def train(model, device, train_loader, optimizer, epoch): global batch_size # model.train() state = model.zero_state(batch_size) for batch_idx, (data, target) in enumerate(train_loader): print(f"The batch_idx value is {batch_idx}") data, target = data.to(device), target.to(devic...
eval_data=val_data_loader, epochs=epochs, verbose=1, save_dir=f'./{model_outputs}{fold}', num_workers=10, callbacks=[earlystop]) from mmrc.utils.eval_utils import CVRecorder recorder = CVRecorder(fold, earlystop.best_value, train_idx=trn_idx, val_idx=val_idx,...
trainlist = data_options['train'] testlist = data_options['valid'] backupdir = data_options['backup'] gpus = data_options['gpus'] # e.g. 0,1,2,3 ngpus = len(gpus.split(',')) num_workers = int(data_options['num_workers']) batch_size = int(net_options['batch']) print("...
I get the embeddings from my model for each image in test dataset: def calculate_embeddings(model, loader): model.eval() embeddings = [] with torch.no_grad(): for batch_idx, images in enumerate(loader): images = images.to(device) batch_embeddings = model(images) embeddings.extend(batch_...
model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer) for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() 把它替换成如下代码,用Arc DGPU训练。并且修改参数 batch_size=32 model.train() model = model.to("xpu") criterion = criterion.to("xp...
model.train() epoch_loss = 0 epoch_acc = 0 for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask
label = self.dataset.loc[idx, "label"] sample = {"text": text, "label": label} # 返回一个 dict return sample # 加载训练集sentiment_train_set = SentimentDataset(data_path + "sentiment.train.data")sentiment_train_loader = DataLoader(sentiment_train_set, batch_size=batch_size, shuffle=Tru...
pro[cessenum]Enumerates all managed processes and the application domains in each process. a[ttach]pidAttaches the debugger to the running process specified by thepidargument. l[ist]modLists the loaded assemblies in the process by AppDomain. ...
Walks the directory tree that is rooted atDrive:Pathand executes theforstatement in each directory of the tree. If no directory is specified after/r, the current directory is used as the root directory. IfSetis just a single period (.), it only enumerates the directory tree. ...
importtorchmodel.train()forbatch_idx, (data,target)inenumerate(train_loader):data,target=data.to(device),target.to(device)optimizer.zero_grad()output=model(data).dequantize()loss=torch.nn.functional.nll_loss(output,target)loss.backward()optimizer.step() ...