mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-12-16 02:19:12 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
d1b643a05a
opencv/modules/dnn/perf/perf_layer.cpp
Haosonn 87f749277d
Merge pull request #24768 from Haosonn:pre-pr-2 
Vulkan backend for NaryEltwiseLayer in DNN module #24768

We improve Vulkan backend for ``NaryEltwiseLayer`` in DNN module by:

- add a basic framework for Vulkan backend in ``NaryEltwiseLayer``
- add a compute shader for binary forwarding (an imitation of what has been done in native OpenCV backend including broadcasting and eltwise-operation)
- typo fixed:
  - Wrong info output in ``context.cpp``

Currently, our implementation (or all layers supporting Vulkan backend) runs pretty slow on discrete GPUs basically due to IO cost in function ``copyToHost``, and we are going to fix that by

- find out the best ``VkMemoryProperty`` for various discrete GPUs

- prevent ``copyToHost`` in middle layers during forwarding, (i.e keep data in GPU memory)
### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

Co-authored-by: IskXCr <IskXCr@outlook.com>
2024-01-29 18:41:49 +03:00

979 lines
29 KiB
C++
Raw Blame History

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "perf_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace opencv_test {
struct Layer_Slice : public TestBaseWithParam<tuple<Backend, Target> >
{
template<int DIMS>
void test_slice(const int* inputShape, const int* begin, const int* end)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0));
for (int i = 0; i < (int)input.total(); ++i)
input.ptr<float>()[i] = (float)(i & 4095);
std::vector<Range> range(DIMS);
for (int i = 0; i < DIMS; ++i)
range[i] = Range(begin[i], end[i]);
Net net;
LayerParams lp;
lp.type = "Slice";
lp.name = "testLayer";
lp.set("begin", DictValue::arrayInt<int*>((int*)&begin[0], DIMS));
lp.set("end", DictValue::arrayInt<int*>((int*)&end[0], DIMS));
net.addLayerToPrev(lp.name, lp.type, lp);
// warmup
{
net.setInput(input);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
EXPECT_GT(cv::norm(out, NORM_INF), 0);
#if 0
//normAssert(out, input(range));
cout << input(range).clone().reshape(1, 1) << endl;
cout << out.reshape(1, 1) << endl;
#endif
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
};
static std::set<std::string> nary_eltwise_cuda_deny_ops = {"equal", "greater", "less", "mean", "pow", "sub"};
struct Layer_NaryEltwise : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& a_shape, const std::vector<int>& b_shape, const String op, bool isRef = false)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
if (!isRef && backendId == DNN_BACKEND_CUDA)
{
if (a_shape.size() != b_shape.size())
throw SkipTestException("The test is skipped because inputs with different shape size are not supported.");
for(int i = 0; i < a_shape.size(); i++)
if (a_shape[i] != b_shape[i] && a_shape[i] != 1 && b_shape[i] != 1)
throw SkipTestException("The test is skipped because inputs are not supported.");
if (nary_eltwise_cuda_deny_ops.find(op) != nary_eltwise_cuda_deny_ops.end())
throw SkipTestException("The operator '" + op + "' is skipped because is not support with cuda currently.");
}
Mat a(a_shape, CV_32FC1);
Mat b(b_shape, CV_32FC1);
Scalar mean = 0.f;
Scalar std = 1.f;
randn(a, mean, std);
randn(b, mean, std);
Net net;
LayerParams lp;
if (isRef)
lp.type = "Eltwise";
else
lp.type = "NaryEltwise";
lp.name = "testLayer";
lp.set("operation", op);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 1, id, 1);
// warmup
{
std::vector<String> inpNames(2);
inpNames[0] = "a";
inpNames[1] = "b";
net.setInputsNames(inpNames);
net.setInput(a, inpNames[0]);
net.setInput(b, inpNames[1]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 8;
