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yolo.py
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from collections import OrderedDict
import torch
import torch.nn as nn
from nets.CSPdarknet import darknet53
def conv2d(filter_in, filter_out, kernel_size, stride=1):
pad = (kernel_size - 1) // 2 if kernel_size else 0
return nn.Sequential(OrderedDict([
("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=stride, padding=pad, bias=False)),
("bn", nn.BatchNorm2d(filter_out)),
("relu", nn.LeakyReLU(0.1)),
]))
#---------------------------------------------------#
# SPP结构，利用不同大小的池化核进行池化
# 池化后堆叠
#---------------------------------------------------#
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size//2) for pool_size in pool_sizes])
def forward(self, x):
features = [maxpool(x) for maxpool in self.maxpools[::-1]]
features = torch.cat(features + [x], dim=1)
return features
#---------------------------------------------------#
# 卷积 + 上采样
#---------------------------------------------------#
class Upsample(nn.Module):
def __init__(self, in_channels, out_channels):
super(Upsample, self).__init__()
self.upsample = nn.Sequential(
conv2d(in_channels, out_channels, 1),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x,):
x = self.upsample(x)
return x
#---------------------------------------------------#
# 三次卷积块
#---------------------------------------------------#
def make_three_conv(filters_list, in_filters):
m = nn.Sequential(
conv2d(in_filters, filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
conv2d(filters_list[1], filters_list[0], 1),
)
return m
#---------------------------------------------------#
# 五次卷积块
#---------------------------------------------------#
def make_five_conv(filters_list, in_filters):
m = nn.Sequential(
conv2d(in_filters, filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
conv2d(filters_list[1], filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
conv2d(filters_list[1], filters_list[0], 1),
)
return m
#---------------------------------------------------#
# 最后获得yolov4的输出
#---------------------------------------------------#
def yolo_head(filters_list, in_filters):
m = nn.Sequential(
conv2d(in_filters, filters_list[0], 3),
nn.Conv2d(filters_list[0], filters_list[1], 1),
)
return m
#---------------------------------------------------#
# yolo_body
#---------------------------------------------------#
class YoloBody(nn.Module):
def __init__(self, anchors_mask, num_classes, pretrained = False):
super(YoloBody, self).__init__()
#---------------------------------------------------#
# 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
#---------------------------------------------------#
self.backbone = darknet53(pretrained)
self.conv1 = make_three_conv([512,1024],1024)
self.SPP = SpatialPyramidPooling()
self.conv2 = make_three_conv([512,1024],2048)
self.upsample1 = Upsample(512,256)
self.conv_for_P4 = conv2d(512,256,1)
self.make_five_conv1 = make_five_conv([256, 512],512)
self.upsample2 = Upsample(256,128)
self.conv_for_P3 = conv2d(256,128,1)
self.make_five_conv2 = make_five_conv([128, 256],256)
# 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75
self.yolo_head3 = yolo_head([256, len(anchors_mask[0]) * (5 + num_classes)],128)
self.down_sample1 = conv2d(128,256,3,stride=2)
self.make_five_conv3 = make_five_conv([256, 512],512)
# 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75
self.yolo_head2 = yolo_head([512, len(anchors_mask[1]) * (5 + num_classes)],256)
self.down_sample2 = conv2d(256,512,3,stride=2)
self.make_five_conv4 = make_five_conv([512, 1024],1024)
# 3*(5+num_classes)=3*(5+20)=3*(4+1+20)=75
self.yolo_head1 = yolo_head([1024, len(anchors_mask[2]) * (5 + num_classes)],512)
def forward(self, x):
# backbone
x2, x1, x0 = self.backbone(x)
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,2048
P5 = self.conv1(x0)
P5 = self.SPP(P5)
# 13,13,2048 -> 13,13,512 -> 13,13,1024 -> 13,13,512
P5 = self.conv2(P5)
# 13,13,512 -> 13,13,256 -> 26,26,256
P5_upsample = self.upsample1(P5)
# 26,26,512 -> 26,26,256
P4 = self.conv_for_P4(x1)
# 26,26,256 + 26,26,256 -> 26,26,512
P4 = torch.cat([P4,P5_upsample],axis=1)
# 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
P4 = self.make_five_conv1(P4)
# 26,26,256 -> 26,26,128 -> 52,52,128
P4_upsample = self.upsample2(P4)
# 52,52,256 -> 52,52,128
P3 = self.conv_for_P3(x2)
# 52,52,128 + 52,52,128 -> 52,52,256
P3 = torch.cat([P3,P4_upsample],axis=1)
# 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
P3 = self.make_five_conv2(P3)
# 52,52,128 -> 26,26,256
P3_downsample = self.down_sample1(P3)
# 26,26,256 + 26,26,256 -> 26,26,512
P4 = torch.cat([P3_downsample,P4],axis=1)
# 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
P4 = self.make_five_conv3(P4)
# 26,26,256 -> 13,13,512
P4_downsample = self.down_sample2(P4)
# 13,13,512 + 13,13,512 -> 13,13,1024
P5 = torch.cat([P4_downsample,P5],axis=1)
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
P5 = self.make_five_conv4(P5)
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size,75,52,52)
#---------------------------------------------------#
out2 = self.yolo_head3(P3)
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size,75,26,26)
#---------------------------------------------------#
out1 = self.yolo_head2(P4)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size,75,13,13)
#---------------------------------------------------#
out0 = self.yolo_head1(P5)
return out0, out1, out2