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AveragePool2d.cpp
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AveragePool2d.cpp
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#include <ATen/ATen.h>
#include <ATen/NativeFunctions.h>
#include <ATen/native/Pool.h>
namespace at {
namespace meta{
using namespace native;
TORCH_META_FUNC(avg_pool2d) (
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
bool ceil_mode,
bool count_include_pad,
c10::optional<int64_t> divisor_override
) {
// #20866, #22032: Guarantee this for the official C++ API?
TORCH_CHECK(kernel_size.size() == 1 || kernel_size.size() == 2,
"avg_pool2d: kernel_size must either be a single int, or a tuple of two ints");
const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
TORCH_CHECK(stride.empty() || stride.size() == 1 || stride.size() == 2,
"avg_pool2d: stride must either be omitted, a single int, or a tuple of two ints");
const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
const int dW = stride.empty() ? kW :
stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
TORCH_CHECK(padding.size() == 1 || padding.size() == 2,
"avg_pool2d: padding must either be a single int, or a tuple of two ints");
const int padH = safe_downcast<int, int64_t>(padding[0]);
const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0,
"divisor must be not zero");
/* sizes */
const int64_t nbatch = input.ndimension() == 4 ? input.size(-4) : 1;
const int64_t nInputPlane = input.size(-3);
const int64_t inputHeight = input.size(-2);
const int64_t inputWidth = input.size(-1);
const int64_t outputHeight = pooling_output_shape<int64_t>(inputHeight, kH, padH, dH, 1, ceil_mode);
const int64_t outputWidth = pooling_output_shape<int64_t>(inputWidth, kW, padW, dW, 1, ceil_mode);
auto memory_format = input.suggest_memory_format();
pool2d_shape_check(
input,
kH, kW, dH, dW, padH, padW, 1, 1,
nInputPlane,
inputHeight, inputWidth,
outputHeight, outputWidth, memory_format);
/* resize output */
if (input.ndimension() == 3) {
set_output(0, {nInputPlane, outputHeight, outputWidth}, input.options());
}
else {
set_output(0, {nbatch, nInputPlane, outputHeight, outputWidth}, input.options().memory_format(memory_format));
}
}
TORCH_META_FUNC(avg_pool2d_backward) (
const Tensor& gradOutput_,
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
bool ceil_mode,
bool count_include_pad,
c10::optional<int64_t> divisor_override
) {
// #20866, #22032: Guarantee this for the official C++ API?
TORCH_CHECK(kernel_size.size() == 1 || kernel_size.size() == 2,
"avg_pool2d: kernel_size must either be a single int, or a tuple of two ints");
const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
TORCH_CHECK(stride.empty() || stride.size() == 1 || stride.size() == 2,
"avg_pool2d: stride must either be omitted, a single int, or a tuple of two ints");
const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
const int dW = stride.empty() ? kW :
stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
TORCH_CHECK(padding.size() == 1 || padding.size() == 2,
"avg_pool2d: padding must either be a single int, or a tuple of two ints");
const int padH = safe_downcast<int, int64_t>(padding[0]);
const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0, "divisor must be not zero");
/* sizes */
const int64_t nbatch = input.ndimension() == 4 ? input.size(-4) : 1;
const int64_t nInputPlane = input.size(-3); // number of channels (or colors)
const int64_t inputHeight = input.size(-2);
const int64_t inputWidth = input.size(-1);
const int64_t outputWidth = pooling_output_shape<int64_t>(inputWidth, kW, padW, dW, 1, ceil_mode);
const int64_t outputHeight = pooling_output_shape<int64_t>(inputHeight, kH, padH, dH, 1, ceil_mode);
auto memory_format = input.suggest_memory_format();
avg_pool2d_backward_shape_check(
input,
gradOutput_,
nbatch,
kH, kW, dH, dW, padH, padW,
nInputPlane,
inputHeight, inputWidth,
outputHeight, outputWidth,
memory_format);
/* resize output */
set_output(0, input.sizes(), input.options().memory_format(memory_format));
}
} // namespace meta
namespace native {
TORCH_IMPL_FUNC(avg_pool2d_out_cpu) (
const Tensor &input,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
bool ceil_mode,
bool count_include_pad,
c10::optional<int64_t> divisor_override,
const Tensor &output
) {
const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
const int dW = stride.empty() ? kW :
stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
const int padH = safe_downcast<int, int64_t>(padding[0]);
const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
avg_pool2d_kernel(
kCPU, output, input,
kW, kH, dW, dH, padW, padH,
count_include_pad, divisor_override);
}
TORCH_IMPL_FUNC(avg_pool2d_backward_out_cpu) (
const Tensor& gradOutput,
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
bool ceil_mode,
bool count_include_pad,
c10::optional<int64_t> divisor_override,
const Tensor& gradInput
) {
const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
const int dW = stride.empty() ? kW :
stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
const int padH = safe_downcast<int, int64_t>(padding[0]);
const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0, "divisor must be not zero");
TORCH_CHECK(input.dtype() == gradOutput.dtype(),
"expected dtype ", input.dtype(), " for `gradOutput` but got dtype ", gradOutput.dtype());
/* zero the gradient */
gradInput.zero_();
avg_pool2d_backward_kernel(
kCPU, gradInput, gradOutput,
kW, kH, dW, dH, padW, padH,
count_include_pad, divisor_override);
}
DEFINE_DISPATCH(avg_pool2d_kernel);
DEFINE_DISPATCH(avg_pool2d_backward_kernel);
} // at::native
} // at