"""Darknet-53 for yolo v3.
"""
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dense
from tensorflow.keras.layers import add, Activation, BatchNormalization
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.regularizers import l2
def conv2d_unit(x, filters, kernels, strides=1):
"""Convolution Unit
This function defines a 2D convolution operation with BN and LeakyReLU.
# Arguments
x: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernels: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and
height. Can be a single integer to specify the same value for
all spatial dimensions.
# Returns
Output tensor.
"""
x = Conv2D(filters, kernels,
padding='same',
strides=strides,
activation='linear',
kernel_regularizer=l2(5e-4))(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.1)(x)
return x
def residual_block(inputs, filters):
"""Residual Block
This function defines a 2D convolution operation with BN and LeakyReLU.
# Arguments
x: Tensor, input tensor of residual block.
kernels: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
# Returns
Output tensor.
"""
x = conv2d_unit(inputs, filters, (1, 1))
x = conv2d_unit(x, 2 * filters, (3, 3))
x = add([inputs, x])
x = Activation('linear')(x)
return x
def stack_residual_block(inputs, filters, n):
"""Stacked residual Block
"""
x = residual_block(inputs, filters)
for i in range(n - 1):
x = residual_block(x, filters)
return x
def darknet_base(inputs):
"""Darknet-53 base model.
"""
x = conv2d_unit(inputs, 32, (3, 3))
x = conv2d_unit(x, 64, (3, 3), strides=2)
x = stack_residual_block(x, 32, n=1)
x = conv2d_unit(x, 128, (3, 3), strides=2)
x = stack_residual_block(x, 64, n=2)
x = conv2d_unit(x, 256, (3, 3), strides=2)
x = stack_residual_block(x, 128, n=8)
x = conv2d_unit(x, 512, (3, 3), strides=2)
x = stack_residual_block(x, 256, n=8)
x = conv2d_unit(x, 1024, (3, 3), strides=2)
x = stack_residual_block(x, 512, n=4)
return x
def darknet():
"""Darknet-53 classifier.
"""
inputs = Input(shape=(416, 416, 3))
x = darknet_base(inputs)
x = GlobalAveragePooling2D()(x)
x = Dense(1000, activation='softmax')(x)
model = Model(inputs, x)
return model
if __name__ == '__main__':
model = darknet()
model.summary()
