Add Proposal Layer as a native UDF
This commit is contained in:
Alexey Reznichenko
Родитель
a69d06231a
Коммит
65471cdcd9
21
CNTK.sln
21
CNTK.sln
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@ -1613,6 +1613,13 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CNTKLibraryCSTrainingTest",
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{50EF9EE6-5018-453E-A063-F77044EF1A97} = {50EF9EE6-5018-453E-A063-F77044EF1A97}
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ProposalLayer", "ProposalLayer", "{3631994A-59E6-4CD6-99A4-6D332F8DABE2}"
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EndProject
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Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ProposalLayerLib", "Examples\Extensibility\ProposalLayer\ProposalLayerLib\ProposalLayerLib.vcxproj", "{91EA9F28-B9B6-4FC7-A47D-9838F5915700}"
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ProjectSection(ProjectDependencies) = postProject
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{E5606ECE-48CA-4464-BB12-09D81D02B9EF} = {E5606ECE-48CA-4464-BB12-09D81D02B9EF}
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EndProjectSection
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug_CpuOnly|x64 = Debug_CpuOnly|x64
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@ -2297,6 +2304,18 @@ Global
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{0DF2109B-BB85-4718-82DE-1C0536D4F2C3}.Release_UWP|x64.ActiveCfg = Release_NoOpt|x64
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{0DF2109B-BB85-4718-82DE-1C0536D4F2C3}.Release|x64.ActiveCfg = Release|x64
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{0DF2109B-BB85-4718-82DE-1C0536D4F2C3}.Release|x64.Build.0 = Release|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Debug_UWP|x64.ActiveCfg = Debug_CpuOnly|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Debug|x64.ActiveCfg = Debug|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Debug|x64.Build.0 = Debug|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release_CpuOnly|x64.ActiveCfg = Release_CpuOnly|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release_NoOpt|x64.ActiveCfg = Release_NoOpt|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release_NoOpt|x64.Build.0 = Release_NoOpt|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release_UWP|x64.ActiveCfg = Release_CpuOnly|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release|x64.ActiveCfg = Release|x64
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700}.Release|x64.Build.0 = Release|x64
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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@ -2521,5 +2540,7 @@ Global
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{B6DED59B-B52A-4D44-9B61-26FF0382764A} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
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{B3B46744-DBB5-42C2-BAD7-9151D9486045} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5}
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{0DF2109B-BB85-4718-82DE-1C0536D4F2C3} = {B3B46744-DBB5-42C2-BAD7-9151D9486045}
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{3631994A-59E6-4CD6-99A4-6D332F8DABE2} = {3BF56127-6F0F-41CF-BFCE-31165A0A5E73}
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{91EA9F28-B9B6-4FC7-A47D-9838F5915700} = {3631994A-59E6-4CD6-99A4-6D332F8DABE2}
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EndGlobalSection
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EndGlobal
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@ -0,0 +1,49 @@
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//
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// Copyright (c) Microsoft. All rights reserved.
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// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
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//
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#pragma once
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#include <vector>
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#include "Rectangle2D.h"
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// TODO: the current implementation is O(N^2), it should be possible to do this in O(N logN),
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// see the sweeping line algorithm: http://algs4.cs.princeton.edu/93intersection/
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// Returns first topN elements from the input vector that do not overlap with any preceeding rectangle
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// by more than a threshold ratio.
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std::vector<Rectangle2D> NonMaximumSupression(const std::vector<Rectangle2D>& in, float threshold, size_t topN)
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{
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std::vector<Rectangle2D> out;
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out.reserve(topN);
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std::vector<bool> suppressed(in.size(), false);
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for (auto i = 0; i < in.size() ; i++)
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{
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const auto& box1 = in[i];
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if (suppressed[i])
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continue;
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out.push_back(box1);
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if (out.size() == topN)
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break;
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for (auto j = i+1; j < in.size(); j++)
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{
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if (suppressed[j])
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continue;
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const auto& box2 = in[j];
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float overlap = box1.Overlap(box2);
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float overlapRatio = overlap / (box1.Area() + box2.Area() - overlap);
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if (overlapRatio >= threshold)
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suppressed[j] = true;
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}
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}
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return out;
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}
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@ -0,0 +1,12 @@
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#include "ProposalLayerLib.h"
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using namespace CNTK;
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extern "C"
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#ifdef _WIN32
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__declspec (dllexport)
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#endif
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Function* CreateProposalLayer(const Variable* operands, size_t /*numOperands*/, const Dictionary* attributes, const wchar_t* name)
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{
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return new ProposalLayer({operands[0], operands[1], operands[2]}, *attributes, name);
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}
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@ -0,0 +1,243 @@
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//
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// Copyright (c) Microsoft. All rights reserved.
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// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
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//
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#pragma once
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#include "CNTKLibrary.h"
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#include "RpnUtils.h"
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#include "NmsUtils.h"
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#include <queue>
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using namespace CNTK;
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using ScoredRectangle2D = std::pair<float, Rectangle2D>;
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// returns true if the left box score is greater than the right box score.
