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4f34cd6d1e
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
730 lines
32 KiB
C++
730 lines
32 KiB
C++
#include "caffe2/operators/generate_proposals_op.h"
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#include <gtest/gtest.h>
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#include "caffe2/core/flags.h"
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#include "caffe2/core/macros.h"
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#include "caffe2/operators/generate_proposals_op_util_boxes.h"
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#include <c10/util/irange.h>
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namespace caffe2 {
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static void AddConstInput(
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const vector<int64_t>& shape,
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const float value,
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const string& name,
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Workspace* ws) {
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DeviceOption option;
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CPUContext context(option);
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Blob* blob = ws->CreateBlob(name);
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auto* tensor = BlobGetMutableTensor(blob, CPU);
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tensor->Resize(shape);
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math::Set<float, CPUContext>(
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tensor->numel(), value, tensor->template mutable_data<float>(), &context);
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return;
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}
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static void AddLinSpacedInput(
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const vector<int64_t>& shape,
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const float min_val,
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const float max_val,
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const string& name,
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Workspace* ws) {
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DeviceOption option;
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CPUContext context(option);
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Blob* blob = ws->CreateBlob(name);
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auto* tensor = BlobGetMutableTensor(blob, CPU);
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tensor->Resize(shape);
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EigenVectorMap<float> tensor_vec(
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tensor->template mutable_data<float>(), tensor->numel());
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tensor_vec.setLinSpaced(min_val, max_val);
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return;
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}
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static void AddInput(
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const vector<int64_t>& shape,
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const vector<float>& values,
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const string& name,
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Workspace* ws) {
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DeviceOption option;
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CPUContext context(option);
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Blob* blob = ws->CreateBlob(name);
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auto* tensor = BlobGetMutableTensor(blob, CPU);
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tensor->Resize(shape);
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EigenVectorMap<float> tensor_vec(
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tensor->template mutable_data<float>(), tensor->numel());
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tensor_vec.array() = utils::AsEArrXt(values);
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return;
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}
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TEST(GenerateProposalsTest, TestComputeAllAnchors) {
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ERMatXf anchors(3, 4);
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anchors << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
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int height = 4;
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int width = 3;
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float feat_stride = 16;
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ERMatXf all_anchors_gt(36, 4);
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all_anchors_gt << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103,
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-22, -16, 69, 31, -68, -40, 115, 55, -160, -88, 207, 103, -6, -16, 85, 31,
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-52, -40, 131, 55, -144, -88, 223, 103, -38, 0, 53, 47, -84, -24, 99, 71,
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-176, -72, 191, 119, -22, 0, 69, 47, -68, -24, 115, 71, -160, -72, 207,
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119, -6, 0, 85, 47, -52, -24, 131, 71, -144, -72, 223, 119, -38, 16, 53,
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63, -84, -8, 99, 87, -176, -56, 191, 135, -22, 16, 69, 63, -68, -8, 115,
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87, -160, -56, 207, 135, -6, 16, 85, 63, -52, -8, 131, 87, -144, -56, 223,
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135, -38, 32, 53, 79, -84, 8, 99, 103, -176, -40, 191, 151, -22, 32, 69,
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79, -68, 8, 115, 103, -160, -40, 207, 151, -6, 32, 85, 79, -52, 8, 131,
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103, -144, -40, 223, 151;
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Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
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Eigen::Map<ERMatXf>(
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anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
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anchors;
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auto result =
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utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
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Eigen::Map<const ERMatXf> all_anchors_result(
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result.data(), height * width * anchors.rows(), 4);
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EXPECT_EQ((all_anchors_result - all_anchors_gt).norm(), 0);
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}
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TEST(GenerateProposalsTest, TestComputeSortedAnchors) {
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ERMatXf anchors(3, 4);
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anchors << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
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int height = 4;
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int width = 3;
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int A = anchors.rows();
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float feat_stride = 16;
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int total = height * width * A;
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// Generate all anchors for ground truth
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Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
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Eigen::Map<ERMatXf>(
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anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
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anchors;
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auto all_anchors =
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utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
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Eigen::Map<const ERMatXf> all_anchors_result(
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all_anchors.data(), height * width * A, 4);
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Eigen::Map<const ERArrXXf> anchors_map(
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anchors.data(), anchors.rows(), anchors.cols());
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// Test with random subsets and ordering of indices
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vector<int> indices(total);
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std::iota(indices.begin(), indices.end(), 0);
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std::random_device rd;
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std::mt19937 gen(rd());
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std::shuffle(indices.begin(), indices.end(), gen);
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for (int count = 0; count <= total; ++count) {
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vector<int> order(indices.begin(), indices.begin() + count);
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auto result = utils::ComputeSortedAnchors(
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anchors_map, height, width, feat_stride, order);
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// Compare the result of ComputeSortedAnchors with first generating all
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// anchors via ComputeAllAnchors and then applying ordering and filtering.
