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Backprop support for pooling ops. (#652)

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* Backprop support for pooling ops.

* max-pool gradient.
This commit is contained in:
Laurent Mazare
2023-08-29 10:17:59 +01:00
committed by GitHub
parent 4b8d57ba15
commit d0a330448d
1 changed files with 35 additions and 2 deletions
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37
candle-core/src/backprop.rs
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@ -219,8 +219,41 @@ impl Tensor {
Op::ConvTranspose2D { .. } => Err(Error::BackwardNotSupported {
op: "conv-transpose2d",
})?,
Op::AvgPool2D { .. } => Err(Error::BackwardNotSupported { op: "avg-pool2d" })?,
Op::MaxPool2D { .. } => Err(Error::BackwardNotSupported { op: "max-pool2d" })?,
Op::AvgPool2D {
arg,
kernel_size,
stride,
} => {
if kernel_size != stride {
crate::bail!("backward not supported for avgpool2d if ksize {kernel_size:?} != stride {stride:?}")
}
let (_n, _c, h, w) = arg.dims4()?;
let grad_arg = grad.upsample_nearest2d(h, w)?;
let grad_arg =
(grad_arg * (1f64 / (kernel_size.0 * kernel_size.1) as f64))?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
}
Op::MaxPool2D {
arg,
kernel_size,
stride,
} => {
if kernel_size != stride {
crate::bail!("backward not supported for maxpool2d if ksize {kernel_size:?} != stride {stride:?}")
}
let (_n, _c, h, w) = arg.dims4()?;
// For computing the max-pool gradient, we compute a mask where a 1 means
// that the element is the maximum, then we apply this mask to the
// upsampled gradient (taking into account that multiple max may exist so
// we scale the gradient for this case).
let node_upsampled = node.upsample_nearest2d(h, w)?;
let mask = arg.eq(&node_upsampled)?.to_dtype(arg.dtype())?;
let avg = mask.avg_pool2d(*kernel_size, *stride)?;
let grad_arg = ((grad * avg)?.upsample_nearest2d(h, w)? * mask)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = sum_grad.add(&grad_arg)?;
}
Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
op: "upsample-nearest2d",
})?,