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static auto cmp = [](const ScoredRectangle2D& left, const ScoredRectangle2D& right)
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{
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if (left.first == right.first)
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return left.second.Area() > right.second.Area();
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return (left.first > right.first);
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};
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using SortedRectangle2DPriorityQueue = std::priority_queue<ScoredRectangle2D, std::vector<ScoredRectangle2D>, decltype(cmp)>;
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class ProposalLayer final : public Function
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{
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uint32_t m_featStride{16};
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std::vector<Rectangle2D> m_anchors;
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size_t m_preNMSTopN, m_postNMSTopN;
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float m_NMSThresh, m_minSize;
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bool m_trainMode;
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enum Inputs : uint32_t {
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SCORES,
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BBOX_DELTAS,
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IM_INFO
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};
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public:
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ProposalLayer(const std::vector<Variable>& inputs, const Dictionary& attributes, const std::wstring& name = L"ProposalLayer")
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: Function(inputs, attributes, name),
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m_preNMSTopN{ 0 }, m_postNMSTopN{ 0 }, m_NMSThresh{ 0 }, m_minSize{0}, m_trainMode(true)
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{
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if (attributes.Contains(L"feat_stride"))
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m_featStride = static_cast<uint32_t>(attributes[L"feat_stride"].Value<int>());
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std::vector<uint32_t> scales{ { 8, 16, 32 } };
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if (attributes.Contains(L"scales"))
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{
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scales.clear();
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const auto& valueVector = attributes[L"scales"].Value<std::vector<DictionaryValue>>();
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for (const auto& value : valueVector)
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scales.push_back(static_cast<uint32_t>(value.Value<int>()));
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}
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ChangeConfiguration(m_trainMode);
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m_anchors = GenerateAnchors(scales);
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}
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private:
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void ChangeConfiguration(bool train)
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{
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const auto& attributes = Attributes();
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if (train)
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{
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m_preNMSTopN = attributes[L"train_pre_nms_topN"].Value<int>();
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m_postNMSTopN = attributes[L"train_post_nms_topN"].Value<int>();
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m_NMSThresh = static_cast<float>(attributes[L"train_nms_thresh"].Value<double>());
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m_minSize = static_cast<float>(attributes[L"train_min_size"].Value<double>());
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}
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else
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{
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m_preNMSTopN = attributes[L"test_pre_nms_topN"].Value<int>();
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m_postNMSTopN = attributes[L"test_post_nms_topN"].Value<int>();
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m_NMSThresh = static_cast<float>(attributes[L"test_nms_thresh"].Value<double>());
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m_minSize = static_cast<float>(attributes[L"test_min_size"].Value<double>());
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}
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}
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std::vector<Rectangle2D> GetAllShiftedAnchors(size_t w, size_t h)
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{
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std::vector<Rectangle2D> anchors;
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anchors.reserve(w * h * m_anchors.size());
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for (const auto& anchor : m_anchors)
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{
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for (size_t j = 0; j < h; j++)
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{
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auto y = float(j * m_featStride);
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for (size_t i = 0; i < w; i++)
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{
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auto x = float(i * m_featStride);
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anchors.emplace_back(anchor.xmin + x, anchor.ymin + y, anchor.xmax + x, anchor.ymax + y);
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}
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}
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}
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return anchors;
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}
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BackPropStatePtr Forward(const std::vector<ValuePtr>& inputValues,
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std::unordered_map<Variable, ValuePtr>& outputs,
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const DeviceDescriptor& computeDevice,
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const std::unordered_set<Variable>& outputsToRetainBackwardStateFor) override
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{
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bool trainMode = outputsToRetainBackwardStateFor.size() != 0;
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if (m_trainMode != trainMode)
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{
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m_trainMode = trainMode;
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ChangeConfiguration(m_trainMode);
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}
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if (computeDevice.Type() != DeviceKind::CPU)
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throw std::runtime_error("ProposalLayer: only CPU evaluation is supported at the moment.");
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auto scoresShape = inputValues[Inputs::SCORES]->Shape();
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if (scoresShape[scoresShape.Rank() - 1] != 1)
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throw std::runtime_error("ProposalLayer: only single item batches are supported");
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auto height = scoresShape[0];
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auto width = scoresShape[1];
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// the first set of N (= m_anchors.size()) channels are bg probs
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// the second set are the fg probs, which we want
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std::vector<size_t> offsets{ 0,0,0,0 };
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offsets[2] = m_anchors.size();
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auto extent = scoresShape.Dimensions();
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extent[2] -= m_anchors.size();
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auto fgSlice = inputValues[Inputs::SCORES]->Data()->SliceView(offsets, extent, true);
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auto scores = fgSlice->DataBuffer<float>();
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// Enumerate all shifted anchors, which will produce (A * H * W) anchors
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auto anchors = std::move(GetAllShiftedAnchors(width, height));
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// Convert anchors into proposals via bbox transformations,
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// input bbox deltas are stored as (H, W, 4 * A, 1)
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const float* bboxDeltas = inputValues[Inputs::BBOX_DELTAS]->Data()->DataBuffer<float>();
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auto proposals = std::move(TransformBboxInv(anchors, bboxDeltas, width * height));
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// 2. clip predicted boxes to image
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const float* imInfo = inputValues[Inputs::IM_INFO]->Data()->DataBuffer<float>();
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ClipBoxes(proposals, imInfo);
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m_preNMSTopN = (m_preNMSTopN > 0) ? std::min(proposals.size(), m_preNMSTopN) : proposals.size();
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// 3. remove predicted boxes with either height or width < threshold
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// (NOTE: convert min_size to input image scale. Original size = im_info[4:6], scaled size = im_info[2:4])
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// 4. Take top pre_nms_topN(e.g. 6000) proposals with highest scores.