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// Need to convert order from (A, H, W) to (H, W, A) format before for this.
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const auto& order_AHW = utils::AsEArrXt(order);
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const auto& order_AH = order_AHW / width;
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const auto& order_W = order_AHW - order_AH * width;
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const auto& order_A = order_AH / height;
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const auto& order_H = order_AH - order_A * height;
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const auto& order_HWA = (order_H * width + order_W) * A + order_A;
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ERArrXXf gt;
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utils::GetSubArrayRows(all_anchors_result.array(), order_HWA, >);
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EXPECT_EQ((result.matrix() - gt.matrix()).norm(), 0);
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}
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}
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TEST(GenerateProposalsTest, TestComputeAllAnchorsRotated) {
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// Similar to TestComputeAllAnchors but for rotated boxes with angle info.
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ERMatXf anchors_xyxy(3, 4);
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anchors_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
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// Convert to RRPN format and add angles
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ERMatXf anchors(3, 5);
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anchors.block(0, 0, 3, 4) = utils::bbox_xyxy_to_ctrwh(
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anchors_xyxy.array(), true /* legacy_plus_one */);
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std::vector<float> angles{0.0, 45.0, -120.0};
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for (int i = 0; i < anchors.rows(); ++i) {
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anchors(i, 4) = angles[i % angles.size()];
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}
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int height = 4;
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int width = 3;
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float feat_stride = 16;
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ERMatXf all_anchors_gt_xyxy(36, 4);
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all_anchors_gt_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191,
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103, -22, -16, 69, 31, -68, -40, 115, 55, -160, -88, 207, 103, -6, -16,
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85, 31, -52, -40, 131, 55, -144, -88, 223, 103, -38, 0, 53, 47, -84, -24,
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99, 71, -176, -72, 191, 119, -22, 0, 69, 47, -68, -24, 115, 71, -160, -72,
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207, 119, -6, 0, 85, 47, -52, -24, 131, 71, -144, -72, 223, 119, -38, 16,
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53, 63, -84, -8, 99, 87, -176, -56, 191, 135, -22, 16, 69, 63, -68, -8,
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115, 87, -160, -56, 207, 135, -6, 16, 85, 63, -52, -8, 131, 87, -144, -56,
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223, 135, -38, 32, 53, 79, -84, 8, 99, 103, -176, -40, 191, 151, -22, 32,
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69, 79, -68, 8, 115, 103, -160, -40, 207, 151, -6, 32, 85, 79, -52, 8,
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131, 103, -144, -40, 223, 151;
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// Convert gt to RRPN format and add angles
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ERMatXf all_anchors_gt(36, 5);
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all_anchors_gt.block(0, 0, 36, 4) = utils::bbox_xyxy_to_ctrwh(
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all_anchors_gt_xyxy.array(), true /* legacy_plus_one */);
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for (int i = 0; i < all_anchors_gt.rows(); ++i) {
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all_anchors_gt(i, 4) = angles[i % angles.size()];
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}
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Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
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Eigen::Map<ERMatXf>(
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anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
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anchors;
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auto result =
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utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
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Eigen::Map<const ERMatXf> all_anchors_result(
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result.data(), height * width * anchors.rows(), 5);
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EXPECT_EQ((all_anchors_result - all_anchors_gt).norm(), 0);
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}
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TEST(GenerateProposalsTest, TestComputeSortedAnchorsRotated) {
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// Similar to TestComputeSortedAnchors but for rotated boxes with angle info.
