 1), padding='same')(act_xg)
sigmoid_xg = layers.Activation('sigmoid')(psi)
shape_sigmoid = K.int_shape(sigmoid_xg)
upsample_psi = layers.UpSampling2D(size=(shape_x[1] // shape_sigmoid[1],
shape_x[2] // shape_sigmoid[2]))(
sigmoid_xg) # 32
upsample_psi = expend_as(upsample_psi, shape_x[3])
y = layers.multiply([upsample_psi, x])
result = layers.Conv2D(shape_x[3], (1, 1), padding='same')(y)
result_bn = layers.BatchNormalization()(result)
return result_bn
def _create_generator(self):
inputs = layers.Input(shape=(self.args.latent_dims, ))
x = layers.Dense(128 * 16 * 16)(inputs)
x = layers.LeakyReLU()(x)
x = layers.Reshape((16, 16, 128))(x)
x = layers.Conv2D(256, kernel_size=5, strides=1, padding='same')(x)
x = layers.LeakyReLU()(x)
# we use a kernel-size which is a multiple of the strides to don't have artifacts when up-sampling
x = layers.Conv2DTranspose(256,
kernel_size=4,
strides=2,
padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(256, kernel_size=5, padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(256, kernel_size=5, padding='same')(x)
x = layers.LeakyReLU()(x)
outputs = layers.Conv2D(CHANNELS,
kernel_size=7,
activation='tanh',
padding='same')(x)
generator = models.Model(inputs, outputs)
return generator
def conv_block(input_tensor, num_filters):
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
return encoder
def decoder_block(input_tensor, concat_tensor, num_filters):
decoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)
decoder = layers.concatenate([concat_tensor, decoder], axis=-1)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
return decoder
def residual_block(y,
nb_channels_in,
nb_channels_out,
_strides=(1, 1),
_project_shortcut=False):
"""
Our network consists of a stack of residual blocks. These blocks have the same topology,
and are subject to two simple rules:
- If producing spatial maps of the same size, the blocks share the same hyper-parameters (width and filter sizes).
- Each time the spatial map is down-sampled by a factor of 2, the width of the blocks is multiplied by a factor of 2.
"""
shortcut = y
# we modify the residual building block as a bottleneck design to make the network more economical
y = layers.Conv2D(nb_channels_in,
kernel_size=(1, 1),
strides=(1, 1),
padding='same')(y)
y = add_common_layers(y)
# ResNeXt (identical to ResNet when `cardinality` == 1)
y = grouped_convolution(y, nb_channels_in, _strides=_strides)
y = add_common_layers(y)
y = layers.Conv2D(nb_channels_out,
kernel_size=(1, 1),
strides=(1, 1),
padding='same')(y)
# batch normalization is employed after aggregating the transformations and before adding to the shortcut
y = layers.BatchNormalization()(y)
# identity shortcuts used directly when the input and output are of the same dimensions
if _project_shortcut or _strides != (1, 1):
# when the dimensions increase projection shortcut is used to match dimensions (done by 1×1 convolutions)
# when the shortcuts go across feature maps of two sizes, they are performed with a stride of 2
shortcut = layers.Conv2D(nb_channels_out,
kernel_size=(1, 1),
strides=_strides,
padding='same')(shortcut)
shortcut = layers.BatchNormalization()(shortcut)
y = layers.add([shortcut, y])
# relu is performed right after each batch normalization,
# expect for the output of the block where relu is performed after the adding to the shortcut
y = layers.LeakyReLU()(y)
return y
def attention_refinment_module(self, input, name):
global_pool = tf.reduce_mean(input, [1, 2], keep_dims=True)
