model.train() loss = 0 for step, batch in enumerate(tqdm(data_loader, desc="Iteration")): batch = batch.to(device) if batch.x.shape[0] == 1 or batch.batch[-1] == 0: # 跳过只有一个样本的batch pass else: ## ignore nan targets (unlabeled) when computing training loss. is_...
init_height),shuffle=False,transform=transforms.Compose([transforms.ToTensor(),]),train=True,seen=cur_model.seen,batch_size=batch_size,num_workers=num_workers),batch_size=batch_size,shuffle=False,**kwargs)metaset=dataset.MetaDataset(metafiles=metadict,train=True)metaloader=torch.utils.data.DataLoad...
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("...
false + +# Data loader configs +batch_size: 6 +num_rays: 2048 +num_workers: 4 +no_shuffle: false + +# Loss configs +loss_class: model.loss.Loss +loss_args: + rgb_loss_weight: 1.0 + mask_loss_weight: 10.0 + mask_alpha: 50.0 + mask_target_gamma: 10.0 + flame_loss_weight: 1....
pin_memory = True) return trainloader, validloader def train_batch_loop(self,model,trainloader,i): epoch_loss = 0.0 epoch_acc = 0.0 pbar_train = tqdm(trainloader, desc = "Epoch" + " [TRAIN] " + str(i+1)) for t,data in enumerate(pbar_train): images,labels = data images = ...
(self,model,trainloader,i): epoch_loss = 0.0 epoch_acc = 0.0 pbar_train = tqdm(trainloader, desc = "Epoch" + " [TRAIN] " + str(i+1)) for t,data in enumerate(pbar_train): images,labels = data images = images.to(device) labels = labels.to(device) logits = model(images,...
I get an error when I run the following line of code: !yolo task=detect mode=train epochs=50 batch=40 plots=True model=weights/yolov10n.pt data=/home/data.yaml Error: /home/miniconda3/lib/python3.10/site-packages/albumentations/core/comp...
forepochinrange(epochs):tloss,vloss=0.0,0.0top1,top5=0.0,0.0pbar=tqdm(enumerate(train_loader),total=len(train_loader),bar_format=TQDM_BAR_FORMAT)fori,(data,target)inpbar:model.train()data=data.to('xpu')target=target.to('xpu')withtorch.xpu.amp.autocast():output=model(data)loss=...
(self,model,trainloader,i):epoch_loss = 0.0epoch_acc = 0.0pbar_train = tqdm(trainloader, desc = "Epoch" + " [TRAIN] " + str(i+1))for t,data in enumerate(pbar_train):images,labels = dataimages = images.to(device)labels = labels.to(device)logits = model(images,labels)loss = ...
Creates a Learner, which combines an optimizer, a model, and the data to train on. Each application (vision, text, tabular) has a customized function that creates a Learner, which automatically handles whatever details it can for the user. For instance, in this image class...