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Commit 328e8546 authored by Grégoire Kubler's avatar Grégoire Kubler
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feat : TESTING - added groundtruth tests with pytorch values and benchmarking

parent a71400ba
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Tags v0.4.0
3 merge requests!50version 0.2.0,!47Draft: TEST_CI_FORK_DO_NOT_MERGE,!42feat/operator_globalAveragePooling
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include/aidge/backend/cpu/operator/GlobalAveragePoolingImpl_forward_kernels.hpp
+ 1
− 2
View file @ 328e8546
......@@ -53,12 +53,11 @@ void GlobalAveragePoolingImpl_cpu_forward_kernel(
const I *filter_start = std::next(
input, batch * in_batch_nb_elems + (channel * in_channel_nb_elems));
// I sum = std::accumulate(&filter_start[0],
// &filter_start[in_batch_nb_elems + 1], 0);
// &filter_start[in_batch_nb_elems + 1], 0.f);
I sum = 0;
for (size_t i = 0; i < in_channel_nb_elems; ++i) {
sum += filter_start[i];
}
output[batch * out_batch_nb_elems + channel] =
sum / static_cast<I>(in_channel_nb_elems);
}
......
unit_tests/operator/Test_GlobalAveragePoolingImpl.cpp
+ 403
− 28
View file @ 328e8546
......@@ -25,6 +25,17 @@
#include "aidge/operator/GlobalAveragePooling.hpp"
#include "aidge/utils/TensorUtils.hpp"
// debug print function
void print_tensor(Aidge::Tensor &T) {
// Print tensors
std::cout << "Tensor : size = [";
for (auto &dim : T.dims()) {
std::cout << dim << " , ";
}
std::cout << "]" << std::endl;
T.print();
}
namespace Aidge {
TEST_CASE("[cpu/operator] GlobalAveragePooling",
"[GlobalAveragePooling][CPU]") {
......@@ -80,7 +91,6 @@ TEST_CASE("[cpu/operator] GlobalAveragePooling",
std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1),
std::multiplies<std::size_t>());
// without broadcasting
float *array0 = new float[nb_elements];
float *result = new float[nb_elements];
......@@ -107,6 +117,10 @@ TEST_CASE("[cpu/operator] GlobalAveragePooling",
const DimSize_t in_batch_nb_elems = in_nb_elems / dims_in[0];
const DimSize_t in_channel_nb_elems = in_batch_nb_elems / dims_in[1];
number_of_operation +=
in_nb_elems +
dims_in[1]; // averaging per channel : 1 addition per element in
// the channel + 1 division this for every batch
// create out nb_elems
std::vector<std::size_t> dims_out{dims_in[0], dims_in[1]};
const std::size_t out_nb_elems =
......@@ -118,7 +132,6 @@ TEST_CASE("[cpu/operator] GlobalAveragePooling",
float *array0 = new float[in_nb_elems];
float *result = new float[out_nb_elems];
float val = valueDist(gen);
std::cout << "val = " << val << std::endl;
for (std::size_t batch = 0; batch < dims_in[0]; ++batch) {
for (std::size_t channel = 0; channel < dims_in[1]; ++channel) {
for (std::size_t i = 0; i < in_channel_nb_elems; ++i)
......@@ -146,42 +159,404 @@ TEST_CASE("[cpu/operator] GlobalAveragePooling",
duration +=
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
// Print tensors
std::cout << "input : size = [";
for (auto &dim : op->getInput(0)->dims()) {
std::cout << dim << " , ";
}
std::cout << "]" << std::endl;
// T0->print();
std::cout << "output : size = [";
for (auto &dim : op->getOutput(0)->dims()) {
std::cout << dim << " , ";
}
std::cout << "]" << std::endl;
op->getOutput(0)->print();
std::cout << "ref Tres : size = output size if no error occurred"
<< std::endl;
std::cout << "ref Tres: size = [";
for (auto &dim : Tres->dims()) {
std::cout << dim << " , ";
}
std::cout << "]" << std::endl;
CHECK(Tres->nbDims() == op->getOutput(0)->nbDims());
