def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
# 52 52 256
# 26 26 512
# 13 13 1024
feat1, feat2, feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
# 第一个特征层
# y1=(batch_size,13,13,3,25)
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, feat2])
# 第二个特征层
# y2=(batch_size,26,26,3,25)
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, feat1])
# 第三个特征层
# y3=(batch_size,52,52,3,25)
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
feat1, feat2, feat3 = darknet_body(inputs) #darknet53获得的特征层
#52*52*256,26*26*512,13*13*1024
darknet = Model(inputs, feat3)
# 第一个特征层
# y1=(batch_size,13,13,3,25)
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
x = compose(
DarknetConv2D_BN_Leaky(256, (1, 1)), #一次卷积
UpSampling2D(2))(x) #上采样
x = Concatenate()([x, feat2]) #与第二特征层输出结合
# 第二个特征层
# y2=(batch_size,26,26,3,25)
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, feat1])
# 第三个特征层
# y3=(batch_size,52,52,3,25)
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
# Darknet53
feat1, feat2, feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
# fist stage's outputs
# y1 = (batch_size, 13, 13 , 3, 85)
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
# feature fusion
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, feat2])
# second stage's outputs
# y2 = (batch_size, 26, 26, 3, 85)
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, feat1])
# third stage's outputs
# y3 = (batch_size, 52, 52, 3, 85)
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
feat1, feat2, feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
# 第一个特征层
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
# 得到y1 = 【batch_size,13,13,255】
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
# 得到x=【batch_size,26,26,256】，feat2 = 【batch_size,26,26,512】
x = Concatenate()([x, feat2])
# 得到x = 【batch_size,26,26,768】
# 第二个特征层
# y2=(batch_size,26,26,3,75)
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
# 得到y2 =【batch_size,26,26,255】，x = 【batch_size,26,26,256】
x = compose(
DarknetConv2D_BN_Leaky(128, (1, 1)), # 得到x =【batch_size,26,26,128】
UpSampling2D(2))(x)
# 得到x =【batch_size,52,52,128】，feat1 = 【batch_size,52,52,256】
x = Concatenate()([x, feat1])
# 得到x =【batch_size,52,52,384】
# 第三个特征层
# y3=(batch_size,52,52,3,85)
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
# 得到y2 =【batch_size,52,52,255】，x = 【batch_size,52,52,128】
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
#---------------------------------------------------#
# 生成darknet53的主干模型
# 获得三个有效特征层，他们的shape分别是：
# 13,13,256
# 26,26,512
# 13,13,1024
#---------------------------------------------------#
feat1,feat2,feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size,13,13,3,85)
#---------------------------------------------------#
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))
# 13,13,512 -> 13,13,256 -> 26,26,256
x = compose(
DarknetConv2D_BN_Leaky(256, (1,1)),
UpSampling2D(2))(x)
# 26,26,256 + 26,26,512 -> 26,26,768
x = Concatenate()([x,feat2])
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size,26,26,3,85)
#---------------------------------------------------#
# 26,26,768 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))
# 26,26,256 -> 26,26,128 -> 52,52,128
x = compose(
DarknetConv2D_BN_Leaky(128, (1,1)),
UpSampling2D(2))(x)
# 52,52,128 + 52,52,256 -> 52,52,384
x = Concatenate()([x,feat1])
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size,52,52,3,85)
#---------------------------------------------------#
# 52,52,384 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
return Model(inputs, [y1,y2,y3])
def yolo_body(inputs, num_anchors, num_classes):
feat1, feat2, feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
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, feat2])
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, feat1])
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
"""Gaussian YOLOv3
Gaussian YOLOv3 can predict a bounding box distribution.
We model the distribution by a gaussian distribution.
p(y|x) = N(μ, δ)
We can use δ to represent the uncertainty about bounding box.
we change [dx, dy, dw, dh] to [μ(dx), μ(dy), μ(dw), μ(dh), δ(dx), δ(dy), δ(dw), δ(dh)]
Args:
inputs: [batch, height, width, channels]
num_anchors: usually be 3
num_classes: number of your classes
Returns:
model
"""
# Darknet53
feat1, feat2, feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
# fist stage's outputs
# y1 = (batch_size, 13, 13 , 3, 89)
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 9))
# feature fusion
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, feat2])
# second stage's outputs
# y2 = (batch_size, 26, 26, 3, 89)
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 9))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, feat1])
# third stage's outputs
# y3 = (batch_size, 52, 52, 3, 89)
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 9))
return Model(inputs, [y1, y2, y3])
def yolo_body(inputs, num_anchors, num_classes):
'''特征层->最后的输出
Parameters
----------
inputs:
num_anchors:
num_classes:
Returns
-------
Model(inputs, [y1,y2,y3]):
'''
# 生成darknet53的主干模型
feat1,feat2,feat3 = darknet_body(inputs)
darknet = Model(inputs, feat3)
# 第一个特征层
# y1=(batch_size,13,13,3,25)
#
x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))
x = compose(
DarknetConv2D_BN_Leaky(256, (1,1)),
# 执行2倍上采样
UpSampling2D(2))(x)
# 将x与darknet的第152层拼接，feat2为第152层输出
x = Concatenate()([x,feat2])
# 第二个特征层
# y2=(batch_size,26,26,3,25)
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,feat1])
# 第三个特征层
# y3=(batch_size,52,52,3,25)
x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
# 根据整个逻辑的输入和输出，构建模型。输入inputs依然保持不变，
# 即(?, 416, 416, 3)，而输出则转换为3个尺度的预测层，即[y1, y2, y3]
return Model(inputs, [y1,y2,y3])
