Add maxpool and avgpool operators (#17)

* ADD:maxpool&&avgpool operators.

add OperatorObj::getDType()

clang format

FIX:timeit API has changed.

* Fix: Tensor::getInputs is const method

* Chore

Co-authored-by: Liyan Zheng <liyan-zheng@outlook.com>

CMakeLists.txt

@@ -14,7 +14,7 @@ set(DEFAULT_BUILD_TYPE "RelWithDebInfo")
 
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_EXTENSIONS OFF) # -std=gnu++11 when on, -std=c++11 when off
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Werror -Wno-error=deprecated-declarations")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -Wall -Werror -Wno-error=deprecated-declarations")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -UNDEBUG") # Enable assertion
 set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -UNDEBUG") # Enable assertion
 

include/core/kernel.h

@@ -64,7 +64,9 @@ class KernelRegistry {
         return true;
     }
     Kernel *getKernel(const KernelAttrs &kernelAttrs) const {
-        return std::get<0>(kernels.at(kernelAttrs));
+        auto it = kernels.find(kernelAttrs);
+        IT_ASSERT(it != kernels.end(), "Kernel not found.");
+        return std::get<0>(it->second);
     }
     const KernelRecord &getKernelItem(const KernelAttrs &kernelAttrs) const {
         return kernels.at(kernelAttrs);

include/core/operator.h

@@ -169,13 +169,14 @@ class OperatorObj : public Object {
     const TensorVec &getInputs() const { return inputs; }
     // TensorVec getOutputs() { return outputs; }
     const TensorVec &getOutputs() const { return outputs; }
-    Tensor getInputs(size_t i) { return inputs.at(i); }
+    Tensor getInputs(size_t i) const { return inputs.at(i); }
     Tensor getOutput() const {
         IT_ASSERT(outputs.size() == 1, "Unimplemented");
         return outputs[0];
     }
     OpType getOpType() const { return type; }
-
+    // HACK: set correct data type
+    DataType getDType() const { return getInputs(0)->getDType(); }
     virtual int numInputs() const = 0;
     virtual int numOutputs() const = 0;
 

include/core/tensor.h

@@ -49,8 +49,18 @@ class TensorObj : public TensorBaseObj {
     void copyData(const Tensor &src) { copyData(src.get()); }
     void setData(
         const std::function<void(void *, size_t, DataType)> &generator) const {
+        IT_ASSERT(data != nullptr);
+        if (!runtime->isCpu()) {
+            IT_TODO_HALT();
+        }
         generator(data->getPtr<void *>(), size(), dtype);
     }
+    Tensor clone(Runtime runtime) {
+        auto obj = make_ref<TensorObj>(shape, dtype, runtime);
+        obj->dataMalloc();
+        obj->copyData(this);
+        return obj;
+    }
 
     void printData() const;
     bool equalData(const Tensor &rhs) const;

include/operators/pooling.h

@@ -0,0 +1,54 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+
+class PoolingObj : public OperatorObj {
+  private:
+    int kh, kw;
+    int dh, dw;
+    int ph, pw;
+    int sh, sw;
+    int n, c, h, w;
+
+  public:
+    PoolingObj(GraphObj *graph, OpType optype, Tensor input, Tensor output,
+               int kh, int kw, int dh, int dw, int ph, int pw, int sh, int sw);
+
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    std::string toString() const override;
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return 1; }
+
+    int getKh() const { return kh; }
+    int getKw() const { return kw; }
+    int getDh() const { return dh; }
+    int getDw() const { return dw; }
+    int getPh() const { return ph; }
+    int getPw() const { return pw; }
+    int getSh() const { return sh; }
+    int getSw() const { return sw; }
+
+    auto getPadStrideDilation() const { return tuple(ph, pw, sh, sw, dh, dw); }
+    auto getNCHWRS() const { return tuple(n, c, h, w, kh, kw); }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
+
+class MaxPoolObj : public PoolingObj {
+  public:
+    MaxPoolObj(GraphObj *graph, Tensor input, Tensor output, int kh, int kw,
+               int dh, int dw, int ph, int pw, int sh, int sw)
+        : PoolingObj(graph, OpType::MaxPool, input, output, kh, kw, dh, dw, ph,
+                     pw, sh, sw) {}
+};
+class AvgPoolObj : public PoolingObj {
+  public:
+    AvgPoolObj(GraphObj *graph, Tensor input, Tensor output, int kh, int kw,
+               int dh, int dw, int ph, int pw, int sh, int sw)
+        : PoolingObj(graph, OpType::AvgPool, input, output, kh, kw, dh, dw, ph,
+                     pw, sh, sw) {}
+};
+}; // namespace infini

