/******************************************************************************* * Copyright 2020-2021 Intel Corporation * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *******************************************************************************/ #ifndef CPU_X64_UNI_RESAMPLING_HPP #define CPU_X64_UNI_RESAMPLING_HPP #include "common/c_types_map.hpp" #include "common/primitive.hpp" #include "cpu/cpu_resampling_pd.hpp" #include "cpu/x64/cpu_isa_traits.hpp" #include "cpu/x64/injectors/jit_uni_postops_injector.hpp" #include "cpu/x64/jit_primitive_conf.hpp" #include "cpu/x64/jit_uni_resampling_kernel.hpp" namespace dnnl { namespace impl { namespace cpu { namespace x64 { struct jit_uni_resampling_fwd_t : public primitive_t { struct pd_t : public cpu_resampling_fwd_pd_t { using cpu_resampling_fwd_pd_t::cpu_resampling_fwd_pd_t; DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit:", conf_.isa, ""), jit_uni_resampling_fwd_t); status_t init(engine_t *engine); const jit_resampling_conf_t &get_conf() const { return conf_; } private: void fill_format_tag_info(); jit_resampling_conf_t conf_; }; jit_uni_resampling_fwd_t(const pd_t *apd) : primitive_t(apd) {} virtual ~jit_uni_resampling_fwd_t() = default; status_t init(engine_t *engine) override; status_t execute(const exec_ctx_t &ctx) const override; private: status_t fill_data_for_interpolation(); /* * Fills indices_ with the data that contains the corresponding * input point for each output point. * The data is arranged as follows: * od_0 = id_0 * stride_w * od_1 = id_1 * stride_w * od_2 = id_2 * stride_w * ... * ih_0 = ih_0 * stride_h * ih_1 = ih_1 * stride_h * ... * iw_0 = iw_1 * stride_w * ... */ status_t fill_data_for_nearest(); /* * Fills indices_ with the data that contains the corresponding * input point for each output point. * The data is arranged as follows: * od_0 = id_0 * od_1 = id_1 * od_2 = id_2 * ... * oh_0 = ih_0 * oh_1 = ih_1 * ... * ow_0 = iw_0 * ... */ status_t fill_data_for_linear(); /* * Fills indices_ with the data that contains the corresponding * corners from input tensor for each output point and fills * weights_ with with the data that contains weights for * corners from input tensor for each output point. * The data is arranged as follows: * NSPC and BLOCKED: * * indices_: * ow_0 = iw_0_left * ow_0 = iw_0_right * ow_1 = iw_1_left * ow_1 = iw_1_right * ... * oh_0 = ih_0_top * oh_1 = ih_1_top * ... * oh_0 = ih_0_bottom * oh_1 = ih_1_bottom * ... * od_0 = id_0_front * od_1 = id_1_front * ... * od_0 = id_0_back * od_1 = id_1_back * ... * * weights_: * ow_0 = weight_0_left * ow_0 = weight_0_right * ow_1 = weight_1_left * ow_1 = weight_1_right * ... * oh_0 = weight_0_top * oh_1 = weight_1_top * ... * oh_0 = weight_0_bottom * oh_1 = weight_1_bottom * ... * od_0 = weight_0_front * od_1 = weight_1_front * ... * od_0 = weight_0_back * od_1 = weight_1_back * ... * * NCSP: * * indices_: * sp_0 = id_0_front + ih_0_top + iw_0_left * sp_0 = id_0_front + ih_0_top + iw_0_right * sp_0 = id_0_front + ih_0_bottom + iw_0_left * sp_0 = id_0_front + ih_0_bottom + iw_0_right * sp_0 = id_0_back + ih_0_top + iw_0_left * sp_0 = id_0_back + ih_0_top + iw_0_right * sp_0 = id_0_back + ih_0_bottom + iw_0_left * sp_0 = id_0_back + ih_0_bottom + iw_0_right * sp_1 = id_1_front + ih_1_top + iw_1_left * sp_1 = id_1_front + ih_1_top + iw_1_right * sp_1 = id_1_front + ih_1_bottom + iw_1_left * ... * * weights_: * sp_0 = weight_0_front * weight_0_top * weight_0_left * sp_0 = weight_0_front * weight_0_top * weight_0_right * sp_0 = weight_0_front * weight_0_bottom * weight_0_left * sp_0 = weight_0_front * weight_0_bottom * weight_0_right * sp_0 = weight_0_back * weight_0_top * weight_0_left * sp_0 = weight_0_back * weight_0_top * weight_0_right * sp_0 = weight_0_back * weight_0_bottom * weight_0_left * sp_0 = weight_0_back * weight_0_bottom * weight_0_right * sp_1 = weight_1_front * weight_1_top * weight_1_left * sp_1 = weight_1_front * weight_1_top * weight_1_right * sp_1 = weight_1_front * weight_1_bottom * weight_1_left * ... */ status_t interpolate_nearest(const uint8_t *src, uint8_t *dst, const std::vector &post_ops_args) const; status_t interpolate_linear(const uint8_t *src, uint8_t *dst, const std::vector &post_ops_args) const; status_t get_proper_kernel_for_avx512( const memory_desc_t *dst_md, const jit_resampling_conf_t &conf); status_t get_proper_kernel_for_avx( const memory_desc_t *dst_md, const jit_resampling_conf_t &conf); status_t get_proper_kernel_for_sse( const memory_desc_t *dst_md, const jit_resampling_conf_t &conf); const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); } std::unique_ptr kernel_; std::vector indices_; std::vector weights_; }; } // namespace x64 } // namespace cpu } // namespace impl } // namespace dnnl #endif