#conv_1 = keras_ly.Conv2D(2048, [1, 1], padding='SAME', name=name+'_conv1')(global_pool)
conv_1 = keras_ly.Conv2D(input.get_shape()[3], [1, 1], padding='SAME', name=name+'_conv1')(global_pool)
sigmoid = tf.sigmoid(conv_1, name=name+'_sigmoid')
mul_out = tf.multiply(input, sigmoid, name=name+'_multiply')
return mul_out
def create_model(dropout_rate):
model = models.Sequential()
model.add(
layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(dropout_rate))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
return model
def conv2_bn(x, filts, k=3, stride=1, rate=1, name=None, pad='same'):
x = l.Conv2D(filts, (k, k),
strides=(stride, stride),
dilation_rate=rate,
padding=pad,
name=name)(x)
x = l.BatchNormalization(name=name + '_bn')(x)
x = l.Activation('relu', name=name + '_relu')(x)
return x
def attention_block(x, gating, inter_shape, name):
"""
self gated attention, attention mechanism on spatial dimension
:param x: input feature map
:param gating: gate signal, feature map from the lower layer
:param inter_shape: intermedium channle numer
:param name: name of attention layer, for output
:return: attention weighted on spatial dimension feature map
"""
shape_x = K.int_shape(x)
shape_g = K.int_shape(gating)
theta_x = layers.Conv2D(inter_shape, (2, 2),
strides=(2, 2),
padding='same')(x) # 16
shape_theta_x = K.int_shape(theta_x)
phi_g = layers.Conv2D(inter_shape, (1, 1), padding='same')(gating)
upsample_g = layers.Conv2DTranspose(
inter_shape, (3, 3),
strides=(shape_theta_x[1] // shape_g[1],
shape_theta_x[2] // shape_g[2]),
padding='same')(phi_g) # 16
# upsample_g = layers.UpSampling2D(size=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),
# data_format="channels_last")(phi_g)
concat_xg = layers.add([upsample_g, theta_x])
act_xg = layers.Activation('relu')(concat_xg)
psi = layers.Conv2D(1, (1, 1), padding='same')(act_xg)
sigmoid_xg = layers.Activation('sigmoid')(psi)
shape_sigmoid = K.int_shape(sigmoid_xg)
upsample_psi = layers.UpSampling2D(size=(shape_x[1] // shape_sigmoid[1],
shape_x[2] // shape_sigmoid[2]),
name=name + '_weight')(sigmoid_xg) # 32
upsample_psi = expend_as(upsample_psi, shape_x[3])
y = layers.multiply([upsample_psi, x])
result = layers.Conv2D(shape_x[3], (1, 1), padding='same')(y)
result_bn = layers.BatchNormalization()(result)
return result_bn
def classifier_model(): #Building of the CNN
model = models.Sequential()
model.add(
layers.Conv2D(1, [2, 40],
input_shape=(1, 40, 173),
strides=(1, 1),
padding='valid',
activation='relu'))
#
#model.add(layers.MaxPool1D(pool_size=2, strides=2, padding='valid'))
#
model.add(
layers.Conv2D(1, [2, 20],
strides=(1, 1),
padding='valid',
activation='relu',
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
#
# model.add(layers.MaxPool1D(pool_size=2, strides=2, padding='valid'))
#
#
model.add(
layers.Conv2D(1, [2, 10],
strides=(3, 3),
padding='valid',
activation='relu',
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
#
# model.add(layers.MaxPool1D(pool_size=2, strides=2, padding='valid'))
#
model.add(layers.Flatten())
#
model.add(
layers.Dense(1,
kernel_initializer=initializers.glorot_normal(),
bias_initializer=initializers.Zeros()))
print(model.summary())
return model
def residual_layer(input_tensor, nb_in_filters=64, nb_bottleneck_filters=16, filter_sz=3, stage=0, reg=0.0):
bn_name = 'bn' + str(stage)
conv_name = 'conv' + str(stage)
relu_name = 'relu' + str(stage)
merge_name = 'add' + str(stage)
# batchnorm-relu-conv, from nb_in_filters to nb_bottleneck_filters via 1x1 conv
if stage>1: # first activation is just after conv1