src/core/operator.cc

@@ -62,7 +62,7 @@ bool OperatorObj::checkValid(GraphObj *graph) {
             IT_ASSERT(!outputs[i]);
             outputs[i] = graph->addTensor(shapes[i], dataTypes[i]);
         }
-    } else { // if graph is not empty, check outputs match inferred shapes
+    } else { // if outputs have been created, check their shapes
         for (size_t i = 0; i < shapes.size(); ++i) {
             if (shapes[i] != outputs[i]->getDims())
                 return false;

src/core/runtime.cc

@@ -22,9 +22,7 @@ void CpuRuntimeObj::run(const Graph &graph, bool tune, bool profiling) const {
     std::map<OpType, int> opCnt;
 
     for (auto &op : graph->getOperators()) {
-        // HACK: set correct data type
+        auto kernelAttrs = KernelAttrs{device, op->getOpType(), op->getDType()};
-        auto kernelAttrs =
-            KernelAttrs{device, op->getOpType(), DataType::UInt32};
         Kernel *kernel = kernelRegistry.getKernel(kernelAttrs);
         auto perfKey = PerfEngine::Key{kernelAttrs, op->getOpPerfKey()};
         std::optional<PerfRecord> perfData = perfEngine.getPerfData(perfKey);
@@ -72,9 +70,7 @@ double RuntimeObj::getPerfTime(const Graph &graph, bool profiling) const {
     std::map<OpType, int> opCnt;
 
     for (auto &op : graph->getOperators()) {
-        // HACK: set correct data type
+        auto kernelAttrs = KernelAttrs{device, op->getOpType(), op->getDType()};
-        auto kernelAttrs =
-            KernelAttrs{device, op->getOpType(), DataType::UInt32};
         Kernel *kernel = kernelRegistry.getKernel(kernelAttrs);
         auto perfKey = PerfEngine::Key{kernelAttrs, op->getOpPerfKey()};
         std::optional<PerfRecord> perfData = perfEngine.getPerfData(perfKey);

