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26740f853e10e34a7e8d2b72b950875796350418
pytorch/caffe2/operators/generate_proposals_op_gpu_test.cc
Will Constable 4f34cd6d1e Replace all CHECK_ and DCHECK_ with TORCH_* macros (#82032) 
Avoid exposing defines that conflict with google logging, since this blocks external usage of libtorch in certain cases.

All the 'interesting' changes should be in these two files, and the rest should just be mechanical changes via sed.
c10/util/logging_is_not_google_glog.h
c10/util/logging_is_google_glog.h

Fixes https://github.com/pytorch/pytorch/issues/81415

cc @miladm @malfet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82032
Approved by: https://github.com/soumith, https://github.com/miladm
2022-07-26 01:20:44 +00:00

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#include "caffe2/operators/generate_proposals_op.h"
#include <gtest/gtest.h>
#include "caffe2/core/flags.h"
#include "caffe2/core/macros.h"
#include "caffe2/core/context.h"
#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/generate_proposals_op_util_boxes.h"
namespace caffe2 {
static void AddLinSpacedInput(
const vector<int64_t>& shape,
const float min_val,
const float max_val,
const string& name,
Workspace* ws) {
DeviceOption option;
CPUContext context(option);
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.setLinSpaced(min_val, max_val);
return;
}
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);
}
TEST(GenerateProposalsTest, TestRealDownSampledGPU) {
if (!HasCudaGPU())
return;
Workspace ws;
OperatorDef def;
def.set_name("test");
def.set_type("GenerateProposals");
def.add_input("scores");
def.add_input("bbox_deltas");
def.add_input("im_info");
def.add_input("anchors");
def.add_output("rois");
def.add_output("rois_probs");
def.mutable_device_option()->set_device_type(PROTO_CUDA);
const int img_count = 2;
const int A = 2;
const int H = 4;
const int W = 5;
vector<float> scores{
5.44218998e-03f, 1.19207997e-03f, 1.12379994e-03f, 1.17181998e-03f,
1.20544003e-03f, 6.17993006e-04f, 1.05261997e-05f, 8.91025957e-06f,
9.29536981e-09f, 6.09605013e-05f, 4.72735002e-04f, 1.13482002e-10f,
1.50015003e-05f, 4.45032993e-06f, 3.21612994e-08f, 8.02662980e-04f,
1.40488002e-04f, 3.12508007e-07f, 3.02616991e-06f, 1.97759000e-08f,
2.66913995e-02f, 5.26766013e-03f, 5.05053019e-03f, 5.62100019e-03f,
5.37420018e-03f, 5.26280981e-03f, 2.48894998e-04f, 1.06842002e-04f,
3.92931997e-06f, 1.79388002e-03f, 4.79440019e-03f, 3.41609990e-07f,
5.20430971e-04f, 3.34090000e-05f, 2.19159006e-07f, 2.28786003e-03f,
5.16703985e-05f, 4.04523007e-06f, 1.79227004e-06f, 5.32449000e-08f};
vector<float> bbx{
-1.65040009e-02f, -1.84051003e-02f, -1.85930002e-02f, -2.08263006e-02f,
-1.83814000e-02f, -2.89172009e-02f, -3.89706008e-02f, -7.52277970e-02f,
-1.54091999e-01f, -2.55433004e-02f, -1.77490003e-02f, -1.10340998e-01f,
-4.20190990e-02f, -2.71421000e-02f, 6.89801015e-03f, 5.71171008e-02f,
-1.75665006e-01f, 2.30021998e-02f, 3.08554992e-02f, -1.39333997e-02f,
3.40579003e-01f, 3.91070992e-01f, 3.91624004e-01f, 3.92527014e-01f,
3.91445011e-01f, 3.79328012e-01f, 4.26631987e-01f, 3.64892989e-01f,
2.76894987e-01f, 5.13985991e-01f, 3.79999995e-01f, 1.80457994e-01f,
4.37402993e-01f, 4.18545991e-01f, 2.51549989e-01f, 4.48318988e-01f,
