for batch_idx, (data, target) in enumerate(train_loader): #data = data.view(-1, 28*28) data, target = data.to(device), target.to(device) logits = net(data) loss = criteon(logits, target) optimizer.zero_grad() loss.backward() optimizer.step() if batch_idx % 100 == 0: print(...
def train(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=None): model.train() ddp_loss = torch.zeros(2).to(rank) if sampler: sampler.set_epoch(epoch) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(rank), target.to(rank)...
def train(model: nn.Module, train_loader: torch.utils.data.DataLoader, optimizer: Any, epoch: int): """ Train the model """ model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output =...
(train_dir, sub_dir) elif i < valid_point: out_dir = os.path.join(valid_dir, sub_dir) else: out_dir = os.path.join(test_dir, sub_dir) makedir(out_dir) target_path = os.path.join(out_dir, imgs[i]) src_path = os.path.join(dataset_dir, sub_dir, imgs[i]) shutil.copy...
deftrain(train_loader,model,criterion,optimizer):model.train()train_loss=0.0fori,(input,target)inenumerate(train_loader):input=input.cuda(non_blocking=True)target=target.cuda(non_blocking=True)# compute output output=model(input)loss=criterion(output,target)# compute gradient anddoSGDstep ...
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True) start = datetime.now() total_step =len(train_loader) for epoch inrange(args.epochs): for i, (images, labels) inenumerate(train_loader): ...
- train :True= 训练集,False= 测试集 - download :True= 从互联上下载数据,并将其放在root目录下。如果数据集已经下载,什么都不干。 返回类型:tuple,返回参数:(image,target)其中target应是目标类的类索引。 举例: from torch.utils.data.sampler importSampler ...
for batch_idx, (images, target) in enumerate(data_loader): images = images.cuda(cfg.gpu, non_blocking=True) target = target.cuda(cfg.gpu, non_blocking=True) # compute output with torch.cuda.amp.autocast(enabled=cfg.use_amp):
for i, l in enumerate(label): l[:, 0] = i # add target image index for build_targets() return torch.stack(img, 0), torch.cat(label, 0), path, shapes @staticmethod def collate_fn4(batch): img, label, path, shapes = zip(*batch) # transposed ...
for i, data in enumerate(train_loader): images = data[0]["data"] target = data[0]["label"].squeeze(-1).long() # model training More information about framework integration can be found in theframework plug-ins sectionof the documentation. ...