mirror of https://github.com/tracel-ai/burn.git
Fix tensor data elem type conversion in book (#2211)
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@ -68,8 +68,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
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fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
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let images = items
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.iter()
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.map(|item| TensorData::from(item.image))
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.map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device))
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.map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
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.map(|data| Tensor::<B, 2>::from_data(data, &self.device))
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.map(|tensor| tensor.reshape([1, 28, 28]))
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// Normalize: make between [0,1] and make the mean=0 and std=1
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// values mean=0.1307,std=0.3081 are from the PyTorch MNIST example
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@ -119,8 +119,8 @@ images.
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```rust, ignore
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let images = items // take items Vec<MnistItem>
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.iter() // create an iterator over it
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.map(|item| TensorData::from(item.image)) // for each item, convert the image to float32 data struct
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.map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device)) // for each data struct, create a tensor on the device
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.map(|item| TensorData::from(item.image).convert::<B::FloatElem>()) // for each item, convert the image to float data struct
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.map(|data| Tensor::<B, 2>::from_data(data, &self.device)) // for each data struct, create a tensor on the device
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.map(|tensor| tensor.reshape([1, 28, 28])) // for each tensor, reshape to the image dimensions [C, H, W]
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.map(|tensor| ((tensor / 255) - 0.1307) / 0.3081) // for each image tensor, apply normalization
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.collect(); // consume the resulting iterator & collect the values into a new vector
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@ -138,5 +138,6 @@ a targets tensor that contains the indexes of the correct digit class. The first
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the image array into a `TensorData` struct. Burn provides the `TensorData` struct to encapsulate
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tensor storage information without being specific for a backend. When creating a tensor from data,
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we often need to convert the data precision to the current backend in use. This can be done with the
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`.convert()` method. While importing the `burn::tensor::ElementConversion` trait, you can call
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`.elem()` on a specific number to convert it to the current backend element type in use.
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`.convert()` method (in this example, the data is converted backend's float element type
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`B::FloatElem`). While importing the `burn::tensor::ElementConversion` trait, you can call `.elem()`
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on a specific number to convert it to the current backend element type in use.
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@ -24,8 +24,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
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fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
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let images = items
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.iter()
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.map(|item| TensorData::from(item.image))
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.map(|data| Tensor::<B, 2>::from_data(data.convert::<B::FloatElem>(), &self.device))
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.map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
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.map(|data| Tensor::<B, 2>::from_data(data, &self.device))
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.map(|tensor| tensor.reshape([1, 28, 28]))
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// normalize: make between [0,1] and make the mean = 0 and std = 1
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// values mean=0.1307,std=0.3081 were copied from Pytorch Mist Example
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