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generate_proposals_op_test.cc
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generate_proposals_op_test.cc
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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/operators/generate_proposals_op_util_boxes.h"
namespace caffe2 {
static void AddConstInput(
const vector<int64_t>& shape,
const float value,
const string& name,
Workspace* ws) {
DeviceOption option;
CPUContext context(option);
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, CPU);
tensor->Resize(shape);
math::Set<float, CPUContext>(
tensor->numel(), value, tensor->template mutable_data<float>(), &context);
return;
}
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;
}
static void AddInput(
const vector<int64_t>& shape,
const vector<float>& values,
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.array() = utils::AsEArrXt(values);
return;
}
TEST(GenerateProposalsTest, TestComputeAllAnchors) {
ERMatXf anchors(3, 4);
anchors << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
int height = 4;
int width = 3;
float feat_stride = 16;
ERMatXf all_anchors_gt(36, 4);
all_anchors_gt << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103,
-22, -16, 69, 31, -68, -40, 115, 55, -160, -88, 207, 103, -6, -16, 85, 31,
-52, -40, 131, 55, -144, -88, 223, 103, -38, 0, 53, 47, -84, -24, 99, 71,
-176, -72, 191, 119, -22, 0, 69, 47, -68, -24, 115, 71, -160, -72, 207,
119, -6, 0, 85, 47, -52, -24, 131, 71, -144, -72, 223, 119, -38, 16, 53,
63, -84, -8, 99, 87, -176, -56, 191, 135, -22, 16, 69, 63, -68, -8, 115,
87, -160, -56, 207, 135, -6, 16, 85, 63, -52, -8, 131, 87, -144, -56, 223,
135, -38, 32, 53, 79, -84, 8, 99, 103, -176, -40, 191, 151, -22, 32, 69,
79, -68, 8, 115, 103, -160, -40, 207, 151, -6, 32, 85, 79, -52, 8, 131,
103, -144, -40, 223, 151;
Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
Eigen::Map<ERMatXf>(
anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
anchors;
auto result =
utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
Eigen::Map<const ERMatXf> all_anchors_result(
result.data(), height * width * anchors.rows(), 4);
EXPECT_EQ((all_anchors_result - all_anchors_gt).norm(), 0);
}
TEST(GenerateProposalsTest, TestComputeSortedAnchors) {
ERMatXf anchors(3, 4);
anchors << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
int height = 4;
int width = 3;
int A = anchors.rows();
float feat_stride = 16;
int total = height * width * A;
// Generate all anchors for ground truth
Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
Eigen::Map<ERMatXf>(
anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
anchors;
auto all_anchors =
utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
Eigen::Map<const ERMatXf> all_anchors_result(
all_anchors.data(), height * width * A, 4);
Eigen::Map<const ERArrXXf> anchors_map(
anchors.data(), anchors.rows(), anchors.cols());
// Test with random subsets and ordering of indices
vector<int> indices(total);
std::iota(indices.begin(), indices.end(), 0);
std::random_device rd;
std::mt19937 gen(rd());
std::shuffle(indices.begin(), indices.end(), gen);
for (int count = 0; count <= total; ++count) {
vector<int> order(indices.begin(), indices.begin() + count);
auto result = utils::ComputeSortedAnchors(
anchors_map, height, width, feat_stride, order);
// Compare the result of ComputeSortedAnchors with first generating all
// anchors via ComputeAllAnchors and then applying ordering and filtering.
// Need to convert order from (A, H, W) to (H, W, A) format before for this.
const auto& order_AHW = utils::AsEArrXt(order);
const auto& order_AH = order_AHW / width;
const auto& order_W = order_AHW - order_AH * width;
const auto& order_A = order_AH / height;
const auto& order_H = order_AH - order_A * height;
const auto& order_HWA = (order_H * width + order_W) * A + order_A;
ERArrXXf gt;
utils::GetSubArrayRows(all_anchors_result.array(), order_HWA, >);
EXPECT_EQ((result.matrix() - gt.matrix()).norm(), 0);
}
}
TEST(GenerateProposalsTest, TestComputeAllAnchorsRotated) {
// Similar to TestComputeAllAnchors but for rotated boxes with angle info.
