435 lines
15 KiB
C++
435 lines
15 KiB
C++
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// ======================================================================== //
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// Copyright 2009-2019 Intel Corporation //
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// //
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// Licensed under the Apache License, Version 2.0 (the "License"); //
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// you may not use this file except in compliance with the License. //
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// You may obtain a copy of the License at //
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// //
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// http://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, //
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
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// See the License for the specific language governing permissions and //
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// limitations under the License. //
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// ======================================================================== //
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#include "network.h"
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#include "upsample.h"
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#include "weights_reorder.h"
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#include <cstring>
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namespace oidn {
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template<int K>
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Network<K>::Network(const Ref<Device>& device, const std::map<std::string, Tensor>& weightMap)
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: device(device),
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eng(engine::cpu, 0),
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sm(eng),
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weightMap(weightMap)
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{
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}
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template<int K>
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void Network<K>::execute(const Progress& progress, int taskIndex)
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{
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if (progress.func)
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{
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const double value = double(taskIndex) / double(progress.taskCount);
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if (!progress.func(progress.userPtr, value))
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throw Exception(Error::Cancelled, "execution was cancelled");
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}
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for (size_t i = 0; i < nodes.size(); ++i)
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{
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nodes[i]->execute(sm);
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if (progress.func)
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{
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const double value = (double(taskIndex) + double(i+1) / double(nodes.size())) / double(progress.taskCount);
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if (!progress.func(progress.userPtr, value))
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throw Exception(Error::Cancelled, "execution was cancelled");
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}
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}
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}
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template<int K>
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std::shared_ptr<memory> Network<K>::allocTensor(const memory::dims& dims,
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memory::format_tag format,
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void* data)
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{
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if (format == memory::format_tag::any)
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{
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if (dims.size() == 4)
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format = BlockedFormat<K>::nChwKc;
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else if (dims.size() == 1)
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format = memory::format_tag::x;
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else
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assert(0);
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}
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memory::desc desc(dims, memory::data_type::f32, format);
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if (data == nullptr)
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{
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const size_t bytes = getTensorSize(dims) * sizeof(float);
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if (format == BlockedFormat<K>::nChwKc)
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activationAllocBytes += bytes;
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totalAllocBytes += bytes;
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return std::make_shared<memory>(desc, eng);
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}
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else
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{
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return std::make_shared<memory>(desc, eng, data);
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}
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}
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template<int K>
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std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
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const std::shared_ptr<memory>& src,
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size_t srcOffset,
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memory::format_tag format)
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{
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const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
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MAYBE_UNUSED(srcDesc);
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assert(srcDesc.data_type == memory::data_type::f32);
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assert(getTensorSize(src) >= srcOffset + getTensorSize(dims));
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if (format == memory::format_tag::any)
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{
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if (dims.size() == 4)
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format = BlockedFormat<K>::nChwKc;
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else if (dims.size() == 1)