int C = 256;
int H = 128;
int W = 100;
};
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_add)
{
test_layer({N, C, H, W}, {N, C, H, W}, "add");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_div)
{
test_layer({N, C, H, W}, {N, C, H, W}, "div");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_div)
{
test_layer({N, C, H, W}, {N, C, H, W}, "div", true);
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_equal)
{
test_layer({N, C, H, W}, {N, C, H, W}, "equal");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_greater)
{
test_layer({N, C, H, W}, {N, C, H, W}, "greater");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_less)
{
test_layer({N, C, H, W}, {N, C, H, W}, "less");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_max)
{
test_layer({N, C, H, W}, {N, C, H, W}, "max");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_max)
{
test_layer({N, C, H, W}, {N, C, H, W}, "max", true);
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mean)
{
test_layer({N, C, H, W}, {N, C, H, W}, "mean");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_min)
{
test_layer({N, C, H, W}, {N, C, H, W}, "min");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_min)
{
test_layer({N, C, H, W}, {N, C, H, W}, "min", true);
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mul)
{
test_layer({N, C, H, W}, {N, C, H, W}, "mul");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_mul)
{
test_layer({N, C, H, W}, {N, C, H, W}, "prod", true);
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_pow)
{
test_layer({N, C, H, W}, {N, C, H, W}, "pow");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sub)
{
test_layer({N, C, H, W}, {N, C, H, W}, "sub");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sum)
{
test_layer({N, C, H, W}, {N, C, H, W}, "sum");
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_sum)
{
test_layer({N, C, H, W}, {N, C, H, W}, "sum", true);
}
PERF_TEST_P_(Layer_NaryEltwise, NCHW_C_sum)
{
test_layer({N, C, H, W}, {C, 1, 1}, "sum");
}
PERF_TEST_P_(Layer_NaryEltwise, NHWC_C)
{
test_layer({N, H, W, C}, {1, C}, "sum");
}
PERF_TEST_P_(Layer_NaryEltwise, NHWC_H)
{
test_layer({N, H, W, C}, {1, H, 1, 1}, "sum");
}
PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_1)
{
const int inputShape[4] = {1, 64, 104, 104};
const int begin[] = {0, 32, 0, 0};
const int end[] = {1, 64, 104, 104};
test_slice<4>(inputShape, begin, end);
}
PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_2)
{
const int inputShape[4] = {1, 128, 52, 52};
const int begin[] = {0, 64, 0, 0};
const int end[] = {1, 128, 52, 52};
test_slice<4>(inputShape, begin, end);
}
PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_3)
{
const int inputShape[4] = {1, 256, 26, 26};
const int begin[] = {0, 128, 0, 0};
const int end[] = {1, 256, 26, 26};
test_slice<4>(inputShape, begin, end);
}
PERF_TEST_P_(Layer_Slice, FastNeuralStyle_eccv16)
{
const int inputShape[4] = {1, 128, 80, 100};
const int begin[] = {0, 0, 2, 2};
const int end[] = {1, 128, 76, 96};
test_slice<4>(inputShape, begin, end);
}
using Layer_Scatter = TestBaseWithParam<tuple<std::vector<int>, std::string, int, tuple<Backend, Target>>>;
PERF_TEST_P_(Layer_Scatter, scatter) {
std::vector<int> shape = get<0>(GetParam());
std::string reduction = get<1>(GetParam());
int axis = get<2>(GetParam());
int backend_id = get<0>(get<3>(GetParam()));
int target_id = get<1>(get<3>(GetParam()));
Mat data(shape, CV_32FC1);
Mat indices(shape, CV_32FC1);
Mat updates(shape, CV_32FC1);
randn(data, 0.f, 1.f);
randu(indices, 0, shape[axis]);
randn(updates, 0.f, 1.f);
indices.convertTo(indices, CV_32SC1, 1, -1);
Net net;
LayerParams lp;
lp.type = "Scatter";
lp.name = "testLayer";
lp.set("reduction", reduction);
