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static void max_pool3d_with_indices_backward_out_frame(
scalar_t *gradInput_data,
scalar_t *gradOutput_data,
int64_t *indices_data,
int64_t nbatch,
int64_t nslices,
int64_t istride, int64_t ostride,
int64_t itime, int64_t iwidth, int64_t iheight,
int64_t otime, int64_t owidth, int64_t oheight,
int dT, int dW, int dH,
int pT, int pW, int pH,
int dilationT, int dilationW, int dilationH)
{
at::parallel_for(0, nbatch, 0, [&](int64_t start, int64_t end) {
for (const auto p : c10::irange(start, end)) {
max_pool3d_with_indices_backward_single_out_frame<scalar_t>(
gradInput_data + p * istride,
gradOutput_data + p * ostride,
indices_data + p * ostride,
nslices,
itime, iwidth, iheight,
otime, owidth, oheight,
dT, dW, dH,
pT, pW, pH,
dilationT, dilationW, dilationH
);
}
});
}
Tensor& max_pool3d_with_indices_backward_out_cpu_template(
Tensor& gradInput,
const Tensor& gradOutput_,
const Tensor& input,
const Tensor& indices,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
IntArrayRef dilation,
bool ceil_mode)
{
// #20866, #22032: Guarantee this for the official C++ API?
TORCH_CHECK(kernel_size.size() == 1 || kernel_size.size() == 3,
"max_pool3d: kernel_size must either be a single int, or a tuple of three ints")
const int kT = safe_downcast<int, int64_t>(kernel_size[0]);
const int kH = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[1]);
const int kW = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[2]);
TORCH_CHECK(stride.size() == 0 || stride.size() == 1 || stride.size() == 3,
"max_pool3d: stride must either be omitted, a single int, or a tuple of three ints")
const int dT = stride.empty() ? kT : safe_downcast<int, int64_t>(stride[0]);
const int dH = stride.empty() ? kH :
stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[1]);
const int dW = stride.empty() ? kW :
stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[2]);
TORCH_CHECK(padding.size() == 1 || padding.size() == 3,
"max_pool3d: padding must be either be a single int, or a tuple of three ints");
const int pT = safe_downcast<int, int64_t>(padding[0]);
const int pH = padding.size() == 1 ? pT : safe_downcast<int, int64_t>(padding[1]);
const int pW = padding.size() == 1 ? pT : safe_downcast<int, int64_t>(padding[2]);
TORCH_CHECK(dilation.size() == 1 || dilation.size() == 3,
"max_pool3d: dilation must be either a single int, or a tuple of three ints");
const int dilationT = safe_downcast<int, int64_t>(dilation[0]);
const int dilationH = dilation.size() == 1 ? dilationT : safe_downcast<int, int64_t>(dilation[1]);
const int dilationW = dilation.size() == 1 ? dilationT : safe_downcast<int, int64_t>(dilation[2]);
TORCH_CHECK((input.ndimension() == 4 || input.ndimension() == 5),
"non-empty 4D or 5D (batch mode) tensor expected for input");
const int64_t nslices = input.size(-4);
const int64_t itime = input.size(-3);
const int64_t iheight = input.size(-2);
const int64_t iwidth = input.size(-1);
/* get contiguous gradOutput */
Tensor gradOutput = gradOutput_.contiguous();
/* resize */
gradInput.resize_as_(input);
gradInput.zero_();
const int64_t otime = gradOutput.size(-3);
const int64_t oheight = gradOutput.size(-2);
const int64_t owidth = gradOutput.size(-1);
max_pool3d_backward_shape_check(
input,
gradOutput,
indices,
nslices,
kT, kH, kW,
dT, dH, dW,
pT, pH, pW,
dilationT, dilationH, dilationW,
itime, iheight, iwidth,
otime, oheight, owidth,
"max_pool3d_with_indices_backward_out_cpu_template()");