def yolo_body(inputs, num_anchors, num_classes):
feat1, feat2, feat3 = darknet_body(inputs)
# y1=(batch_size,13,13,3,85)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(feat3)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
maxpool1 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1),
padding='same')(P5)
maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1),
padding='same')(P5)
maxpool3 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1),
padding='same')(P5)
P5 = Concatenate()([maxpool1, maxpool2, maxpool3, P5])
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5_upsample = compose(DarknetConv2D_BN_Leaky(256, (1, 1)),
UpSampling2D(2))(P5)
P4 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
P4 = Concatenate()([P4, P5_upsample])
P4 = make_five_convs(P4, 256)
P4_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P4)
P3 = DarknetConv2D_BN_Leaky(128, (1, 1))(feat1)
P3 = Concatenate()([P3, P4_upsample])
P3 = make_five_convs(P3, 128)
P3_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P3)
P3_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P3_output)
# 26,26 output
P3_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P3)
P3_downsample = DarknetConv2D_BN_Leaky(256, (3, 3),
strides=(2, 2))(P3_downsample)
P4 = Concatenate()([P3_downsample, P4])
P4 = make_five_convs(P4, 256)
P4_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P4_output)
# 13,13 output
P4_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P4)
P4_downsample = DarknetConv2D_BN_Leaky(512, (3, 3),
strides=(2, 2))(P4_downsample)
P5 = Concatenate()([P4_downsample, P5])
P5 = make_five_convs(P5, 512)
P5_output = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P5_output)
return Model(inputs, [P5_output, P4_output, P3_output])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
feat1, feat2, feat3 = darknet_body(inputs)
# feat3进行三次卷积进入SPP结构
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(feat3)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# 使用了SPP结构，即不同尺度的最大池化后堆叠。
maxpool1 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1), padding='same')(P5)
maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1), padding='same')(P5)
maxpool3 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1), padding='same')(P5)
P5 = Concatenate()([maxpool1, maxpool2, maxpool3, P5])
#SPP聚合后的三次卷积
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
#卷积+上采样
P5_upsample = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(P5)
P4 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
P4 = Concatenate()([P4, P5_upsample])
P4 = make_five_convs(P4, 256)
P4_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(P4)
P3 = DarknetConv2D_BN_Leaky(128, (1, 1))(feat1)
P3 = Concatenate()([P3, P4_upsample])
P3 = make_five_convs(P3, 128)
P3_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P3)
# num_anchors = 3
# num_classes+1+4(1是先验框是不是包含物体，4是先验框的参数)
P3_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P3_output)
# 38x38 output
P3_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P3)
P3_downsample = DarknetConv2D_BN_Leaky(256, (3, 3), strides=(2, 2))(P3_downsample)
P4 = Concatenate()([P3_downsample, P4])
P4 = make_five_convs(P4, 256)
P4_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P4_output)
# 19x19 output
P4_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P4)
P4_downsample = DarknetConv2D_BN_Leaky(512, (3, 3), strides=(2, 2))(P4_downsample)
P5 = Concatenate()([P4_downsample, P5])
P5 = make_five_convs(P5, 512)
P5_output = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P5_output)
return Model(inputs, [P5_output, P4_output, P3_output])
def yolo_body(inputs, num_anchors, num_classes):
#---------------------------------------------------#
# 生成CSPdarknet53的主干模型
# 获得三个有效特征层，他们的shape分别是：
# 52,52,256
# 26,26,512
# 13,13,1024
#---------------------------------------------------#
feat1, feat2, feat3 = darknet_body(inputs)
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,2048 -> 13,13,512 -> 13,13,1024 -> 13,13,512
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(feat3)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# 使用了SPP结构，即不同尺度的最大池化后堆叠。
maxpool1 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1),
padding='same')(P5)
maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1),
padding='same')(P5)
maxpool3 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1),
padding='same')(P5)
P5 = Concatenate()([maxpool1, maxpool2, maxpool3, P5])
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# 13,13,512 -> 13,13,256 -> 26,26,256
P5_upsample = compose(DarknetConv2D_BN_Leaky(256, (1, 1)),
UpSampling2D(2))(P5)
# 26,26,512 -> 26,26,256
P4 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
# 26,26,256 + 26,26,256 -> 26,26,512
P4 = Concatenate()([P4, P5_upsample])
# 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
P4 = make_five_convs(P4, 256)
# 26,26,256 -> 26,26,128 -> 52,52,128
P4_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P4)
# 52,52,256 -> 52,52,128
P3 = DarknetConv2D_BN_Leaky(128, (1, 1))(feat1)
# 52,52,128 + 52,52,128 -> 52,52,256
P3 = Concatenate()([P3, P4_upsample])
# 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
P3 = make_five_convs(P3, 128)
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size,52,52,3,85)
#---------------------------------------------------#
P3_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P3)
P3_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P3_output)
# 52,52,128 -> 26,26,256
P3_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P3)
