/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://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. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author raver119@gmail.com // #include #include #include #include #include "mkldnnUtils.h" #include using namespace dnnl; namespace nd4j { namespace ops { namespace platforms { PLATFORM_IMPL(maxpool3dnew, ENGINE_CPU) { auto input = INPUT_VARIABLE( 0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto output = OUTPUT_VARIABLE( 0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW) int kD = INT_ARG(0); // filter(kernel) depth int kH = INT_ARG(1); // filter(kernel) height int kW = INT_ARG(2); // filter(kernel) width int sD = INT_ARG(3); // strides depth int sH = INT_ARG(4); // strides height int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width int dD = INT_ARG(9); // dilations depth int dH = INT_ARG(10); // dilations height int dW = INT_ARG(11); // dilations width int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID // int extraParam0 = INT_ARG(13); // unnecessary for max case, required only for avg and pnorm cases int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW REQUIRE_TRUE(input->rankOf() == 5, 0, "MAXPOOL3DNEW OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "MAXPOOL3DNEW op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); std::string expectedOutputShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx( {bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2})); REQUIRE_TRUE(expectedOutputShape == ShapeUtils::shapeAsString(output), 0, "MAXPOOL3D op: wrong shape of output array, expected is %s, but got %s instead !", expectedOutputShape.c_str(), ShapeUtils::shapeAsString(output).c_str()); // REQUIRE_TRUE(iD >= kD && iH >= kH && iW >= kW, 0, "MAXPOOL3D OP: the input depth/height/width must be greater or equal to kernel(filter) depth/height/width, but got [%i, %i, %i] and [%i, %i, %i] correspondingly !", iD,iH,iW, kD,kH,kW); // REQUIRE_TRUE(kD/2 >= pD && kH/2 >= pH && kW/2 >= pW, 0, "MAXPOOL3D OP: pad depth/height/width must not be greater than half of kernel depth/height/width, but got [%i, %i, %i] and [%i, %i, %i] correspondingly !", pD,pH,pW, kD,kH,kW); if (!isNCDHW) { input = new NDArray( input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] output = new NDArray( output->permute({0, 4, 1, 2, 3})); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW] } if (isSameMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); auto poolingMode = PoolingType::MAX_POOL; auto extraParam0 = 1; dnnl_memory_desc_t empty; dnnl::memory::desc pool_src_md(empty), pool_dst_md(empty); dnnl::memory::desc user_src_md(empty), user_dst_md(empty); dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r; dnnl::algorithm algorithm; mkldnnUtils::getMKLDNNMemoryDescPool3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0, true, bS, iC, iD, iH, iW, oC, oD, oH, oW, input, nullptr, output, algorithm, &pool_src_md, nullptr, &pool_dst_md, &user_src_md, nullptr, &user_dst_md, pool_strides, pool_kernel, pool_padding, pool_padding_r); auto pool_desc = pooling_forward::desc(prop_kind::forward_inference, algorithm, pool_src_md, pool_dst_md, pool_strides, pool_kernel, pool_padding, pool_padding_r); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); dnnl::stream stream(engine); auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine); auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer()); auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer()); auto pool_src_memory = user_src_memory; if (pool_prim_desc.src_desc() != user_src_memory.get_desc()) { pool_src_memory = dnnl::memory(pool_prim_desc.src_desc(), engine); reorder(user_src_memory, pool_src_memory).execute(stream, user_src_memory, pool_src_memory); } auto pool_dst_memory = user_dst_memory; if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) { pool_dst_memory = dnnl::memory(pool_prim_desc.dst_desc(), engine); } pooling_forward(pool_prim_desc).execute(stream, {{DNNL_ARG_SRC, pool_src_memory}, {DNNL_ARG_DST, pool_dst_memory}}); if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) { reorder(pool_dst_memory, user_dst_memory).execute(stream, pool_dst_memory, user_dst_memory); } stream.wait(); if (!isNCDHW) { delete input; delete output; } return Status::OK(); } PLATFORM_CHECK(maxpool3dnew, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); } } } }