fengyuentau
3 years ago
parent
04ebedb6f0
commit
d24d8f2abe
14 changed files with 728 additions and 10 deletions
@ -0,0 +1,202 @@ |
||||
// 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 "../precomp.hpp" |
||||
#include "layers_common.hpp" |
||||
|
||||
#include <algorithm> // for std::max & std::min |
||||
|
||||
namespace cv { namespace dnn { |
||||
|
||||
class ScatterNDLayerImpl CV_FINAL : public ScatterNDLayer |
||||
{ |
||||
public: |
||||
enum class REDUCTION |
||||
{ |
||||
NONE = 1, |
||||
ADD, |
||||
MUL, |
||||
MAX, |
||||
MIN |
||||
} reduction; |
||||
|
||||
ScatterNDLayerImpl(const LayerParams& params) |
||||
{ |
||||
setParamsFrom(params); |
||||
|
||||
String reduction_name = toLowerCase(params.get<String>("reduction", "none")); |
||||
if (reduction_name == "none") |
||||
reduction = REDUCTION::NONE; |
||||
else if (reduction_name == "add") |
||||
reduction = REDUCTION::ADD; |
||||
else if (reduction_name == "mul") |
||||
reduction = REDUCTION::MUL; |
||||
else if (reduction_name == "max") |
||||
reduction = REDUCTION::MAX; |
||||
else if (reduction_name == "min") |
||||
reduction = REDUCTION::MIN; |
||||
else |
||||
CV_Error(cv::Error::StsBadArg, "Unkown reduction \"" + reduction_name + "\""); |
||||
} |
||||
|
||||
virtual bool supportBackend(int backendId) CV_OVERRIDE |
||||
{ |
||||
return backendId == DNN_BACKEND_OPENCV; |
||||
} |
||||
|
||||
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs, |
||||
const int requiredOutputs, |
||||
std::vector<MatShape> &outputs, |
||||
std::vector<MatShape> &internals) const CV_OVERRIDE |
||||
{ |
||||
CV_CheckEQ(inputs.size(), 3ull, "ScatterND: require three inputs."); |
||||
|
||||
size_t r = inputs[0].size(), q = inputs[1].size(), p = inputs[2].size(), k = inputs[1].back(); |
||||
CV_CheckEQ(r + q - inputs[1].back() - 1, p, "ScatterND: updates should have rank of data.dims + indices.dims - indices.size[-1] - 1"); |
||||
CV_CheckLE(k, r, "ScatterND: indices.shape[-1] must be less than (or equal to) the rank of input data."); |
||||
|
||||
for (int i = 0; i < q - 1; i++) // np.ndindex(indices.shape[-1])
|
||||
{ |
||||
CV_CheckEQ(inputs[2][i], inputs[1][i], "ScatterND: updates.shape[0 : rank(indices)-1] must equal to indices.shape[0 : rank(indices)-1]."); |
||||
} |
||||
for (int i = q - 1, j = k, m = 0; i + m < p; m++) |
||||
{ |
||||
CV_CheckEQ(inputs[2][i + m], inputs[0][j + m], "ScatterND: updates.shape[rank(indices)-1 : ] must equal to data[indices.shape[-1] : rank(data)-1]."); |
||||
} |
||||
|
||||
outputs.assign(1, inputs[0]); |
||||
return false; |
||||
} |
||||
|
||||
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE |
||||
{ |
||||
CV_TRACE_FUNCTION(); |
||||
CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
||||
|
||||
std::vector<Mat> inputs, outputs; |
||||
inputs_arr.getMatVector(inputs); |
||||
outputs_arr.getMatVector(outputs); |
||||
|
||||
const Mat& data = inputs[0]; |
||||
const Mat& indices = inputs[1]; |
||||
const Mat& updates = inputs[2]; |
||||
Mat& out = outputs[0]; |
||||
|
||||
typeDispatch(outputs[0].type(), data, indices, updates, out); |
||||
} |
||||
|
||||
// NOTE: This impl does not check whether indices have duplicate entries.
