def yolo_body(inputs, num_anchors, num_classes):
#---------------------------------------------------#
# 生成CSPdarknet53_tiny的主干模型
# feat1的shape为26,26,256
# feat2的shape为13,13,512
#---------------------------------------------------#
feat1, feat2 = darknet_body(inputs)
# 13,13,512 -> 13,13,256
P5 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
# 13,13,256 -> 13,13,512 -> 13,13,255
P5_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P5_output)
# 13,13,256 -> 13,13,128 -> 26,26,128
P5_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P5)
# 26,26,256 + 26,26,128 -> 26,26,384
P4 = Concatenate()([P5_upsample, feat1])
# 26,26,384 -> 26,26,256 -> 26,26,255
P4_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P4_output)
return Model(inputs, [P5_output, P4_output])
def yolo_body(input_shape, anchors_mask, num_classes, phi=0):
inputs = Input(input_shape)
#---------------------------------------------------#
# 生成CSPdarknet53_tiny的主干模型
# feat1的shape为26,26,256
# feat2的shape为13,13,512
#---------------------------------------------------#
feat1, feat2 = darknet_body(inputs)
if phi >= 1 and phi <= 4:
feat1 = attention[phi - 1](feat1, name='feat1')
feat2 = attention[phi - 1](feat2, name='feat2')
# 13,13,512 -> 13,13,256
P5 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
# 13,13,256 -> 13,13,512 -> 13,13,255
P5_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P5)
P5_output = DarknetConv2D(
len(anchors_mask[0]) * (num_classes + 5), (1, 1))(P5_output)
# 13,13,256 -> 13,13,128 -> 26,26,128
P5_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P5)
if phi >= 1 and phi <= 4:
P5_upsample = attention[phi - 1](P5_upsample, name='P5_upsample')
# 26,26,256 + 26,26,128 -> 26,26,384
P4 = Concatenate()([P5_upsample, feat1])
# 26,26,384 -> 26,26,256 -> 26,26,255
P4_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P4)
P4_output = DarknetConv2D(
len(anchors_mask[1]) * (num_classes + 5), (1, 1))(P4_output)
return Model(inputs, [P5_output, P4_output])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
# 首先我们会获取到两个有效特征层
# feat1 26x26x256
# feat2 13x13x512
feat1, feat2 = darknet_body(inputs)
# 13x13x512 -> 13x13x256
P5 = DarknetConv2D_BN_Leaky(256, (1, 1))(feat2)
P5_output = DarknetConv2D_BN_Leaky(512, (3, 3))(P5)
P5_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P5_output)
# Conv+UpSampling2D 13x13x256 -> 26x26x128
P5_upsample = compose(DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(P5)
# 26x26x(128+256) 26x26x384
P4 = Concatenate()([feat1, P5_upsample])
P4_output = DarknetConv2D_BN_Leaky(256, (3, 3))(P4)
P4_output = DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1))(P4_output)
return Model(inputs, [P5_output, P4_output])
def yolo_body(inputs, num_anchors, num_classes):
#---------------------------------------------------#
# 生成CSPdarknet53_tiny的主干模型
# feat1的shape为26,26,256
# feat2的shape为13,13,512
#---------------------------------------------------#
feat1, feat2 = darknet_body(inputs)
P5 = DarknetConv2D_BN_Leaky(256, (1,1))(feat2)
P5 = DarknetConv2D_BN_Leaky(512, (3,3))(P5)
P5 = DarknetConv2D_BN_Leaky(256, (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, P5])
P5 = DarknetConv2D_BN_Leaky(256, (1,1))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (3,3))(P5)
P5 = DarknetConv2D_BN_Leaky(256, (1,1))(P5)
P5 = DarknetConv2D_BN_Leaky(512, (3,3))(P5)
P5 = DarknetConv2D_BN_Leaky(256, (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))(feat1)
P4 = DarknetConv2D_BN_Leaky(512, (3,3))(P4)
P4 = DarknetConv2D_BN_Leaky(256, (1,1))(P4)
maxpool1 = MaxPooling2D(pool_size=(13,13), strides=(1,1), padding='same')(P4)
maxpool2 = MaxPooling2D(pool_size=(9,9), strides=(1,1), padding='same')(P4)
P4 = Concatenate()([maxpool1, maxpool2, P4])
P4 = DarknetConv2D_BN_Leaky(256, (1,1))(P4)
P4 = DarknetConv2D_BN_Leaky(512, (3,3))(P4)
# 26,26,256 + 26,26,256 -> 26,26,512
P4 = Concatenate()([P4, P5_upsample])
#---------------------------------------------------#
# 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)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size,13,13,3,85)
#---------------------------------------------------#
P5_output = DarknetConv2D_BN_Leaky(512, (3,3))(P5)
P5_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(P5_output)
return Model(inputs, [P5_output, P4_output])
def yolo_body(inputs, num_anchors, num_classes):
# 生成darknet53的主干模型
feat1,feat2 = darknet_body(inputs)
# 第一个特征层
# y1=(batch_size,13,13,3,85)
P5 = DarknetConv2D_BN_Leaky(256, (1,1))(feat2)
P5_output = DarknetConv2D_BN_Leaky(512, (3,3))(P5)
P5_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(P5_output)
P5_upsample = compose(DarknetConv2D_BN_Leaky(128, (1,1)), UpSampling2D(2))(P5)
P4 = Concatenate()([feat1, P5_upsample])
P4_output = DarknetConv2D_BN_Leaky(256, (3,3))(P4)
P4_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(P4_output)
return Model(inputs, [P5_output, P4_output])