145 lines
8.9 KiB
C++
145 lines
8.9 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author saudet
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// @author raver119@gmail.com
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace dnnl;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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PLATFORM_IMPL(avgpool3dnew) {
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auto input = INPUT_VARIABLE(
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0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto output = OUTPUT_VARIABLE(
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0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW)
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int kD = INT_ARG(0); // filter(kernel) depth
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int kH = INT_ARG(1); // filter(kernel) height
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int kW = INT_ARG(2); // filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int extraParam0 = INT_ARG(13); // unnecessary for max case, required only for avg and pnorm cases
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
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REQUIRE_TRUE(input->rankOf() == 5, 0,
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"MAXPOOL3DNEW OP: rank of input array must be equal to 5, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"MAXPOOL3DNEW op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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std::string expectedOutputShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx(
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{bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}));
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REQUIRE_TRUE(expectedOutputShape == ShapeUtils::shapeAsString(output), 0,
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"MAXPOOL3D op: wrong shape of output array, expected is %s, but got %s instead !",
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expectedOutputShape.c_str(), ShapeUtils::shapeAsString(output).c_str());
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// 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);
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// 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);
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if (!isNCDHW) {
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input = new NDArray(
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input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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output = new NDArray(
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output->permute({0, 4, 1, 2, 3})); // [bS, oD, oH, oW, iC] -> [bS, iC, oD, oH, oW]
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}
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if (isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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auto poolingMode = PoolingType::AVG_POOL;
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dnnl_memory_desc_t empty;
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dnnl::memory::desc pool_src_md(empty), pool_dst_md(empty);
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dnnl::memory::desc user_src_md(empty), user_dst_md(empty);
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dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r;
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dnnl::algorithm algorithm;
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mkldnnUtils::getMKLDNNMemoryDescPool3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode,
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extraParam0, true,
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bS, iC, iD, iH, iW, oC, oD, oH, oW, input, nullptr, output,
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algorithm,
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&pool_src_md, nullptr, &pool_dst_md, &user_src_md, nullptr,
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&user_dst_md,
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pool_strides, pool_kernel, pool_padding, pool_padding_r);
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auto pool_desc = pooling_forward::desc(prop_kind::forward_inference, algorithm, pool_src_md,
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pool_dst_md,
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pool_strides, pool_kernel, pool_padding, pool_padding_r);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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dnnl::stream stream(engine);
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auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine);
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auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer());
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auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer());
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auto pool_src_memory = user_src_memory;
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if (pool_prim_desc.src_desc() != user_src_memory.get_desc()) {
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pool_src_memory = dnnl::memory(pool_prim_desc.src_desc(), engine);
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reorder(user_src_memory, pool_src_memory).execute(stream, user_src_memory, pool_src_memory);
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}
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auto pool_dst_memory = user_dst_memory;
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if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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pool_dst_memory = dnnl::memory(pool_prim_desc.dst_desc(), engine);
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}
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pooling_forward(pool_prim_desc).execute(stream, {{DNNL_ARG_SRC, pool_src_memory},
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{DNNL_ARG_DST, pool_dst_memory}});
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if (pool_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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reorder(pool_dst_memory, user_dst_memory).execute(stream, pool_dst_memory, user_dst_memory);
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}
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stream.wait();
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if (!isNCDHW) {
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delete input;
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delete output;
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}
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return Status::OK();
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}
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PLATFORM_CHECK(avgpool3dnew) {
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// we don't want to use mkldnn if cpu doesn't support avx/avx2
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if (::optimalLevel() < 2)
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return false;
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
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}
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}
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}
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} |