|
||||
// The last duplicate entry will overwrite the previous.
|
||||
template<typename T, typename Functor> |
||||
void forward_impl(const Functor& rd, const Mat& data, const Mat& indices, const Mat& updates, Mat& out) |
||||
{ |
||||
data.copyTo(out); |
||||
|
||||
const int* shape = data.size.p; |
||||
const size_t* step = data.step.p; |
||||
|
||||
const int ind_ndims = indices.dims; |
||||
const int* ind_shape = indices.size.p; |
||||
const T* p_indices = indices.ptr<const T>(); |
||||
|
||||
const int upd_ndims = updates.dims; |
||||
const int* upd_shape = updates.size.p; |
||||
const T* p_updates = updates.ptr<const T>(); |
||||
|
||||
T* p_out = out.ptr<T>(); |
||||
|
||||
int k = ind_shape[ind_ndims - 1]; // last dim of indices
|
||||
size_t total = (size_t)(indices.total() / k); |
||||
|
||||
size_t updates_size = 1; |
||||
for (int i = ind_ndims - 1; i < upd_ndims; i++) |
||||
updates_size *= upd_shape[i]; |
||||
|
||||
size_t inp_start_offset = 0; |
||||
size_t ind_start_offset = 0; |
||||
size_t upd_start_offset = 0; |
||||
for (size_t i = 0; i < total; i++, ind_start_offset += k, upd_start_offset += updates_size) |
||||
{ |
||||
const T* tmp_p_indices = p_indices + ind_start_offset; |
||||
inp_start_offset = 0; |
||||
for (int j = 0; j < k; j++) |
||||
{ |
||||
CV_Assert(tmp_p_indices[j] < shape[j] && tmp_p_indices[j] > -shape[j]); |
||||
inp_start_offset += (((int)tmp_p_indices[j] + shape[j]) % shape[j]) * step[j]; |
||||
} |
||||
inp_start_offset /= sizeof(T); |
||||
|
||||
const T* tmp_p_updates = p_updates + upd_start_offset; |
||||
T* tmp_p_out = p_out + inp_start_offset; |
||||
for (int j = 0; j < updates_size; j++) |
||||
tmp_p_out[j] = rd(tmp_p_out[j], tmp_p_updates[j]); |
||||
} |
||||
} |
||||
|
||||
template<typename... Args> |
||||
inline void typeDispatch(const int type, Args&&... args) |
||||
{ |
||||
switch (type) |
||||
{ |
||||
case CV_8U: |
||||
reductionDispatch<uint8_t>(std::forward<Args>(args)...); |
||||
break; |
||||
case CV_32S: |
||||
reductionDispatch<int32_t>(std::forward<Args>(args)...); |
||||
break; |
||||
case CV_32F: |
||||
reductionDispatch<float>(std::forward<Args>(args)...); |
||||
break; |
||||
default: |
||||
CV_Error(cv::Error::BadDepth, "Unsupported type."); |
||||
}; |
||||
} |
||||
|
||||
template<typename T, typename... Args> |
||||
inline void reductionDispatch(Args&&... args) |
||||
{ |
||||
switch (reduction) |
||||
{ |
||||
case REDUCTION::NONE: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return b; }; // a from input data, b from updates
|
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::ADD: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return a + b; }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MUL: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return a * b; }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MAX: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return std::max(a, b); }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MIN: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return std::min(a, b); }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
default: |
||||
CV_Error(Error::StsBadArg, "Unsupported reduction."); |
||||
}; |
||||
} |
||||
}; |
||||
|
||||
Ptr<ScatterNDLayer> ScatterNDLayer::create(const LayerParams& params) |
||||
{ |
||||
return makePtr<ScatterNDLayerImpl>(params); |
||||
} |
||||
|
||||
}} // namespace cv::dnn
|
||||
@ -0,0 +1,208 @@ |
||||