src/kernels/cpu/pooling.cc

@@ -0,0 +1,91 @@
+#include "operators/pooling.h"
+#include "core/kernel.h"
+
+namespace infini {
+template <typename T> class NativePooling : public Kernel {
+    virtual T getPoolingValue(int kh, int kw, int posh, int posw, int ih,
+                              int iw, T *inptr) const = 0;
+    void compute(const Operator &_op, const PerfRecord &record,
+                 const RuntimeObj *context) const override {
+        auto op = as<PoolingObj>(_op);
+        T *inptr = op->getInputs(0)->getRawDataPtr<T *>();
+        T *outptr = op->getOutput()->getRawDataPtr<T *>();
+        const auto [n, c, ih, iw, kh, kw] = op->getNCHWRS();
+        const auto [ph, pw, sh, sw, dh, dw] = op->getPadStrideDilation();
+        if (dh != 1 || dw != 1)
+            IT_TODO_HALT(); // To support dailated pooling
+        auto outDim = op->getOutput()->getDims();
+        int oh = outDim[2], ow = outDim[3];
+        for (auto i = 0; i < n; i++) {
+            for (auto j = 0; j < c; j++) {
+                auto inoffset = i * (c * ih * iw) + j * ih * iw;
+                for (auto h = 0; h < oh; h++) {
+                    for (auto w = 0; w < ow; w++) {
+                        T val =
+                            getPoolingValue(kh, kw, h * sh - ph, w * sw - pw,
+                                            ih, iw, inptr + inoffset);
+                        auto outoffset =
+                            w + h * ow + j * (oh * ow) + i * (c * oh * ow);
+                        outptr[outoffset] = val;
+                    }
+                }
+            }
+        }
+    }
+
+    void compute(const Operator &op, const RuntimeObj *context) const override {
+        compute(op, {}, context);
+    }
+
+    PerfRecord tune(const Operator &op,
+                    const RuntimeObj *context) const override {
+        PerfRecord perfrcd(timeit([&]() { compute(op, context); }));
+        return perfrcd;
+    }
+};
+
+template <typename T> class NaiveMaxPool : public NativePooling<T> {
+    T getPoolingValue(int kh, int kw, int posh, int posw, int ih, int iw,
+                      T *inptr) const override {
+        T maxval = 0;
+        for (auto k = 0; k < kh; k++) {
+            for (auto l = 0; l < kw; l++) {
+                auto inPosH = posh + k;
+                auto inPosW = posw + l;
+                if (inPosH < 0 || inPosH >= ih || inPosW < 0 || inPosW >= iw)
+                    continue;
+                auto offset = (posh + k) * iw + posw + l;
+                auto val = inptr[offset];
+                if (maxval < val)
+                    maxval = val;
+            }
+        }
+        return maxval;
+    }
+};
+
+template <typename T> class NaiveAvgPool : public NativePooling<T> {
+    T getPoolingValue(int kh, int kw, int posh, int posw, int ih, int iw,
+                      T *inptr) const override {
+        T sum = 0;
+        for (auto k = 0; k < kh; k++) {
+            for (auto l = 0; l < kw; l++) {
+                auto inPosH = posh + k;
+                auto inPosW = posw + l;
+                if (inPosH < 0 || inPosH >= ih || inPosW < 0 || inPosW >= iw)
+                    continue;
+                auto offset = (posh + k) * iw + posw + l;
+                sum += inptr[offset];
+            }
+        }
+        return T(sum / (kh * kw));
+    }
+};
+
+REGISTER_KERNEL(Device::CPU, OpType::MaxPool, DataType::UInt32,
+                NaiveMaxPool<uint32_t>, "maxPoolNaive_CPU_uint32");
+REGISTER_KERNEL(Device::CPU, OpType::MaxPool, DataType::Float32,
+                NaiveMaxPool<float>, "maxPoolNaive_CPU_float32");
+REGISTER_KERNEL(Device::CPU, OpType::AvgPool, DataType::Float32,
+                NaiveAvgPool<float>, "AvgPoolNaive_CPU_float32");
+} // namespace infini

