def tiny_yolo_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 model CNN body in keras.'''
x1 = compose(
DarknetConv2D_BN_Leaky(16, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
DarknetConv2D_BN_Leaky(32, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
DarknetConv2D_BN_Leaky(64, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
DarknetConv2D_BN_Leaky(128, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
DarknetConv2D_BN_Leaky(256, (3, 3)))(inputs)
x2 = compose(
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
DarknetConv2D_BN_Leaky(512, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same'),
DarknetConv2D_BN_Leaky(1024, (3, 3)),
DarknetConv2D_BN_Leaky(256, (1, 1)))(x1)
y1 = compose(DarknetConv2D_BN_Leaky(512, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2)
x2 = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x2)
y2 = compose(Concatenate(), DarknetConv2D_BN_Leaky(256, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1)))([x2, x1])
return Model(inputs, [y1, y2])
def mobilenetv2_yolo_body(inputs, num_anchors, num_classes):
#net, endpoint = inception_v2.inception_v2(inputs)
mobilenet = MobileNetV2(input_tensor=inputs, weights='imagenet')
# input: 416 x 416 x 3
# conv_pw_13_relu :13 x 13 x 1024
# conv_pw_11_relu :26 x 26 x 512
# conv_pw_5_relu : 52 x 52 x 256
f1 = mobilenet.get_layer('out_relu').output
# f1 :13 x 13 x 1024
x, y1 = make_last_layers(f1, 512, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
f2 = mobilenet.get_layer('block_13_expand_relu').output
# f2: 26 x 26 x 512
x = Concatenate()([x, f2])
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
f3 = mobilenet.get_layer('block_6_expand_relu').output
# f3 : 52 x 52 x 256
x = Concatenate()([x, f3])
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs=inputs, outputs=[y1, y2, y3])
def squeezenet_yolo_body(inputs, num_anchors, num_classes):
#net, endpoint = inception_v2.inception_v2(inputs)
mobilenet = squeezenet_body(input_tensor=inputs)
# input: 416 x 416 x 3
# contatenate_10 :12 x 12 x 640
# contatenate_6 :25 x 25 x 384
# contatenate_4 : 51 x 51 x 256
f1 = mobilenet.get_layer('concatenate_10').output
# f1 :13 x 13 x 1024
x, y1 = make_last_layers(f1, 512, num_anchors * (num_classes + 5))
x = compose(
DarknetConv2D_BN_Leaky(256, (1,1)),
UpSampling2D(2))(x)
f2 = mobilenet.get_layer('concatenate_6').output
# f2: 26 x 26 x 512
x = Concatenate()([x,f2])
x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))
x = compose(
DarknetConv2D_BN_Leaky(128, (1,1)),
UpSampling2D(2))(x)
f3 = mobilenet.get_layer('concatenate_4').output
# f3 : 52 x 52 x 256
x = Concatenate()([x, f3])
x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
return Model(inputs = inputs, outputs=[y1,y2,y3])
def make_last_layers(x, num_filters, out_filters):
'''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(x)
y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D(out_filters, (1, 1)))(x)
return x, y
def make_last_layers(x,filters,out_filters):
""""6 Convolution Leaky_RELU Layers followed by a linear Convolution layer"""
x = compose(
DarknetConv2D_BN_Leaky(filters,(1,1)),
DarknetConv2D_BN_Leaky(filters * 2,(3,3)),
DarknetConv2D_BN_Leaky(filters,(1,1)),
DarknetConv2D_BN_Leaky(filters * 2,(3,3)),
DarknetConv2D_BN_Leaky(filters,(1,1)))(x)
y = compose(
DarknetConv2D_BN_Leaky(filters * 2,(3,3)),
DarknetConv2D(out_filters,(1,1)))(x)
return x,y
def yolo_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 model CNN body in Keras."""
darknet = Model(inputs, darknet_body(inputs))
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[152].output])
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[92].output])
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def DarknetConv2D_BN_Leaky(*args,**kwargs):
"""Darknet Convolution followed by Batch Normalization and LeakyReLU"""
no_bias_kwargs = {'use_bias':False}
no_bias_kwargs.update(kwargs)
return compose(
DarknetConv2D(*args, **no_bias_kwargs),
BatchNormalization(),
LeakyReLU(alpha=0.1))
def make_last_layers(input_layer, num_filters, out_filters):
"""
yolov3网络的输出层，由5个卷积层和1个卷积层加1个线性输出层构成，
其中5个卷积层的输出会连接到下一个输出层,
这里的结构类似FPN特征金字塔
:param input_layer: 输入
:param num_filters: 中间层的卷积核数
:param out_filters: 输出层的卷积核数
:return: yolo输出x，以及下一层的输入y
"""
x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(input_layer)
y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D(out_filters, (1, 1)))(x)
return x, y
def resblock_body(x, num_filters, num_blocks):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
x = ZeroPadding2D(((1, 0), (1, 0)))(x)
x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x)
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x)
x = Add()([x, y])
return x
def resblock_body(x,filters,num_blocks):
"""Residual blocks for Darknet"""
x = ZeroPadding2D(((1,0),(1,0)))(x)
x = DarknetConv2D_BN_Leaky(filters,(3,3),strides=(2,2))(x)
for _ in range(num_blocks):
y = compose(
DarknetConv2D_BN_Leaky(filters//2,(1,1)),
DarknetConv2D_BN_Leaky(filters,(3,3)))(x)
x = Add()([x,y])
return x
def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""
yolov3网络中的“卷积+标准化+激活层”结构：conv -> BN -> activate
:param args: **
:param kwargs: **
:return:
"""
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(DarknetConv2D(*args, **no_bias_kwargs),
BatchNormalization(), LeakyReLU(alpha=0.1))
def yolo_body(inputs, num_anchors, num_classes):
"""
yolov3的卷积神经网络主体
:param inputs: 输入
:param num_anchors: 锚框数
:param num_classes: 类数
:return: Model
"""
darknet = Model(inputs, darknet_body(inputs))
# 第一个输出13*13的小尺度特征图，对应检索大尺度特征
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[152].output])
# 第二个输出26*26的中尺度特征图，对应检索中尺度特征
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[92].output])
# 第三个输出52*52的大尺度特征图，对应检索小尺度特征
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def resblock_body(input_layer, num_filters, num_blocks):
"""
yolov3网络的残差块结构:conv -> res ->out
:param input_layer: 网络输入
:param num_filters: 卷积核数
:param num_blocks: 残差块结构数
:return: 输出
"""
x = ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x)
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x)
x = Add()([x, y]) # 残差相加
return x