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ERMatXf anchors_xyxy(3, 4);
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anchors_xyxy << -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103;
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// Convert to RRPN format and add angles
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ERMatXf anchors(3, 5);
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anchors.block(0, 0, 3, 4) = utils::bbox_xyxy_to_ctrwh(
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anchors_xyxy.array(), true /* legacy_plus_one */);
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std::vector<float> angles{0.0, 45.0, -120.0};
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for (int i = 0; i < anchors.rows(); ++i) {
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anchors(i, 4) = angles[i % angles.size()];
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}
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int height = 4;
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int width = 3;
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int A = anchors.rows();
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float feat_stride = 16;
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int total = height * width * A;
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// Generate all anchors for ground truth
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Tensor anchors_tensor(vector<int64_t>{anchors.rows(), anchors.cols()}, CPU);
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Eigen::Map<ERMatXf>(
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anchors_tensor.mutable_data<float>(), anchors.rows(), anchors.cols()) =
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anchors;
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auto all_anchors =
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utils::ComputeAllAnchors(anchors_tensor, height, width, feat_stride);
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Eigen::Map<const ERMatXf> all_anchors_result(
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all_anchors.data(), height * width * A, 5);
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Eigen::Map<const ERArrXXf> anchors_map(
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anchors.data(), anchors.rows(), anchors.cols());
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// Test with random subsets and ordering of indices
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vector<int> indices(total);
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std::iota(indices.begin(), indices.end(), 0);
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std::random_device rd;
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std::mt19937 gen(rd());
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std::shuffle(indices.begin(), indices.end(), gen);
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for (int count = 0; count <= total; ++count) {
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vector<int> order(indices.begin(), indices.begin() + count);
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auto result = utils::ComputeSortedAnchors(
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anchors_map, height, width, feat_stride, order);
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// Compare the result of ComputeSortedAnchors with first generating all
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// anchors via ComputeAllAnchors and then applying ordering and filtering.
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// Need to convert order from (A, H, W) to (H, W, A) format before for this.
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const auto& order_AHW = utils::AsEArrXt(order);
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const auto& order_AH = order_AHW / width;
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const auto& order_W = order_AHW - order_AH * width;
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const auto& order_A = order_AH / height;
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const auto& order_H = order_AH - order_A * height;
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const auto& order_HWA = (order_H * width + order_W) * A + order_A;
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ERArrXXf gt;
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utils::GetSubArrayRows(all_anchors_result.array(), order_HWA, >);
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EXPECT_EQ((result.matrix() - gt.matrix()).norm(), 0);
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}
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}
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TEST(GenerateProposalsTest, TestEmpty) {
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Workspace ws;
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OperatorDef def;
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def.set_name("test");
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def.set_type("GenerateProposals");
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def.add_input("scores");
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def.add_input("bbox_deltas");
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def.add_input("im_info");
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def.add_input("anchors");
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def.add_output("rois");
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def.add_output("rois_probs");
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const int img_count = 3;
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const int A = 4;
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const int H = 10;
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const int W = 8;
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AddConstInput(vector<int64_t>{img_count, A, H, W}, 1., "scores", &ws);
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AddLinSpacedInput(
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vector<int64_t>{img_count, 4 * A, H, W}, 0, 10, "bbox_deltas", &ws);
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AddConstInput(vector<int64_t>{img_count, 3}, 0.1, "im_info", &ws);
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AddConstInput(vector<int64_t>{A, 4}, 1.0, "anchors", &ws);
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def.add_arg()->CopyFrom(MakeArgument("spatial_scale", 2.0f));
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unique_ptr<OperatorBase> op(CreateOperator(def, &ws));
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EXPECT_NE(nullptr, op.get());
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EXPECT_TRUE(op->Run());
