dataloader

问题 I'm trying to figure out the best way to handle a one-to-many relationship using type-graphql and typeorm with a postgresql db (using apollo server with express). I have a user table which has a one-to-many relation with a courses table. The way I am currently handling this is to use the @RelationId field to create a column of userCourseIds and then using @FieldResolver with dataloader to batch fetch the courses that belong to that user(s). My issue is that with the @RelationId field, a

问题 I have the following pieces of code: # Device configuration device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') seed = 42 np.random.seed(seed) torch.manual_seed(seed) # split the dataset into validation and test sets len_valid_set = int(0.1*len(dataset)) len_train_set = len(dataset) - len_valid_set print("The length of Train set is {}".format(len_train_set)) print("The length of Test set is {}".format(len_valid_set)) train_dataset , valid_dataset, = torch.utils.data.random

问题 I have the following pieces of code: # Device configuration device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') seed = 42 np.random.seed(seed) torch.manual_seed(seed) # split the dataset into validation and test sets len_valid_set = int(0.1*len(dataset)) len_train_set = len(dataset) - len_valid_set print("The length of Train set is {}".format(len_train_set)) print("The length of Test set is {}".format(len_valid_set)) train_dataset , valid_dataset, = torch.utils.data.random

来源： https://stackoverflow.com/questions/60244596/how-to-improve-performance-on-nested-graphql-connections-when-using-pagination

问题 I am attempting to use the DataLoader feature within the graphql-java-kickstart library: https://github.com/graphql-java-kickstart My application is a Spring Boot application using 2.3.0.RELEASE. And I using version 7.0.1 of the graphql-spring-boot-starter library. The library is pretty easy to use and it works when I don't use the data loader. However, I am plagued by the N+1 SQL problem and as a result need to use the data loader to help alleviate this issue. When I execute a request, I end

问题 I'm using a couple of dataloaders that use injected query services (which in turn have dependencies on a DbContext). It looks something like this: Field<ListGraphType<UserType>>( "Users", resolve: context => { var loader = accessor.Context.GetOrAddBatchLoader<Guid, IEnumerable<User>>( "MyUserLoader", userQueryService.MyUserFunc); return loader.LoadAsync(context.Source.UserId); }); Field<ListGraphType<GroupType>>( "Groups", resolve: context => { var loader = accessor.Context

问题 I am new to pytorch. I am trying to create a DataLoader for a dataset of images where each image got a corresponding ground truth (same name): root: --->RGB: ------>img1.png ------>img2.png ------>... ------>imgN.png --->GT: ------>img1.png ------>img2.png ------>... ------>imgN.png When I use the path for root folder (that contains RGB and GT folders) as input for the torchvision.datasets.ImageFolder it reads all of the images as if they were all intended for input (classified as RGB and GT)

问题 The question is about the data loading tutorial from the PyTorch website. I don't know how they write the value of mean_pix and std_pix of the in transforms.Normalize without calculation I'm unable to find any explanation relevant to this question on StackOverflow. import torch from torchvision import transforms, datasets data_transform = transforms.Compose([ transforms.RandomSizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0

问题 I'm training a model using PyTorch. To load the data, I'm using torch.utils.data.DataLoader . The data loader is using a custom database I've implemented. A strange problem has occurred, every time the second for in the following code executes, the number of threads/processes increases and a huge amount of memory is allocated for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): epoch_start_time = time.time() if epoch != start_epoch: epoch_iter = epoch_iter % dataset_size for i,