// 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 "../precomp.hpp" |
||||
#include "layers_common.hpp" |
||||
|
||||
#include <algorithm> // for std::max & std::min |
||||
|
||||
namespace cv { namespace dnn { |
||||
|
||||
class ScatterLayerImpl CV_FINAL : public ScatterLayer |
||||
{ |
||||
public: |
||||
enum class REDUCTION |
||||
{ |
||||
NONE = 1, |
||||
ADD, |
||||
MUL, |
||||
MAX, |
||||
MIN |
||||
} reduction; |
||||
|
||||
ScatterLayerImpl(const LayerParams& params) |
||||
{ |
||||
setParamsFrom(params); |
||||
|
||||
axis = params.get<int>("axis", 0); |
||||
String reduction_name = toLowerCase(params.get<String>("reduction", "none")); |
||||
if (reduction_name == "none") |
||||
reduction = REDUCTION::NONE; |
||||
else if (reduction_name == "add") |
||||
reduction = REDUCTION::ADD; |
||||
else if (reduction_name == "mul") |
||||
reduction = REDUCTION::MUL; |
||||
else if (reduction_name == "max") |
||||
reduction = REDUCTION::MAX; |
||||
else if (reduction_name == "min") |
||||
reduction = REDUCTION::MIN; |
||||
else |
||||
CV_Error(cv::Error::StsBadArg, "Unkown reduction \"" + reduction_name + "\""); |
||||
} |
||||
|
||||
virtual bool supportBackend(int backendId) CV_OVERRIDE |
||||
{ |
||||
return backendId == DNN_BACKEND_OPENCV; |
||||
} |
||||
|
||||
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs, |
||||
const int requiredOutputs, |
||||
std::vector<MatShape> &outputs, |
||||
std::vector<MatShape> &internals) const CV_OVERRIDE |
||||
{ |
||||
CV_CheckEQ(inputs.size(), 3ull, "Scatter: require three inputs."); |
||||
CV_CheckEQ(inputs[0].size(), inputs[1].size(), "Scatter: input data should have the same ndim with indices."); |
||||
CV_CheckEQ(inputs[0].size(), inputs[2].size(), "Scatter: input data should have the same ndim with updates."); |
||||
for (size_t i = 0; i < inputs[0].size(); i++) |
||||
{ |
||||
CV_CheckGE(inputs[0][i], inputs[1][i], "Scatter: each dim of input data should be greater than (or equal to) indices'."); |
||||
CV_CheckEQ(inputs[1][i], inputs[2][i], "Scatter: each dim of indices should be equal to updates'."); |
||||
} |
||||
outputs.assign(1, inputs[0]); |
||||
return false; |
||||
} |
||||
|
||||
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE |
||||
{ |
||||
CV_TRACE_FUNCTION(); |
||||
CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
||||
|
||||
std::vector<Mat> inputs, outputs; |
||||
inputs_arr.getMatVector(inputs); |
||||
outputs_arr.getMatVector(outputs); |
||||
|
||||
const Mat& data = inputs[0]; |
||||
const Mat& indices = inputs[1]; |
||||
const Mat& updates = inputs[2]; |
||||
Mat& out = outputs[0]; |
||||
|
||||
typeDispatch(outputs[0].type(), data, indices, updates, out); |
||||
} |
||||
|
||||
template<typename T, typename Functor> |
||||
void forward_impl(const Functor& rd, const Mat& data, const Mat& indices, const Mat& updates, Mat& out) |
||||
{ |
||||
data.copyTo(out); |
||||
|
||||
const int ndims = data.dims; |
||||
const int* shape = data.size.p; |
||||
const size_t* step = data.step.p; |
||||
|
||||
const int* ind_shape = indices.size.p; |
||||
const size_t* ind_step = indices.step.p; |
||||
|
||||
size_t inp_offset = 0; |
||||
size_t ind_offset = 0; |
||||
const T* p_index = indices.ptr<const T>(); |
||||
const T* p_update = updates.ptr<const T>(); |
||||
T* p_out = out.ptr<T>(); |
||||
|
||||
size_t total = indices.total(); |
||||
|
||||
int j, offset_at_idx, index; |
||||
size_t t, idx; |
||||
