140 lines
6.5 KiB
C++
140 lines
6.5 KiB
C++
/*******************************************************************************
|
|
* 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 saudet
|
|
// @author raver119@gmail.com
|
|
//
|
|
|
|
#include <ops/declarable/PlatformHelper.h>
|
|
#include <ops/declarable/OpRegistrator.h>
|
|
#include <platform_boilerplate.h>
|
|
|
|
#include <helpers/MKLDNNStream.h>
|
|
#include "mkldnnUtils.h"
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
|
|
using namespace dnnl;
|
|
using namespace samediff;
|
|
|
|
namespace nd4j {
|
|
namespace ops {
|
|
namespace platforms {
|
|
PLATFORM_IMPL(avgpool2d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "Input should have rank of 4, but got %i instead",
|
|
input->rankOf());
|
|
|
|
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
auto argI = *(block.getIArguments());
|
|
auto output = OUTPUT_VARIABLE(0);
|
|
|
|
const auto kH = INT_ARG(0);
|
|
const auto kW = INT_ARG(1);
|
|
const auto sH = INT_ARG(2);
|
|
const auto sW = INT_ARG(3);
|
|
int pH = INT_ARG(4);
|
|
int pW = INT_ARG(5);
|
|
const auto dH = INT_ARG(6);
|
|
const auto dW = INT_ARG(7);
|
|
const auto isSameMode = static_cast<bool>(INT_ARG(8));
|
|
const auto extraParam0 = INT_ARG(9);
|
|
|
|
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}",
|
|
dH, dW);
|
|
|
|
int oH = 0;
|
|
int oW = 0;
|
|
|
|
int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
|
|
|
|
const int iH = static_cast<int>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
|
|
const int iW = static_cast<int>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
|
|
|
|
if (!isNCHW) {
|
|
input = new NDArray(
|
|
input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
|
|
output = new NDArray(
|
|
output->permute({0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
|
|
}
|
|
|
|
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
|
|
|
|
if (isSameMode)
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
|
|
|
|
const int bS = input->sizeAt(0);
|
|
const int iC = input->sizeAt(1);
|
|
const int oC = output->sizeAt(1);
|
|
|
|
auto poolingMode = PoolingType::AVG_POOL;
|
|
|
|
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::getMKLDNNMemoryDescPool2d(kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0,
|
|
true,
|
|
bS, iC, iH, iW, oC, 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());
|
|
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;
|
|
dnnl::stream stream(engine);
|
|
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();
|
|
|
|
//streams[0].submitAndWait();
|
|
|
|
if (!isNCHW) {
|
|
delete input;
|
|
delete output;
|
|
}
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
PLATFORM_CHECK(avgpool2d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto output = OUTPUT_VARIABLE(0);
|
|
|
|
return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
|
|
}
|
|
}
|
|
}
|
|
} |