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train.py
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#-------------------------------------#
# 对数据集进行训练
#-------------------------------------#
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from nets.retinanet import Retinanet
from nets.retinanet_training import FocalLoss, LossHistory, weights_init
from utils.dataloader import RetinanetDataset, retinanet_dataset_collate
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
#---------------------------------------------------#
# 获得类和先验框
#---------------------------------------------------#
def get_classes(classes_path):
'''loads the classes'''
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def fit_one_epoch(net,focal_loss,epoch,epoch_size,epoch_size_val,gen,genval,Epoch,cuda):
total_loss = 0
val_loss = 0
net.train()
print('Start Train')
with tqdm(total=epoch_size,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen):
if iteration >= epoch_size:
break
images, targets = batch[0], batch[1]
with torch.no_grad():
if cuda:
images = torch.from_numpy(images).type(torch.FloatTensor).cuda()
targets = [torch.from_numpy(ann).type(torch.FloatTensor).cuda() for ann in targets]
else:
images = torch.from_numpy(images).type(torch.FloatTensor)
targets = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in targets]
optimizer.zero_grad()
#-------------------#
# 获得预测结果
#-------------------#
_, regression, classification, anchors = net(images)
#-------------------#
# 计算损失
#-------------------#
loss, _, _ = focal_loss(classification, regression, anchors, targets, cuda=cuda)
loss.backward()
torch.nn.utils.clip_grad_norm_(net.parameters(), 1e-2)
optimizer.step()
total_loss += loss.item()
pbar.set_postfix(**{'total_loss': total_loss / (iteration + 1),
'lr' : get_lr(optimizer)})
pbar.update(1)
net.eval()
print('Start Validation')
with tqdm(total=epoch_size_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
for iteration, batch in enumerate(genval):
if iteration >= epoch_size_val:
break
images_val, targets_val = batch[0], batch[1]
with torch.no_grad():
if cuda:
images_val = torch.from_numpy(images_val).type(torch.FloatTensor).cuda()
targets_val = [torch.from_numpy(ann).type(torch.FloatTensor).cuda() for ann in targets_val]
else:
images_val = torch.from_numpy(images_val).type(torch.FloatTensor)
targets_val = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in targets_val]
optimizer.zero_grad()
_, regression, classification, anchors = net(images_val)
loss, _, _ = focal_loss(classification, regression, anchors, targets_val, cuda=cuda)
val_loss += loss.item()
pbar.set_postfix(**{'total_loss': val_loss / (iteration + 1)})
pbar.update(1)
loss_history.append_loss(total_loss/(epoch_size+1), val_loss/(epoch_size_val+1))
print('Finish Validation')
print('Epoch:'+ str(epoch+1) + '/' + str(Epoch))
print('Total Loss: %.4f || Val Loss: %.4f ' % (total_loss/(epoch_size+1),val_loss/(epoch_size_val+1)))
print('Saving state, iter:', str(epoch+1))
torch.save(model.state_dict(), 'logs/Epoch%d-Total_Loss%.4f-Val_Loss%.4f.pth'%((epoch+1),total_loss/(epoch_size+1),val_loss/(epoch_size_val+1)))
return val_loss/(epoch_size_val+1)
if __name__ == "__main__":
#--------------------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#--------------------------------------------#
Cuda = True
#--------------------------------------------#
# 输入图像大小
#--------------------------------------------#
input_shape = (600, 600)
#--------------------------------------------#
# phi == 0 : resnet18
# phi == 1 : resnet34
# phi == 2 : resnet50
# phi == 3 : resnet101
# phi == 4 : resnet152
#--------------------------------------------#
phi = 2
#--------------------------------------------#
# 训练前一定要注意注意修改
# classes_path对应的txt的内容
# 修改成自己需要分的类
#--------------------------------------------#
classes_path = 'model_data/xinye_classes.txt'
#--------------------------------------------#
# 获取classes和数量
#--------------------------------------------#
class_names = get_classes(classes_path)
num_classes = len(class_names)
#----------------------------------------------------#
# 获取Retinanet模型
#----------------------------------------------------#
model = Retinanet(num_classes, phi, False)
weights_init(model)
#----------------------------------------------------#
# 权值文件请看README，百度网盘下载
#----------------------------------------------------#
model_path = "model_data/retinanet_resnet50.pth"
print('Loading weights into state dict...')