for (size_t i = 0; i < total; i++) |
||||
{ |
||||
t = i; |
||||
inp_offset = 0; |
||||
ind_offset = 0; |
||||
int offset_at_axis = 0; |
||||
for (j = ndims - 1; j >= 0; j--) |
||||
{ |
||||
idx = t / ind_shape[j]; |
||||
offset_at_idx = (int)(t - idx * ind_shape[j]); |
||||
ind_offset += offset_at_idx * ind_step[j]; |
||||
inp_offset += offset_at_idx * step[j]; |
||||
t = idx; |
||||
if (j == axis) |
||||
{ |
||||
offset_at_axis = offset_at_idx * step[j]; |
||||
} |
||||
} |
||||
ind_offset /= sizeof(T); |
||||
|
||||
// get index and overwrite current indices
|
||||
const T* tmp_p_index = p_index + ind_offset; |
||||
index = (int)(*tmp_p_index); |
||||
CV_Assert(index < shape[axis] && index > -shape[axis]); |
||||
|
||||
inp_offset = inp_offset - offset_at_axis + ((index + shape[axis]) % shape[axis]) * step[axis]; |
||||
inp_offset /= sizeof(T); |
||||
|
||||
const T* tmp_p_update = p_update + ind_offset; |
||||
T* tmp_p_out = p_out + inp_offset; |
||||
*tmp_p_out = rd(*tmp_p_out, *tmp_p_update); |
||||
} |
||||
} |
||||
|
||||
template<typename... Args> |
||||
inline void typeDispatch(const int type, Args&&... args) |
||||
{ |
||||
switch (type) |
||||
{ |
||||
case CV_8U: |
||||
reductionDispatch<uint8_t>(std::forward<Args>(args)...); |
||||
break; |
||||
case CV_32S: |
||||
reductionDispatch<int32_t>(std::forward<Args>(args)...); |
||||
break; |
||||
case CV_32F: |
||||
reductionDispatch<float>(std::forward<Args>(args)...); |
||||
break; |
||||
default: |
||||
CV_Error(cv::Error::BadDepth, "Unsupported type."); |
||||
}; |
||||
} |
||||
|
||||
template<typename T, typename... Args> |
||||
inline void reductionDispatch(Args&&... args) |
||||
{ |
||||
switch (reduction) |
||||
{ |
||||
case REDUCTION::NONE: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return b; }; // a from input data, b from updates
|
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::ADD: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return a + b; }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MUL: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return a * b; }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MAX: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return std::max(a, b); }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
case REDUCTION::MIN: |
||||
{ |
||||
auto rd = [](const T& a, const T& b) { return std::min(a, b); }; |
||||
forward_impl<T>(rd, std::forward<Args>(args)...); |
||||
break; |
||||
} |
||||
default: |
||||
CV_Error(Error::StsBadArg, "Unsupported reduction."); |
||||
}; |
||||
} |
||||
|
||||
private: |
||||
// Attributes
|
||||
int axis; |
||||
}; |
||||
|
||||
Ptr<ScatterLayer> ScatterLayer::create(const LayerParams& params) |
||||
{ |
||||
return makePtr<ScatterLayerImpl>(params); |
||||
} |
||||
|
||||
}} // namespace cv::dnn
|
||||
@ -1,2 +1,15 @@ |
||||
"test_averagepool_3d_default", |
||||
"test_maxpool_3d_default", |
||||
"test_scatter_elements_with_axis", |
||||
"test_scatter_elements_with_duplicate_indices", |
||||
"test_scatter_elements_with_negative_indices", |
||||
"test_scatter_elements_with_reduction_max", |
||||
"test_scatter_elements_with_reduction_min", |
||||
"test_scatter_elements_without_axis", |
||||
"test_scatter_with_axis", |
||||
"test_scatter_without_axis", |
||||
"test_scatternd", |
||||
"test_scatternd_add", |
||||
"test_scatternd_max", |
||||
"test_scatternd_min", |
||||
"test_scatternd_multiply", |
||||
|
||||
Loading…
Reference in new issue