1.68564007e-01f, 4.65440989e-01f, 4.21891987e-01f, 4.45928007e-01f,
3.27155995e-03f, 3.71480011e-03f, 3.60032008e-03f, 4.27092984e-03f,
3.74579988e-03f, 5.95752988e-03f, -3.14473989e-03f, 3.52022005e-03f,
-1.88564006e-02f, 1.65188999e-03f, 1.73791999e-03f, -3.56074013e-02f,
-1.66615995e-04f, 3.14146001e-03f, -1.11830998e-02f, -5.35363983e-03f,
6.49790000e-03f, -9.27671045e-03f, -2.83346009e-02f, -1.61233004e-02f,
-2.15505004e-01f, -2.19910994e-01f, -2.20872998e-01f, -2.12831005e-01f,
-2.19145000e-01f, -2.27687001e-01f, -3.43973994e-01f, -2.75869995e-01f,
-3.19516987e-01f, -2.50418007e-01f, -2.48537004e-01f, -5.08224010e-01f,
-2.28724003e-01f, -2.82402009e-01f, -3.75815988e-01f, -2.86352992e-01f,
-5.28333001e-02f, -4.43836004e-01f, -4.55134988e-01f, -4.34897989e-01f,
-5.65053988e-03f, -9.25739005e-04f, -1.06790999e-03f, -2.37016007e-03f,
-9.71166010e-04f, -8.90910998e-03f, -1.17592998e-02f, -2.08992008e-02f,
-4.94231991e-02f, 6.63906988e-03f, 3.20469006e-03f, -6.44695014e-02f,
-3.11607006e-03f, 2.02738005e-03f, 1.48096997e-02f, 4.39785011e-02f,
-8.28424022e-02f, 3.62076014e-02f, 2.71668993e-02f, 1.38250999e-02f,
6.76669031e-02f, 1.03252999e-01f, 1.03255004e-01f, 9.89722982e-02f,
1.03646003e-01f, 4.79663983e-02f, 1.11014001e-01f, 9.31736007e-02f,
1.15768999e-01f, 1.04014002e-01f, -8.90677981e-03f, 1.13103002e-01f,
1.33085996e-01f, 1.25405997e-01f, 1.50051996e-01f, -1.13038003e-01f,
7.01059997e-02f, 1.79651007e-01f, 1.41055003e-01f, 1.62841007e-01f,
-1.00247003e-02f, -8.17587040e-03f, -8.32176022e-03f, -8.90108012e-03f,
-8.13035015e-03f, -1.77263003e-02f, -3.69572006e-02f, -3.51580009e-02f,
-5.92143014e-02f, -1.80795006e-02f, -5.46086021e-03f, -4.10550982e-02f,
-1.83081999e-02f, -2.15411000e-02f, -1.17953997e-02f, 3.33894007e-02f,
-5.29635996e-02f, -6.97528012e-03f, -3.15250992e-03f, -3.27355005e-02f,
1.29676998e-01f, 1.16080999e-01f, 1.15947001e-01f, 1.21797003e-01f,
1.16089001e-01f, 1.44875005e-01f, 1.15617000e-01f, 1.31586999e-01f,
1.74735002e-02f, 1.21973999e-01f, 1.31596997e-01f, 2.48907991e-02f,
6.18605018e-02f, 1.12855002e-01f, -6.99798986e-02f, 9.58312973e-02f,
1.53593004e-01f, -8.75087008e-02f, -4.92327996e-02f, -3.32239009e-02f};
vector<float> im_info{60, 80, 0.166667f};
vector<float> anchors{-38, -16, 53, 31, -120, -120, 135, 135};
// Doubling everything related to images, to simulate
// num_images = 2
scores.insert(scores.begin(), scores.begin(), scores.end());
bbx.insert(bbx.begin(), bbx.begin(), bbx.end());
im_info.insert(im_info.begin(), im_info.begin(), im_info.end());
ERMatXf rois_gt(18, 5);
rois_gt << 0, 0, 0, 79, 59, 0, 0, 5.0005703f, 51.6324f, 42.6950f, 0,
24.13628387f, 7.51243401f, 79, 45.0663f, 0, 0, 7.50924301f, 67.4779f,
45.0336, 0, 0, 23.09477997f, 50.61448669f, 59, 0, 0, 39.52141571f,
51.44710541f, 59, 0, 23.57396317f, 29.98791885f, 79, 59, 0, 0,
41.90219116f, 79, 59, 0, 0, 23.30098343f, 78.2413f, 58.7287f, 1, 0, 0, 79,
59, 1, 0, 5.0005703f, 51.6324f, 42.6950f, 1, 24.13628387f, 7.51243401f,
79, 45.0663f, 1, 0, 7.50924301f, 67.4779f, 45.0336, 1, 0, 23.09477997f,
50.61448669f, 59, 1, 0, 39.52141571f, 51.44710541f, 59, 1, 23.57396317f,
29.98791885f, 79, 59, 1, 0, 41.90219116f, 79, 59, 1, 0, 23.30098343f,
78.2413f, 58.7287f;
vector<float> rois_probs_gt{2.66913995e-02f,
