Fixed unreasonable type of parameter in broadcast (#505)
Change type of shape from int to uint & add ut for broadcast From github issue https://github.com/VeriSilicon/TIM-VX/issues/376 Define opversion to avoid incompatibility with upper level software Type: Code Improvement Signed-off-by: Feiyue Chen <Feiyue.Chen@verisilicon.com>
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Chen Feiyue
GitHub
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@ -24,7 +24,7 @@
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#ifndef OVXLIBXX_OPERATIONS_BROADCAST_H_
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#define OVXLIBXX_OPERATIONS_BROADCAST_H_
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#include "tim/vx/builtin_op.h"
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#define BROADCAST_OPVERSION 1
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namespace tim {
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namespace vx {
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namespace ops {
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@ -45,12 +45,12 @@ namespace ops {
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class Broadcast : public BuiltinOp {
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public:
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Broadcast(Graph* graph, const std::vector<int32_t>& shape, const std::vector<int32_t>& dimensions = {});
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Broadcast(Graph* graph, const std::vector<uint32_t>& shape, const std::vector<int32_t>& dimensions = {});
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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protected:
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const std::vector<int32_t> shape_;
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protected:
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std::vector<uint32_t> shape_;
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std::vector<int32_t> dimensions_;
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};
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@ -30,23 +30,24 @@
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namespace tim {
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namespace vx {
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namespace ops {
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Broadcast::Broadcast(Graph* graph, const std::vector<int32_t>& shape,
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Broadcast::Broadcast(Graph* graph, const std::vector <uint32_t>& shape,
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const std::vector<int32_t>& dimensions)
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: BuiltinOp(graph, VSI_NN_OP_EXPAND_BROADCAST),
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shape_(shape),
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dimensions_(dimensions) {
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this->impl()->node()->nn_param.expand_broadcast.dim_num = shape_.size();
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this->impl()->node()->nn_param.expand_broadcast.shape = (uint32_t*)shape_.data();
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this->impl()->node()->nn_param.expand_broadcast.shape = shape_.data();
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#ifdef VSI_EXPAND_BROADCAST_ENABLE_DIMENSIONS
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this->impl()->node()->nn_param.expand_broadcast.dimensions_num = dimensions_.size();
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if (dimensions.size() > 0)
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{
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int dim_num = shape.size();
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for (uint32_t i = 0; i < dimensions.size(); ++i) {
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dimensions_[i] += (dimensions[i] < 0 ? dim_num : 0U);
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for (uint32_t i = 0; i < dimensions.size(); i++)
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{
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dimensions_[i] += (dimensions[i] < 0 ? shape_.size() : 0U );
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}
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this->impl()->node()->nn_param.expand_broadcast.dimensions = (uint32_t*)dimensions_.data();
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} else {
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} else
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{
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this->impl()->node()->nn_param.expand_broadcast.dimensions = nullptr;
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}
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#endif
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@ -62,7 +62,7 @@ TEST(Broadcast, ScalarTo2D_2x3) {
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std::vector<float> golden = {
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2.25f, 2.25f, 2.25f, 2.25f, 2.25f, 2.25f,
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};
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std::vector<int32_t> shape = {3, 2};
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std::vector<uint32_t> shape = {3, 2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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@ -93,7 +93,7 @@ TEST(Broadcast, 1DTo2D) {
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std::vector<float> golden = {
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1.f, 2.f, 3.f, 1.f, 2.f, 3.f,
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};
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std::vector<int32_t> shape = {3, 2};
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std::vector<uint32_t> shape = {3, 2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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@ -125,7 +125,7 @@ TEST(Broadcast, 1DTo2D_WithDims0) {
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1.f, 2.f,
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1.f, 2.f,
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};
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std::vector<int32_t> shape = {2, 2};
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std::vector<uint32_t> shape = {2, 2};
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std::vector<int32_t> dimensions = {0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -158,7 +158,7 @@ TEST(Broadcast, 1DTo2D_WithDims1) {
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1.f, 1.f,
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2.f, 2.f,
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};
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std::vector<int32_t> shape = {2, 2};
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std::vector<uint32_t> shape = {2, 2};
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std::vector<int32_t> dimensions = {1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -170,6 +170,44 @@ TEST(Broadcast, 1DTo2D_WithDims1) {
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CheckResult(graph, golden, output_tensor);
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}
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TEST(Broadcast, 1DTo2D_WithDimsMinus2) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({3});
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tim::vx::ShapeType output_shape({3, 2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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1.f, 2.f, 3.f
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};
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std::vector<float> golden = {
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1.f, 2.f, 3.f,
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1.f, 2.f, 3.f
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};
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std::vector<uint32_t> shape = {3, 2};
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std::vector<int32_t> dimensions = {-2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Broadcast, 1DTo3D_WithDims0) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -190,7 +228,7 @@ TEST(Broadcast, 1DTo3D_WithDims0) {
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std::vector<float> golden = {
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1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f,
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};
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std::vector<int32_t> shape = {2, 2, 2};
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std::vector<uint32_t> shape = {2, 2, 2};
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std::vector<int32_t> dimensions = {0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -222,7 +260,7 @@ TEST(Broadcast, 1DTo3D_WithDims1) {
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std::vector<float> golden = {
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1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f,
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};
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std::vector<int32_t> shape = {2, 2, 2};
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std::vector<uint32_t> shape = {2, 2, 2};
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std::vector<int32_t> dimensions = {1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -254,7 +292,7 @@ TEST(Broadcast, 1DTo3D_WithDims2) {
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std::vector<float> golden = {
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1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f,
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};
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std::vector<int32_t> shape = {2, 2, 2};
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std::vector<uint32_t> shape = {2, 2, 2};
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std::vector<int32_t> dimensions = {2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -286,7 +324,7 @@ TEST(Broadcast, 2DTo3D_WithDims02) {
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std::vector<float> golden = {
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1.f, 5.f, 1.f, 5.f, 2.f, 6.f, 2.f, 6.f,
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};
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std::vector<int32_t> shape = {2, 2, 2};
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std::vector<uint32_t> shape = {2, 2, 2};
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std::vector<int32_t> dimensions = {0, 2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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@ -318,7 +356,7 @@ TEST(Broadcast, 2DTo3D_WithDims12) {
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std::vector<float> golden = {
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1.f, 1.f, 5.f, 5.f, 2.f, 2.f, 6.f, 6.f,
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};
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std::vector<int32_t> shape = {2, 2, 2};
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std::vector<uint32_t> shape = {2, 2, 2};
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std::vector<int32_t> dimensions = {1, 2};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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