ERMatXf anchors_xyxy(3, 4);
anchors_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
// Convert to RRPN format and add angles
ERMatXf anchors(3, 5);
anchors.block(0, 0, 3, 4) = utils::bbox_xyxy_to_ctrwh(
anchors_xyxy.array(), true /* legacy_plus_one */);
std::vector<float> angles{0.0, 45.0, -120.0};
for (int i = 0; i < anchors.rows(); ++i) {
anchors(i, 4) = angles[i % angles.size()];
}
int height = 4;
int width = 3;
float feat_stride = 16;
ERMatXf all_anchors_gt_xyxy(36, 4);
all_anchors_gt_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191,
103, -22, -16, 69, 31, -68, -40, 115, 55, -160, -88, 207, 103, -6, -16,
85, 31, -52, -40, 131, 55, -144, -88, 223, 103, -38, 0, 53, 47, -84, -24,
99, 71, -176, -72, 191, 119, -22, 0, 69, 47, -68, -24, 115, 71, -160, -72,
207, 119, -6, 0, 85, 47, -52, -24, 131, 71, -144, -72, 223, 119, -38, 16,
53, 63, -84, -8, 99, 87, -176, -56, 191, 135, -22, 16, 69, 63, -68, -8,
115, 87, -160, -56, 207, 135, -6, 16, 85, 63, -52, -8, 131, 87, -144, -56,
223, 135, -38, 32, 53, 79, -84, 8, 99, 103, -176, -40, 191, 151, -22, 32,
69, 79, -68, 8, 115, 103, -160, -40, 207, 151, -6, 32, 85, 79, -52, 8,
131, 103, -144, -40, 223, 151;
// Convert gt to RRPN format and add angles
ERMatXf all_anchors_gt(36, 5);
all_anchors_gt.block(0, 0, 36, 4) = utils::bbox_xyxy_to_ctrwh(
all_anchors_gt_xyxy.array(), true /* legacy_plus_one */);
for (int i = 0; i < all_anchors_gt.rows(); ++i) {
all_anchors_gt(i, 4) = angles[i % angles.size()];
}
Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
Eigen::Map<ERMatXf>(
anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
anchors;
auto result =
utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
Eigen::Map<const ERMatXf> all_anchors_result(
result.data(), height * width * anchors.rows(), 5);
EXPECT_EQ((all_anchors_result - all_anchors_gt).norm(), 0);
}
TEST(GenerateProposalsTest, TestComputeSortedAnchorsRotated) {
// Similar to TestComputeSortedAnchors but for rotated boxes with angle info.
ERMatXf anchors_xyxy(3, 4);
anchors_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
// Convert to RRPN format and add angles
ERMatXf anchors(3, 5);
anchors.block(0, 0, 3, 4) = utils::bbox_xyxy_to_ctrwh(
anchors_xyxy.array(), true /* legacy_plus_one */);
std::vector<float> angles{0.0, 45.0, -120.0};
for (int i = 0; i < anchors.rows(); ++i) {
anchors(i, 4) = angles[i % angles.size()];
}
int height = 4;
int width = 3;
int A = anchors.rows();
float feat_stride = 16;
int total = height * width * A;
// Generate all anchors for ground truth
Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
Eigen::Map<ERMatXf>(
anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
anchors;
auto all_anchors =
utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
Eigen::Map<const ERMatXf> all_anchors_result(
all_anchors.data(), height * width * A, 5);
Eigen::Map<const ERArrXXf> anchors_map(
anchors.data(), anchors.rows(), anchors.cols());
// Test with random subsets and ordering of indices
vector<int> indices(total);
std::iota(indices.begin(), indices.end(), 0);
std::random_device rd;
std::mt19937 gen(rd());
std::shuffle(indices.begin(), indices.end(), gen);
for (int count = 0; count <= total; ++count) {
vector<int> order(indices.begin(), indices.begin() + count);
auto result = utils::ComputeSortedAnchors(
anchors_map, height, width, feat_stride, order);
// Compare the result of ComputeSortedAnchors with first generating all
// anchors via ComputeAllAnchors and then applying ordering and filtering.