lp.set("axis", axis);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> input_names{"data", "indices", "updates"};
net.setInputsNames(input_names);
net.setInput(data, input_names[0]);
net.setInput(indices, input_names[1]);
net.setInput(updates, input_names[2]);
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
Mat out = net.forward();
}
// perf
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Scatter, Combine(
Values(std::vector<int>{2, 128, 64, 50}),
Values(std::string("none"), std::string("add")),
Values(0), // use Values(0, 1, 2, 3) for more details
dnnBackendsAndTargets(/* withInferenceEngine= */ false,
/* withHalide= */ false,
/* withCpuOCV= */ true,
/* withVkCom= */ false,
/* withCUDA= */ false,
/* withNgraph= */ false,
/* withWebnn= */ false,
/* withCann= */ false) // only test on CPU
));
using Layer_ScatterND = TestBaseWithParam<tuple<std::vector<int>, std::string, tuple<Backend, Target>>>;
PERF_TEST_P_(Layer_ScatterND, scatterND) {
std::vector<int> shape = get<0>(GetParam());
std::string reduction = get<1>(GetParam());
int backend_id = get<0>(get<2>(GetParam()));
int target_id = get<1>(get<2>(GetParam()));
std::vector<int> indices_shape(shape);
indices_shape.push_back(int(shape.size()));
Mat data(shape, CV_32FC1);
Mat indices(indices_shape, CV_32FC1);
Mat updates(shape, CV_32FC1);
randn(data, 0.f, 1.f);
randn(updates, 0.f, 1.f);
// Create indices such that indices[n_i, c_j, h_k, w_l, :4] = [i, j, k, l]
std::vector<int> current_index_tuple(shape.size());
int total = data.total();
std::vector<int> indices_step;
for (int i = 0; i < indices.dims; i++)
{
int step = indices.step.p[i] / sizeof(float);
indices_step.push_back(step);
}
int t, j, idx, offset_at_idx, offset;
auto *indices_ptr = indices.ptr<float>();
for (int i = 0; i < total; i++)
{
t = i;
for (j = shape.size() - 1; j >= 0; j--)
{
idx = t / shape[j];
offset_at_idx = (int)(t - idx * shape[j]);
current_index_tuple[j] = offset_at_idx;
t = idx;
}
offset = 0;
for (j = 0; j < shape.size(); j++)
offset += current_index_tuple[j] * indices_step[j];
for (j = 0; j < shape.size(); j++)
indices_ptr[offset + j] = current_index_tuple[j];
}
Net net;
LayerParams lp;
lp.type = "ScatterND";
lp.name = "testLayer";
lp.set("reduction", reduction);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> input_names{"data", "indices", "updates"};
net.setInputsNames(input_names);
net.setInput(data, input_names[0]);
net.setInput(indices, input_names[1]);
net.setInput(updates, input_names[2]);
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, Combine(
Values(std::vector<int>{2, 128, 64, 50}),
Values(std::string("none"), std::string("add")),
dnnBackendsAndTargets(/* withInferenceEngine= */ false,
/* withHalide= */ false,
/* withCpuOCV= */ true,
/* withVkCom= */ false,
/* withCUDA= */ false,
/* withNgraph= */ false,
/* withWebnn= */ false,
/* withCann= */ false) // only test on CPU
));
struct Layer_LayerNorm : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& x_shape)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat x(x_shape, CV_32FC1);
Mat scale(x_shape.back(), 1, CV_32FC1);
Mat b(x_shape.back(), 1, CV_32FC1);
randu(x, 0.f, 1.f);
randu(scale, 0.f, 1.f);
randu(b, 0.f, 1.f);
Net net;
LayerParams lp;
lp.type = "LayerNormalization";
lp.name = "testLayer";
lp.set("axis", 2);
lp.set("hasBias", true);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> inpNames(3);
inpNames[0] = "x";
inpNames[1] = "scale";
inpNames[2] = "b";
net.setInputsNames(inpNames);
net.setInput(x, inpNames[0]);
net.setInput(scale, inpNames[1]);
net.setInput(b, inpNames[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 1;
int H = 50;
int W = 768;
};
PERF_TEST_P_(Layer_LayerNorm, LayerNorm)
{
test_layer({N, H ,W});
}