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format = memory::format_tag::x;
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else
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assert(0);
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}
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memory::desc desc(dims, memory::data_type::f32, format);
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float* srcPtr = (float*)src->get_data_handle() + srcOffset;
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return std::make_shared<memory>(desc, eng, srcPtr);
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}
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template<int K>
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std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
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const std::shared_ptr<memory>& src,
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const memory::dims& srcOffset)
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{
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return castTensor(dims, src, getTensorSize(srcOffset));
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}
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template<int K>
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void Network<K>::zeroTensor(const std::shared_ptr<memory>& dst)
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{
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assert(getTensorType(dst) == memory::data_type::f32);
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memset(dst->get_data_handle(), 0, getTensorSize(dst)*sizeof(float));
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}
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template<int K>
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memory::dims Network<K>::getInputReorderDims(const memory::dims& srcDims, int alignment)
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{
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memory::dims dstDims = srcDims;
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dstDims[1] = getPadded<K>(srcDims[1]); // round up C
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dstDims[2] = roundUp(srcDims[2], memory::dim(alignment)); // round up H
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dstDims[3] = roundUp(srcDims[3], memory::dim(alignment)); // round up W
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return dstDims;
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}
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template<int K>
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std::shared_ptr<Node> Network<K>::addInputReorder(const Image& color,
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const Image& albedo,
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const Image& normal,
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const std::shared_ptr<TransferFunction>& transferFunc,
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int alignment,
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const std::shared_ptr<memory>& userDst)
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{
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assert(color);
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int inputC = 3;
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if (albedo) inputC += 3;
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if (normal) inputC += 3;
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memory::dims srcDims = {1, inputC, color.height, color.width};
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memory::dims dstDims = getInputReorderDims(srcDims, alignment);
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// Allocate padded memory
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auto dst = userDst;
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if (!dst)
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dst = allocTensor(dstDims);
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// Push node
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std::shared_ptr<Node> node;
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if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
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node = std::make_shared<InputReorderNode<K, LinearTransferFunction>>(color, albedo, normal, dst, tf);
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else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
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node = std::make_shared<InputReorderNode<K, GammaTransferFunction>>(color, albedo, normal, dst, tf);
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else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
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node = std::make_shared<InputReorderNode<K, LogTransferFunction>>(color, albedo, normal, dst, tf);
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else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
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node = std::make_shared<InputReorderNode<K, PQXTransferFunction>>(color, albedo, normal, dst, tf);
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else
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assert(0);
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nodes.push_back(node);
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return node;
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}
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template<int K>
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std::shared_ptr<Node> Network<K>::addOutputReorder(const std::shared_ptr<memory>& src,
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const std::shared_ptr<TransferFunction>& transferFunc,
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const Image& output)
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{
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memory::dims srcDims = getTensorDims(src);
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assert(srcDims[1] == K);
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// Push node
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std::shared_ptr<Node> node;
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if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
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node = std::make_shared<OutputReorderNode<K, LinearTransferFunction>>(src, output, tf);
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else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
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node = std::make_shared<OutputReorderNode<K, GammaTransferFunction>>(src, output, tf);
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else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
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node = std::make_shared<OutputReorderNode<K, LogTransferFunction>>(src, output, tf);
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else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
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node = std::make_shared<OutputReorderNode<K, PQXTransferFunction>>(src, output, tf);