// Need to convert order from (A, H, W) to (H, W, A) format before for this.
const auto& order_AHW = utils::AsEArrXt(order);
const auto& order_AH = order_AHW / width;
const auto& order_W = order_AHW - order_AH * width;
const auto& order_A = order_AH / height;
const auto& order_H = order_AH - order_A * height;
const auto& order_HWA = (order_H * width + order_W) * A + order_A;
ERArrXXf gt;
utils::GetSubArrayRows(all_anchors_result.array(), order_HWA, >);
EXPECT_EQ((result.matrix() - gt.matrix()).norm(), 0);
}
}
TEST(GenerateProposalsTest, TestEmpty) {
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");
const int img_count = 3;
const int A = 4;
const int H = 10;
const int W = 8;
AddConstInput(vector<int64_t>{img_count, A, H, W}, 1., "scores", &ws);
AddLinSpacedInput(
vector<int64_t>{img_count, 4 * A, H, W}, 0, 10, "bbox_deltas", &ws);
AddConstInput(vector<int64_t>{img_count, 3}, 0.1, "im_info", &ws);
AddConstInput(vector<int64_t>{A, 4}, 1.0, "anchors", &ws);
def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 2.0f));
unique_ptr<OperatorBase> op(CreateOperator(def, &ws));
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
Blob* rois_blob = ws.GetBlob("rois");
EXPECT_NE(nullptr, rois_blob);
auto& rois = rois_blob->Get<TensorCPU>();
EXPECT_EQ(rois.numel(), 0);
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs = rois_probs_blob->Get<TensorCPU>();
EXPECT_EQ(rois_probs.numel(), 0);
}
TEST(GenerateProposalsTest, TestRealDownSampled) {
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");
const int img_count = 1;
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};
ERMatXf rois_gt(9, 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;
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};
AddInput(vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput(vector<int64_t>{img_count, 4 * A, H, W}, bbx, "bbox_deltas", &ws);
AddInput(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput(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 = rois_blob->Get<TensorCPU>();
EXPECT_EQ(rois.sizes(), (vector<int64_t>{rois_gt.rows(), rois_gt.cols()}));
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-4);
// test rois_probs
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs = rois_probs_blob->Get<TensorCPU>();
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);
}
TEST(GenerateProposalsTest, TestRealDownSampledRotatedAngle0) {
// Similar to TestRealDownSampled but for rotated boxes with angle info.
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");
const int img_count = 1;
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();
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};
// Results should exactly be the same as TestRealDownSampled 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(9, 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;
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(), 1);
// 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};
AddInput(vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput(
vector<int64_t>{img_count, box_dim * A, H, W},
bbx_with_angle,
"bbox_deltas",
&ws);
AddInput(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput(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 = rois_blob->Get<TensorCPU>();
EXPECT_EQ(rois.sizes(), (vector<int64_t>{rois_gt.rows(), rois_gt.cols()}));
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 = rois_probs_blob->Get<TensorCPU>();
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);
}
TEST(GenerateProposalsTest, TestRealDownSampledRotated) {
// Similar to TestRealDownSampled but for rotated boxes with angle info.
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");
const int img_count = 1;
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();
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};
AddInput(vector<int64_t>{img_count, A, H, W}, scores, "scores", &ws);
AddInput(
vector<int64_t>{img_count, box_dim * A, H, W},
bbx_with_angle,
"bbox_deltas",
&ws);
AddInput(vector<int64_t>{img_count, 3}, im_info, "im_info", &ws);
AddInput(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());
Blob* rois_blob = ws.GetBlob("rois");
EXPECT_NE(nullptr, rois_blob);
auto& rois = rois_blob->Get<TensorCPU>();
EXPECT_EQ(rois.sizes(), (vector<int64_t>{13, 6}));
Blob* rois_probs_blob = ws.GetBlob("rois_probs");
EXPECT_NE(nullptr, rois_probs_blob);
auto& rois_probs = rois_probs_blob->Get<TensorCPU>();
EXPECT_EQ(rois_probs.sizes(), (vector<int64_t>{13}));
// Verify that the resulting angles are correct
auto rois_data =
Eigen::Map<const ERMatXf>(rois.data<float>(), rois.size(0), rois.size(1));
for (int i = 0; i < rois.size(0); ++i) {
EXPECT_LE(std::abs(rois_data(i, 5) - expected_angle), 1e-4);
}
}
} // namespace caffe2