( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
x = torch.empty((8, 42), device = args.device) net = Network().to(device = args.device) 1. 2. 这可以在许多情况下用于生成设备不可知代码。以下是使用 dataloader 的例子: cuda0 = torch.device('cuda:0') # CUDA GPU 0 for i, x in enumerate(train_loader): x = x.to(cuda0) 1. ...
for batch_idx, (data, target) in enumerate(train_loader): data, target = Variable(data), Variable(target) # resize data from (batch_size, 1, 28, 28) to (batch_size, 28*28) data = data.view(-1, 28*28) optimizer.zero_grad() net_out = net(data) loss = criterion(net_out, ta...
for idx, batch in enumerate(tqdm(train_dataloader)): cnn.train() B1, B2, _, H, W = batch['patches'].shape Expand Down Expand Up @@ -281,19 +296,22 @@ def train_patches(opt): if scheduler.get_last_lr()[-1] > 5e-5: scheduler.step() opt.pretrained_model = path opt.pretra...
3)to obtain smoother outputs in the application of video matting a one-frame delay (OFD) trick as post-processing 优点 1)faster:63FPS (1080Ti,512×512) 2)SOTA: (1)bjective decomposition and concurrent optimization;(2)specific supervisions for each of the sub-objectives ...
for(k, batch)inenumerate(dataloader): # compute current value of loss function via forward pass output =gnn_model(batch) loss_function_value =loss_function(output[:,0], torch.tensor(batch.y, dtype = torch.float32)) # set past gradient to zero ...
y_test = train_test_split(X_pa_finetuned, y, test_size=0.2, random_state=42) # Re-encode the labels to ensure they are consecutive integers starting from 0 unique_labels = sorted(set(y_train) | set(y_test)) label_mapping = {label: idx for idx, label in enumerate...
train_loader = DataLoader(train_t, batch_size=data_yaml['batch_size'], shuffle=True) test_t = dataset_tensor(X_test, data_yaml['embed_val']) test_loader = DataLoader(test_t, batch_size=data_yaml['batch_size'], shuffle=True)
for batch_idx, (data, target) in enumerate(train_loader): metax, mask = metaloader.next() t2 = time.time() adjust_learning_rate(optimizer, processed_batches) processed_batches = processed_batches + 1 if use_cuda: data = data.cuda() metax = metax.cuda() mask = mask.cuda() # target...
path.isfile("dataloader_state.pt"): state_dict = torch.load("dataloader_state.pt") dataloader.load_state_dict(state_dict) # Iterate over the data for batch_idx, batch in enumerate(dataloader): # Store the state every 1000 batches if batch_idx % 1000 == 0: torch.save(dataloader.state...