Dataloader传入数据(这个数据包括:训练数据和标签),batchsize(代表的是将数据分成batch=[len(train_ids[0])除以batchsize],每一份包括的数据是batchsize) 三. 对enumerate的理解: enumerate返回值有两个:一个是序号,也就是在这里的batch地址,一个是数据train_ids for i, data in enumerate(train_loader,1): ...
for i in range(num_epochs):total_loss = 0for bidx, (x,_) in enumerate(tqdm(train_loader, desc=f"Epoch {i+1}/{num_epochs}")):x = x.cuda()x = F.pad(x, (2,2,2,2))t = torch.randint(0,num_time_steps,(batch...
importtorch.cuda.nvtxasnvtxnvtx.range_push("Batch 0")nvtx.range_push("Load Data")fori,(input_data,target)inenumerate(train_loader):input_data=input_data.cuda(non_blocking=True)target=target.cuda(non_blocking=True)nvtx.range_pop();nvtx.range_push("Forward")output=model(input_data)nvtx.rang...
model.train() t0 = time.perf_counter()summ = 0count = 0 for step, data in enumerate(train_loader):# copy data to GPUinputs = data[0].to(device=device, non_blocking=True)label = data[1].squeeze(-1).to(device=device, non_blocking=Tru...
forbatch_idx, (data, target)inenumerate(train_loader): images= images.cuda(non_blocking=True)#将数据放入cuda target= target.cuda(non_blocking=True)#将标签放入cuda ... output=model(images) loss=criterion(output, target) ... optimizer.zero_grad() ...
foridx,(x,y)inenumerate(train_loader):pred=model(x)loss=criterion(pred,y)loss.backwardstep()optimizer.zero_grad()if(idx+1)%eval_steps==0:eval() 如果你想设置batch_size=64结果爆显存了,那么不妨设置batch_size=16,然后定义一个变量accum_steps=4,每个mini-batch仍然正常前向传播以及反向传播,但是...
train_sampler.set_epoch(epoch)forbatch_idx, (data, target)inenumerate(train_loader): data=data.cuda() target=target.cuda() ... output=model(images) loss=criterion(output, target) ... optimizer.zero_grad() loss.backward() optimizer.step()if__name__=="__main__": ...
forbatch_idx, (data, target)inenumerate(train_loader): images = images.cuda(non_blocking=True) target = target.cuda(non_blocking=True) ... output = model(images) loss = criterion(output, target) ... optimizer.zero_grad loss.backward ...
for batch_idx, (data, target) in enumerate(train_loader): images = images.cuda(non_blocking=True) target = target.cuda(non_blocking=True) ... output = model(images) loss = criterion(output, target) ... optimizer.zero_grad() loss.backward() ...
def train(epoch): for i, data in enumerate(train_loader): # 一个batch inputs, labels, inputs_path = data inputs = inputs.unsqueeze(1) labels = labels.unsqueeze(1) # 将这些数据转换成Variable类型 inputs, labels = Variable(inputs), Variable(labels) ...