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Fix tensor data elem type conversion in book (#2211)

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Guillaume Lagrange 2024-08-28 10:55:10 -04:00 committed by GitHub
parent 0292967000
commit 40d321cc0d
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2 changed files with 9 additions and 8 deletions
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13
burn-book/src/basic-workflow/data.md
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@ -68,8 +68,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
let images = items
.iter()
.map(|item| TensorData::from(item.image))
.map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device))
.map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
.map(|data| Tensor::<B, 2>::from_data(data, &self.device))
.map(|tensor| tensor.reshape([1, 28, 28]))
// Normalize: make between [0,1] and make the mean=0 and std=1
// values mean=0.1307,std=0.3081 are from the PyTorch MNIST example
@ -119,8 +119,8 @@ images.
```rust, ignore
let images = items // take items Vec<MnistItem>
.iter() // create an iterator over it
.map(|item| TensorData::from(item.image)) // for each item, convert the image to float32 data struct
.map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device)) // for each data struct, create a tensor on the device
.map(|item| TensorData::from(item.image).convert::<B::FloatElem>()) // for each item, convert the image to float data struct
.map(|data| Tensor::<B, 2>::from_data(data, &self.device)) // for each data struct, create a tensor on the device
.map(|tensor| tensor.reshape([1, 28, 28])) // for each tensor, reshape to the image dimensions [C, H, W]
.map(|tensor| ((tensor / 255) - 0.1307) / 0.3081) // for each image tensor, apply normalization
.collect(); // consume the resulting iterator & collect the values into a new vector
@ -138,5 +138,6 @@ a targets tensor that contains the indexes of the correct digit class. The first
the image array into a `TensorData` struct. Burn provides the `TensorData` struct to encapsulate
tensor storage information without being specific for a backend. When creating a tensor from data,
we often need to convert the data precision to the current backend in use. This can be done with the
`.convert()` method. While importing the `burn::tensor::ElementConversion` trait, you can call
`.elem()` on a specific number to convert it to the current backend element type in use.
`.convert()` method (in this example, the data is converted backend's float element type
`B::FloatElem`). While importing the `burn::tensor::ElementConversion` trait, you can call `.elem()`
on a specific number to convert it to the current backend element type in use.

4
examples/guide/src/data.rs
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@ -24,8 +24,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
let images = items
.iter()
.map(|item| TensorData::from(item.image))
.map(|data| Tensor::<B, 2>::from_data(data.convert::<B::FloatElem>(), &self.device))
.map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
.map(|data| Tensor::<B, 2>::from_data(data, &self.device))
.map(|tensor| tensor.reshape([1, 28, 28]))
// normalize: make between [0,1] and make the mean = 0 and std = 1
// values mean=0.1307,std=0.3081 were copied from Pytorch Mist Example