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CPU implementation for upsample-nearest2d. (#339)
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@ -672,6 +672,43 @@ impl Map1 for AvgPool2D {
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}
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}
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struct UpsampleNearest2D(usize, usize);
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impl Map1 for UpsampleNearest2D {
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fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
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// TODO: Specialized implementation for the case 2*h, 2*w?
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let (dst_h, dst_w) = (self.0, self.1);
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let (b_sz, c, src_h, src_w) = layout.shape().dims4()?;
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let stride = layout.stride();
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let (stride_h, stride_w) = (stride[2], stride[3]);
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let src_index = layout.start_offset();
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let scale_h = src_h as f64 / dst_h as f64;
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let scale_w = src_w as f64 / dst_w as f64;
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let mut dst = vec![T::zero(); b_sz * c * dst_h * dst_w];
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let src_h_idxs = (0..src_h)
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.map(|h_idx| usize::min(src_h - 1, (h_idx as f64 * scale_h) as usize))
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.collect::<Vec<_>>();
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let src_w_idxs = (0..src_w)
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.map(|w_idx| usize::min(src_w - 1, (w_idx as f64 * scale_w) as usize))
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.collect::<Vec<_>>();
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for b_idx in 0..b_sz {
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let dst = &mut dst[b_idx * c * dst_h * dst_w..];
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let src_index = src_index + b_idx * stride[0];
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for c_idx in 0..c {
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let dst = &mut dst[c_idx * dst_h * dst_w..];
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let src_index = src_index + c_idx * stride[1];
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for (h_idx, src_h_idx) in src_h_idxs.iter().enumerate() {
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for (w_idx, src_w_idx) in src_w_idxs.iter().enumerate() {
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let src_index = src_index + src_h_idx * stride_h + src_w_idx * stride_w;
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dst[h_idx * dst_w + w_idx] = src[src_index]
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}
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}
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}
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}
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Ok(dst)
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}
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}
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struct Gather<'a, I: IntDType> {
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ids: &'a [I],
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ids_l: &'a Layout,
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@ -1577,6 +1614,10 @@ impl BackendStorage for CpuStorage {
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AvgPool2D(kernel_size, stride).map(self, layout)
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}
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fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
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UpsampleNearest2D(h, w).map(self, layout)
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}
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fn elu(&self, layout: &Layout, alpha: f64) -> Result<Self> {
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// TODO: Have some generic map for functions that apply on num_traits::Float elements.
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match self {
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