struct Layer_LayerNormExpanded : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& x_shape)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat x(x_shape, CV_32FC1);
Mat scale(1, x_shape.back(), CV_32FC1); // transpose to pass shape check
Mat b(1, x_shape.back(), CV_32FC1); // transpose to pass shape check
randu(x, 0.f, 1.f);
randu(scale, 0.f, 1.f);
randu(b, 0.f, 1.f);
// sub graph structure:
// -> ReduceMean -> -> Pow(2) -> ReduceMean -> Add(epsilon) -> Sqrt ->
// x Sub Div -> Mul(scale) -> Add(bias)
// ---------------> ------------------------------------------------->
Net net;
LayerParams lp_rm;
lp_rm.type = "Reduce";
lp_rm.name = "reducemean1";
lp_rm.set("reduce", "AVE");
std::vector<int> deleteDims(1, x_shape.back());
lp_rm.set("deleted_dims", DictValue::arrayInt(&deleteDims[0], deleteDims.size()));
std::vector<int> targetDims(x_shape.begin(), x_shape.end());
targetDims[x_shape.size() - 1] = 1;
lp_rm.set("target_dims", DictValue::arrayInt(&targetDims[0], targetDims.size()));
int id_rm = net.addLayerToPrev(lp_rm.name, lp_rm.type, lp_rm);
net.connect(0, 0, id_rm, 0);
LayerParams lp_sub;
lp_sub.type = "NaryEltwise";
lp_sub.name = "sub1";
lp_sub.set("operation", "sub");
int id_sub = net.addLayer(lp_sub.name, lp_sub.type, lp_sub);
net.connect(0, 0, id_sub, 0);
net.connect(id_rm, 0, id_sub, 1);
Mat pow_const(1, 1, CV_32FC1);
pow_const.at<float>(0) = 2.f;
LayerParams lp_pow_const;
lp_pow_const.type = "Const";
lp_pow_const.name = "const1";
lp_pow_const.blobs.push_back(pow_const);
int id_pow_const = net.addLayer(lp_pow_const.name, lp_pow_const.type, lp_pow_const);
LayerParams lp_pow;
lp_pow.type = "NaryEltwise";
lp_pow.name = "pow1";
lp_pow.set("operation", "pow");
int id_pow = net.addLayer(lp_pow.name, lp_pow.type, lp_pow);
net.connect(id_sub, 0, id_pow, 0);
net.connect(id_pow_const, 0, id_pow, 1);
LayerParams lp_rm1;
lp_rm1.type = "Reduce";
lp_rm1.name = "reducemean2";
lp_rm1.set("reduce", "AVE");
lp_rm1.set("deleted_dims", DictValue::arrayInt(&deleteDims[0], deleteDims.size()));
lp_rm1.set("target_dims", DictValue::arrayInt(&targetDims[0], targetDims.size()));
int id_rm1 = net.addLayer(lp_rm1.name, lp_rm1.type, lp_rm1);
net.connect(id_pow, 0, id_rm1, 0);
Mat add_const(1, 1, CV_32F);
add_const.at<float>(0) = 1e-5;
LayerParams lp_add_const;
lp_add_const.type = "Const";
lp_add_const.name = "const2";
lp_add_const.blobs.push_back(add_const);
int id_add_const = net.addLayer(lp_add_const.name, lp_add_const.type, lp_add_const);
LayerParams lp_add;
lp_add.type = "NaryEltwise";
lp_add.name = "add1";
lp_add.set("operation", "add");
int id_add = net.addLayer(lp_add.name, lp_add.type, lp_add);
net.connect(id_rm1, 0, id_add, 0);
net.connect(id_add_const, 0, id_add, 1);
LayerParams lp_sqrt;
lp_sqrt.type = "Sqrt";
lp_sqrt.name = "sqrt1";
int id_sqrt = net.addLayer(lp_sqrt.name, lp_sqrt.type, lp_sqrt);
net.connect(id_add, 0, id_sqrt, 0);
LayerParams lp_div;
lp_div.type = "NaryEltwise";
lp_div.name = "div1";
lp_div.set("operation", "div");
int id_div = net.addLayer(lp_div.name, lp_div.type, lp_div);
net.connect(id_sub, 0, id_div, 0);
net.connect(id_sqrt, 0, id_div, 1);
LayerParams lp_mul;
lp_mul.type = "NaryEltwise";
lp_mul.name = "mul1";
lp_mul.set("operation", "mul");
int id_mul = net.addLayer(lp_mul.name, lp_mul.type, lp_mul);
net.connect(id_div, 0, id_mul, 0);
net.connect(0, 1, id_mul, 1);
LayerParams lp_add1;
lp_add1.type = "NaryEltwise";
lp_add1.name = "add2";
lp_add1.set("operation", "add");
int id_add1 = net.addLayer(lp_add1.name, lp_add1.type, lp_add1);
net.connect(id_mul, 0, id_add1, 0);
net.connect(0, 2, id_add1, 1);
// warmup
{
std::vector<String> inpNames(3);
inpNames[0] = "x";
inpNames[1] = "scale";
inpNames[2] = "b";
net.setInputsNames(inpNames);