5.44218998e-03f,
1.20544003e-03f,
1.19207997e-03f,
6.17993006e-04f,
4.72735002e-04f,
6.09605013e-05f,
1.50015003e-05f,
8.91025957e-06f};
// Doubling everything related to images, to simulate
// num_images = 2
rois_probs_gt.insert(
rois_probs_gt.begin(), rois_probs_gt.begin(), rois_probs_gt.end());
AddInput<CUDAContext>(
vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput<CUDAContext>(
vector<int64_t>{img_count, 4 * A, H, W}, bbx, "bbox_deltas", &ws);
AddInput<CUDAContext>(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput<CUDAContext>(vector<int64_t>{A, 4}, anchors, "anchors", &ws);
def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 1.0f / 16.0f));
def.add_arg()->CopyFrom(MakeArgument("pre_nms_topN", 6000));
def.add_arg()->CopyFrom(MakeArgument("post_nms_topN", 300));
def.add_arg()->CopyFrom(MakeArgument("nms_thresh", 0.7f));
def.add_arg()->CopyFrom(MakeArgument("min_size", 16.0f));
def.add_arg()->CopyFrom(MakeArgument("correct_transform_coords", true));
unique_ptr<OperatorBase> op(CreateOperator(def, &ws));
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
// test rois
Blob* rois_blob = ws.GetBlob("rois");
EXPECT_NE(nullptr, rois_blob);
auto& rois_gpu = rois_blob->Get<TensorCUDA>();
Tensor rois{CPU};
rois.CopyFrom(rois_gpu);
EXPECT_EQ(rois.sizes(), (vector<int64_t>{rois_gt.rows(), rois_gt.cols()}));
auto rois_data =
Eigen::Map<const ERMatXf>(rois.data<float>(), rois.dim(0), rois.dim(1));
EXPECT_NEAR((rois_data.matrix() - rois_gt).cwiseAbs().maxCoeff(), 0, 1e-4);
// test rois_probs
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs_gpu = rois_probs_blob->Get<TensorCUDA>();
Tensor rois_probs{CPU};
rois_probs.CopyFrom(rois_probs_gpu);
EXPECT_EQ(
rois_probs.sizes(), (vector<int64_t>{int64_t(rois_probs_gt.size())}));
auto rois_probs_data =
ConstEigenVectorArrayMap<float>(rois_probs.data<float>(), rois.dim(0));
EXPECT_NEAR(
(rois_probs_data.matrix() - utils::AsEArrXt(rois_probs_gt).matrix())
.cwiseAbs()
.maxCoeff(),
0,
1e-4);
}
#if defined(CV_MAJOR_VERSION) && (CV_MAJOR_VERSION >= 3)
TEST(GenerateProposalsTest, TestRealDownSampledRotatedAngle0GPU) {
// Similar to TestRealDownSampledGPU but for rotated boxes with angle info.
if (!HasCudaGPU())
return;
const float angle = 0;
const float delta_angle = 0;
const float clip_angle_thresh = 1.0;
const int box_dim = 5;
Workspace ws;
OperatorDef def;
def.set_name("test");
def.set_type("GenerateProposals");
def.add_input("scores");
def.add_input("bbox_deltas");
def.add_input("im_info");
def.add_input("anchors");
def.add_output("rois");
def.add_output("rois_probs");
def.mutable_device_option()->set_device_type(PROTO_CUDA);
const int img_count = 2;
const int A = 2;
const int H = 4;
const int W = 5;
vector<float> scores{
5.44218998e-03f, 1.19207997e-03f, 1.12379994e-03f, 1.17181998e-03f,
1.20544003e-03f, 6.17993006e-04f, 1.05261997e-05f, 8.91025957e-06f,
9.29536981e-09f, 6.09605013e-05f, 4.72735002e-04f, 1.13482002e-10f,
1.50015003e-05f, 4.45032993e-06f, 3.21612994e-08f, 8.02662980e-04f,
1.40488002e-04f, 3.12508007e-07f, 3.02616991e-06f, 1.97759000e-08f,
2.66913995e-02f, 5.26766013e-03f, 5.05053019e-03f, 5.62100019e-03f,
5.37420018e-03f, 5.26280981e-03f, 2.48894998e-04f, 1.06842002e-04f,
3.92931997e-06f, 1.79388002e-03f, 4.79440019e-03f, 3.41609990e-07f,
