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Update Div OP - add scale param (#203) 

Update Div OP - add scale param
This commit is contained in:
Kee 2021-11-04 10:44:52 +08:00 committed by GitHub
parent e4cc133d36
commit c9086e0afe
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 123 additions and 2 deletions
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8
include/tim/vx/ops/elementwise.h
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@ -78,7 +78,6 @@ DECLARE_ELEMENTWISE_OP(Minimum)
DECLARE_ELEMENTWISE_OP(Maximum)
DECLARE_ELEMENTWISE_OP(Add)
DECLARE_ELEMENTWISE_OP(Sub)
DECLARE_ELEMENTWISE_OP(Div)
DECLARE_ELEMENTWISE_OP(Pow)
DECLARE_ELEMENTWISE_OP(FloorDiv)
@ -89,6 +88,13 @@ class Multiply : public Operation {
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
};
class Div : public Operation {
public:
Div(Graph* graph, float scale = 1.0f);
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
};
#undef DECLARE_ELEMENTWISE_OP
} // namespace ops

12
src/tim/vx/ops/elementwise.cc
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@ -41,7 +41,6 @@ DEFINE_ELEMENTWISE_OP(Minimum, VSI_NN_OP_MINIMUM)
DEFINE_ELEMENTWISE_OP(Maximum, VSI_NN_OP_MAXIMUM)
DEFINE_ELEMENTWISE_OP(Add, VSI_NN_OP_ADD)
DEFINE_ELEMENTWISE_OP(Sub, VSI_NN_OP_SUBTRACT)
DEFINE_ELEMENTWISE_OP(Div, VSI_NN_OP_DIVIDE)
DEFINE_ELEMENTWISE_OP(Pow, VSI_NN_OP_POW)
DEFINE_ELEMENTWISE_OP(FloorDiv, VSI_NN_OP_FLOORDIV)
@ -58,6 +57,17 @@ std::shared_ptr<Operation> Multiply::Clone(
this->impl_->node_->nn_param.multiply.scale);
}
Div::Div(Graph* graph, float scale)
: Operation(graph, VSI_NN_OP_DIVIDE, 2, 1) {
this->impl()->node()->nn_param.divide.scale = scale;
}
std::shared_ptr<Operation> Div::Clone(
std::shared_ptr<Graph>& graph) const {
return graph->CreateOperation<Div>(
this->impl_->node_->nn_param.divide.scale);
}
} // namespace ops
} // namespace vx
} // namespace tim

105
src/tim/vx/ops/elementwise_test.cc
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@ -130,3 +130,108 @@ TEST(FloorDiv, shape_5_1_broadcast_uint8) {
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(Div, shape_1_fp32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor_x = graph->CreateTensor(input_spec);
auto input_tensor_y = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data_x = { 1 };
std::vector<float> in_data_y = { 0 };
std::vector<float> golden = { std::numeric_limits<float>::infinity() };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::Div>();
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(1);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(Div, shape_5_1_broadcast_uint8) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape_x({1});
tim::vx::ShapeType in_shape_y({5, 1});
tim::vx::ShapeType out_shape({5, 1});
tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
tim::vx::Quantization quant_out(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8,
in_shape_x, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8,
in_shape_y, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT, quant_out);
auto input_tensor_x = graph->CreateTensor(input_spec_x);
auto input_tensor_y = graph->CreateTensor(input_spec_y);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data_x = { 255 };
std::vector<uint8_t> in_data_y = { 1, 2, 3, 0, 255 };
std::vector<uint8_t> golden = { 255, 255, 170, 255, 2 };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()));
auto op = graph->CreateOperation<tim::vx::ops::Div>();
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(Div, shape_5_1_broadcast_scale_uint8) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape_x({1});
tim::vx::ShapeType in_shape_y({5, 1});
tim::vx::ShapeType out_shape({5, 1});
tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
tim::vx::Quantization quant_out(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8,
in_shape_x, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8,
in_shape_y, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT, quant_out);
auto input_tensor_x = graph->CreateTensor(input_spec_x);
auto input_tensor_y = graph->CreateTensor(input_spec_y);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data_x = { 128 };
std::vector<uint8_t> in_data_y = { 1, 2, 3, 0, 255 };
std::vector<uint8_t> golden = { 128, 64, 43, 255, 1 };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()));
auto op = graph->CreateOperation<tim::vx::ops::Div>(0.5f);
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}