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// Priority queue here is effectively a min heap, the top of the pq has the lowest score
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// among the top-N elements.
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SortedRectangle2DPriorityQueue pq(cmp);
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auto cntkImageScale = imInfo[2] / imInfo[4];
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auto minSize = m_minSize * cntkImageScale;
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for (size_t i = 0; i < proposals.size(); i++)
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{
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const auto& box = proposals[i];
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if (box.Width() < minSize || box.Height() < minSize)
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continue;
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pq.emplace(scores[i], box);
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if (pq.size() > m_preNMSTopN)
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pq.pop();
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}
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proposals.erase(proposals.begin() + pq.size(), proposals.end());
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for (size_t i = 0; !pq.empty(); i++)
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{
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proposals[pq.size()-1] = std::move(pq.top().second);
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pq.pop();
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}
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// at this point, proposals contains m_preNMSTopN boxes sorted in the descending order of
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// their scores (from the highest to lowest).
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// 5. apply nms(e.g.threshold = 0.7), take after_nms_topN(e.g. 300) top proposals.
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m_postNMSTopN = (m_postNMSTopN > 0) ? std::min(proposals.size(), m_postNMSTopN) : proposals.size();
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proposals = NonMaximumSupression(proposals, m_NMSThresh, m_postNMSTopN);
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auto numFoundProposals = proposals.size();
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// 6. pad with zeros if too few rois were found
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if (numFoundProposals < m_postNMSTopN)
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proposals.resize(numFoundProposals, Rectangle2D(0, 0, 0, 0));
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// 7. create the output value and copy the data.
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NDArrayView outputData(DataType::Float, NDShape({ 4, numFoundProposals }),
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proposals.data(), numFoundProposals * 4 * sizeof(float), computeDevice);
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auto outputValue = MakeSharedObject<Value>(
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MakeSharedObject<NDArrayView>(DataType::Float, NDShape({ 4, numFoundProposals }), computeDevice));
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outputValue->Data()->CopyFrom(outputData);
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// Output rois blob
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outputs[this->Output()] = outputValue;
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return nullptr;
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}
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void Backward(const BackPropStatePtr& state,
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const std::unordered_map<Variable, ValuePtr>& rootGradientValues,
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std::unordered_map<Variable, ValuePtr>& backPropagatedGradientValuesForInputs) override
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{
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state; rootGradientValues; backPropagatedGradientValuesForInputs;
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return;
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}
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const std::wstring& OpName() const override
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{
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static const std::wstring opName = L"ProposalLayerOp";
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return opName;
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}
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size_t CurrentVersion() const override { NOT_IMPLEMENTED; }
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||||
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void InferOutputs(std::vector<Variable>& outputs) override
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||||
{
|
||||
auto firstOperand = Function::Inputs()[0];
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auto proposalShape = NDShape({ 4, NDShape::FreeDimension });
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auto dtype = firstOperand.GetDataType();
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auto dynamicAxes = firstOperand.DynamicAxes();
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outputs.push_back(OutputVariable(proposalShape, dtype, dynamicAxes , false, L"rpn_rois_raw"));
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||||
}
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FunctionPtr Clone(const std::vector<Variable>& clonedInputs) override
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{
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return AsComposite(MakeSharedObject<ProposalLayer>(clonedInputs, this->Attributes(), this->Name()));
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||||
}
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||||
};
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@ -0,0 +1,124 @@
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|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||
<ItemGroup Label="ProjectConfigurations">
|
||||
<ProjectConfiguration Include="Debug|x64">
|
||||
<Configuration>Debug</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release_NoOpt|x64">