5.20430971e-04f, 3.34090000e-05f, 2.19159006e-07f, 2.28786003e-03f,
5.16703985e-05f, 4.04523007e-06f, 1.79227004e-06f, 5.32449000e-08f};
vector<float> bbx{
-1.65040009e-02f, -1.84051003e-02f, -1.85930002e-02f, -2.08263006e-02f,
-1.83814000e-02f, -2.89172009e-02f, -3.89706008e-02f, -7.52277970e-02f,
-1.54091999e-01f, -2.55433004e-02f, -1.77490003e-02f, -1.10340998e-01f,
-4.20190990e-02f, -2.71421000e-02f, 6.89801015e-03f, 5.71171008e-02f,
-1.75665006e-01f, 2.30021998e-02f, 3.08554992e-02f, -1.39333997e-02f,
3.40579003e-01f, 3.91070992e-01f, 3.91624004e-01f, 3.92527014e-01f,
3.91445011e-01f, 3.79328012e-01f, 4.26631987e-01f, 3.64892989e-01f,
2.76894987e-01f, 5.13985991e-01f, 3.79999995e-01f, 1.80457994e-01f,
4.37402993e-01f, 4.18545991e-01f, 2.51549989e-01f, 4.48318988e-01f,
1.68564007e-01f, 4.65440989e-01f, 4.21891987e-01f, 4.45928007e-01f,
3.27155995e-03f, 3.71480011e-03f, 3.60032008e-03f, 4.27092984e-03f,
3.74579988e-03f, 5.95752988e-03f, -3.14473989e-03f, 3.52022005e-03f,
-1.88564006e-02f, 1.65188999e-03f, 1.73791999e-03f, -3.56074013e-02f,
-1.66615995e-04f, 3.14146001e-03f, -1.11830998e-02f, -5.35363983e-03f,
6.49790000e-03f, -9.27671045e-03f, -2.83346009e-02f, -1.61233004e-02f,
-2.15505004e-01f, -2.19910994e-01f, -2.20872998e-01f, -2.12831005e-01f,
-2.19145000e-01f, -2.27687001e-01f, -3.43973994e-01f, -2.75869995e-01f,
-3.19516987e-01f, -2.50418007e-01f, -2.48537004e-01f, -5.08224010e-01f,
-2.28724003e-01f, -2.82402009e-01f, -3.75815988e-01f, -2.86352992e-01f,
-5.28333001e-02f, -4.43836004e-01f, -4.55134988e-01f, -4.34897989e-01f,
-5.65053988e-03f, -9.25739005e-04f, -1.06790999e-03f, -2.37016007e-03f,
-9.71166010e-04f, -8.90910998e-03f, -1.17592998e-02f, -2.08992008e-02f,
-4.94231991e-02f, 6.63906988e-03f, 3.20469006e-03f, -6.44695014e-02f,
-3.11607006e-03f, 2.02738005e-03f, 1.48096997e-02f, 4.39785011e-02f,
-8.28424022e-02f, 3.62076014e-02f, 2.71668993e-02f, 1.38250999e-02f,
6.76669031e-02f, 1.03252999e-01f, 1.03255004e-01f, 9.89722982e-02f,
1.03646003e-01f, 4.79663983e-02f, 1.11014001e-01f, 9.31736007e-02f,
1.15768999e-01f, 1.04014002e-01f, -8.90677981e-03f, 1.13103002e-01f,
1.33085996e-01f, 1.25405997e-01f, 1.50051996e-01f, -1.13038003e-01f,
7.01059997e-02f, 1.79651007e-01f, 1.41055003e-01f, 1.62841007e-01f,
-1.00247003e-02f, -8.17587040e-03f, -8.32176022e-03f, -8.90108012e-03f,
-8.13035015e-03f, -1.77263003e-02f, -3.69572006e-02f, -3.51580009e-02f,
-5.92143014e-02f, -1.80795006e-02f, -5.46086021e-03f, -4.10550982e-02f,
-1.83081999e-02f, -2.15411000e-02f, -1.17953997e-02f, 3.33894007e-02f,
-5.29635996e-02f, -6.97528012e-03f, -3.15250992e-03f, -3.27355005e-02f,
1.29676998e-01f, 1.16080999e-01f, 1.15947001e-01f, 1.21797003e-01f,
1.16089001e-01f, 1.44875005e-01f, 1.15617000e-01f, 1.31586999e-01f,
1.74735002e-02f, 1.21973999e-01f, 1.31596997e-01f, 2.48907991e-02f,
6.18605018e-02f, 1.12855002e-01f, -6.99798986e-02f, 9.58312973e-02f,
1.53593004e-01f, -8.75087008e-02f, -4.92327996e-02f, -3.32239009e-02f};
// Add angle in bbox deltas
int num_boxes = scores.size();
TORCH_CHECK_EQ(bbx.size() / 4, num_boxes);
vector<float> bbx_with_angle(num_boxes * box_dim);
// bbx (deltas) is in shape (A * 4, H, W). Insert angle delta
// at each spatial location for each anchor.
