Datasets:
shibing624
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source_code

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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 int64_t dH = stride.empty() ? kH : stride[0];
const int64_t dW = stride.empty() ? kW : stride.size() == 1 ? dH : 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 int64_t padH = padding[0];
const int64_t padW = padding.size() == 1 ? padH : padding[1];
TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0,
"divisor must be not zero");
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));
}
return TORCH_PRECOMPUTE_STRUCT(avg_pool2d)().set_kH(kH).set_kW(kW).set_dH(dH).set_dW(dW).set_padH(padH).set_padW(padW);
}
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);