for (DimSize_t i = 0; i < op->getOutput(0)->nbDims(); ++i) {
CHECK(Tres->dims().at(i) == op->getOutput(0)->dims().at(i));
}
Tres->print();
CHECK(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] array0;
delete[] result;
}
SECTION("Using result from a pytorch function as groundtruth") {}
SECTION("random testing") {}
SECTION("random testing") {
for (int trial = 0; trial < NBTRIALS; ++trial) {
// generate the tensor
const std::size_t nbDims = nbHighDimsDist(gen);
std::vector<std::size_t> dims_in;
for (std::size_t i = 0; i < nbDims; ++i) {
dims_in.push_back(dimSizeDist(gen));
}
// create in nb_elems
const std::size_t in_nb_elems =
std::accumulate(dims_in.cbegin(), dims_in.cend(), std::size_t(1),
std::multiplies<std::size_t>());
const DimSize_t in_batch_nb_elems = in_nb_elems / dims_in[0];
const DimSize_t in_channel_nb_elems = in_batch_nb_elems / dims_in[1];
number_of_operation +=
in_nb_elems +
dims_in[1]; // averaging per channel : 1 addition per element in
// the channel + 1 division this for every batch
// create out nb_elems
std::vector<std::size_t> dims_out{dims_in[0], dims_in[1]};
const std::size_t out_nb_elems =
std::accumulate(dims_out.cbegin(), dims_out.cend(),
std::size_t(1), std::multiplies<std::size_t>());
const DimSize_t out_batch_nb_elems = out_nb_elems / dims_out[0];
// iterate over each batch/channel
float *array0 = new float[in_nb_elems];
float *result = new float[out_nb_elems];
for (std::size_t batch = 0; batch < dims_in[0]; ++batch) {
for (std::size_t channel = 0; channel < dims_in[1]; ++channel) {
float channel_sum = 0;
for (std::size_t i = 0; i < in_channel_nb_elems; ++i)
{
float val = valueDist(gen);
array0[batch * in_batch_nb_elems +
channel * in_channel_nb_elems + i] = val;
channel_sum += val;
}
result[batch * out_batch_nb_elems + channel] =
channel_sum / in_channel_nb_elems;
}
}
// input0
T0->resize(dims_in);
T0->getImpl()->setRawPtr(array0, in_nb_elems);
// results
Tres->resize(dims_out);
Tres->getImpl()->setRawPtr(result, out_nb_elems);
op->computeOutputDims();
start = std::chrono::system_clock::now();
REQUIRE_NOTHROW(globAvgPool->forward());
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(
end - start);
CHECK(Tres->nbDims() == op->getOutput(0)->nbDims());
for (DimSize_t i = 0; i < op->getOutput(0)->nbDims(); ++i) {
CHECK(Tres->dims().at(i) == op->getOutput(0)->dims().at(i));
}
CHECK(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] array0;
delete[] result;
}
}
SECTION("Using result from a pytorch function as groundtruth") {
DimSize_t batch_size = 2;
DimSize_t channels = 3;
DimSize_t height = 4;
DimSize_t width = 3;
DimSize_t depth = 2;
SECTION("2D_img") {
const std::vector<DimSize_t> in_dims{batch_size, channels, height,
width};
const std::vector<DimSize_t> out_dims{batch_size, channels};
DimSize_t in_nb_elems = batch_size * channels * height * width;
DimSize_t out_nb_elems = batch_size * channels;
number_of_operation +=
in_nb_elems +
channels; // averaging per channel : 1 addition per element in
// the channel + 1 division this for every batch
auto input = new float[in_nb_elems];
auto result = new float[out_nb_elems];
input[0] = 0.1807716;
input[1] = -0.0699881;
input[2] = -0.3596235;
input[3] = -0.9152045;
input[4] = 0.6257653;
input[5] = 0.0255099;
input[6] = 0.9545137;
input[7] = 0.0643485;
input[8] = 0.3611506;
input[9] = 1.1678782;
input[10] = -1.3498932;
input[11] = -0.5101767;
input[12] = 0.2359577;
input[13] = -0.2397784;
input[14] = -0.9211147;
input[15] = 1.5432971;
input[16] = 1.3488258;