int i = 0, j = 0;
for (int a = 0; a < A; ++a) {
for (int k = 0; k < 4 * H * W; ++k) {
bbx_with_angle[i++] = bbx[j++];
}
for (int k = 0; k < H * W; ++k) {
bbx_with_angle[i++] = delta_angle;
}
}
vector<float> im_info{60, 80, 0.166667f};
// vector<float> anchors{-38, -16, 53, 31, -120, -120, 135, 135};
// Anchors in [x_ctr, y_ctr, w, h, angle] format
vector<float> anchors{7.5, 7.5, 92, 48, angle, 7.5, 7.5, 256, 256, angle};
// Doubling everything related to images, to simulate
// num_images = 2
scores.insert(scores.begin(), scores.begin(), scores.end());
bbx_with_angle.insert(
bbx_with_angle.begin(), bbx_with_angle.begin(), bbx_with_angle.end());
im_info.insert(im_info.begin(), im_info.begin(), im_info.end());
// Results should exactly be the same as TestRealDownSampledGPU since
// angle = 0 for all boxes and clip_angle_thresh > 0 (which means
// all horizontal boxes will be clipped to maintain backward compatibility).
ERMatXf rois_gt_xyxy(18, 5);
rois_gt_xyxy << 0, 0, 0, 79, 59, 0, 0, 5.0005703f, 51.6324f, 42.6950f, 0,
24.13628387f, 7.51243401f, 79, 45.0663f, 0, 0, 7.50924301f, 67.4779f,
45.0336, 0, 0, 23.09477997f, 50.61448669f, 59, 0, 0, 39.52141571f,
51.44710541f, 59, 0, 23.57396317f, 29.98791885f, 79, 59, 0, 0,
41.90219116f, 79, 59, 0, 0, 23.30098343f, 78.2413f, 58.7287f, 1, 0, 0, 79,
59, 1, 0, 5.0005703f, 51.6324f, 42.6950f, 1, 24.13628387f, 7.51243401f,
79, 45.0663f, 1, 0, 7.50924301f, 67.4779f, 45.0336, 1, 0, 23.09477997f,
50.61448669f, 59, 1, 0, 39.52141571f, 51.44710541f, 59, 1, 23.57396317f,
29.98791885f, 79, 59, 1, 0, 41.90219116f, 79, 59, 1, 0, 23.30098343f,
78.2413f, 58.7287f;
ERMatXf rois_gt(rois_gt_xyxy.rows(), 6);
// Batch ID
rois_gt.block(0, 0, rois_gt.rows(), 1) =
rois_gt_xyxy.block(0, 0, rois_gt.rows(), 0);
// rois_gt in [x_ctr, y_ctr, w, h] format
rois_gt.block(0, 1, rois_gt.rows(), 4) = utils::bbox_xyxy_to_ctrwh(
rois_gt_xyxy.block(0, 1, rois_gt.rows(), 4).array(),
true /* legacy_plus_one */);
// Angle
rois_gt.block(0, 5, rois_gt.rows(), 1) =
ERMatXf::Constant(rois_gt.rows(), 1, angle);
vector<float> rois_probs_gt{2.66913995e-02f,
5.44218998e-03f,
1.20544003e-03f,
1.19207997e-03f,
6.17993006e-04f,
4.72735002e-04f,
6.09605013e-05f,
1.50015003e-05f,
8.91025957e-06f};
// Doubling everything related to images, to simulate
// num_images = 2
rois_probs_gt.insert(
rois_probs_gt.begin(), rois_probs_gt.begin(), rois_probs_gt.end());
AddInput<CUDAContext>(
vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput<CUDAContext>(
vector<int64_t>{img_count, box_dim * A, H, W},
bbx_with_angle,
"bbox_deltas",
&ws);
AddInput<CUDAContext>(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput<CUDAContext>(vector<int64_t>{A, box_dim}, anchors, "anchors", &ws);
def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 1.0f / 16.0f));
def.add_arg()->CopyFrom(MakeArgument("pre_nms_topN", 6000));
def.add_arg()->CopyFrom(MakeArgument("post_nms_topN", 300));
def.add_arg()->CopyFrom(MakeArgument("nms_thresh", 0.7f));
def.add_arg()->CopyFrom(MakeArgument("min_size", 16.0f));
def.add_arg()->CopyFrom(MakeArgument("correct_transform_coords", true));
def.add_arg()->CopyFrom(MakeArgument("clip_angle_thresh", clip_angle_thresh));
unique_ptr<OperatorBase> op(CreateOperator(def, &ws));
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
// test rois
Blob* rois_blob = ws.GetBlob("rois");
EXPECT_NE(nullptr, rois_blob);
auto& rois_gpu = rois_blob->Get<TensorCUDA>();
Tensor rois{CPU};
rois.CopyFrom(rois_gpu);
EXPECT_EQ(rois.sizes(), (vector<int64_t>{rois_gt.rows(), rois_gt.cols()}));
auto rois_data =
Eigen::Map<const ERMatXf>(rois.data<float>(), rois.dim(0), rois.dim(1));
EXPECT_NEAR((rois_data.matrix() - rois_gt).cwiseAbs().maxCoeff(), 0, 1e-4);
// test rois_probs
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs_gpu = rois_probs_blob->Get<TensorCUDA>();
Tensor rois_probs{CPU};
rois_probs.CopyFrom(rois_probs_gpu);
EXPECT_EQ(
rois_probs.sizes(), (vector<int64_t>{int64_t(rois_probs_gt.size())}));
auto rois_probs_data =
ConstEigenVectorArrayMap<float>(rois_probs.data<float>(), rois.dim(0));
EXPECT_NEAR(
(rois_probs_data.matrix() - utils::AsEArrXt(rois_probs_gt).matrix())
.cwiseAbs()
.maxCoeff(),
0,
1e-4);
}
TEST(GenerateProposalsTest, TestRealDownSampledRotatedGPU) {
// Similar to TestRealDownSampledGPU but for rotated boxes with angle info.
