Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #20818 from rogday:yolov4x_mish_cuda

Browse Source
pull/20851/head
Alexander Alekhin 3 years ago
parent cca4c47781 9c84749e2c
commit 4672dbda2a
5 changed files with 73 additions and 28 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 68
      modules/dnn/src/cuda/region.cu
  2. 2
      modules/dnn/src/cuda4dnn/kernels/region.hpp
  3. 6
      modules/dnn/src/cuda4dnn/primitives/region.hpp
  4. 5
      modules/dnn/src/layers/region_layer.cpp
  5. 10
      modules/dnn/test/test_darknet_importer.cpp

68
modules/dnn/src/cuda/region.cu
Unescape View File

@ -31,7 +31,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
size_type boxes_per_cell, size_type box_size,
size_type rows, size_type cols, T scale_x_y,
size_type height_norm, size_type width_norm,
T object_prob_cutoff)
T object_prob_cutoff, bool new_coords)
{
using vector2_type = get_vector_type_t<T, 2>;
auto bias_vPtr = vector2_type::get_pointer(bias.data());
@ -47,22 +47,43 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
const auto y = (box_index % batch_inner_size) / row_inner_size;
const auto x = (box_index % row_inner_size) / col_inner_size;
using device::fast_sigmoid;
/* When new_coords is true, we shouldn't use logistic activation again */
T objectness_prob;
if (new_coords)
{
const auto tmp_x = (input[box_offset + 0] - static_cast<T>(0.5)) * scale_x_y + static_cast<T>(0.5);
const auto tmp_y = (input[box_offset + 1] - static_cast<T>(0.5)) * scale_x_y + static_cast<T>(0.5);
output[box_offset + 0] = fast_divide_ftz(static_cast<T>(x) + tmp_x, static_cast<T>(cols));
output[box_offset + 1] = fast_divide_ftz(static_cast<T>(y) + tmp_y, static_cast<T>(rows));
vector2_type bias_xy;
v_load(bias_xy, bias_vPtr[box_of_the_cell]);
output[box_offset + 2] = input[box_offset + 2] * input[box_offset + 2] *
static_cast<T>(4) * bias_xy.data[0] / static_cast<T>(width_norm);
output[box_offset + 3] = input[box_offset + 3] * input[box_offset + 3] *
static_cast<T>(4) * bias_xy.data[1] / static_cast<T>(height_norm);
objectness_prob = input[box_offset + 4];
}
else
{
const auto tmp_x = (fast_sigmoid(input[box_offset + 0]) - static_cast<T>(0.5)) * scale_x_y + static_cast<T>(0.5);
const auto tmp_y = (fast_sigmoid(input[box_offset + 1]) - static_cast<T>(0.5)) * scale_x_y + static_cast<T>(0.5);
output[box_offset + 0] = (T(x) + tmp_x) / T(cols);
output[box_offset + 1] = (T(y) + tmp_y) / T(rows);
output[box_offset + 0] = fast_divide_ftz(static_cast<T>(x) + tmp_x, static_cast<T>(cols));
output[box_offset + 1] = fast_divide_ftz(static_cast<T>(y) + tmp_y, static_cast<T>(rows));
vector2_type bias_xy;
v_load(bias_xy, bias_vPtr[box_of_the_cell]);
using device::fast_exp;
output[box_offset + 2] = fast_exp(input[box_offset + 2]) * bias_xy.data[0] / T(width_norm);
output[box_offset + 3] = fast_exp(input[box_offset + 3]) * bias_xy.data[1] / T(height_norm);
output[box_offset + 2] = fast_exp(input[box_offset + 2]) * bias_xy.data[0] / static_cast<T>(width_norm);
output[box_offset + 3] = fast_exp(input[box_offset + 3]) * bias_xy.data[1] / static_cast<T>(height_norm);
/* squash objectness score into a probability */
using device::fast_sigmoid;
T objectness_prob = fast_sigmoid(input[box_offset + 4]);
objectness_prob = fast_sigmoid(input[box_offset + 4]);
}
/* ignore prediction if the objectness probability is less than the cutoff */
if (objectness_prob < object_prob_cutoff)
@ -73,7 +94,8 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
}
template <class T>
__global__ void region_sigmoid_class_score(Span<T> output, View<T> input, T class_prob_cutoff, size_type box_size)
__global__ void region_sigmoid_class_score(Span<T> output, View<T> input, T class_prob_cutoff,
size_type box_size, bool new_coords)
{
for (auto idx : grid_stride_range(output.size())) {
const index_type box_no = idx / box_size;
@ -92,9 +114,20 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
*
* to obtain the actual class probability, we multiply the conditional probability
* with the object probability
*
* when new_coords is true, we shouldn't use logistic activation again.
*/
using device::fast_sigmoid;
auto actual_class_prob = objectness_prob * fast_sigmoid(input[idx]);
T actual_class_prob;
if (new_coords)
{
actual_class_prob = objectness_prob * input[idx];
}
else
{
actual_class_prob = objectness_prob * fast_sigmoid(input[idx]);
}
if (actual_class_prob <= class_prob_cutoff)
actual_class_prob = T(0);
output[idx] = actual_class_prob;
@ -147,7 +180,8 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
std::size_t boxes_per_cell, std::size_t box_size,
std::size_t rows, std::size_t cols, T scale_x_y,
std::size_t height_norm, std::size_t width_norm,
bool if_true_sigmoid_else_softmax /* true = sigmoid, false = softmax */)
bool if_true_sigmoid_else_softmax, /* true = sigmoid, false = softmax */
bool new_coords)
{
CV_Assert(output.size() == input.size());
CV_Assert(output.size() % box_size == 0);
@ -158,12 +192,12 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
launch_kernel(box_kernel, box_policy,
output, input, bias, boxes_per_cell, box_size,
rows, cols, scale_x_y, height_norm, width_norm,
object_prob_cutoff);
object_prob_cutoff, new_coords);
if (if_true_sigmoid_else_softmax) {
auto kernel_score = raw::region_sigmoid_class_score<T>;
auto policy_score = make_policy(kernel_score, output.size(), 0, stream);
launch_kernel(kernel_score, policy_score, output, input, class_prob_cutoff, box_size);
launch_kernel(kernel_score, policy_score, output, input, class_prob_cutoff, box_size, new_coords);
} else {
auto kernel_score = raw::region_softmax_class_score<T>;
auto policy_score = make_policy(kernel_score, output.size(), 0, stream);
@ -173,10 +207,10 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void region(const Stream&, Span<__half>, View<__half>, View<__half>,
__half, __half, std::size_t, std::size_t, std::size_t, std::size_t, __half, std::size_t, std::size_t, bool);
__half, __half, std::size_t, std::size_t, std::size_t, std::size_t, __half, std::size_t, std::size_t, bool, bool);
#endif
template void region(const Stream&, Span<float>, View<float>, View<float>,
float, float, std::size_t, std::size_t, std::size_t, std::size_t, float, std::size_t, std::size_t, bool);
float, float, std::size_t, std::size_t, std::size_t, std::size_t, float, std::size_t, std::size_t, bool, bool);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */