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else
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assert(0);
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nodes.push_back(node);
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return node;
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}
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template<int K>
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memory::dims Network<K>::getConvDims(const std::string& name, const memory::dims& srcDims)
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{
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auto b = weightMap[name + "/b"];
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memory::dims dstDims = srcDims;
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dstDims[1] = getPadded<K>(b.dims[0]); // dstDims[C] = getPadded(OC)
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return dstDims;
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}
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template<int K>
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std::shared_ptr<Node> Network<K>::addConv(const std::string& name,
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const std::shared_ptr<memory>& src,
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const std::shared_ptr<memory>& userDst,
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bool relu)
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{
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const memory::dims strides = {1, 1};
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const memory::dims padding = {1, 1};
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memory::dims srcDims = getTensorDims(src);
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// Get the weights
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const auto& W = weightMap[name + "/W"];
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if (W.ndims() != 4 || W.format != "oihw")
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throw Exception(Error::InvalidOperation, "invalid convolution weights");
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memory::dims weightsDims = W.dims;
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auto userWeights = allocTensor(weightsDims, memory::format_tag::oihw, W.data);
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// Pad the weights
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memory::dims weightsPadDims = weightsDims;
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weightsPadDims[1] = getPadded<K>(weightsDims[1]); // IC
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weightsPadDims[0] = getPadded<K>(weightsDims[0]); // OC
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assert(srcDims[1] == weightsPadDims[1]); // srcDims[C] == weightsPadDims[IC]
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auto weightsPad = allocTensor(weightsPadDims, memory::format_tag::oihw);
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WeightsReorderNode<K>(userWeights, weightsPad).execute(sm);
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// Get the biases
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const auto& b = weightMap[name + "/b"];
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if (b.ndims() != 1)
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throw Exception(Error::InvalidOperation, "invalid convolution biases");
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memory::dims biasDims = b.dims;
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// Copy/pad the biases
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memory::dims biasPadDims = {getPadded<K>(biasDims[0])};
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auto bias = allocTensor(biasPadDims);
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if (biasDims[0] != biasPadDims[0])
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memset(bias->get_data_handle(), 0, biasPadDims[0]*sizeof(float));
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memcpy(bias->get_data_handle(), b.data, biasDims[0]*sizeof(float));
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// Allocate memory for destination
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memory::dims dstDims = srcDims;
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dstDims[1] = weightsPadDims[0]; // dstDims[C] = weightsPadDims[OC]
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std::shared_ptr<memory> dst;
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if (!userDst)
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dst = allocTensor(dstDims);
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else if (getTensorDims(userDst) == dstDims)
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dst = userDst;
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else
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dst = castTensor(dstDims, userDst);
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// Create a convolution
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// Let the convolution primitive choose the weights format
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auto weightsDesc = memory::desc({ weightsPadDims }, memory::data_type::f32, memory::format_tag::any);
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auto convAlgo = (K == 16) ? convolution_winograd : convolution_direct;
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auto convDesc = convolution_forward::desc(
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prop_kind::forward_inference, convAlgo,
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src->get_desc(),
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weightsDesc,
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bias->get_desc(),
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dst->get_desc(),
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strides, padding, padding, padding_kind::zero);
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// Incorporate relu
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mkldnn::primitive_attr convAttr;
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if (relu)
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{
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mkldnn::post_ops ops;
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ops.append_eltwise(
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1.f, // scale factor, not used
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algorithm::eltwise_relu,
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0.f, // max with
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0.f // unused
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);
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convAttr.set_post_ops(ops);
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}
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convAttr.set_scratchpad_mode(scratchpad_mode_user);
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auto convPrimDesc = convolution_forward::primitive_desc(convDesc, convAttr, eng);
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// Reorder the weights to the final format, if necessary