if (!HasCudaGPU())
return;
const float angle = 45.0;
const float delta_angle = 0.174533; // 0.174533 radians -> 10 degrees
const float expected_angle = 55.0;
const float clip_angle_thresh = 1.0;
const int box_dim = 5;
Workspace ws;
OperatorDef def;
def.set_name("test");
def.set_type("GenerateProposals");
def.add_input("scores");
def.add_input("bbox_deltas");
def.add_input("im_info");
def.add_input("anchors");
def.add_output("rois");
def.add_output("rois_probs");
def.mutable_device_option()->set_device_type(PROTO_CUDA);
const int img_count = 2;
const int A = 2;
const int H = 4;
const int W = 5;
vector<float> scores{
5.44218998e-03f, 1.19207997e-03f, 1.12379994e-03f, 1.17181998e-03f,
1.20544003e-03f, 6.17993006e-04f, 1.05261997e-05f, 8.91025957e-06f,
9.29536981e-09f, 6.09605013e-05f, 4.72735002e-04f, 1.13482002e-10f,
1.50015003e-05f, 4.45032993e-06f, 3.21612994e-08f, 8.02662980e-04f,
1.40488002e-04f, 3.12508007e-07f, 3.02616991e-06f, 1.97759000e-08f,
2.66913995e-02f, 5.26766013e-03f, 5.05053019e-03f, 5.62100019e-03f,
5.37420018e-03f, 5.26280981e-03f, 2.48894998e-04f, 1.06842002e-04f,
3.92931997e-06f, 1.79388002e-03f, 4.79440019e-03f, 3.41609990e-07f,
5.20430971e-04f, 3.34090000e-05f, 2.19159006e-07f, 2.28786003e-03f,
5.16703985e-05f, 4.04523007e-06f, 1.79227004e-06f, 5.32449000e-08f};
vector<float> bbx{
-1.65040009e-02f, -1.84051003e-02f, -1.85930002e-02f, -2.08263006e-02f,
-1.83814000e-02f, -2.89172009e-02f, -3.89706008e-02f, -7.52277970e-02f,
-1.54091999e-01f, -2.55433004e-02f, -1.77490003e-02f, -1.10340998e-01f,
-4.20190990e-02f, -2.71421000e-02f, 6.89801015e-03f, 5.71171008e-02f,
-1.75665006e-01f, 2.30021998e-02f, 3.08554992e-02f, -1.39333997e-02f,
3.40579003e-01f, 3.91070992e-01f, 3.91624004e-01f, 3.92527014e-01f,
3.91445011e-01f, 3.79328012e-01f, 4.26631987e-01f, 3.64892989e-01f,
2.76894987e-01f, 5.13985991e-01f, 3.79999995e-01f, 1.80457994e-01f,
4.37402993e-01f, 4.18545991e-01f, 2.51549989e-01f, 4.48318988e-01f,
1.68564007e-01f, 4.65440989e-01f, 4.21891987e-01f, 4.45928007e-01f,
3.27155995e-03f, 3.71480011e-03f, 3.60032008e-03f, 4.27092984e-03f,
3.74579988e-03f, 5.95752988e-03f, -3.14473989e-03f, 3.52022005e-03f,
-1.88564006e-02f, 1.65188999e-03f, 1.73791999e-03f, -3.56074013e-02f,