net.setInput(x, inpNames[0]);
net.setInput(scale, inpNames[1]);
net.setInput(b, inpNames[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 1;
int H = 50;
int W = 768;
};
PERF_TEST_P_(Layer_LayerNormExpanded, DISABLED_LayerNormExpanded)
{
test_layer({N, H ,W});
}
struct Layer_GatherElements : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& data_shape, const std::vector<int>& indices_shape, int axis = 0)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat data(data_shape, CV_32FC1);
Mat indices(indices_shape, CV_32FC1);
randu(data, 0.f, 1.f);
randu(indices, 0, data_shape[axis]);
Net net;
LayerParams lp;
lp.type = "GatherElements";
lp.name = "testLayer";
lp.set("axis", axis);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
// warmup
{
std::vector<String> inpNames(3);
inpNames[0] = "data";
inpNames[1] = "indices";
net.setInputsNames(inpNames);
net.setInput(data, inpNames[0]);
net.setInput(indices, inpNames[1]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
};
PERF_TEST_P_(Layer_GatherElements, GatherElements)
{
test_layer({2700, 1, 2914}, {2700, 1, 81}, 2);
}
struct Layer_InstanceNorm : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& x_shape)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat x(x_shape, CV_32FC1);
Mat scale(x_shape[1], 1, CV_32FC1);
Mat b(x_shape[1], 1, CV_32FC1);
randu(x, 0.f, 1.f);
randu(scale, 0.f, 1.f);
randu(b, 0.f, 1.f);
Net net;
LayerParams lp;
lp.type = "InstanceNormalization";
lp.name = "testLayer";
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> inpNames{"x", "scale", "b"};
net.setInputsNames(inpNames);
net.setInput(x, inpNames[0]);
net.setInput(scale, inpNames[1]);
net.setInput(b, inpNames[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 2;
int C = 64;
int H = 180;
int W = 240;
};
PERF_TEST_P_(Layer_InstanceNorm, InstanceNorm)
{
test_layer({N, C, H, W});
}
struct Layer_Attention : public TestBaseWithParam<tuple<Backend, Target>> {
void test_layer(const std::vector<int> x_shape, const std::vector<int> qkv_hidden_sizes, const int num_heads) {
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
auto qk_hidden_size = qkv_hidden_sizes[0];
auto v_hidden_size = qkv_hidden_sizes[2];
auto input_hidden_size = x_shape[2];
auto hidden_size = qk_hidden_size + qk_hidden_size + v_hidden_size;
Mat x(x_shape, CV_32F);
Mat weight(std::vector<int>{input_hidden_size, hidden_size}, CV_32F);
Mat bias(std::vector<int>{hidden_size}, CV_32F);
randu(x, 0.f, 1.f);
randu(weight, 0.f, 1.f);
randu(bias, 0.f, 1.f);
LayerParams lp;
lp.type = "Attention";
lp.name = "testLayer";
lp.set("num_heads", num_heads);
lp.set("qkv_hidden_sizes", DictValue::arrayInt(qkv_hidden_sizes.data(), qkv_hidden_sizes.size()));
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
{
std::vector<std::string> input_names{"x", "weight", "bias"};
net.setInputsNames(input_names);
net.setInput(x, input_names[0]);
net.setInput(weight, input_names[1]);
net.setInput(bias, input_names[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat out = net.forward();
}
SANITY_CHECK_NOTHING();
}
};
PERF_TEST_P_(Layer_Attention, VisionTransformer) {
test_layer({1, 197, 768}, {768, 768, 768}, 12);
}
struct Layer_GroupNorm : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& x_shape, int num_groups)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat x(x_shape, CV_32FC1);
Mat scale(x_shape[1], 1, CV_32FC1);
Mat b(x_shape[1], 1, CV_32FC1);
randu(x, 0.f, 1.f);
randu(scale, 0.f, 1.f);
randu(b, 0.f, 1.f);
Net net;
LayerParams lp;
lp.type = "GroupNormalization";
lp.name = "testLayer";
lp.set("num_groups", num_groups);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> inpNames{"x", "scale", "b"};
net.setInputsNames(inpNames);
net.setInput(x, inpNames[0]);