2
modules/dnn/src/cuda4dnn/kernels/region.hpp
Unescape View File

@ -18,7 +18,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
std::size_t boxes_per_cell, std::size_t box_size,
std::size_t rows, std::size_t cols, T scale_x_y,
std::size_t height_norm, std::size_t width_norm,
bool if_true_sigmoid_else_softmax);
bool if_true_sigmoid_else_softmax, bool new_coords);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */

6
modules/dnn/src/cuda4dnn/primitives/region.hpp
Unescape View File

@ -60,6 +60,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
T class_prob_cutoff;
T nms_iou_threshold;
bool new_coords;
};
template <class T>
@ -87,6 +88,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
class_prob_cutoff = config.class_prob_cutoff;
nms_iou_threshold = config.nms_iou_threshold;
new_coords = config.new_coords;
}
void forward(
@ -115,7 +117,8 @@ namespace cv { namespace dnn { namespace cuda4dnn {
boxes_per_cell, cell_box_size,
rows, cols, scale_x_y,
height_norm, width_norm,
if_true_sigmoid_else_softmax
if_true_sigmoid_else_softmax,
new_coords
);
if (nms_iou_threshold > 0) {
@ -176,6 +179,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
T object_prob_cutoff, class_prob_cutoff;
T nms_iou_threshold;
bool new_coords;
};
}}} /* namespace cv::dnn::cuda4dnn */

5
modules/dnn/src/layers/region_layer.cpp
Unescape View File

@ -125,7 +125,7 @@ public:
#endif
#ifdef HAVE_CUDA
if (backendId == DNN_BACKEND_CUDA)
return new_coords == 0;
return true;
#endif
return backendId == DNN_BACKEND_OPENCV;
}
@ -437,11 +437,12 @@ public:
config.scale_x_y = scale_x_y;
config.object_prob_cutoff = (classfix == -1) ? 0.5 : 0.0;
config.object_prob_cutoff = (classfix == -1) ? thresh : 0.f;
config.class_prob_cutoff = thresh;
config.nms_iou_threshold = nmsThreshold;
config.new_coords = (new_coords == 1);
return make_cuda_node<cuda4dnn::RegionOp>(preferableTarget, std::move(context->stream), blobs[0], config);
}
#endif

10
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -745,8 +745,14 @@ TEST_P(Test_Darknet_nets, YOLOv4x_mish)
};
Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.042 : 3e-4;
double scoreDiff = 8e-5;
double iouDiff = 3e-4;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.006;
iouDiff = 0.042;
}
std::string config_file = "yolov4x-mish.cfg";
std::string weights_file = "yolov4x-mish.weights";

Write Preview
Loading…
Cancel
Save