|
||||
<Configuration>Release_NoOpt</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release|x64">
|
||||
<Configuration>Release</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Debug_CpuOnly|x64">
|
||||
<Configuration>Debug_CpuOnly</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release_CpuOnly|x64">
|
||||
<Configuration>Release_CpuOnly</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClCompile Include="ProposalLayerLib.cpp" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClInclude Include="NmsUtils.h" />
|
||||
<ClInclude Include="ProposalLayerLib.h" />
|
||||
<ClInclude Include="Rectangle2D.h" />
|
||||
<ClInclude Include="RpnUtils.h" />
|
||||
</ItemGroup>
|
||||
<PropertyGroup Label="Globals">
|
||||
<ProjectGuid>{91EA9F28-B9B6-4FC7-A47D-9838F5915700}</ProjectGuid>
|
||||
<Keyword>Win32Proj</Keyword>
|
||||
<RootNamespace>ProposalLayerLib</RootNamespace>
|
||||
<ProjectName>ProposalLayerLib</ProjectName>
|
||||
</PropertyGroup>
|
||||
<Import Project="$(SolutionDir)\CNTK.Cpp.props" />
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
||||
<PropertyGroup Condition="$(DebugBuild)" Label="Configuration">
|
||||
<ConfigurationType>DynamicLibrary</ConfigurationType>
|
||||
<UseDebugLibraries>true</UseDebugLibraries>
|
||||
<PlatformToolset>v140</PlatformToolset>
|
||||
<CharacterSet>Unicode</CharacterSet>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="$(ReleaseBuild)" Label="Configuration">
|
||||
<ConfigurationType>DynamicLibrary</ConfigurationType>
|
||||
<UseDebugLibraries>false</UseDebugLibraries>
|
||||
<PlatformToolset>v140</PlatformToolset>
|
||||
<WholeProgramOptimization>true</WholeProgramOptimization>
|
||||
<CharacterSet>Unicode</CharacterSet>
|
||||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings" />
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
|
||||
</ImportGroup>
|
||||
<PropertyGroup Label="UserMacros" />
|
||||
<PropertyGroup Condition="$(DebugBuild)">
|
||||
<LinkIncremental>true</LinkIncremental>
|
||||
<TargetName>Cntk.$(ProjectName)-$(CntkComponentVersion)</TargetName>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="$(ReleaseBuild)">
|
||||
<LinkIncremental>false</LinkIncremental>
|
||||
<TargetName>Cntk.$(ProjectName)-$(CntkComponentVersion)</TargetName>
|
||||
</PropertyGroup>
|
||||
<ItemDefinitionGroup>
|
||||
<ClCompile>
|
||||
<AdditionalIncludeDirectories>$(SolutionDir)Source\CNTKv2LibraryDll\API</AdditionalIncludeDirectories>
|
||||
</ClCompile>
|
||||
<Link>
|
||||
<AdditionalLibraryDirectories>$(OutDir);$(SolutionDir)$(Platform)\$(Configuration)</AdditionalLibraryDirectories>
|
||||
</Link>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="$(DebugBuild)">
|
||||
<ClCompile>
|
||||
<PrecompiledHeader>NotUsing</PrecompiledHeader>
|
||||
<WarningLevel>Level4</WarningLevel>
|
||||
<Optimization>Disabled</Optimization>
|
||||
<PreprocessorDefinitions>WIN32;_DEBUG;_USRDLL;_WINDOWS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<SDLCheck>true</SDLCheck>
|
||||
<TreatWarningAsError>false</TreatWarningAsError>
|
||||
<AdditionalOptions>/bigobj %(AdditionalOptions)</AdditionalOptions>
|
||||
</ClCompile>
|
||||
<Link>
|
||||
<SubSystem>Console</SubSystem>
|
||||
<GenerateDebugInformation>true</GenerateDebugInformation>
|
||||
<AdditionalDependencies>Cntk.Core-$(CntkComponentVersion).lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
|
||||
</Link>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="$(ReleaseBuild)">
|
||||
<ClCompile>
|
||||
<WarningLevel>Level4</WarningLevel>
|
||||
<PrecompiledHeader>NotUsing</PrecompiledHeader>
|
||||
<Optimization>MaxSpeed</Optimization>
|
||||
<FunctionLevelLinking>true</FunctionLevelLinking>
|
||||
<IntrinsicFunctions>true</IntrinsicFunctions>
|
||||
<PreprocessorDefinitions>WIN32;NDEBUG;_USRDLL;_WINDOWS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<SDLCheck>true</SDLCheck>
|
||||
<FavorSizeOrSpeed>Speed</FavorSizeOrSpeed>
|
||||
<AdditionalOptions>/d2Zi+ %(AdditionalOptions)</AdditionalOptions>
|
||||
<TreatWarningAsError>false</TreatWarningAsError>
|
||||
</ClCompile>
|
||||
<Link>
|
||||
<SubSystem>Console</SubSystem>
|
||||
<GenerateDebugInformation>true</GenerateDebugInformation>
|
||||
<EnableCOMDATFolding>true</EnableCOMDATFolding>
|
||||
<OptimizeReferences>true</OptimizeReferences>
|
||||
<AdditionalDependencies>Cntk.Core-$(CntkComponentVersion).lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
|
||||
</Link>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="$(CpuOnlyBuild)">
|
||||
<ClCompile>
|
||||
<PreprocessorDefinitions>CPUONLY;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Debug_CpuOnly|x64'">MultiThreadedDebug</RuntimeLibrary>
|
||||
<RuntimeLibrary Condition="'$(Configuration)|$(Platform)'=='Release_CpuOnly|x64'">MultiThreaded</RuntimeLibrary>
|
||||
</ClCompile>
|
||||
</ItemDefinitionGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||
<ItemGroup>
|
||||
<Filter Include="Source Files">
|
||||
<UniqueIdentifier>{4FC737F1-C7A5-4376-A066-2A32D752A2FF}</UniqueIdentifier>
|
||||
<Extensions>cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx</Extensions>
|
||||
</Filter>
|
||||
<Filter Include="Header Files">
|
||||
<UniqueIdentifier>{93995380-89BD-4b04-88EB-625FBE52EBFB}</UniqueIdentifier>
|
||||
<Extensions>h;hh;hpp;hxx;hm;inl;inc;xsd</Extensions>
|
||||
</Filter>
|
||||
<Filter Include="Resource Files">
|
||||
<UniqueIdentifier>{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}</UniqueIdentifier>
|
||||
<Extensions>rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms</Extensions>
|
||||
</Filter>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClCompile Include="ProposalLayerLib.cpp">
|
||||
<Filter>Source Files</Filter>
|
||||
</ClCompile>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClInclude Include="ProposalLayerLib.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
<ClInclude Include="NmsUtils.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
<ClInclude Include="Rectangle2D.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
<ClInclude Include="RpnUtils.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
//
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
|
||||
//
|
||||
|
||||
#pragma once
|
||||
#include <algorithm>
|
||||
#include <stdexcept>
|
||||
|
||||
//
|
||||
// An immutable data type to encapsulate a two-dimensional axis-aligned rectangle
|
||||
// with float-value coordinates. The rectangle is closed — it includes the points on the boundary.