src/kernels/cuda/pooling.cc

@@ -0,0 +1,89 @@
+#include "operators/pooling.h"
+#include "core/kernel.h"
+#include "cuda/cuda_runtime.h"
+
+namespace infini {
+class poolingCudnn : public Kernel {
+    virtual cudnnPoolingMode_t getPoolingMode() const = 0;
+    void compute(const Operator &_op, const PerfRecord &record,
+                 const RuntimeObj *_context) const override {
+        auto op = as<PoolingObj>(_op);
+        auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
+        cudnnStatus_t stat;
+        void *const inData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const outData = (op->getOutput()->getRawDataPtr<void *>());
+
+        const auto [n, c, h, w, kh, kw] = op->getNCHWRS();
+        const auto [ph, pw, sh, sw, dh, dw] = op->getPadStrideDilation();
+
+        // get inputs
+        cudnnTensorDescriptor_t inDesc;
+        checkCudnnError(cudnnCreateTensorDescriptor(&inDesc));
+        checkCudnnError(cudnnSetTensor4dDescriptor(
+            inDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w));
+
+        // get maxpool descriptor
+        cudnnPoolingDescriptor_t poolingDesc;
+        checkCudnnError(cudnnCreatePoolingDescriptor(&poolingDesc));
+        checkCudnnError(cudnnSetPooling2dDescriptor(
+            poolingDesc, getPoolingMode(), CUDNN_NOT_PROPAGATE_NAN, kh, kw, ph,
+            pw, sh, sw));
+
+        // get outputs
+        int outn, outc, outh, outw;
+        checkCudnnError(cudnnGetPooling2dForwardOutputDim(
+            poolingDesc, inDesc, &outn, &outc, &outh, &outw));
+        cudnnTensorDescriptor_t outDesc;
+        checkCudnnError(cudnnCreateTensorDescriptor(&outDesc));
+        checkCudnnError(cudnnSetTensor4dDescriptor(outDesc, CUDNN_TENSOR_NCHW,
+                                                   CUDNN_DATA_FLOAT, outn, outc,
+                                                   outh, outw));
+        IT_ASSERT((vector{outn, outc, outh, outw}) ==
+                      op->getOutput()->getDims(),
+                  "cuDNN output shape mismatches with OP output shape");
+
+        float alpha = 1.f, beta = 0.f;
+        stat = cudnnPoolingForward(context->cudnnHandle(), poolingDesc, &alpha,
+                                   inDesc, inData, &beta, outDesc, outData);
+        if (stat != CUDNN_STATUS_SUCCESS)
+            return;
+
+        // Destories in CUDA does not require sync. But cuDNN does not state
+        // whether sync is required before destories.
+        checkCudnnError(cudnnDestroyTensorDescriptor(inDesc));
+        checkCudnnError(cudnnDestroyTensorDescriptor(outDesc));
+        checkCudnnError(cudnnDestroyPoolingDescriptor(poolingDesc));
+    }
+
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        compute(_op, {}, _context);
+    }
+    // Premise: op is idempotent since it is called multiple times.
+    PerfRecord tune(const Operator &_op,
+                    const RuntimeObj *_context) const override {
+        PerfRecord ret;
+        auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
+        ret.time = timeit([&]() { compute(_op, _context); },
+                          [&]() { context->sync(); });
+        return ret;
+    }
+};
+
+class maxPoolCudnn : public poolingCudnn {
+    cudnnPoolingMode_t getPoolingMode() const override {
+        return CUDNN_POOLING_MAX;
+    }
+};
+
+class avgPoolCudnn : public poolingCudnn {
+    cudnnPoolingMode_t getPoolingMode() const override {
+        return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
+    }
+};
+
+REGISTER_KERNEL(Device::CUDA, OpType::MaxPool, DataType::Float32, maxPoolCudnn,
+                "MaxPool_cuDNN_CUDA_Float32");
+REGISTER_KERNEL(Device::CUDA, OpType::AvgPool, DataType::Float32, avgPoolCudnn,
+                "AvgPool_cuDNN_CUDA_Float32");
+}; // namespace infini

src/operators/pooling.cc

@@ -0,0 +1,52 @@
+#include "operators/pooling.h"
+
+namespace infini {
+
+PoolingObj::PoolingObj(GraphObj *graph, OpType optype, Tensor input,
+                       Tensor output, int kh, int kw, int dh, int dw, int ph,
+                       int pw, int sh, int sw)
+    : OperatorObj(optype, {input}, {output}), kh(kh), kw(kw), dh(dh), dw(dw),
+      ph(ph), pw(pw), sh(sh), sw(sw) {
+    n = input->getDims()[0];
+    c = input->getDims()[1];
+    h = input->getDims()[2], w = input->getDims()[3];
+
+    IT_ASSERT(checkValid(graph));
+}
+
+optional<vector<Shape>> PoolingObj::inferShape(const TensorVec &inputs) const {
+    const auto &input = inputs[0];
+    auto h = input->getDims()[input->getDims().size() - 2],
+         w = input->getDims()[input->getDims().size() - 1];
+    int oh = (h - (kh - sh) + ph * 2) / sh;
+    int ow = (w - (kw - sw) + pw * 2) / sw;
+    auto ret = input->getDims();
+    ret[input->getDims().size() - 2] = oh;
+    ret[input->getDims().size() - 1] = ow;
+    return {{ret}};
+}
+
+std::string PoolingObj::toString() const {
+    std::ostringstream os;
+    os << "Maxpool[" << getGuid() << "]";
+    os << "(";
+    os << "k=[" << kh << "," << kw << "],";
+    os << "p=[" << ph << "," << pw << "],";
+    os << "s=[" << sh << "," << sw << "],";
+    os << "d=[" << dh << "," << dw << "],";
+    os << "input=" << inputs[0]->getGuid() << ",";
+    os << "output=" << outputs[0]->getGuid() << ")";
+    return os.str();
+}
+
+vector<int> PoolingObj::getWorkloadVector() const {
+    return {
+        enum_to_underlying(type), n, c, h, w, kh, kw, ph, pw, sh, sw, dh, dw};
+}
+
+vector<int> PoolingObj::getOpAttrVector() const {
+    IT_TODO_HALT();
+    return {enum_to_underlying(type), kh, kw, ph, pw, sh, sw, dh, dw};
+}
+
+}; // namespace infini

