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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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pytorch/interfacing_data/README.md
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@ -16,9 +16,18 @@ There are options to interface your data.
## torch.utils.data.Dataset
## [torch.utils.data.Dataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
In the case we might not be able to load the fully dataset into memory, the **torch.utils.data.Dataset** is very helpful.
```python
CLASS torch.utils.data.Dataset(*args, **kwds)
```
> An abstract class representing a Dataset.
>
> All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite **\_\_getitem\_\_()**, supporting fetching a data sample for a given key. Subclasses could also optionally overwrite **\_\_len\_\_()**, which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement **\_\_getitems\_\_()**, for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.
In the case we might not be able to load the fully dataset into memory, the torch.utils.data.Dataset is very helpful.
We need to create a new class which is derived from **torch.utils.data.Dataset**. We can do what every we want in this class as long as we service the functions
* **\_\_len\_\_()** : gives us the number of pattern in the dataset