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auto weights = weightsPad;
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if (convPrimDesc.weights_desc() != weightsPad->get_desc())
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{
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weights = std::make_shared<memory>(convPrimDesc.weights_desc(), eng);
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ReorderNode(weightsPad, weights).execute(sm);
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}
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// Create convolution node and add it to the net
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auto node = std::make_shared<ConvNode>(convPrimDesc, src, weights, bias, dst);
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nodes.push_back(node);
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return node;
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}
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template<int K>
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memory::dims Network<K>::getPoolDims(const memory::dims& srcDims)
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{
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memory::dims dstDims = srcDims;
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dstDims[2] /= 2; // H/2
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dstDims[3] /= 2; // W/2
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return dstDims;
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}
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template<int K>
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std::shared_ptr<Node> Network<K>::addPool(const std::shared_ptr<memory>& src,
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const std::shared_ptr<memory>& userDst)
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{
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const memory::dims kernel = {2, 2};
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const memory::dims strides = {2, 2};
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const memory::dims padding = {0, 0};
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memory::dims srcDims = getTensorDims(src);
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memory::dims dstDims = getPoolDims(srcDims);
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std::shared_ptr<memory> dst;
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if (!userDst)
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dst = allocTensor(dstDims);
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else if (getTensorDims(userDst) == dstDims)
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dst = userDst;
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else
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dst = castTensor(dstDims, userDst);
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auto poolDesc = pooling_forward::desc(
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prop_kind::forward_inference, pooling_max,
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src->get_desc(),
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dst->get_desc(),
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strides, kernel, padding, padding, padding_kind::zero);
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mkldnn::primitive_attr poolAttr;
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poolAttr.set_scratchpad_mode(scratchpad_mode_user);
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auto poolPrimDesc = pooling_forward::primitive_desc(poolDesc, poolAttr, eng);
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auto node = std::make_shared<PoolNode>(poolPrimDesc, src, dst);
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nodes.push_back(node);
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return node;
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}
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template<int K>
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memory::dims Network<K>::getUpsampleDims(const memory::dims& srcDims)
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{
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memory::dims dstDims = srcDims;
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dstDims[2] *= 2; // H*2
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|
dstDims[3] *= 2; // W*2
|
||
|
|
return dstDims;
|
||
|
|
}
|
||
|
|
|
||
|
|
template<int K>
|
||
|
|
std::shared_ptr<Node> Network<K>::addUpsample(const std::shared_ptr<memory>& src,
|
||
|
|
const std::shared_ptr<memory>& userDst)
|
||
|
|
{
|
||
|
|
memory::dims srcDims = getTensorDims(src);
|
||
|
|
memory::dims dstDims = getUpsampleDims(srcDims);
|
||
|
|
|
||
|
|
std::shared_ptr<memory> dst;
|
||
|
|
if (!userDst)
|
||
|
|
dst = allocTensor(dstDims);
|
||
|
|
else if (getTensorDims(userDst) == dstDims)
|
||
|
|
dst = userDst;
|
||
|
|
else
|
||
|
|
dst = castTensor(dstDims, userDst);
|
||
|
|
|
||
|
|
// Create upsampling node and add it to net
|
||
|
|
auto node = std::make_shared<UpsampleNode<K>>(src, dst);
|
||
|
|
nodes.push_back(node);
|
||
|
|
return node;
|
||
|
|
}
|
||
|
|
|
||
|
|
template<int K>
|
||
|
|
memory::dims Network<K>::getConcatDims(const memory::dims& src1Dims, const memory::dims& src2Dims)
|
||
|
|
{
|
||
|
|
assert(src1Dims[0] == src2Dims[0]); // N
|
||
|
|
assert(src1Dims[2] == src2Dims[2]); // H
|
||
|
|
assert(src1Dims[3] == src2Dims[3]); // W
|
||
|
|
|
||
|
|
memory::dims dstDims = src1Dims;
|
||
|
|
dstDims[1] += src2Dims[1]; // C
|
||
|
|
return dstDims;
|
||
|
|
}
|
||
|
|
|
||
|
|
template<int K>
|
||
|
|
std::shared_ptr<Node> Network<K>::addAutoexposure(const Image& color,
|
||
|
|
const std::shared_ptr<HDRTransferFunction>& transferFunc)
|
||
|
|
{
|
||
|
|
auto node = std::make_shared<AutoexposureNode>(color, transferFunc);
|
||
|
|
nodes.push_back(node);
|
||
|
|
return node;
|
||
|
|
}
|
||
|
|
|
||
|
|
template <int K>
|
||
|
|
void Network<K>::finalize()
|
||
|
|
{
|
||
|
|
// Compute the size of the scratchpad
|
||
|
|
size_t scratchpadSize = 0;
|
||
|
|
for (const auto& node : nodes)
|
||
|
|
scratchpadSize = max(scratchpadSize, node->getScratchpadSize());
|
||
|
|
|
||
|
|
// Allocate the scratchpad
|
||
|
|
memory::dims scratchpadDims = { memory::dim(scratchpadSize) };
|
||
|
|
memory::desc scratchpadDesc(scratchpadDims, memory::data_type::u8, memory::format_tag::x);
|
||
|
|
auto scratchpad = std::make_shared<memory>(scratchpadDesc, eng);
|
||
|
|
activationAllocBytes += scratchpadSize;
|
||
|
|
totalAllocBytes += scratchpadSize;
|
||
|
|
|
||
|
|
// Set the scratchpad for the nodes
|
||
|
|
for (auto& node : nodes)
|
||
|
|
node->setScratchpad(scratchpad);
|
||
|
|
|
||
|
|
// Free the weights
|
||
|
|
weightMap.clear();
|
||
|
|
|
||
|
|
// Print statistics
|
||
|
|
if (device->isVerbose(2))
|
||
|
|
{
|
||
|
|
std::cout << "Activation bytes: " << activationAllocBytes << std::endl;
|
||
|
|
std::cout << "Scratchpad bytes: " << scratchpadSize << std::endl;
|
||
|
|
std::cout << "Total bytes : " << totalAllocBytes << std::endl;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template class Network<8>;
|
||
|
|
template class Network<16>;
|
||
|
|
|
||
|
|
} // namespace oidn
|