test/core/test_hash.cc

@@ -14,8 +14,8 @@ TEST(Hash, OperatorHash) {
         Tensor o0 = g->addTensor({1, 2, 4}, DataType::UInt32);
         auto matmul = g->addOpWithOutputs<MatmulObj>(i0, w0, o0);
         key1 = matmul->getOpPerfKey();
-        EXPECT_NE(key1.hash, 0);
+        EXPECT_NE(key1.hash, (HashType)0);
-        EXPECT_GT(key1.attrs.size(), 5);
+        EXPECT_GT(key1.attrs.size(), (size_t)5);
     }
     { // build with addOp
         Graph g = make_ref<GraphObj>(nullptr);
@@ -23,7 +23,7 @@ TEST(Hash, OperatorHash) {
         Tensor w0 = g->addTensor({2, 3, 4}, DataType::UInt32);
         auto matmul = g->addOp<MatmulObj>(i0, w0, nullptr);
         key2 = matmul->getOpPerfKey();
-        EXPECT_NE(key2.hash, 0);
+        EXPECT_NE(key2.hash, (HashType)0);
     }
     EXPECT_NE(key1.hash, key2.hash);
 }

test/operators/test_pooling.cc

@@ -0,0 +1,122 @@
+#include "core/graph.h"
+#include "core/runtime.h"
+#include "cuda/cuda_runtime.h"
+#include "cuda/cuda_utility.h"
+#include "operators/pooling.h"
+#include "test.h"
+
+namespace infini {
+using KDPS = vector<int>;
+using ExpectOutput = vector<float>;
+TEST(MaxPool, ShapeInference) {
+    Runtime cpuRuntime = CpuRuntimeObj::getInstance();
+    {
+        Graph g = make_ref<GraphObj>(cpuRuntime);
+        Tensor i = g->addTensor({1, 64, 162, 162}, DataType::UInt32);
+        const int kh = 3, kw = 3, dh = 1, dw = 1, ph = 0, pw = 0, sh = 2,
+                  sw = 2;
+        auto op =
+            g->addOp<MaxPoolObj>(i, nullptr, kh, kw, dh, dw, ph, pw, sh, sw);
+        EXPECT_EQ(op->getOutput()->getDims(), (Shape{1, 64, 80, 80}));
+    }
+
+    { // dilation & stride
+        Graph g = make_ref<GraphObj>(cpuRuntime);
+        Tensor i = g->addTensor({1, 64, 162, 162}, DataType::UInt32);
+        auto op = g->addOp<MaxPoolObj>(i, nullptr, 4, 3, 1, 1, 2, 1, 1, 2);
+        EXPECT_EQ(op->getOutput()->getDims(), (Shape{1, 64, 163, 81}));
+    }
+}
+
+TEST(MaxPool, NaiveCPU) {
+    Runtime cpuRuntime = CpuRuntimeObj::getInstance();
+    Graph g = make_ref<GraphObj>(cpuRuntime);
+    Tensor i = g->addTensor({1, 2, 5, 5}, DataType::UInt32);
+    auto op = g->addOp<MaxPoolObj>(i, nullptr, 3, 3, 1, 1, 1, 1, 2, 2);
+
+    g->dataMalloc();
+    i->setData(IncrementalGenerator());
+    cpuRuntime->run(g, true, true);
+    double perfTime = cpuRuntime->getPerfTime(g);
+    // The example matmul takes 0.0036ms with one core
+    EXPECT_GT(perfTime, 0);
+    EXPECT_LT(perfTime, 5);
+    // check answer
+    vector<uint32_t> ans = {6,  8,  9,  16, 18, 19, 21, 23, 24,
+                            31, 33, 34, 41, 43, 44, 46, 48, 49};
+    EXPECT_TRUE(op->getOutput()->equalData(ans));
