View source: R/as_dataloader.R
| as_dataloader | R Documentation |
as_dataloader is used internally by luz to convert input
data and valid_data as passed to fit.luz_module_generator() to a
torch::dataloader
as_dataloader(x, ...)
## S3 method for class 'dataset'
as_dataloader(x, ..., batch_size = 32)
## S3 method for class 'list'
as_dataloader(x, ...)
## S3 method for class 'dataloader'
as_dataloader(x, ...)
## S3 method for class 'matrix'
as_dataloader(x, ...)
## S3 method for class 'numeric'
as_dataloader(x, ...)
## S3 method for class 'array'
as_dataloader(x, ...)
## S3 method for class 'torch_tensor'
as_dataloader(x, ...)
x |
the input object. |
... |
Passed to |
batch_size |
(int, optional): how many samples per batch to load
(default: |
as_dataloader methods should have sensible defaults for batch_size,
parallel workers, etc.
It allows users to quickly experiment with fit.luz_module_generator() by not requiring
to create a torch::dataset and a torch::dataloader in simple
experiments.
as_dataloader(dataset): Converts a torch::dataset() to a torch::dataloader().
as_dataloader(list): Converts a list of tensors or arrays with the same
size in the first dimension to a torch::dataloader()
as_dataloader(dataloader): Returns the same dataloader
as_dataloader(matrix): Converts the matrix to a dataloader
as_dataloader(numeric): Converts the numeric vector to a dataloader
as_dataloader(array): Converts the array to a dataloader
as_dataloader(torch_tensor): Converts the tensor to a dataloader
You can implement your own as_dataloader S3 method if you want your data
structure to be automatically supported by luz's fit.luz_module_generator().
The method must satisfy the following conditions:
The method should return a torch::dataloader().
The only required argument is x. You have good default for all other
arguments.
It's better to avoid implementing as_dataloader methods for common S3 classes
like data.frames. In this case, its better to assign a different class to
the inputs and implement as_dataloader for it.
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