net.setInput(scale, inpNames[1]);
net.setInput(b, inpNames[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 2;
int C = 64;
int H = 180;
int W = 240;
int num_groups = 16;
};
PERF_TEST_P_(Layer_GroupNorm, GroupNorm)
{
test_layer({N, C, H, W}, num_groups);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
#ifdef HAVE_CUDA
INSTANTIATE_TEST_CASE_P(CUDA, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)));
#endif
#ifdef HAVE_VULKAN
INSTANTIATE_TEST_CASE_P(VULKAN, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_VKCOM, DNN_TARGET_VULKAN)));
#endif
INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNormExpanded, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_GatherElements, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_InstanceNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_Attention, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_GroupNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
typedef TestBaseWithParam<tuple<Vec4i, int, bool, tuple<Backend, Target> > > Layer_FullyConnected;
PERF_TEST_P_(Layer_FullyConnected, fc)
{
std::vector<int> inpShape;
inpShape.reserve(4);
for (int i = 0; i < 4; ++i) {
int dim = get<0>(GetParam())[i];
if (dim == 0)
break;
inpShape.push_back(dim);
}
Mat input(inpShape, CV_32F);
randn(input, 0, 1);
int axis = input.dims - 1;
int outDims = get<1>(GetParam());
bool isMatMul = get<2>(GetParam());
int backendId = get<0>(get<3>(GetParam()));
int targetId = get<1>(get<3>(GetParam()));
if (inpShape.size() == 4 && inpShape[0] == 5 && inpShape[1] == 16 && inpShape[2] == 512 && inpShape[3] == 128 && outDims >= 512)
applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG);
std::vector<int> weightShape;
if (isMatMul) {
weightShape = inpShape;
weightShape[weightShape.size() - 2] = outDims;
} else {
weightShape = {outDims, (int)input.total(axis, input.dims)};
}
Mat weights(weightShape, CV_32F);
randn(weights, 0, 1);
LayerParams lp;
lp.set("axis", input.dims - 1);
lp.set("is_matmul", weights.dims > 2);
lp.set("bias_term", false);
lp.set("num_output", (int)weights.total(0, weights.dims - 1));
lp.blobs.resize(1, weights);
Net net;
net.addLayerToPrev("matmul", "InnerProduct", lp);
net.setInput(input);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
// warmup
Mat output = net.forward();
TEST_CYCLE()
{
net.forward();
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_FullyConnected, Combine(
Values( // input size
Vec4i(5, 512, 384),
Vec4i(5, 16, 512, 128)
),
Values(256, 512, 1024), // output dimension
testing::Bool(), // is_matmul
dnnBackendsAndTargets()
));
typedef TestBaseWithParam<tuple<std::vector<int>, int, tuple<Backend, Target> > > Layer_Softmax;
PERF_TEST_P_(Layer_Softmax, softmax_3d) {
std::vector<int> shape = get<0>(GetParam());
int axis = get<1>(GetParam());
int backendId = get<0>(get<2>(GetParam()));
int targetId = get<1>(get<2>(GetParam()));
Mat data(shape, CV_32FC1);
Scalar mean = 0.f;
Scalar std = 1.f;
randn(data, mean, std);
Net net;
LayerParams lp;
lp.type = "Softmax";
lp.name = "testLayer";
lp.set("axis", axis);
net.addLayerToPrev(lp.name, lp.type, lp);
// warmup
{
net.setInput(data);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE() {
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Softmax, Combine(
Values( // input size
std::vector<int>({16, 50, 50}),
std::vector<int>({16, 197, 197}),
std::vector<int>({16, 1024, 1024})
),
Values(0, 1, 2), // axis
dnnBackendsAndTargets(/* withInferenceEngine= */ false,
/* withHalide= */ false,
/* withCpuOCV= */ true,
/* withVkCom= */ false,
/* withCUDA= */ false,
/* withNgraph= */ false,
/* withWebnn= */ false,
/* withCann= */ false) // only test on CPU
));
} // namespace
Reference in New Issue View Git Blame Copy Permalink