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#include "caffe2/quantization/server/resize_nearest_dnnlowp_op.h"
namespace caffe2 {
template <typename T>
bool ResizeNearestDNNLowPOp<T>::RunOnDevice() {
using namespace dnnlowp;
this->ParseDNNLowPOperatorArguments_();
// Choose quantization params
in_qparams_[0] = GetInputTensorQuantizationParamsOf(this, 0, qfactory_.get());
const auto& X = InputTensorCPU_(0);
auto* Y = OutputTensorCPU_(0);
CAFFE_ENFORCE_EQ(X.ndim(), 4);
const int N = X.dim32(0);
const int IH = X.dim32(1);
const int IW = X.dim32(2);
const int C = X.dim32(3);
const int OW = IW * width_scale_;
const int OH = IH * height_scale_;
Y->Resize(N, OH, OW, C);
const T* X_data = X.template data<T>();
T* Y_data = Y->template mutable_data<T>();
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int n = 0; n < N; ++n) {
for (int y = 0; y < OH; ++y) {
const int in_y = std::min((int)(y / height_scale_), (IH - 1));
for (int x = 0; x < OW; ++x) {
const int in_x = std::min((int)(x / width_scale_), (IW - 1));
std::memcpy(
&Y_data[((n * OH + y) * OW + x) * C],
&X_data[((n * IH + in_y) * IW + in_x) * C],
C * sizeof(T));
}
}
}
// Even if there is a pre-chosen quantization parameters for the output,
// it is ignored because resize nearest output quantization should be same
// as the input.
PropagateOutputTensorQuantizationParams(this, 0, in_qparams_[0]);
return true;
}
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Int8ResizeNearest,
DNNLOWP,
ResizeNearestDNNLowPOp<uint8_t>);
} // namespace caffe2
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