x = layers.BatchNormalization(axis=-1, name=bn_name+'a')(input_tensor)
x = layers.Activation('relu', name=relu_name+'a')(x)
else:
x = input_tensor
x = layers.Conv2D(nb_bottleneck_filters, (1, 1),
kernel_initializer='glorot_normal',
kernel_regularizer=regularizers.l2(reg),
use_bias=False,
name=conv_name+'a')(x)
# batchnorm-relu-conv, from nb_bottleneck_filters to nb_bottleneck_filters via FxF conv
x = layers.BatchNormalization(axis=-1, name=bn_name+'b')(x)
x = layers.Activation('relu', name=relu_name+'b')(x)
x = layers.Conv2D(nb_bottleneck_filters, (filter_sz, filter_sz),
padding='same',
kernel_initializer='glorot_normal',
kernel_regularizer=regularizers.l2(reg),
use_bias = False,
name=conv_name+'b')(x)
# batchnorm-relu-conv, from nb_in_filters to nb_bottleneck_filters via 1x1 conv
x = layers.BatchNormalization(axis=-1, name=bn_name+'c')(x)
x = layers.Activation('relu', name=relu_name+'c')(x)
x = layers.Conv2D(nb_in_filters, (1, 1),
kernel_initializer='glorot_normal',
kernel_regularizer=regularizers.l2(reg),
name=conv_name+'c')(x)
# merge
x = layers.add([x, input_tensor], name=merge_name)
return x
def classifier_model(): # linear stack of
################################################################################
############################ YOUR CODE HERE ################################
# Define a Sequential model
model = models.Sequential()
# The first two layers are convolutional layers. For the first layer, we must specify the input shape.
model.add(
layers.Conv2D(
NUM_FILTERS_1,
3,
strides=(2, 2),
activation='relu',
padding='same',
input_shape=(28, 28, 1))) # we have to add 2d convolutional layer
# a conv layer is defined by the number of filters
# second dimension we have to choose a stride of 2 : amount of shift that we are using for shift
# papdding same = output is cropped , output should be 28*28
# specify the kind of activate : RELU
# first layer of input dimension
# we initialize the biases to 0
# we set kernel initializer
model.add(
layers.Conv2D(NUM_FILTERS_2,
3,
strides=(2, 2),
activation='relu',
padding='same'))
# also a convolutional layer
# 3x3 filters , 2x2 strides
# we don't specify the input shape
# The final layer is a dense, 1 dimensional layer. We must therefore first flatten the result of the
# previous layer
model.add(
layers.Flatten()
) # we want to reduce to 1 dimension to be able to have a dense layer of 1 dim
# convert 2-3 layer dimension into a vextor
model.add(layers.Dense(10))
return model
def classifier_model(): #Building of the CNN
model = models.Sequential()
model.add(
layers.Conv2D(1, [2, 10],
input_shape=(40, 44, 1),
strides=(1, 1),
padding='valid',
activation='relu',
data_format='channels_last'))
model.add(
layers.MaxPooling2D(pool_size=(2, 2),
strides=None,
padding='valid',
data_format=None))
model.add(
layers.Conv2D(1, [2, 6],
strides=(1, 1),
padding='valid',
activation='relu'))
model.add(
layers.MaxPooling2D(pool_size=(2, 2),
strides=None,
padding='valid',
data_format=None))
model.add(
layers.Conv2D(1, [2, 3],
strides=(1, 1),
padding='valid',
activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(1))
print(model.summary())
return model
def conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1, 1)):
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = 3
x = layers.Conv2D(filters, (num_row, num_col),
strides=strides,
padding=padding)(x) # use_bias=False,
x = layers.BatchNormalization(axis=bn_axis)(x) # scale=False,
x = layers.Activation('relu')(x)
return x
def _create_discriminator(self):
inputs = layers.Input(shape=(HEIGHT, WIDTH, CHANNELS))