-1.66615995e-04f, 3.14146001e-03f, -1.11830998e-02f, -5.35363983e-03f,
6.49790000e-03f, -9.27671045e-03f, -2.83346009e-02f, -1.61233004e-02f,
-2.15505004e-01f, -2.19910994e-01f, -2.20872998e-01f, -2.12831005e-01f,
-2.19145000e-01f, -2.27687001e-01f, -3.43973994e-01f, -2.75869995e-01f,
-3.19516987e-01f, -2.50418007e-01f, -2.48537004e-01f, -5.08224010e-01f,
-2.28724003e-01f, -2.82402009e-01f, -3.75815988e-01f, -2.86352992e-01f,
-5.28333001e-02f, -4.43836004e-01f, -4.55134988e-01f, -4.34897989e-01f,
-5.65053988e-03f, -9.25739005e-04f, -1.06790999e-03f, -2.37016007e-03f,
-9.71166010e-04f, -8.90910998e-03f, -1.17592998e-02f, -2.08992008e-02f,
-4.94231991e-02f, 6.63906988e-03f, 3.20469006e-03f, -6.44695014e-02f,
-3.11607006e-03f, 2.02738005e-03f, 1.48096997e-02f, 4.39785011e-02f,
-8.28424022e-02f, 3.62076014e-02f, 2.71668993e-02f, 1.38250999e-02f,
6.76669031e-02f, 1.03252999e-01f, 1.03255004e-01f, 9.89722982e-02f,
1.03646003e-01f, 4.79663983e-02f, 1.11014001e-01f, 9.31736007e-02f,
1.15768999e-01f, 1.04014002e-01f, -8.90677981e-03f, 1.13103002e-01f,
1.33085996e-01f, 1.25405997e-01f, 1.50051996e-01f, -1.13038003e-01f,
7.01059997e-02f, 1.79651007e-01f, 1.41055003e-01f, 1.62841007e-01f,
-1.00247003e-02f, -8.17587040e-03f, -8.32176022e-03f, -8.90108012e-03f,
-8.13035015e-03f, -1.77263003e-02f, -3.69572006e-02f, -3.51580009e-02f,
-5.92143014e-02f, -1.80795006e-02f, -5.46086021e-03f, -4.10550982e-02f,
-1.83081999e-02f, -2.15411000e-02f, -1.17953997e-02f, 3.33894007e-02f,
-5.29635996e-02f, -6.97528012e-03f, -3.15250992e-03f, -3.27355005e-02f,
1.29676998e-01f, 1.16080999e-01f, 1.15947001e-01f, 1.21797003e-01f,
1.16089001e-01f, 1.44875005e-01f, 1.15617000e-01f, 1.31586999e-01f,
1.74735002e-02f, 1.21973999e-01f, 1.31596997e-01f, 2.48907991e-02f,
6.18605018e-02f, 1.12855002e-01f, -6.99798986e-02f, 9.58312973e-02f,
1.53593004e-01f, -8.75087008e-02f, -4.92327996e-02f, -3.32239009e-02f};
// Add angle in bbox deltas
int num_boxes = scores.size();
TORCH_CHECK_EQ(bbx.size() / 4, num_boxes);
vector<float> bbx_with_angle(num_boxes * box_dim);
// bbx (deltas) is in shape (A * 4, H, W). Insert angle delta
// at each spatial location for each anchor.