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if np.shape(model_dict[k]) == np.shape(v)}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
focal_loss = FocalLoss()
loss_history = LossHistory("logs/")
#----------------------------------------------------#
# 获得图片路径和标签
#----------------------------------------------------#
annotation_path = 'xinye.txt'
#----------------------------------------------------------------------#
# 验证集的划分在train.py代码里面进行
# 2007_test.txt和2007_val.txt里面没有内容是正常的。训练不会使用到。
# 当前划分方式下，验证集和训练集的比例为1:9
#----------------------------------------------------------------------#
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines) * val_split)
num_train = len(lines) - num_val
#------------------------------------------------------#
# 主干特征提取网络特征通用，冻结训练可以加快训练速度
# 也可以在训练初期防止权值被破坏。
# Init_Epoch为起始世代
# Freeze_Epoch为冻结训练的世代
# Epoch总训练世代
# 提示OOM或者显存不足请调小Batch_size
#------------------------------------------------------#
if True:
#--------------------------------------------#
# BATCH_SIZE不要太小，不然训练效果很差
#--------------------------------------------#
lr = 1e-4
Batch_size = 4
Init_Epoch = 0
Freeze_Epoch = 25
optimizer = optim.Adam(net.parameters(),lr)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=2, verbose=True)
train_dataset = RetinanetDataset(lines[:num_train], (input_shape[0], input_shape[1]), True)
val_dataset = RetinanetDataset(lines[num_train:], (input_shape[0], input_shape[1]), False)
gen = DataLoader(train_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=retinanet_dataset_collate)
gen_val = DataLoader(val_dataset, shuffle=True, batch_size=Batch_size, num_workers=4,pin_memory=True,
drop_last=True, collate_fn=retinanet_dataset_collate)
epoch_size = num_train // Batch_size
epoch_size_val = num_val // Batch_size
if epoch_size == 0 or epoch_size_val == 0:
raise ValueError("数据集过小，无法进行训练，请扩充数据集。")
#------------------------------------#
# 冻结一定部分训练
#------------------------------------#
for param in model.backbone_net.parameters():
param.requires_grad = False
for epoch in range(Init_Epoch,Freeze_Epoch):
val_loss = fit_one_epoch(net,focal_loss,epoch,epoch_size,epoch_size_val,gen,gen_val,Freeze_Epoch,Cuda)
lr_scheduler.step(val_loss)
if True:
#--------------------------------------------#
# BATCH_SIZE不要太小，不然训练效果很差
#--------------------------------------------#
lr = 1e-5
Batch_size = 2
Freeze_Epoch = 25
Unfreeze_Epoch = 100
optimizer = optim.Adam(net.parameters(),lr)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=2, verbose=True)
train_dataset = RetinanetDataset(lines[:num_train], (input_shape[0], input_shape[1]), True)
val_dataset = RetinanetDataset(lines[num_train:], (input_shape[0], input_shape[1]), False)
gen = DataLoader(train_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=retinanet_dataset_collate)
gen_val = DataLoader(val_dataset, shuffle=True, batch_size=Batch_size, num_workers=4,pin_memory=True,
drop_last=True, collate_fn=retinanet_dataset_collate)
epoch_size = num_train // Batch_size
epoch_size_val = num_val // Batch_size
if epoch_size == 0 or epoch_size_val == 0:
raise ValueError("数据集过小，无法进行训练，请扩充数据集。")
#------------------------------------#
# 解冻后训练
#------------------------------------#
for param in model.backbone_net.parameters():
param.requires_grad = True
for epoch in range(Freeze_Epoch,Unfreeze_Epoch):
val_loss = fit_one_epoch(net,focal_loss,epoch,epoch_size,epoch_size_val,gen,gen_val,Unfreeze_Epoch,Cuda)
lr_scheduler.step(val_loss)