input[17] = -0.1396417;
input[18] = 0.2857972;
input[19] = 0.9651205;
input[20] = -2.0371499;
input[21] = 0.4931363;
input[22] = 1.4869986;
input[23] = 0.5910330;
input[24] = 0.1260297;
input[25] = -1.5626874;
input[26] = -1.1601028;
input[27] = -0.3348408;
input[28] = 0.4477722;
input[29] = -0.8016447;
input[30] = 1.5236114;
input[31] = 2.5085869;
input[32] = -0.6630959;
input[33] = -0.2512752;
input[34] = 1.0101448;
input[35] = 0.1215468;
input[36] = 0.1583993;
input[37] = 1.1340188;
input[38] = -1.1538976;
input[39] = -0.2983968;
input[40] = -0.5075365;
input[41] = -0.9239212;
input[42] = 0.5467061;
input[43] = -1.4947776;
input[44] = -1.2057148;
input[45] = 0.5718198;
input[46] = -0.5973545;
input[47] = -0.6936757;
input[48] = 1.6455388;
input[49] = -0.8029931;
input[50] = 1.3514109;
input[51] = -0.2759193;
input[52] = -1.5108346;
input[53] = 2.1047730;
input[54] = 2.7629590;
input[55] = -1.7465292;
input[56] = 0.8353187;
input[57] = -1.9560477;
input[58] = -0.8002653;
input[59] = -0.5044988;
input[60] = -0.0711742;
input[61] = -0.5130699;
input[62] = -1.0307810;
input[63] = 0.9154347;
input[64] = -0.2282317;
input[65] = -0.6884708;
input[66] = 0.1832259;
input[67] = 0.6003584;
input[68] = -1.5429375;
input[69] = -0.3465560;
input[70] = -0.1476223;
input[71] = 0.6469797;
result[0] = 0.0145876;
result[1] = 0.3010401;
result[2] = 0.0803371;
result[3] = -0.3720275;
result[4] = 0.0919094;
result[5] = -0.1852371;
// input0
T0->resize(in_dims);
T0->getImpl()->setRawPtr(input, in_nb_elems);
// results
Tres->resize(out_dims);
Tres->getImpl()->setRawPtr(result, out_nb_elems);
op->computeOutputDims();
start = std::chrono::system_clock::now();
REQUIRE_NOTHROW(globAvgPool->forward());
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(
end - start);
CHECK(Tres->nbDims() == op->getOutput(0)->nbDims());
for (DimSize_t i = 0; i < op->getOutput(0)->nbDims(); ++i) {
CHECK(Tres->dims().at(i) == op->getOutput(0)->dims().at(i));
}
CHECK(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] input;
delete[] result;
}
SECTION("3D_img") {
const std::vector<DimSize_t> in_dims{batch_size, channels, height,
width, depth};
const std::vector<DimSize_t> out_dims{batch_size, channels};
DimSize_t in_nb_elems =
batch_size * channels * height * width * depth;
number_of_operation +=
in_nb_elems +
channels; // averaging per channel : 1 addition per element in
// the channel + 1 division this for every batch
DimSize_t out_nb_elems = batch_size * channels;
auto input = new float[in_nb_elems];
auto result = new float[out_nb_elems];
input[0] = 0.0061403;
input[1] = -0.9665052;
input[2] = 0.3582928;
input[3] = 0.1072854;
input[4] = 1.2463317;
input[5] = 1.2460036;
input[6] = 0.3534451;
input[7] = 0.9425349;
input[8] = -0.2103887;
input[9] = -0.7959853;
input[10] = 0.1297970;
input[11] = -1.9445597;
input[12] = 0.0609514;
input[13] = -0.2379328;
input[14] = 1.9020044;
input[15] = -1.1762751;
input[16] = 0.3404147;
input[17] = 1.1685153;
input[18] = -0.6526139;
input[19] = 0.3767620;
input[20] = 0.1887376;
input[21] = 0.5154487;
input[22] = 0.6371427;
input[23] = -0.3948864;
input[24] = -1.1571540;
input[25] = 0.2896117;
input[26] = 0.6163548;
input[27] = -0.4370409;
input[28] = 0.6589766;
input[29] = 0.6587803;
input[30] = -1.3702172;
input[31] = -1.6210355;
input[32] = 0.5872851;
input[33] = 0.2860694;
input[34] = 0.0082870;
input[35] = -0.2523253;
input[36] = -1.3247224;
input[37] = 0.1891782;
input[38] = 0.0211001;
input[39] = 0.9404197;
input[40] = -0.5576900;
input[41] = -0.6939272;
