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Upsample grad (#1420)

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* encode size of upsample in enum

* working convolution method for limited 2d kernels

* add test for sf 3 interpolation

* add higher dimensional tests, fix to work with multichannel input

* Remove commented out line.

---------

Co-authored-by: Laurent <laurent.mazare@gmail.com>
This commit is contained in:
KGrewal1
2023-12-10 07:43:24 +00:00
committed by GitHub
parent 9bd94c1ffa
commit 18eb87f25f
4 changed files with 192 additions and 6 deletions
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26
candle-core/src/backprop.rs
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@ -114,7 +114,7 @@ impl Tensor {
| Op::Unary(_node, UnaryOp::Round) => nodes,
Op::Reshape(node)
| Op::UpsampleNearest1D(node)
| Op::UpsampleNearest2D(node)
| Op::UpsampleNearest2D { arg: node, .. }
| Op::AvgPool2D { arg: node, .. }
| Op::MaxPool2D { arg: node, .. }
| Op::Copy(node)
@ -350,9 +350,27 @@ impl Tensor {
Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
op: "upsample-nearest1d",
})?,
Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
op: "upsample-nearest2d",
})?,
Op::UpsampleNearest2D {
arg,
target_h,
target_w,
} => {
let (_n, c, h, w) = arg.dims4()?;
if target_h % h != 0 || target_w % w != 0 {
crate::bail!("backward not supported for non integer upscaling factors")
}
let scale_h = target_h / h;
let scale_w = target_w / w;
if scale_h != scale_w {
crate::bail!("backward not supported for non uniform upscaling factors")
};
let kernel =
Tensor::ones((c, 1, scale_h, scale_w), arg.dtype(), arg.device())?;
let conv_sum = grad.conv2d(&kernel, 0, scale_h, 1, c)?;
let sum_grad = grads.or_insert(arg)?;
*sum_grad = conv_sum;
}
Op::SliceScatter0(lhs, rhs, start_rhs) => {
let rhs_sum_grad = grads.or_insert(rhs)?;
let rhs_grad = grad.narrow(0, *start_rhs, rhs.dim(0)?)?;