|
||||
//
|
||||
// Ported from java version available at http://algs4.cs.princeton.edu/code/edu/princeton/cs/algs4/Rectangle2D.java.html
|
||||
//
|
||||
|
||||
#pragma pack(push, 1)
|
||||
|
||||
struct Point2D {
|
||||
float x, y;
|
||||
};
|
||||
|
||||
// Immutable data type for 2D axis-aligned rectangle.
|
||||
struct Rectangle2D {
|
||||
float xmin, ymin; // minimum x- and y-coordinates
|
||||
float xmax, ymax; // maximum x- and y-coordinates
|
||||
|
||||
Rectangle2D(float xmin, float ymin, float xmax, float ymax)
|
||||
:xmin{ xmin }, ymin{ ymin }, xmax{ xmax }, ymax{ ymax }
|
||||
{
|
||||
if (xmax < xmin || ymax < ymin)
|
||||
throw std::invalid_argument("Invalid rectangle");
|
||||
}
|
||||
|
||||
Rectangle2D(float w, float h, const Point2D& center)
|
||||
:xmin{ center.x - 0.5f * w },
|
||||
ymin{ center.y - 0.5f * h },
|
||||
xmax{ center.x + 0.5f * w - 1.f },
|
||||
ymax{ center.y + 0.5f * h - 1.f }
|
||||
{
|
||||
if (xmax < xmin || ymax < ymin)
|
||||
throw std::invalid_argument("Invalid rectangle");
|
||||
}
|
||||
|
||||
float Width() const { return xmax - xmin + 1.f; }
|
||||
|
||||
float Height() const { return ymax - ymin + 1.f; }
|
||||
|
||||
float Area() const { return Width() * Height(); }
|
||||
|
||||
bool Intersects(const Rectangle2D& that) const
|
||||
{
|
||||
return xmax >= that.xmin && ymax >= that.ymin
|
||||
&& that.xmax >= xmin && that.ymax >= ymin;
|
||||
}
|
||||
|
||||
float Overlap(const Rectangle2D& that) const
|
||||
{
|
||||
if (!Intersects(that))
|
||||
return 0;
|
||||
Rectangle2D overlap{ std::max(xmin, that.xmin), std::max(ymin, that.ymin),
|
||||
std::min(xmax, that.xmax), std::min(ymax, that.ymax) };
|
||||
return overlap.Area();
|
||||
}
|
||||
|
||||
Point2D Center() const {
|
||||
return { 0.5f * (xmin + xmax + 1.f), 0.5f * (ymin + ymax + 1.f) };
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#pragma pack(pop)
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
//
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
|
||||
//
|
||||
|
||||
#pragma once
|
||||
#include <cmath>
|
||||
#include <vector>
|
||||
#include <stdint.h>
|
||||
#include "Rectangle2D.h"
|
||||
|
||||
// Generate anchor(reference) windows by enumerating aspect ratios X scales
|
||||
// wrt a reference(0, 0, 15, 15) window.