x = layers.Conv2D(128, kernel_size=3)(inputs)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, kernel_size=4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, kernel_size=4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, kernel_size=4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Flatten()(x)
x = layers.Dropout(self.args.dropout)(x)
outputs = layers.Dense(1, activation='sigmoid')(x)
discriminator = models.Model(inputs, outputs)
return discriminator
def model_definition(self):
self.model = models.Sequential()
self.model.add(
layers.Conv2D(64, (5, 5),
activation='relu',
input_shape=self.input_shape))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.Conv2D(64, (5, 5), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Conv2D(128, (3, 3), activation='relu'))
self.model.add(layers.AveragePooling2D())
self.model.add(layers.Conv2D(128, (1, 1), activation='relu'))
self.model.add(layers.MaxPooling2D(pool_size=(2, 2)))
self.model.add(layers.Dropout(0.25))
self.model.add(layers.Flatten())
self.model.add(layers.Dense(3072, activation='relu'))
self.model.add(layers.Dropout(0.5))
self.model.add(layers.Dense(128, activation='relu'))
self.model.add(layers.Dropout(0.5))
self.model.add(layers.Dense(3, activation='softmax'))
adam = optimizers.Adamax()
self.model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['acc'])
def gating_signal(input, out_size, batch_norm=False):
"""
resize the down layer feature map into the same dimension as the up layer feature map
using 1x1 conv
:param input: down-dim feature map
:param out_size:output channel number
:return: the gating feature map with the same dimension of the up layer feature map
"""
x = layers.Conv2D(out_size, (1, 1), padding='same')(input)
if batch_norm:
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
return x
def keras_efficientnet(blocks_args, global_params, training=False):
inp = layers.Input((224, 224, 3))
x = layers.Conv2D(32, 3, padding='same', strides=2, name='stem_conv2d', use_bias=False)(inp)
x = em.batchnorm(name='stem_tpu_batch_normalization')(x)
x = layers.Lambda(lambda x: em.relu_fn(x))(x)
idx = 0
for block in blocks_args:
x = el.mbConvBlock(x, block, global_params, idx, training=training)
# x = MBConvBlock(block, global_params, idx)(x, training=training)
idx += 1
if block.num_repeat > 1:
block = block._replace(
input_filters=block.output_filters, strides=[1, 1])
for _ in range(block.num_repeat - 1):
x = el.mbConvBlock(x, block, global_params, idx, training=training)
idx += 1
x = layers.Conv2D(1280, 1, name='head_conv2d', use_bias=False)(x)
x = em.batchnorm(name='head_tpu_batch_normalization')(x)
x = layers.Lambda(lambda x: em.relu_fn(x))(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(1000, activation='softmax', name='head_dense', )(x)
model = models.Model(inp, x, name='efficientnet-b0')
return model
def create_model(img_shape):
inputs = layers.Input(shape=img_shape)
encoder0_pool, encoder0 = encoder_block(inputs, 32)
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128)
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256)
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512)
center = conv_block(encoder4_pool, 1024)
decoder4 = decoder_block(center, encoder4, 512)
decoder3 = decoder_block(decoder4, encoder3, 256)
decoder2 = decoder_block(decoder3, encoder2, 128)
decoder1 = decoder_block(decoder2, encoder1, 64)
decoder0 = decoder_block(decoder1, encoder0, 32)
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0) # change to perceptron?
