static Ptr<CumSumLayer> create(const LayerParams& params);
};
+ class CV_EXPORTS ScatterLayer : public Layer
+ {
+ public:
+ static Ptr<ScatterLayer> create(const LayerParams& params);
+ };
+
+ class CV_EXPORTS ScatterNDLayer : public Layer
+ {
+ public:
+ static Ptr<ScatterNDLayer> create(const LayerParams& params);
+ };
+
//! @}
//! @}
CV__DNN_INLINE_NS_END
test_slice<4>(inputShape, begin, end);
}
+struct Layer_Scatter : public TestBaseWithParam<tuple<Backend, Target> >
+{
+ void test_layer(const std::vector<int>& shape, const String reduction = "none", int axis = 0)
+ {
+ int backendId = get<0>(GetParam());
+ int targetId = get<1>(GetParam());
+
+ Mat data(shape, CV_32FC1);
+ Mat indices(shape, CV_32FC1);
+ Mat updates(shape, CV_32FC1);
+
+ Scalar mean = 0.f;
+ Scalar std = 1.f;
+ randn(data, mean, std);
+ randu(indices, 0, shape[axis]);
+ randn(updates, mean, std);
+
+ 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> inpNames(3);
+ inpNames[0] = "data";
+ inpNames[1] = "indices";
+ inpNames[2] = "updates";
+ net.setInputsNames(inpNames);
+ net.setInput(data, inpNames[0]);
+ net.setInput(indices, inpNames[1]);
+ net.setInput(updates, inpNames[2]);
+
+ 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_Scatter, DISABLED_Scatter)
+{
+ test_layer({N, C, H, W});
+}
+
+PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter_add)
+{
+ test_layer({N, C, H, W}, "add");
+}
+
+struct Layer_ScatterND : public TestBaseWithParam<tuple<Backend, Target> >
+{
+ void test_layer(const std::vector<int>& shape, const String reduction = "none")
+ {
+ int backendId = get<0>(GetParam());
+ int targetId = get<1>(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);
+
+ Scalar mean = 0.f;
+ Scalar std = 1.f;
+ randn(data, mean, std);
+ randn(updates, mean, std);
+
+ // initialize the indices with index tuples like [0...N, 0...C, 0...H, 0...W]
+ 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;
+ 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.at<float>(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> inpNames(3);
+ inpNames[0] = "data";
+ inpNames[1] = "indices";
+ inpNames[2] = "updates";
+ net.setInputsNames(inpNames);
+ net.setInput(data, inpNames[0]);
+ net.setInput(indices, inpNames[1]);
+ net.setInput(updates, inpNames[2]);
+
+ 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_ScatterND, DISABLED_ScatterND)
+{
+ test_layer({N, C, H ,W});
+}
+
+PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND_add)
+{
+ test_layer({N, C, H , W}, "add");
+}
+
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)));
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Scatter, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
+INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
} // namespace
CV_DNN_REGISTER_LAYER_CLASS(GRU, GRULayer);
CV_DNN_REGISTER_LAYER_CLASS(CumSum, CumSumLayer);
+ CV_DNN_REGISTER_LAYER_CLASS(Scatter, ScatterLayer);
+ CV_DNN_REGISTER_LAYER_CLASS(ScatterND, ScatterNDLayer);
+
CV_DNN_REGISTER_LAYER_CLASS(Quantize, QuantizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Dequantize, DequantizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Requantize, RequantizeLayer);
--- /dev/null
+// 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
--- /dev/null
+// 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
void parseElementWise (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseDepthToSpace (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseRange (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
+ void parseScatter (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseSimpleLayers (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
// Domain: com.microsoft
constBlobsExtraInfo.insert(std::make_pair(node_proto.output(0), TensorInfo(1)));
}
+void ONNXImporter::parseScatter(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
+{
+ CV_CheckEQ(node_proto.input_size(), 3, "Scatter: three inputs are required.");
+ layerParams.type = "Scatter";
+ if (node_proto.op_type() == "ScatterND")
+ layerParams.type = "ScatterND";
+
+ size_t consts = 0;
+ for (size_t i = 0; i < node_proto.input_size(); ++i)
+ if (layer_id.find(node_proto.input(i)) == layer_id.end())
+ ++consts;
+
+ if (consts == node_proto.input_size())