|
||||
std::vector<Rectangle2D> GenerateAnchors(const std::vector<uint32_t>& scales, const std::vector<float>& ratios = { 0.5, 1, 2 }, float baseSize = 16.f)
|
||||
{
|
||||
Rectangle2D base(0.f, 0.f, baseSize-1, baseSize-1);
|
||||
|
||||
auto area = base.Area();
|
||||
auto center = base.Center();
|
||||
|
||||
std::vector<Rectangle2D> anchors;
|
||||
anchors.reserve(ratios.size() * scales.size());
|
||||
|
||||
for (const auto& ratio : ratios)
|
||||
{
|
||||
auto areaRatio = area / ratio;
|
||||
auto w = std::round(sqrt(areaRatio));
|
||||
auto h = std::round(w * ratio);
|
||||
|
||||
for (const auto& scale : scales)
|
||||
{
|
||||
auto scaledW = w * scale;
|
||||
auto scaledH = h * scale;
|
||||
anchors.emplace_back(scaledW, scaledH, center);
|
||||
}
|
||||
}
|
||||
|
||||
return anchors;
|
||||
}
|
||||
|
||||
// Deltas contain a tuple of 4 floats (dx, dy, dw, dh) for every box
|
||||
// in the boxes vector. Returns a vector of boxes transformed according
|
||||
// to the following rule:
|
||||
// new_center_x = dx * widths + center_x
|
||||
// new_w = exp(dw) * width
|
||||
// new_xmin = new_center_x - 0.5 * new_w
|
||||
std::vector<Rectangle2D> TransformBboxInv(const std::vector<Rectangle2D>& boxes, const float* deltas, const size_t stride)
|
||||
{
|
||||
std::vector<Rectangle2D> predBoxes;
|
||||
predBoxes.reserve(boxes.size());
|
||||
|
||||
size_t index = 0;
|
||||
for (const auto& box : boxes)
|
||||
{
|
||||
auto offset = index++;
|
||||
|
||||
if (index % stride == 0)
|
||||
// each block consists of four strides, index points to the end of the first stride,
|
||||
// jump over the remaining three.
|
||||
index += 3 * stride;
|
||||
|
||||
|
||||
float dx = deltas[offset]; offset += stride;
|
||||
float dy = deltas[offset]; offset += stride;
|
||||
float dw = deltas[offset]; offset += stride;
|
||||
float dh = deltas[offset];
|
||||
auto center = box.Center();
|
||||
auto x = std::min(dx, 10.f) * box.Width() + center.x;
|
||||
auto y = std::min(dy, 10.f) * box.Height() + center.y;
|
||||
auto w = std::exp(std::min(dw, 10.f)) * box.Width();
|
||||
auto h = std::exp(std::min(dh, 10.f)) * box.Height();
|
||||
// The following seems incorrect, but it matches Caffe implementation.
|
||||
predBoxes.emplace_back(x - 0.5f * w, y - 0.5f * h, x + 0.5f * w, y + 0.5f * h);
|
||||
}
|
||||
|
||||
return predBoxes;
|
||||
}
|
||||
|
||||
// Clip boxes to image boundaries.
|
||||
// imInfo - a tuple of 6 floats (pad_width, pad_height, scaled_image_width, scaled_image_height, orig_img_width, orig_img_height)
|
||||
// e.g.(1000, 1000, 1000, 600, 500, 300) for an original image of 600x300 that is scaled and padded to 1000x1000
|
||||
void ClipBoxes(std::vector<Rectangle2D>& boxes, const float* imInfo)
|
||||
{
|
||||
auto i = 0;
|
||||
auto pad_width = imInfo[i++];
|
||||
auto pad_height = imInfo[i++];
|
||||
auto scaled_image_width = imInfo[i++];
|
||||
auto scaled_image_height = imInfo[i++];
|
||||
|
||||
auto xmin = (pad_width - scaled_image_width) / 2;
|
||||
auto xmax = xmin + scaled_image_width - 1;
|
||||
|
||||
auto ymin = (pad_height - scaled_image_height) / 2;
|
||||
auto ymax = ymin + scaled_image_height - 1;
|
||||
|
||||
for (auto& box : boxes)
|
||||
{
|
||||
box.xmin = std::max(std::min(box.xmin, xmax), xmin);
|
||||
box.xmax = std::max(std::min(box.xmax, xmax), xmin);
|
||||
box.ymin = std::max(std::min(box.ymin, ymax), ymin);
|
||||
box.ymax = std::max(std::min(box.ymax, ymax), ymin);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
# Licensed under the MIT license. See LICENSE.md file in the project root
|
||||
# for full license information.
|
||||
# ==============================================================================
|
||||
|
||||
import os, sys, argparse, copy
|
||||
import cntk as C
|
||||
from cntk import ops
|
||||
|
||||
abs_path = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.join(abs_path, "..", "..", "Image", "Detection", "FasterRCNN"))
|
||||
|
||||
C.device.try_set_default_device(C.device.cpu())
|
||||
|
||||
from FasterRCNN import eval_faster_rcnn_mAP, set_global_vars
|
||||
from config import cfg
|
||||
|
||||
ops.register_native_user_function('ProposalLayerOp', 'Cntk.ProposalLayerLib-' + C.__version__.rstrip('+'), 'CreateProposalLayer')
|
||||
|
||||
def clone_with_native_proposal_layer(model):
|
||||
def filter(x):
|
||||
return type(x) == C.Function and \
|
||||
x.op_name == 'UserFunction' and \
|
||||
x.name == 'ProposalLayer'
|
||||
|
||||
def converter(x):
|
||||
layer_config = copy.deepcopy(x.attributes)
|
||||
layer_config["test_pre_nms_topN"] = cfg["TEST"].RPN_PRE_NMS_TOP_N
|
||||
layer_config["test_post_nms_topN"] = cfg["TEST"].RPN_POST_NMS_TOP_N
|
||||
layer_config["test_nms_thresh"] = float(cfg["TEST"].RPN_NMS_THRESH)
|
||||
layer_config["test_min_size"] = float(cfg["TEST"].RPN_MIN_SIZE)
|
||||
|
||||
layer_config["train_pre_nms_topN"] = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
|
||||
layer_config["train_post_nms_topN"] = cfg["TRAIN"].RPN_POST_NMS_TOP_N
|
||||
layer_config["train_nms_thresh"] = float(cfg["TRAIN"].RPN_NMS_THRESH)
|
||||
layer_config["train_min_size"] = float(cfg["TRAIN"].RPN_MIN_SIZE)
|
||||
|
||||
return ops.native_user_function('ProposalLayerOp', list(x.inputs), layer_config, 'native_proposal_layer')
|
||||
|
||||
return C.misc.convert(model, filter, converter)
|
||||
|
||||
def convert(model_path):
|
||||
device = C.cpu()
|
||||
model = C.Function.load(model_path, device=device)