+}
+
+TEST(AvgPool, NaiveCPU) {
+    Runtime cpuRuntime = CpuRuntimeObj::getInstance();
+    Graph g = make_ref<GraphObj>(cpuRuntime);
+    Tensor i = g->addTensor({1, 2, 5, 5}, DataType::Float32);
+    auto op = g->addOp<AvgPoolObj>(i, nullptr, 3, 3, 1, 1, 1, 1, 2, 2);
+
+    g->dataMalloc();
+    i->setData(IncrementalGenerator());
+    cpuRuntime->run(g, true, true);
+
+    // check answer
+    vector<float> ans = {
+        1.33333337, 3.0000,  2.66666675, 7.0000,     12.0000,   9.0000,
+        8.0000,     13.0000, 9.33333302, 12.444447,  19.666666, 13.7777777,
+        23.666666,  37.0000, 25.666666,  19.1111107, 29.666666, 20.4444447};
+    EXPECT_TRUE(op->getOutput()->equalData(ans));
+
+    double perfTime = cpuRuntime->getPerfTime(g);
+    // The example matmul takes 0.0036ms with one core
+    EXPECT_GT(perfTime, 0);
+    EXPECT_LT(perfTime, 5);
+}
+
+template <class T>
+void testPoolCudnn(
+    const std::function<void(void *, size_t, DataType)> &generator,
+    const Shape &shape, const KDPS &kdps, const ExpectOutput &ansVec) {
+    EXPECT_TRUE(kdps.size() == 8);
+    Runtime cpuRuntime = CpuRuntimeObj::getInstance();
+    auto cudaRuntime = make_ref<CudaRuntimeObj>();
+
+    // Build input data on CPU
+    Tensor i0cpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
+    i0cpu->dataMalloc();
+    i0cpu->setData(generator);
+
+    // Build CUDA graph
+    Graph g = make_ref<GraphObj>(cudaRuntime);
+    auto i0 = g->cloneTensor(i0cpu);
+    auto pool = g->addOp<T>(i0, nullptr, kdps[0], kdps[1], kdps[2], kdps[3],
+                            kdps[4], kdps[5], kdps[6], kdps[7]);
+
+    // allocate CUDA memory
+    g->dataMalloc();
+
+    // Execute on CUDA
+    cudaRuntime->run(g);
+
+    // clone CUDA output to CPU
+    auto o0 = pool->getOutput();
+    auto cpuo0 = o0->clone(cpuRuntime);
+
+    // check results on CPU
+    EXPECT_TRUE(cpuo0->equalData(ansVec));
+}
+
+TEST(MaxPool, CuDNN) {
+    testPoolCudnn<MaxPoolObj>(IncrementalGenerator(), Shape{1, 2, 5, 5},
+                              KDPS{3, 3, 1, 1, 1, 1, 2, 2},
+                              ExpectOutput{6, 8, 9, 16, 18, 19, 21, 23, 24, 31,
+                                           33, 34, 41, 43, 44, 46, 48, 49});
+}
+
+TEST(AvgPool, CuDNN) {
+    testPoolCudnn<AvgPoolObj>(
+        IncrementalGenerator(), Shape{1, 2, 5, 5}, KDPS{3, 3, 1, 1, 1, 1, 2, 2},
+        ExpectOutput{1.333333, 3.0000, 2.666667, 7.0000, 12.0000, 9.0000,
+                     8.0000, 13.0000, 9.333333, 12.44444, 19.666667, 13.777778,
+                     23.666667, 37.0000, 25.666667, 19.111111, 29.666667,
+                     20.444444});
+}
+
+} // namespace infini