if isinstance(train_dataset, torch.utils.data.IterableDataset): if self.args.world_size > 1: train_dataset = IterableDatasetShard( train_dataset, batch_size=self._train_batch_size, drop_last=self.args.dataloader_drop_last, num_processes=self.args.world_size, process_index=self.args.process_...
def get_lsun_dataloader(path_to_data='/data/dgl/LSUN', dataset='bedroom_train', batch_size=64): """LSUN dataloader with (128, 128) sized images. path_to_data : str One of 'bedroom_val' or 'bedroom_train' """ # Compose transforms transform = transforms.Compose([ transforms.Resize(...
dataloaders = {split:get_dataloader(config, split, get_transform(config, split))forsplitin['train','val']} writer = SummaryWriter(config.train.dir) train(config, model, dataloaders, criterion, optimizer, scheduler, writer, last_epoch+1) 开发者ID:pudae,项目名称:kaggle-hpa,代码行数:24,代码...
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=2) val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=2) 1. 2. 2.3 标签平滑 class LabelSmoothingCrossEntropy(nn.Layer): def __...
training_set_iterator = torch.utils.data.DataLoader(dataset, sampler, config) # tra // Phase 准备训练阶段 training/training_loop_3d.py phases = (G, opt) # G is module // Execute training phases 训练核心部分 training/training_loop_3d.py for phase in zip(phases) ...
test'elseself.train_sourceself.sample_type='all'#全采样data_loader=tordata.DataLoader(dataset=...
讲到这里,Pytorch的DataLoader数据读取机制思路基本上理清楚了,接下来谈一谈图像预处理模块(transforms)。 2、图像预处理模块(transforms) transforms中包含了各种常用的图像预处理方法,存放在torchvision这个计算机视觉工具包中,具体见Pytorch官网https://pytorch.org/vision/stable/index.html,主要包括以下方法: ...
# training steps 的数量: [number of batches] x [number of epochs].total_steps =len(train_dataloader) * epochs# 设计 learning rate schedulerscheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps =50, num_training_steps = total_steps)...
test_data = TFRecordDataLoader(config, mode="test")# initialise the estimatortrainer = RawTrainer(config, model, train_data, val_data, test_data)# start trainingtrainer.run() 開發者ID:maxwellmri,項目名稱:Distributed-Tensorflow-Template,代碼行數:28,代碼來源:task.py ...
deftrain(dataloader,model,loss_fn,optimizer):size=len(dataloader.dataset)model.train()forbatch,(X,y)inenumerate(dataloader):# Compute prediction errorpred=model(X)loss=loss_fn(pred,y)# Backpropagationoptimizer.zero_grad()loss.backward()optimizer.step()ifbatch%100==0:loss,current=loss.item(),...