|
||||
|
||||
# Replace all python proposal layer user-functions with native proposal layer
|
||||
# user functions.
|
||||
return clone_with_native_proposal_layer(model)
|
||||
|
||||
def evaluate(model_path):
|
||||
# ProposalLayer currently only runs on the CPU
|
||||
eval_device = C.cpu()
|
||||
model = C.Function.load(model_path, device=eval_device)
|
||||
set_global_vars(False)
|
||||
return eval_faster_rcnn_mAP(model)
|
||||
|
||||
#############################
|
||||
# main function boilerplate #
|
||||
#############################
|
||||
|
||||
if __name__=='__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-path', '--model_path',
|
||||
help='Filepath of a model created with FasterRCNN.py', required=True)
|
||||
parser.add_argument('-eval', '--eval_model',
|
||||
help='Evaluate a FasterRCNN model (with or without a native Proposal Layer)',
|
||||
required=False, default=False)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.eval_model:
|
||||
evaluate(args.model_path)
|
||||
else:
|
||||
model = convert(args.model_path)
|
||||
path = os.path.dirname(args.model_path)
|
||||
filename = 'native_proposal_layer_' + os.path.basename(args.model_path)
|
||||
model.save(os.path.join(path, filename))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -26,7 +26,6 @@ class ProposalLayer(UserFunction):
|
|||
'''
|
||||
|
||||
def __init__(self, arg1, arg2, arg3, name='ProposalLayer', param_str=None):
|
||||
super(ProposalLayer, self).__init__([arg1, arg2, arg3], name=name)
|
||||
self.param_str_ = param_str if param_str is not None else "'feat_stride': 16\n'scales':\n - 8 \n - 16 \n - 32"
|
||||
|
||||
# parse the layer parameter string, which must be valid YAML
|
||||
|
|
@ -36,6 +35,10 @@ class ProposalLayer(UserFunction):
|
|||
self._anchors = generate_anchors(scales=np.array(anchor_scales))
|
||||
self._num_anchors = self._anchors.shape[0]
|
||||
|
||||
attributes = {'feat_stride' : self._feat_stride, 'scales' : anchor_scales}
|
||||
|
||||
super(ProposalLayer, self).__init__([arg1, arg2, arg3], attributes=attributes, name=name)
|
||||
|
||||
if DEBUG:
|
||||
print ('feat_stride: {}'.format(self._feat_stride))
|
||||
print ('anchors:')
|
||||
|
|
|
|||
21
Makefile
21
Makefile
|
|
@ -565,6 +565,27 @@ $(BINARY_CONVOLUTION_EXAMPLE_LIB): $(BINARY_CONVOLUTION_EXAMPLE_LIBRARY_OBJ) | $
|
|||
$(CXX) $(LDFLAGS) -shared $(patsubst %,-L%, $(LIBDIR)) $(patsubst %,$(RPATH)%, $(LIBDIR) $(ORIGINDIR)) -o $@ $^ -l$(CNTKLIBRARY) $(SOURCEDIR)/../Examples/Extensibility/BinaryConvolution/BinaryConvolutionLib/halide/halide_convolve_nofeatures.a
|
||||
|
||||
|
||||
##############################################
|
||||
# Native implementation of the Proposal Layer
|
||||
##############################################
|
||||
|
||||
PROPOSAL_LAYER_LIBRARY_SRC =\
|
||||
$(SOURCEDIR)/../Examples/Extensibility/ProposalLayer/ProposalLayerLib/ProposalLayerLib.cpp \
|
||||
|
||||
PROPOSAL_LAYER_LIBRARY_OBJ := $(patsubst %.cpp, $(OBJDIR)/%.o, $(PROPOSAL_LAYER_LIBRARY_SRC))
|
||||
|
||||
PROPOSAL_LAYER_LIB:= $(LIBDIR)/Cntk.ProposalLayerLib-$(CNTK_COMPONENT_VERSION).so
|
||||
ALL_LIBS += $(PROPOSAL_LAYER_LIB)
|
||||
PYTHON_LIBS += $(PROPOSAL_LAYER_LIB)
|
||||
SRC += $(PROPOSAL_LAYER_LIBRARY_SRC)
|
||||
|
||||
$(PROPOSAL_LAYER_LIB): $(PROPOSAL_LAYER_LIBRARY_OBJ) | $(CNTKLIBRARY_LIB)
|
||||
@echo $(SEPARATOR)
|
||||
@echo creating $@ for $(ARCH) with build type $(BUILDTYPE)
|
||||
@mkdir -p $(dir $@)
|
||||
$(CXX) $(LDFLAGS) -shared $(patsubst %,-L%, $(LIBDIR)) $(patsubst %,$(RPATH)%, $(LIBDIR) $(ORIGINDIR)) -o $@ $^ -l$(CNTKLIBRARY)
|
||||
|
||||
|
||||
########################################
|
||||
# LibEval
|
||||
########################################
|
||||
|
|
|
|||
|
|
@ -5,12 +5,11 @@
|
|||
