image_row_size = image_size[0] * image_size[1]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
transform = transforms.Compose([
transforms.Resize(image_size),
# transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
path = '/home/aims/Documents/Pytorch/pytorch_exercise/data'
net = Classification()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train_data = CatDogDataset(path + "/" + 'train', transform=transform)
test_data = CatDogDataset(path + "/" + 'val', transform=transform)
trainloader = torch.utils.data.DataLoader(test_data,
batch_size=64,
shuffle=True,
num_workers=4)
testloader = torch.utils.data.DataLoader(test_data,
batch_size=64,
shuffle=True,
num_workers=4)
#Training
for epoch in range(10): # loop over the dataset multiple times
def main():
#define the dataloader
train_loader = DataLoader(SkeletonFeeder(mode='train', debug=False), batch_size=params['batchsize'], shuffle=True, num_workers=params['numworkers'])
val_loader = DataLoader(SkeletonFeeder(mode='valid', debug=False), batch_size=params['batchsize'], shuffle=False, num_workers=params['numworkers'])
n_class = params['n_class']
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
#use the model and transfer to gpu
model = Classification(n_class=n_class) #add model cfg
model = model.cuda(params['gpu'][0])#to do--->
model = nn.DataParallel(model, device_ids=params['gpu'])
if params['retrain']:
trained_dict = torch.load(params['retrain'],map_location='cpu')
model_dict = model.state_dict()
trained_dict = {k:v for k,v in trained_dict.items() if k in model_dict}
model_dict.update(trained_dict)
model.load_state_dict(model_dict)
print('load trained model finish')
model_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(model_params, lr=params['lr'], weight_decay=params['weight_decay'])
schedule = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.333, patience=2, verbose=True)
writer = SummaryWriter()
criterion = nn.CrossEntropyLoss()
min_loss = 1000
print('-------------------start training----------------------')
print('lr:', optimizer.param_groups[0]['lr'])
for i in range(params['epoch']):
train_loss, train_top1, train_top5, batch_time, data_time = train(model, train_loader, optimizer,criterion)
valid_loss, val_top1, val_top5 = valid(model, val_loader,optimizer,criterion)
schedule.step(valid_loss)
f = open(params['log']+'bert_classifylog_'+cur_time+'.txt', 'a')
print('epoch:', str(i + 1) + "/" + str(params['epoch']))
print('data time:%0.3f'%data_time.avg, 'batch time:%0.3f'%batch_time.avg, 'epoch time:%0.3f'%(batch_time.sum))
print('train loss:%0.8f'%train_loss, 'top1:%0.2f'%train_top1, '%', 'top5:%0.2f'%train_top5, '%', 'lr:', optimizer.param_groups[0]['lr'])
print('valid loss:%0.8f'%valid_loss, 'top1:%0.2f'%val_top1, '%', 'top5:%0.2f'%val_top5, '%')
f.write('epoch:'+str(i+1)+"/"+str(params['epoch'])+'\n')
f.write('data time:%0.3f'%data_time.avg+'batch time:%0.3f'%batch_time.avg+'epoch time:%0.3f'%(batch_time.sum)+'\n')
f.write('train loss:%0.8f'%train_loss+'top1:%0.2f'%train_top1+'%'+'top5:%0.2f'%train_top5+'%'+'lr:'+str(optimizer.param_groups[0]['lr'])+'\n')
f.write('valid loss:%0.8f'%valid_loss+'top1:%0.2f'%val_top1+'%'+'top5:%0.2f'%val_top5+'%'+'\n')
f.write('************************************\n')
f.close()
writer.add_scalar('train loss', train_loss, i)
writer.add_scalar('valid loss', valid_loss, i)
writer.add_scalar('train top1', train_top1, i)
writer.add_scalar('valid top1', val_top1, i)
writer.add_scalar('train top5', train_top5, i)
writer.add_scalar('valid top5', val_top5, i)
if valid_loss < min_loss:
torch.save(model.state_dict(), params['save_path']+'bert_classifymodel_'+cur_time+'.pth')
print('saving model successful to --->',params['save_path'])
min_loss = valid_loss
writer.close()
##构建数据迭代器 训练
dataset = MyData(args.datas, transforms=normalize)
valid = MyData(args.test, transforms=normalize)
dataloader = DataLoader(dataset,
shuffle=True,
batch_size=batch_size,
num_workers=2,
collate_fn=collate_fn)
valid_dataloader = DataLoader(valid,
shuffle=True,
batch_size=batch_size,
num_workers=2,
collate_fn=collate_fn)
##定义优化器
optimizer = torch.optim.Adam([{
'params': model.parameters()
}],
lr=args.lr,
betas=(args.beta1, args.beta2))
nums = len(dataloader)
logs = open('logs.txt', 'a+')
valid_logs = open('valid.txt', 'a+')
for epoch in range(args.epochs):
for step, (images, labels) in enumerate(dataloader):
images, labels = images.cuda(), labels.cuda()
images, labels = Variable(images, requires_grad=False), Variable(
labels, requires_grad=False)
##将梯度初始化为零（因为一个batch的loss关于weight的导数是所有sample的loss关于weight的导数的累加和）
optimizer.zero_grad() ##
losses = model(images, labels)