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#include "caffe2/operators/roi_align_op.h"
#include "caffe2/utils/eigen_utils.h"
#include <c10/test/util/Macros.h>
#include "caffe2/core/context_gpu.h"
#include "caffe2/core/flags.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
#include "gtest/gtest.h"
namespace caffe2 {
namespace {
template <class Context>
void AddConstInput(
const vector<int64_t>& shape,
const float value,
const string& name,
Context* context,
Workspace* ws) {
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, Context::GetDeviceType());
tensor->Resize(shape);
math::Set<float, Context>(
tensor->size(), value, tensor->template mutable_data<float>(), context);
return;
}
template <class Context>
void AddInput(
const vector<int64_t>& shape,
const vector<float>& values,
const string& name,
Workspace* ws);
template <>
void AddInput<CPUContext>(
const vector<int64_t>& shape,
const vector<float>& values,
const string& name,
Workspace* ws) {
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, CPU);
tensor->Resize(shape);
EigenVectorMap<float> tensor_vec(
tensor->template mutable_data<float>(), tensor->numel());
tensor_vec.array() = utils::AsEArrXt(values);
}
template <>
void AddInput<CUDAContext>(
const vector<int64_t>& shape,
const vector<float>& values,
const string& name,
Workspace* ws) {
Tensor tmp(shape, CPU);
EigenVectorMap<float> tmp_vec(tmp.mutable_data<float>(), tmp.numel());
tmp_vec.array() = utils::AsEArrXt(values);
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, CUDA);
tensor->CopyFrom(tmp);
}
template <class Context>
DeviceType GetDeviceType() {
return CPU;
}
template <>
DeviceType GetDeviceType<CUDAContext>() {
return CUDA;
}
int randInt(int a, int b) {
static std::random_device rd;
static std::mt19937 gen(rd());
return std::uniform_int_distribution<int>(a, b)(gen);
}
struct TestParams {
int N;
int C;
int H;
int W;
int n_rois;
vector<float> rois_array;
};
template <class Context>
void CreateAndRun(
TensorCPU* outResult,
const string& order,
const TestParams& test_params,
bool random_test) {
Workspace ws;
Context context;
if (random_test) {
const int N = test_params.N;
const int C = test_params.C;
const int H = test_params.H;
const int W = test_params.W;
vector<float> features(N * C * H * W);
std::iota(features.begin(), features.end(), 0);
// utils::AsEArrXt(features) /= features.size();
AddInput<Context>(vector<int64_t>{N, C, H, W}, features, "X", &ws);
const int n_rois = test_params.n_rois;
const vector<float>& rois = test_params.rois_array;
AddInput<Context>(vector<int64_t>{n_rois, 5}, rois, "R", &ws);
} else {
const int N = 2;
const int C = 3;
const int H = 100;
const int W = 110;
vector<float> features(N * C * H * W);
std::iota(features.begin(), features.end(), 0);
// utils::AsEArrXt(features) /= features.size();
AddInput<Context>(vector<int64_t>{N, C, H, W}, features, "X", &ws);
vector<float> rois{0, 0, 0, 79, 59,
0, 0, 5.0005703f, 52.63237f, 43.69501495f,
0, 24.13628387f, 7.51243401f, 79, 46.06628418f,
0, 0, 7.50924301f, 68.47792816f, 46.03357315f,
0, 0, 23.09477997f, 51.61448669f, 59,
0, 0, 39.52141571f, 52.44710541f, 59,
0, 23.57396317f, 29.98791885f, 79, 59,
0, 0, 41.90219116f, 79, 59,
0, 0, 23.30098343f, 79, 59};
AddInput<Context>(vector<int64_t>{9, 5}, rois, "R", &ws);
}
std::vector<unique_ptr<OperatorBase>> ops;
EXPECT_TRUE(order == "NCHW" || order == "NHWC");
if (order == "NCHW") {
OperatorDef def;
def.set_name("test");
def.set_type("RoIAlign");
def.add_input("X");
def.add_input("R");
def.add_output("Y");
def.mutable_device_option()->set_device_type(
TypeToProto(GetDeviceType<Context>()));
def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 1.0f / 16.0f));
def.add_arg()->CopyFrom(MakeArgument("pooled_h", 6));
def.add_arg()->CopyFrom(MakeArgument("pooled_w", 8));
def.add_arg()->CopyFrom(MakeArgument("sampling_ratio", 2));
ops.push_back(CreateOperator(def, &ws));
} else if (order == "NHWC") {
OperatorDef def_roialign;
def_roialign.set_name("test");
