| /*! |
| * Copyright (c) 2017 by Contributors |
| * \file quantize.cc |
| * \brief |
| */ |
| #include "./quantize-inl.h" |
| |
| namespace mxnet { |
| namespace op { |
| DMLC_REGISTER_PARAMETER(QuantizeParam); |
| |
| NNVM_REGISTER_OP(_contrib_quantize) |
| .describe(R"code(Quantize a input tensor from float to `out_type`, |
| with user-specified `min_range` and `max_range`. |
| |
| [min_range, max_range] are scalar floats that spcify the range for |
| the input data. Each value of the tensor will undergo the following: |
| |
| `out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range)` |
| |
| here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()` |
| )code" ADD_FILELINE) |
| .set_attr_parser(ParamParser<QuantizeParam>) |
| .set_num_inputs(3) |
| .set_num_outputs(3) |
| .set_attr<nnvm::FInferShape>("FInferShape", QuantizeShape) |
| .set_attr<nnvm::FInferType>("FInferType", QuantizeType) |
| .set_attr<FCompute>("FCompute<cpu>", QuantizeCompute<cpu>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_quantize"}) |
| .add_argument("input", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`") |
| .add_argument("min_range", "NDArray-or-Symbol", "The minimum scalar value " |
| "possibly produced for the input") |
| .add_argument("max_range", "NDArray-or-Symbol", "The maximum scalar value " |
| "possibly produced for the input") |
| .add_arguments(QuantizeParam::__FIELDS__()); |
| |
| } // namespace op |
| } // namespace mxnet |