input[42] = -0.3252473;
input[43] = 1.2439330;
input[44] = -1.1671864;
input[45] = -0.4091243;
input[46] = 1.2600617;
input[47] = -1.5630058;
input[48] = 1.1346143;
input[49] = -0.0823837;
input[50] = 0.2893163;
input[51] = 0.8357732;
input[52] = -0.2449911;
input[53] = 0.2712233;
input[54] = 0.0936364;
input[55] = -0.8834321;
input[56] = -0.3274170;
input[57] = 0.0783938;
input[58] = -0.3807656;
input[59] = 0.3775077;
input[60] = 0.1119123;
input[61] = 2.3142793;
input[62] = -0.7989057;
input[63] = -0.5643027;
input[64] = -1.1346605;
input[65] = 0.1705271;
input[66] = 0.9946650;
input[67] = 1.2625724;
input[68] = 1.6218156;
input[69] = 1.0774711;
input[70] = 0.5947813;
input[71] = -1.5290873;
input[72] = 2.0437069;
input[73] = -0.1656267;
input[74] = 0.0870704;
input[75] = -0.5276564;
input[76] = -0.1002882;
input[77] = 1.0539219;
input[78] = -0.6230739;
input[79] = -1.5905718;
input[80] = -0.9741858;
input[81] = -0.1869211;
input[82] = 0.5816050;
input[83] = -2.6339815;
input[84] = -1.0764544;
input[85] = 2.5903966;
input[86] = 0.4940658;
input[87] = 0.4671729;
input[88] = 0.6588292;
input[89] = -0.7257792;
input[90] = 1.4280071;
input[91] = -1.2187740;
input[92] = 0.7380729;
input[93] = -1.1599953;
input[94] = -1.4355115;
input[95] = -1.5304037;
input[96] = 0.8474578;
input[97] = 0.0774260;
input[98] = 0.5433396;
input[99] = -0.8438400;
input[100] = -0.1089903;
input[101] = -0.6354192;
input[102] = 0.8772392;
input[103] = 0.2844733;
input[104] = 0.0975270;
input[105] = -0.9785872;
input[106] = -0.4320499;
input[107] = -1.4937501;
input[108] = -2.0644901;
input[109] = 0.0851217;
input[110] = 0.6644159;
input[111] = 0.4168026;
input[112] = 0.0958830;
input[113] = -1.5699565;
input[114] = 0.3739572;
input[115] = -0.1420672;
input[116] = -0.7864021;
input[117] = 0.2443752;
input[118] = -0.9811850;
input[119] = -0.0698569;
input[120] = 0.1463890;
input[121] = 0.2536245;
input[122] = 0.2136150;
input[123] = 0.3113698;
input[124] = 1.8353856;
input[125] = 1.4473228;
input[126] = -0.7373698;
input[127] = 0.2485314;
input[128] = -0.4789796;
input[129] = -0.3396149;
input[130] = 0.6438198;
input[131] = 0.7287521;
input[132] = -1.5119252;
input[133] = -0.1006494;
input[134] = 1.8955028;
input[135] = 1.0871323;
input[136] = 0.3620502;
input[137] = -0.8826663;
input[138] = 1.2220223;
input[139] = -1.2817260;
input[140] = 1.4153577;
input[141] = 0.4148015;
input[142] = 1.3458617;
input[143] = 1.9718349;
result[0] = 0.1333608;
result[1] = -0.1716091;
result[2] = 0.2201060;
result[3] = -0.1585989;
result[4] = -0.2291074;
result[5] = 0.4254351;
// input0
T0->resize(in_dims);
T0->getImpl()->setRawPtr(input, in_nb_elems);
// results
Tres->resize(out_dims);
Tres->getImpl()->setRawPtr(result, out_nb_elems);
op->computeOutputDims();
start = std::chrono::system_clock::now();
REQUIRE_NOTHROW(globAvgPool->forward());
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(
end - start);
CHECK(Tres->nbDims() == op->getOutput(0)->nbDims());
for (DimSize_t i = 0; i < op->getOutput(0)->nbDims(); ++i) {
CHECK(Tres->dims().at(i) == op->getOutput(0)->dims().at(i));
}
CHECK(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] input;
delete[] result;
}
}
std::cout << "GlobalAveragePooling total execution time : "
<< duration.count() << "µs" << std::endl;
std::cout << "Number of operations : " << number_of_operation
<< std::endl;
std::cout << "Operation / µs = " << number_of_operation / duration.count()
<< std::endl;
}
}
}
......
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