# ==============================================================================
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import os, sys
|
||||
import pytest
|
||||
import sys
|
||||
from cntk import load_model
|
||||
from cntk import load_model, cntk_py
|
||||
from cntk.cntk_py import DeviceKind_GPU
|
||||
from cntk.device import try_set_default_device, gpu
|
||||
from cntk.device import try_set_default_device, gpu, cpu
|
||||
from cntk.logging.graph import get_node_outputs
|
||||
from cntk.ops.tests.ops_test_utils import cntk_device
|
||||
from _cntk_py import force_deterministic_algorithms
|
||||
|
|
@ -19,6 +18,7 @@ force_deterministic_algorithms()
|
|||
abs_path = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(abs_path)
|
||||
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Detection", "FasterRCNN"))
|
||||
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Extensibility", "ProposalLayer"))
|
||||
|
||||
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
|
||||
grocery_path = prepare_Grocery_data()
|
||||
|
|
@ -63,6 +63,53 @@ def test_fasterrcnn_grocery_training_e2e(device_id):
|
|||
meanAP = eval_faster_rcnn_mAP(eval_model)
|
||||
assert meanAP > 0.01
|
||||
|
||||
@win35_linux34
|
||||
def test_native_fasterrcnn_eval(tmpdir, device_id):
|
||||
from config import cfg
|
||||
cfg["CNTK"].FORCE_DETERMINISTIC = True
|
||||
cfg["CNTK"].DEBUG_OUTPUT = False
|
||||
cfg["CNTK"].VISUALIZE_RESULTS = False
|
||||
cfg["CNTK"].FAST_MODE = True
|
||||
cfg["CNTK"].MAP_FILE_PATH = grocery_path
|
||||
|
||||
from FasterRCNN import set_global_vars
|
||||
set_global_vars(False)
|
||||
|
||||
if cntk_device(device_id).type() != DeviceKind_GPU:
|
||||
pytest.skip('test only runs on GPU') # it runs very slow in CPU
|
||||
try_set_default_device(cntk_device(device_id))
|
||||
|
||||
# since we do not use a reader for evaluation we need unzipped data
|
||||
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
|
||||
|
||||
if externalData:
|
||||
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
|
||||
model_file = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v0", "AlexNet.model")
|
||||
else:
|
||||
model_file = os.path.join(abs_path, *"../../../../Examples/Image/PretrainedModels/AlexNet.model".split("/"))
|
||||
|
||||
from FasterRCNN import train_faster_rcnn_e2e, eval_faster_rcnn_mAP
|
||||
|
||||
np.random.seed(seed=3)
|
||||
|
||||
eval_model = train_faster_rcnn_e2e(model_file, debug_output=False)
|
||||
|
||||
meanAP_python = eval_faster_rcnn_mAP(eval_model)
|
||||
|
||||
cntk_py.always_allow_setting_default_device()
|
||||
|
||||
try_set_default_device(cpu())
|
||||
|
||||
from native_proposal_layer import clone_with_native_proposal_layer
|
||||
|
||||
model_with_native_pl = clone_with_native_proposal_layer(eval_model)
|
||||
meanAP_native = eval_faster_rcnn_mAP(model_with_native_pl)
|
||||
|
||||
# 0.2067 (python) vs 0.2251 (native) -- the difference stems
|
||||
# from different sorting algorithms: quicksort in python and
|
||||
# heapsort in c++ (both are not stable).
|
||||
assert abs(meanAP_python - meanAP_native) < 0.02
|
||||
|
||||
@win35_linux34
|
||||
def test_fasterrcnn_grocery_training_4stage(device_id):
|
||||
from config import cfg
|
||||
|
|
|
|||
|
|
@ -1621,8 +1621,12 @@ class UserFunction(Function):
|
|||
name (str): name of this function
|
||||
'''
|
||||
|
||||
def __init__(self, inputs, as_numpy=True, name=''):
|
||||
def __init__(self, inputs, as_numpy=True, attributes=None, name=''):
|
||||
if attributes is None:
|
||||
super(UserFunction, self).__init__(inputs, name)
|
||||
else:
|
||||
attributes = _py_dict_to_cntk_dict(attributes)
|
||||
super(UserFunction, self).__init__(inputs, attributes, name)
|
||||
self.set_native(False)
|
||||
self.as_numpy = as_numpy
|
||||
|
||||
|
|
|
|||
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Ссылка в новой задаче