+ {
+ std::vector<Mat> inputs, output;
+ for (size_t i = 0; i < node_proto.input_size(); i++)
+ {
+ Mat blob = getBlob(node_proto, i);
+ if (i == 1) // indices
+ blob.convertTo(blob, CV_32F);
+ inputs.push_back(blob);
+ }
+ runLayer(layerParams, inputs, output);
+ CV_Assert(output.size() == 1);
+ addConstant(node_proto.output(0), output[0]);
+ return;
+ }
+ else if (consts > 0)
+ {
+ for (size_t i = 0; i < node_proto.input_size(); i++)
+ {
+ if (layer_id.find(node_proto.input(i)) == layer_id.end())
+ {
+ Mat blob = getBlob(node_proto, i);
+ if (i == 1) // indices, from int32/int64 to float32
+ blob.convertTo(blob, CV_32F);
+
+ LayerParams constParams;
+ constParams.name = node_proto.input(i);
+ constParams.type = "Const";
+ constParams.blobs.push_back(blob);
+
+ opencv_onnx::NodeProto proto;
+ proto.add_output(constParams.name);
+ addLayer(constParams, proto);
+ }
+ }
+ }
+
+ addLayer(layerParams, node_proto);
+}
+
void ONNXImporter::parseSimpleLayers(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
bool is_all_input_const = true;
dispatch["DetectionOutput"] = &ONNXImporter::parseDetectionOutput;
dispatch["CumSum"] = &ONNXImporter::parseCumSum;
dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter::parseDepthToSpace;
+ dispatch["ScatterElements"] = dispatch["Scatter"] = dispatch["ScatterND"] = &ONNXImporter::parseScatter;
dispatch["Equal"] = dispatch["Greater"] = dispatch["Less"] = dispatch["Pow"] = dispatch["Add"] =
dispatch["Sub"] = dispatch["Mul"] = dispatch["Div"] = &ONNXImporter::parseElementWise;
{"test_scatter_elements_with_axis", 3, 1},
{"test_scatter_elements_with_duplicate_indices", 3, 1},
{"test_scatter_elements_with_negative_indices", 3, 1},
+ {"test_scatter_elements_with_reduction_max", 3, 1},
+ {"test_scatter_elements_with_reduction_min", 3, 1},
{"test_scatter_elements_without_axis", 3, 1},
{"test_scatter_with_axis", 3, 1},
{"test_scatter_without_axis", 3, 1},
{"test_scatternd", 3, 1},
{"test_scatternd_add", 3, 1},
+ {"test_scatternd_max", 3, 1},
+ {"test_scatternd_min", 3, 1},
{"test_scatternd_multiply", 3, 1},
{"test_sce_NCd1_mean_weight_negative_ii", 3, 1},
{"test_sce_NCd1_mean_weight_negative_ii_expanded", 3, 1},
"test_sub_uint8",
"test_tan", // FP16 only
"test_upsample_nearest",
+"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",
"test_sub_uint8",
"test_tanh",
"test_upsample_nearest",
+"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",
// no filter
CASE(test_scatter_elements_with_negative_indices)
// no filter
+CASE(test_scatter_elements_with_reduction_max)
+ // no filter
+CASE(test_scatter_elements_with_reduction_min)
+ // no filter
CASE(test_scatter_elements_without_axis)
// no filter
CASE(test_scatter_with_axis)
// no filter
CASE(test_scatternd_add)
// no filter
+CASE(test_scatternd_max)
+ // no filter
+CASE(test_scatternd_min)
+ // no filter
CASE(test_scatternd_multiply)
// no filter
CASE(test_sce_NCd1_mean_weight_negative_ii)
"test_sub_uint8",
"test_transpose_all_permutations_0",
"test_upsample_nearest",
+"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",
"test_reduce_sum_square_default_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.0183411 vs 0.004
"test_reduce_sum_square_do_not_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02
"test_reduce_sum_square_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02
-"test_reduce_sum_square_negative_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02
\ No newline at end of file
+"test_reduce_sum_square_negative_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02
+"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",
"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",
"test_roialign_aligned_true",
"test_scan9_sum",
"test_scan_sum",
-"test_scatter_elements_with_axis",
-"test_scatter_elements_with_duplicate_indices",
-"test_scatter_elements_with_negative_indices",
-"test_scatter_elements_without_axis",
-"test_scatter_with_axis",
-"test_scatter_without_axis",
-"test_scatternd",
-"test_scatternd_add",
-"test_scatternd_multiply",
"test_sce_NCd1_mean_weight_negative_ii",
"test_sce_NCd1_mean_weight_negative_ii_expanded",
"test_sce_NCd1_mean_weight_negative_ii_log_prob",
projects / platform / upstream / opencv.git / commitdiff