model = models.Model(inputs=[inputs], outputs=[outputs])
return model
def attention_refinment_module_new(self, input, name, last_arm=False):
global_pool = tf.reduce_mean(input, [1, 2], keep_dims=True)
conv_1 = keras_ly.Conv2D(input.get_shape()[3], [1, 1], padding='SAME', name=name+'_conv1')(global_pool)
with tf.variable_scope(name+'_conv1_bn') as scope:
conv_1_bn = slim.batch_norm(conv_1, fused=True, scope=scope)
sigmoid = tf.sigmoid(conv_1_bn, name=name+'_sigmoid')
mul_out = tf.multiply(input, sigmoid, name=name+'_multiply')
if last_arm:
glob_red = tf.reduce_mean(mul_out, [1, 2], keep_dims=True)
out_scale = tf.multiply(glob_red, mul_out)
print('last arm shape')
print(input.shape)
print(out_scale.shape)
return out_scale
else:
return mul_out
def conv_layer(layer_name, layer_num):
w = get_weights(vgg_layers, layer_num)
b = get_bias(vgg_layers, layer_num)
filters = w[3]
kernel_size = [w[0], w[1]]
has_bias = False
bias_initial = None
if b:
has_bias = True
bias_initial = tf.keras.initializers.Constant(b)
conv = layers.Conv2D(filters=filters,
kernel_size=kernel_size,
strides=1,
padding='same',
use_bias=has_bias,
bias_initializer=bias_initial,
activation='relu')
return conv
def shortcut(self, input, residual):
"""Adds a shortcut between input and residual block and merges them with "sum"
"""
# Expand channels of shortcut to match residual.
# Stride appropriately to match residual (width, height)
# Should be int if network architecture is correctly configured.
input_shape = K.int_shape(input)
#residual_shape = K.int_shape(residual)
try:
residual_shape = np.shape(residual).as_list()
except:
residual_shape = np.shape(residual)
stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS]))
stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS]))
equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS]
#equal_width = input_shape[ROW_AXIS] == residual_shape[ROW_AXIS]
#equal_heights = input_shape[COL_AXIS] == residual_shape[COL_AXIS]
shortcut = input
# 1 X 1 conv if shape is different. Else identity.
if stride_width > 1 or stride_height > 1 or not equal_channels:
#if not equal_width or not equal_height or not equal_channels:
shortcut = layers.Conv2D(filters=residual_shape[CHANNEL_AXIS],
kernel_size=(1, 1),
strides=(stride_width, stride_height),
padding="valid",
kernel_initializer="he_normal",
kernel_regularizer=regularizers.l2(0.0001))(input)
return layers.add([shortcut, residual])
def create_model(self, img_shape, num_class):
concat_axis = 3
inputs = layers.Input(shape = img_shape)
conv1 = layers.Conv2D(32, (3, 3), activation='relu', padding='same', name='conv1_1')(inputs)
conv1 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = layers.MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = layers.Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = layers.Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up_conv5 = layers.UpSampling2D(size=(2, 2))(conv5)
ch, cw = self.get_crop_shape(conv4, up_conv5)
crop_conv4 = layers.Cropping2D(cropping=(ch,cw))(conv4)
up6 = layers.concatenate([up_conv5, crop_conv4], axis=concat_axis)
conv6 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up_conv6 = layers.UpSampling2D(size=(2, 2))(conv6)
ch, cw = self.get_crop_shape(conv3, up_conv6)
crop_conv3 = layers.Cropping2D(cropping=(ch,cw))(conv3)
up7 = layers.concatenate([up_conv6, crop_conv3], axis=concat_axis)
conv7 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up_conv7 = layers.UpSampling2D(size=(2, 2))(conv7)
ch, cw = self.get_crop_shape(conv2, up_conv7)
crop_conv2 = layers.Cropping2D(cropping=(ch,cw))(conv2)
up8 = layers.concatenate([up_conv7, crop_conv2], axis=concat_axis)
conv8 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up_conv8 = layers.UpSampling2D(size=(2, 2))(conv8)
ch, cw = self.get_crop_shape(conv1, up_conv8)
crop_conv1 = layers.Cropping2D(cropping=(ch,cw))(conv1)
up9 = layers.concatenate([up_conv8, crop_conv1], axis=concat_axis)
conv9 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
model = models.Model(inputs=inputs, outputs=conv9)
return model
