【Pytorch深度学习50篇】·······第五篇：【YOLO】【3】—–训练篇
一周过去了，我赶在一周的尾巴上来继续写文章了，上周不算忙，至少不用去驻场了，日子好过多了。不过苦了小兄弟们了，他们去驻场了。
来这个现在这个公司快两年了，大概是今年5月份开始，我们一个初创公司开始和这个行业的巨头开始对着干，老板也是真相信我们啊，别人公司什么体量，我们公司什么体量，根本不是一个数量级，就这样还初生牛犊不怕虎，他不怕，我怕，但是又能怎么着呢，环境就是这么残酷，你不硬着头皮上，就只能灰溜溜的走。负重前行吧。明知山有虎，偏向虎上行的精神。
好了，废话说多了，回归主题，YOLO的训练，之前我们网络模型和数据准备都完事了，所以今天的任务就是把它训练起来，我们先上训练代码
3.训练
from models.yolov3 import yolov3
from models.yolov5 import yolov5s
from models.yolov4 import yolov4
from torch.utils.data import DataLoader
import torch
import config
import dataset
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class Yolo_Train:
def __init__(self, img_file_path, anno_file_path, model_size='small', pretrain_flag=True, data_augmentation=False):
self.class_num = len(config.class_name)
self.net, self.save_name = self.select_model(model_size, pretrain_flag)
self.train_data = dataset.Yolo_Dataset(img_file_path, anno_file_path,data_augmentation)
self.train_loader = DataLoader(self.train_data, batch_size=config.hy_params['batch_size'], shuffle=True,
num_workers=2)
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=config.hy_params['lr'])
self.epoches = config.hy_params['epoch']
def train(self):
self.net.train()
for epoch in range(self.epoches):
losss = 0
for index, (img_tensor, label_32, label_16, label_8) in enumerate(self.train_loader):
label_32 = label_32.to(device)
label_16 = label_16.to(device)
label_8 = label_8.to(device)
img = img_tensor.to(device)
output32, output16, output8 = self.net(img)
loss32 = self.loss_function(output32, label_32, 0.84)
loss16 = self.loss_function(output16, label_16, 0.96)
loss8 = self.loss_function(output8, label_8, 0.96)
loss = loss32 + loss16 + loss8
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
losss += loss.item()
print('epoch: [%s/%s] ----iteration[%s/%s]----------- loss:%.8f' % (
epoch + 1, self.epoches, index + 1, len(self.train_loader), losss / (index + 1)))
torch.save(self.net, './weights/' + self.save_name)
def loss_function(self, output, target, alpha):
# output = [batch,21,13,13] target = [batch,13,13,3,7]
output = output.permute(0, 2, 3, 1) # [batch,13,13,21]
output = output.reshape(output.size(0), output.size(1), output.size(2), 3, -1) # [batch,13,13,3,7]
mask_obj = target[..., 0] > 0 # 置信度大于0，有目标
mask_noobj = target[..., 0] == 0 # 没有目标
loss_obj = torch.mean((output[mask_obj] - target[mask_obj]) ** 2)
loss_noobj = torch.mean((output[mask_noobj][:, 0] - target[mask_noobj][:, 0]) ** 2)
loss = loss_obj * alpha + loss_noobj
return loss
def select_model(self, model_size, pretrain_flag):
assert model_size in ['small', 'middle', 'big']
if model_size == 'small':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov5s_net.pth').to(device)
else:
net = yolov5s.YOLO(nc=self.class_num).to(device)
return net, 'yolov5s_net.pth'
elif model_size == 'middle':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov4_net.pth').to(device)
else:
net = yolov4.yolo4(num_class=self.class_num).to(device)
return net, 'yolov4_net.pth'
elif model_size == 'big':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov3_net.pth').to(device)
else:
net = yolov3.yolo3(num_class=self.class_num).to(device)
return net, 'yolov3_net.pth'
if __name__ == '__main__':
img_file_path = r'D:DATASface_maskJPEGImages'
anno_file_path = r'D:DATASface_maskAnnotations'
Trainer = Yolo_Train(img_file_path, anno_file_path,model_size='small')
Trainer.train()
我这里把之前提到的v3,v4,v5模型都涉及进来了。训练的时候我们可以通过model_size来更改模型
我们先来看初始化函数
def __init__(self, img_file_path, anno_file_path, model_size='small', pretrain_flag=True, data_augmentation=False):
self.class_num = len(config.class_name)
self.net, self.save_name = self.select_model(model_size, pretrain_flag)
self.train_data = dataset.Yolo_Dataset(img_file_path, anno_file_path,data_augmentation)
self.train_loader = DataLoader(self.train_data, batch_size=config.hy_params['batch_size'], shuffle=True,
num_workers=2)
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=config.hy_params['lr'])
self.epoches = config.hy_params['epoch']我们定义了类别的数量、选择了网络模型、定义了dataloader（也就是上一篇文章的数据准备）、定义了优化器和训练的epoch数量。
选择网络模型我也定义了一个函数
def select_model(self, model_size, pretrain_flag):
assert model_size in ['small', 'middle', 'big']
if model_size == 'small':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov5s_net.pth').to(device)
else:
net = yolov5s.YOLO(nc=self.class_num).to(device)
return net, 'yolov5s_net.pth'
elif model_size == 'middle':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov4_net.pth').to(device)
else:
net = yolov4.yolo4(num_class=self.class_num).to(device)
return net, 'yolov4_net.pth'
elif model_size == 'big':
if pretrain_flag:
print('加载预训练模型')
net = torch.load('./weights/yolov3_net.pth').to(device)
else:
net = yolov3.yolo3(num_class=self.class_num).to(device)
return net, 'yolov3_net.pth'可以加载yolov3、yolov4、yolov5的模型文件和预训练模型
然后就是训练函数
def train(self):
self.net.train()
for epoch in range(self.epoches):
losss = 0
for index, (img_tensor, label_32, label_16, label_8) in enumerate(self.train_loader):
label_32 = label_32.to(device)
label_16 = label_16.to(device)
label_8 = label_8.to(device)
img = img_tensor.to(device)
output32, output16, output8 = self.net(img)
loss32 = self.loss_function(output32, label_32, 0.84)
loss16 = self.loss_function(output16, label_16, 0.96)
loss8 = self.loss_function(output8, label_8, 0.96)
loss = loss32 + loss16 + loss8
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
losss += loss.item()
print('epoch: [%s/%s] ----iteration[%s/%s]----------- loss:%.8f' % (
epoch + 1, self.epoches, index + 1, len(self.train_loader), losss / (index + 1)))
torch.save(self.net, './weights/' + self.save_name)又是一顿常规操作，损失函数，我选择了一个不是特别的好的MSE，这个玩意貌似不太好，还应该crossentropy和IOUloss，之后我来更新出来吧。
整个项目我在下一篇的推理的时候，会完全给大家开源出来的。
至此，敬礼，solute！！！！
老规矩，上咩咩。