def_roialign.set_type("RoIAlign");
def_roialign.add_input("X_NHWC");
def_roialign.add_input("R");
def_roialign.add_output("Y_NHWC");
def_roialign.mutable_device_option()->set_device_type(
TypeToProto(GetDeviceType<Context>()));
def_roialign.add_arg()->CopyFrom(
MakeArgument("spatial_scale", 1.0f / 16.0f));
def_roialign.add_arg()->CopyFrom(MakeArgument("pooled_h", 6));
def_roialign.add_arg()->CopyFrom(MakeArgument("pooled_w", 8));
def_roialign.add_arg()->CopyFrom(MakeArgument("sampling_ratio", 2));
def_roialign.add_arg()->CopyFrom(MakeArgument<string>("order", "NHWC"));
OperatorDef def_x;
def_x.set_name("test_x");
def_x.set_type("NCHW2NHWC");
def_x.add_input("X");
def_x.add_output("X_NHWC");
def_x.mutable_device_option()->set_device_type(
TypeToProto(GetDeviceType<Context>()));
OperatorDef def_y;
def_y.set_name("test_y");
def_y.set_type("NHWC2NCHW");
def_y.add_input("Y_NHWC");
def_y.add_output("Y");
def_y.mutable_device_option()->set_device_type(
TypeToProto(GetDeviceType<Context>()));
ops.push_back(CreateOperator(def_x, &ws));
ops.push_back(CreateOperator(def_roialign, &ws));
ops.push_back(CreateOperator(def_y, &ws));
}
for (auto const& op : ops) {
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
}
Blob* Y_blob = ws.GetBlob("Y");
EXPECT_NE(nullptr, Y_blob);
auto& Y = Y_blob->Get<Tensor>();
outResult->CopyFrom(Y);
}
} // namespace
TEST(RoiAlignTest, CheckCPUGPUEqual) {
if (!caffe2::HasCudaGPU())
return;
Tensor y_cpu(CPU);
Tensor y_gpu(CPU);
Tensor y_cpu_nhwc(CPU);
// tests using FAIR example
{
TestParams test_params;
CreateAndRun<CPUContext>(&y_cpu, "NCHW", test_params, false);
CreateAndRun<CUDAContext>(&y_gpu, "NCHW", test_params, false);
CreateAndRun<CPUContext>(&y_cpu_nhwc, "NHWC", test_params, false);
EXPECT_EQ(y_cpu.sizes(), y_gpu.sizes());
EXPECT_EQ(y_cpu.sizes(), y_cpu_nhwc.sizes());
ConstEigenVectorMap<float> y_cpu_vec(y_cpu.data<float>(), y_cpu.numel());
ConstEigenVectorMap<float> y_gpu_vec(y_gpu.data<float>(), y_gpu.numel());
ConstEigenVectorMap<float> y_cpu_nhwc_vec(
y_cpu_nhwc.data<float>(), y_cpu_nhwc.numel());
int max_diff_idx = -1;
(y_cpu_vec - y_gpu_vec).cwiseAbs().maxCoeff(&max_diff_idx);
EXPECT_FLOAT_EQ(y_cpu_vec[max_diff_idx], y_gpu_vec[max_diff_idx]);
max_diff_idx = -1;
(y_cpu_vec - y_cpu_nhwc_vec).cwiseAbs().maxCoeff(&max_diff_idx);
EXPECT_FLOAT_EQ(y_cpu_vec[max_diff_idx], y_cpu_nhwc_vec[max_diff_idx]);
}
// random tests
const int random_test_numbers = 100;
for (int i = 0; i < random_test_numbers; i++) {
const int N = randInt(1, 5);
const int C = randInt(1, 5);
const int H = randInt(1, 50);
const int W = randInt(1, 50);
const int n_rois = randInt(1, 30);
vector<float> rois_array;
for (int n = 0; n < n_rois; n++) {
rois_array.push_back(randInt(0, N - 1));
int w1 = randInt(-20, W + 20);
int w2 = randInt(-20, W + 20);
int h1 = randInt(-20, H + 20);
int h2 = randInt(-20, H + 20);
rois_array.push_back(std::min(w1, w2));
rois_array.push_back(std::max(h1, h2));
rois_array.push_back(std::min(w1, w2));
rois_array.push_back(std::max(h1, h2));
}
TestParams test_params{N, C, H, W, n_rois, rois_array};
CreateAndRun<CPUContext>(&y_cpu, "NCHW", test_params, true);
CreateAndRun<CUDAContext>(&y_gpu, "NCHW", test_params, true);
CreateAndRun<CPUContext>(&y_cpu_nhwc, "NHWC", test_params, true);
EXPECT_EQ(y_cpu.sizes(), y_gpu.sizes());
EXPECT_EQ(y_cpu.sizes(), y_cpu_nhwc.sizes());
ConstEigenVectorMap<float> y_cpu_vec(y_cpu.data<float>(), y_cpu.numel());
ConstEigenVectorMap<float> y_gpu_vec(y_gpu.data<float>(), y_gpu.numel());
ConstEigenVectorMap<float> y_cpu_nhwc_vec(
y_cpu_nhwc.data<float>(), y_cpu_nhwc.numel());
int max_diff_idx = -1;
(y_cpu_vec - y_gpu_vec).cwiseAbs().maxCoeff(&max_diff_idx);
EXPECT_NEAR(y_cpu_vec[max_diff_idx], y_gpu_vec[max_diff_idx], 1e-1);
max_diff_idx = -1;
(y_cpu_vec - y_cpu_nhwc_vec).cwiseAbs().maxCoeff(&max_diff_idx);
EXPECT_FLOAT_EQ(y_cpu_vec[max_diff_idx], y_cpu_nhwc_vec[max_diff_idx]);
}
}
} // namespace caffe2
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