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#include <array>
#include "caffe2/core/operator.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
class NormalizePlanarYUVOp : public Operator<CPUContext> {
public:
USE_OPERATOR_FUNCTIONS(CPUContext);
using Operator<CPUContext>::Operator;
bool RunOnDevice() override {
const auto& X = Input(0);
const auto& M = Input(1); // mean
const auto& S = Input(2); // standard deviation
auto* Z = Output(0, X.sizes(), at::dtype<float>());
CAFFE_ENFORCE(X.sizes().size() == 4);
const auto N = X.dim32(0);
auto C = X.size(1);
const auto H = X.size(2);
const auto W = X.size(3);
CAFFE_ENFORCE(C == M.size(1));
CAFFE_ENFORCE(C == S.size(1));
const auto* Xdata = X.data<float>();
auto* Zdata = Z->template mutable_data<float>();
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
int offset = H * W;
for (auto n = 0; n < N; n++) { // realistically N will always be 1
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
int batch_offset = n * C * offset;
for (auto c = 0; c < C; c++) {
ConstEigenVectorMap<float> channel_s(
&Xdata[batch_offset + (c * offset)], offset);
EigenVectorMap<float> channel_d(
&Zdata[batch_offset + (c * offset)], offset);
channel_d = channel_s.array() - M.data<float>()[c];
channel_d = channel_d.array() / S.data<float>()[c];
}
}
return true;
}
};
REGISTER_CPU_OPERATOR(NormalizePlanarYUV, NormalizePlanarYUVOp);
OPERATOR_SCHEMA(NormalizePlanarYUV)
.NumInputs(3)
.NumOutputs(1)
.AllowInplace({{0, 0}});
;
} // namespace
} // namespace caffe2
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