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Blob* rois_blob = ws.GetBlob("rois");
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EXPECT_NE(nullptr, rois_blob);
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auto& rois = rois_blob->Get<TensorCPU>();
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EXPECT_EQ(rois.numel(), 0);
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Blob* rois_probs_blob = ws.GetBlob("rois_probs");
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EXPECT_NE(nullptr, rois_probs_blob);
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auto& rois_probs = rois_probs_blob->Get<TensorCPU>();
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EXPECT_EQ(rois_probs.numel(), 0);
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}
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TEST(GenerateProposalsTest, TestRealDownSampled) {
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Workspace ws;
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OperatorDef def;
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def.set_name("test");
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def.set_type("GenerateProposals");
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def.add_input("scores");
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def.add_input("bbox_deltas");
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def.add_input("im_info");
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def.add_input("anchors");
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def.add_output("rois");
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def.add_output("rois_probs");
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const int img_count = 1;
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const int A = 2;
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const int H = 4;
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const int W = 5;
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vector<float> scores{
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5.44218998e-03f, 1.19207997e-03f, 1.12379994e-03f, 1.17181998e-03f,
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1.20544003e-03f, 6.17993006e-04f, 1.05261997e-05f, 8.91025957e-06f,
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9.29536981e-09f, 6.09605013e-05f, 4.72735002e-04f, 1.13482002e-10f,
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1.50015003e-05f, 4.45032993e-06f, 3.21612994e-08f, 8.02662980e-04f,
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1.40488002e-04f, 3.12508007e-07f, 3.02616991e-06f, 1.97759000e-08f,
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2.66913995e-02f, 5.26766013e-03f, 5.05053019e-03f, 5.62100019e-03f,
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5.37420018e-03f, 5.26280981e-03f, 2.48894998e-04f, 1.06842002e-04f,
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3.92931997e-06f, 1.79388002e-03f, 4.79440019e-03f, 3.41609990e-07f,
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5.20430971e-04f, 3.34090000e-05f, 2.19159006e-07f, 2.28786003e-03f,
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5.16703985e-05f, 4.04523007e-06f, 1.79227004e-06f, 5.32449000e-08f};
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vector<float> bbx{
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-1.65040009e-02f, -1.84051003e-02f, -1.85930002e-02f, -2.08263006e-02f,
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-1.83814000e-02f, -2.89172009e-02f, -3.89706008e-02f, -7.52277970e-02f,
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-1.54091999e-01f, -2.55433004e-02f, -1.77490003e-02f, -1.10340998e-01f,
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-4.20190990e-02f, -2.71421000e-02f, 6.89801015e-03f, 5.71171008e-02f,
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-1.75665006e-01f, 2.30021998e-02f, 3.08554992e-02f, -1.39333997e-02f,
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3.40579003e-01f, 3.91070992e-01f, 3.91624004e-01f, 3.92527014e-01f,
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3.91445011e-01f, 3.79328012e-01f, 4.26631987e-01f, 3.64892989e-01f,
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2.76894987e-01f, 5.13985991e-01f, 3.79999995e-01f, 1.80457994e-01f,
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4.37402993e-01f, 4.18545991e-01f, 2.51549989e-01f, 4.48318988e-01f,
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1.68564007e-01f, 4.65440989e-01f, 4.21891987e-01f, 4.45928007e-01f,
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3.27155995e-03f, 3.71480011e-03f, 3.60032008e-03f, 4.27092984e-03f,
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3.74579988e-03f, 5.95752988e-03f, -3.14473989e-03f, 3.52022005e-03f,
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-1.88564006e-02f, 1.65188999e-03f, 1.73791999e-03f, -3.56074013e-02f,
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-1.66615995e-04f, 3.14146001e-03f, -1.11830998e-02f, -5.35363983e-03f,
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6.49790000e-03f, -9.27671045e-03f, -2.83346009e-02f, -1.61233004e-02f,
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-2.15505004e-01f, -2.19910994e-01f, -2.20872998e-01f, -2.12831005e-01f,
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-2.19145000e-01f, -2.27687001e-01f, -3.43973994e-01f, -2.75869995e-01f,
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-3.19516987e-01f, -2.50418007e-01f, -2.48537004e-01f, -5.08224010e-01f,
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-2.28724003e-01f, -2.82402009e-01f, -3.75815988e-01f, -2.86352992e-01f,
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-5.28333001e-02f, -4.43836004e-01f, -4.55134988e-01f, -4.34897989e-01f,
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-5.65053988e-03f, -9.25739005e-04f, -1.06790999e-03f, -2.37016007e-03f,
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-9.71166010e-04f, -8.90910998e-03f, -1.17592998e-02f, -2.08992008e-02f,
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-4.94231991e-02f, 6.63906988e-03f, 3.20469006e-03f, -6.44695014e-02f,
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-3.11607006e-03f, 2.02738005e-03f, 1.48096997e-02f, 4.39785011e-02f,
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-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
|
|
auto 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};
|
|
|
|
// 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
|
|
auto 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};
|
|
|
|
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 (const auto i : c10::irange(rois.size(0))) {
|
|
EXPECT_LE(std::abs(rois_data(i, 5) - expected_angle), 1e-4);
|
|
}
|
|
}
|
|
|
|
} // namespace caffe2
|