P3_downsample = DarknetConv2D_BN_Leaky(256, (3, 3),
strides=(2, 2))(P3_downsample)
# 26,26,256 + 26,26,256 -> 26,26,512
P4 = Concatenate()([P3_downsample, P4])
# 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
P4 = make_five_convs(P4, 256)
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size,26,26,3,85)
#---------------------------------------------------#
P4_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P4_output)
# 26,26,256 -> 13,13,512
P4_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P4)
P4_downsample = DarknetConv2D_BN_Leaky(512, (3, 3),
strides=(2, 2))(P4_downsample)
# 13,13,512 + 13,13,512 -> 13,13,1024
P5 = Concatenate()([P4_downsample, P5])
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
P5 = make_five_convs(P5, 512)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size,13,13,3,85)
#---------------------------------------------------#
P5_output = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P5_output)
return Model(inputs, [P5_output, P4_output, P3_output])
def yolo_body(inputs, num_anchors, num_classes):
# Generate the backbone model of darknet53
feat1, feat2, feat3 = darknet_body(inputs)
# First feature layer
# y1=(batch_size,13,13,3,85)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(feat3)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# The SPP structure is used, that is, the maximum pooling of different scales is stacked。
maxpool1 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1),
padding='same')(P5)
maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1),
padding='same')(P5)
maxpool3 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1),
padding='same')(P5)
P5 = Concatenate()([maxpool1, maxpool2, maxpool3, P5])
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
P5_upsample = compose(DarknetConv2D_BN_Leaky(256, (1, 1)),
UpSampling2D(2))(P5)
P4 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
P4 = Concatenate()([P4, P5_upsample])
P4 = make_five_convs(P4, 256)
P4_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P4)
P3 = DarknetConv2D_BN_Leaky(128, (1, 1))(feat1)
P3 = Concatenate()([P3, P4_upsample])
P3 = make_five_convs(P3, 128)
P3_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P3)
P3_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P3_output)
# 26,26 output
P3_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P3)
P3_downsample = DarknetConv2D_BN_Leaky(256, (3, 3),
strides=(2, 2))(P3_downsample)
P4 = Concatenate()([P3_downsample, P4])
P4 = make_five_convs(P4, 256)
P4_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P4_output)
# 13,13 output
P4_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P4)
P4_downsample = DarknetConv2D_BN_Leaky(512, (3, 3),
strides=(2, 2))(P4_downsample)
P5 = Concatenate()([P4_downsample, P5])
P5 = make_five_convs(P5, 512)
P5_output = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P5_output)
return Model(inputs, [P5_output, P4_output, P3_output])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
feat1, feat2, feat3 = darknet_body(inputs)
# 第一个特征层
# y1=(batch_size,13,13,3,85) 进行三次卷积
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(feat3)
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# 使用了SPP结构，即不同尺度的最大池化后堆叠。 进行三次池化
maxpool1 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1), padding='same')(P5)
maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1), padding='same')(P5)
maxpool3 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1), padding='same')(P5)
P5 = Concatenate()([maxpool1, maxpool2, maxpool3, P5]) # 进行堆叠 其中有一个特征层没有经过池化
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5) # 再进行三次池化
P5 = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (1, 1))(P5)
# 从这里开始就是PANet的内容了 PANet对CSPDarknet53中的特征层进行融合，并对SPP结构的输出进行了特征融合
P5_upsample = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(P5) # 上采样
P4 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2) # 1*1的卷积改变维度 然后进行堆叠
P4 = Concatenate()([P4, P5_upsample])
P4 = make_five_convs(P4, 256) # 5次卷积
P4_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(P4) # 对P4进行上采样
P3 = DarknetConv2D_BN_Leaky(128, (1, 1))(feat1) # 对P4上采样的结果和feat1进行堆叠
P3 = Concatenate()([P3, P4_upsample])
P3 = make_five_convs(P3, 128) # 进行5次卷积
# 至此 已完成特征金字塔的结构 但是对于yolov4来说 还需要进行一系列的下采样，加强特征融合 提取到更有用的特征
# Yolo Head完成输出
P3_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P3) # 3*3的卷积 可以看作是对之前特征的整合
# batch_size, 52*52, num_anchors=3(代表3个先验框), num_classes(表示先验框包含的物体的种类)+1(是否包含物体)+4(代表先验框的调整参数)
P3_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P3_output) # 1*1的卷积 可以看作是利用这个特征 获得最终的输出
# 26,26 output 利用卷积，进行步长为(2, 2)的下采样
P3_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P3)
P3_downsample = DarknetConv2D_BN_Leaky(256, (3, 3), strides=(2, 2))(P3_downsample)
P4 = Concatenate()([P3_downsample, P4]) # 把下采样的结果和P4进行堆叠
P4 = make_five_convs(P4, 256)
# Yolo Head完成输出
P4_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P4)
# batch_size, 26*26, num_anchors=3(代表3个先验框), num_classes(表示先验框包含的物体的种类)+1(是否包含物体)+4(代表先验框的调整参数)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P4_output)
# 13,13 output 继续进行下采采样
P4_downsample = ZeroPadding2D(((1, 0), (1, 0)))(P4)
P4_downsample = DarknetConv2D_BN_Leaky(512, (3, 3), strides=(2, 2))(P4_downsample)
P5 = Concatenate()([P4_downsample, P5])
P5 = make_five_convs(P5, 512)
# Yolo Head完成输出
P5_output = DarknetConv2D_BN_Leaky(1024, (3, 3))(P5)
# batch_size, 13*13
# num_anchors=3(代表3个先验框), num_classes(表示先验框包含的物体的种类)+1(是否包含物体)+4(代表先验框的调整参数)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(P5_output)
return Model(inputs, [P5_output, P4_output, P3_output])
