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195cba500f
Summary: Maratyszcza harouwu yinghai This is broken since #13065. `c_str()` returns a pointer that isn't permanent. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18109 Differential Revision: D14516622 Pulled By: ezyang fbshipit-source-id: 7113d92eac4f61479c4c7b323cf78cc8aa00b17e
64 lines
2.5 KiB
C++
64 lines
2.5 KiB
C++
#include "caffe2/operators/quantized/int8_average_pool_op.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(
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Int8AveragePool,
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int8::Int8AveragePoolOp<int8::Activation::NONE>);
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REGISTER_CPU_OPERATOR(
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Int8AveragePoolRelu,
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int8::Int8AveragePoolOp<int8::Activation::RELU>);
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const char kAveragePoolDoc_int8[] = R"DOC(
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consumes an input blob X and applies average pooling across the
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the blob according to kernel sizes, stride sizes, and pad lengths defined by the
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ConvPoolOpBase operator. Average pooling consisting of averaging all values of a
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subset of the input tensor according to the kernel size and downsampling the
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data into the output blob Y for further processing.
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)DOC";
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std::function<void(OpSchema&)> AveragePoolDocGenerator(
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const char* dim,
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bool relu_fused = false) {
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return [=](OpSchema& schema) {
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string doc = "AveragePool{dim} {pool_doc}";
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c10::ReplaceAll(doc, "{dim}", dim);
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c10::ReplaceAll(doc, "{pool_doc}", kAveragePoolDoc_int8);
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schema.SetDoc(doc);
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schema.Input(
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0,
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"X",
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"Input data tensor from the previous operator; dimensions depend on "
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"whether the NCHW or NHWC operators are being used. For example, in "
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"the former, the input has size (N x C x H x W), where N is the batch "
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"size, C is the number of channels, and H and W are the height and the "
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"width of the data. The corresponding permutation of dimensions is "
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"used in the latter case.");
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schema.Output(0, "Y", relu_fused ?
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"Output data tensor from average pooling across the input "
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"tensor. Dimensions will vary based on various kernel, stride, and pad "
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"sizes. Output will go through rectified linear "
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"function, where y = max(0, x)." :
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"Output data tensor from average pooling across the input "
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"tensor. Dimensions will vary based on various kernel, stride, and pad "
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"sizes.");
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};
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}
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OPERATOR_SCHEMA(Int8AveragePool)
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.NumInputs(1)
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.NumOutputs(1)
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.Arg("Y_scale", "Output tensor quantization scale")
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.Arg("Y_zero_point", "Output tensor quantization offset")
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.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForPool)
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.FillUsing(AveragePoolDocGenerator(""));
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OPERATOR_SCHEMA(Int8AveragePoolRelu)
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.NumInputs(1)
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.NumOutputs(1)
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.Arg("Y_scale", "Output tensor quantization scale")
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.Arg("Y_zero_point", "Output tensor quantization offset")
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.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForPool)
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.FillUsing(AveragePoolDocGenerator("", true));
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} // namespace caffe2
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