int i = 0, j = 0;
for (int a = 0; a < A; ++a) {
for (int k = 0; k < 4 * H * W; ++k) {
bbx_with_angle[i++] = bbx[j++];
}
for (int k = 0; k < H * W; ++k) {
bbx_with_angle[i++] = delta_angle;
}
}
vector<float> im_info{60, 80, 0.166667f};
// vector<float> anchors{-38, -16, 53, 31, -120, -120, 135, 135};
vector<float> anchors{8, 8, 92, 48, angle, 8, 8, 256, 256, angle};
// Doubling everything related to images, to simulate
// num_images = 2
scores.insert(scores.begin(), scores.begin(), scores.end());
bbx_with_angle.insert(
bbx_with_angle.begin(), bbx_with_angle.begin(), bbx_with_angle.end());
im_info.insert(im_info.begin(), im_info.begin(), im_info.end());
ERMatXf rois_gt(26, 6);
rois_gt <<
0, 6.55346, 25.3227, 253.447, 291.446, expected_angle,
0, 55.3932, 33.3369, 253.731, 289.158, expected_angle,
0, 6.48163, 24.3478, 92.3015, 38.6944, expected_angle,
0, 70.3089, 26.7894, 92.3453, 38.5539, expected_angle,
0, 22.3067, 26.7714, 92.3424, 38.5243, expected_angle,
0, 54.084, 26.8413, 92.3938, 38.798, expected_angle,
0, 38.2894, 26.798, 92.3318, 38.4873, expected_angle,
0, 5.33962, 42.2077, 92.5497, 38.2259, expected_angle,
0, 6.36709, 58.24, 92.16, 37.4372, expected_angle,
0, 69.65, 48.6713, 92.1521, 37.3668, expected_angle,
0, 20.4147, 44.4783, 91.7111, 34.0295, expected_angle,
0, 33.079, 41.5149, 92.3244, 36.4278, expected_angle,
0, 41.8235, 37.291, 90.2815, 34.872, expected_angle,
1, 6.55346, 25.3227, 253.447, 291.446, expected_angle,
1, 55.3932, 33.3369, 253.731, 289.158, expected_angle,
1, 6.48163, 24.3478, 92.3015, 38.6944, expected_angle,
1, 70.3089, 26.7894, 92.3453, 38.5539, expected_angle,
1, 22.3067, 26.7714, 92.3424, 38.5243, expected_angle,
1, 54.084, 26.8413, 92.3938, 38.798, expected_angle,
1, 38.2894, 26.798, 92.3318, 38.4873, expected_angle,
1, 5.33962, 42.2077, 92.5497, 38.2259, expected_angle,
1, 6.36709, 58.24, 92.16, 37.4372, expected_angle,
1, 69.65, 48.6713, 92.1521, 37.3668, expected_angle,
1, 20.4147, 44.4783, 91.7111, 34.0295, expected_angle,
1, 33.079, 41.5149, 92.3244, 36.4278, expected_angle,
1, 41.8235, 37.291, 90.2815, 34.872, expected_angle;
vector<float> rois_probs_gt{2.66913995e-02f,
5.621e-03f,
5.44218998e-03f,
1.20544003e-03f,
1.19207997e-03f,
1.17182e-03f,
1.1238e-03f,
6.17993006e-04f,
4.72735002e-04f,
6.09605013e-05f,
1.50015003e-05f,
8.91025957e-06f,
9.29537e-09f};
// Doubling everything related to images, to simulate
// num_images = 2
rois_probs_gt.insert(
rois_probs_gt.begin(), rois_probs_gt.begin(), rois_probs_gt.end());
AddInput<CUDAContext>(
vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput<CUDAContext>(
vector<int64_t>{img_count, box_dim * A, H, W},
bbx_with_angle,
"bbox_deltas",
&ws);
AddInput<CUDAContext>(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput<CUDAContext>(vector<int64_t>{A, box_dim}, anchors, "anchors", &ws);
def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 1.0f / 16.0f));
def.add_arg()->CopyFrom(MakeArgument("pre_nms_topN", 6000));
def.add_arg()->CopyFrom(MakeArgument("post_nms_topN", 300));
def.add_arg()->CopyFrom(MakeArgument("nms_thresh", 0.7f));
def.add_arg()->CopyFrom(MakeArgument("min_size", 16.0f));
def.add_arg()->CopyFrom(MakeArgument("correct_transform_coords", true));
def.add_arg()->CopyFrom(MakeArgument("clip_angle_thresh", clip_angle_thresh));
unique_ptr<OperatorBase> op(CreateOperator(def, &ws));
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
// test rois
Blob* rois_blob = ws.GetBlob("rois");
EXPECT_NE(nullptr, rois_blob);
auto& rois_gpu = rois_blob->Get<TensorCUDA>();
Tensor rois{CPU};
rois.CopyFrom(rois_gpu);
EXPECT_EQ(rois.sizes(), (vector<int64_t>{26, 6}));
auto rois_data =
Eigen::Map<const ERMatXf>(rois.data<float>(), rois.size(0), rois.size(1));
EXPECT_NEAR((rois_data.matrix() - rois_gt).cwiseAbs().maxCoeff(), 0, 1e-3);
// test rois_probs
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs_gpu = rois_probs_blob->Get<TensorCUDA>();
Tensor rois_probs{CPU};
rois_probs.CopyFrom(rois_probs_gpu);
EXPECT_EQ(
rois_probs.sizes(), (vector<int64_t>{int64_t(rois_probs_gt.size())}));
auto rois_probs_data =
ConstEigenVectorArrayMap<float>(rois_probs.data<float>(), rois.size(0));
EXPECT_NEAR(
(rois_probs_data.matrix() - utils::AsEArrXt(rois_probs_gt).matrix())
.cwiseAbs()
.maxCoeff(),
0,
1e-4);
}
#endif // CV_MAJOR_VERSION >= 3
} // namespace caffe2
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