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Add a case for local response norm (#590) 

Signed-off-by: Chen <jack.chen@verisilicon.com>
Co-authored-by: Chen <jack.chen@verisilicon.com>
This commit is contained in:
chxin66 2023-05-20 16:57:21 +08:00 committed by GitHub
parent ddcb00c11b
commit 99606fd891
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1 changed files with 44 additions and 3 deletions
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47
src/tim/vx/ops/localresponsenormalization_test.cc
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@ -47,16 +47,57 @@ TEST(localresponsenormalization, axis_0_shape_6_1_1_1_float) {
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
in_data.size() * sizeof(float)));
int radius = 5;
int radius = 2;
int size = radius * 2;
float alpha = 4.0, beta = 0.5, bias = 9.0;
auto op = graph->CreateOperation<tim::vx::ops::LocalResponseNormalization>(
radius, alpha, beta, bias, 0);
size, alpha, beta, bias, 0);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(18);
std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(localresponsenormalization, axis_1_shape_2_6_float) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({2, 6});
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 = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {-1.100000023841858f, -1.100000023841858f, 0.6000000238418579f,
0.6000000238418579f, 0.699999988079071f, 0.699999988079071f,
1.2000000476837158f, 1.2000000476837158f, -0.699999988079071f,
-0.699999988079071f, 0.10000000149011612f, 0.10000000149011612f};
std::vector<float> golden = {-0.26492568850517273f, -0.26492568850517273f, 0.12510864436626434f,
0.12510864436626434f, 0.14011213183403015f, 0.14011213183403015f,
0.267261266708374f, 0.267261266708374f, -0.16178755462169647f,
-0.16178755462169647f, 0.024426599964499474f, 0.024426599964499474f};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
in_data.size() * sizeof(float)));
int radius = 2;
int size = radius * 2;
float alpha = 4.0, beta = 0.5, bias = 9.0;
auto op = graph->CreateOperation<tim::vx::ops::LocalResponseNormalization>(
size, alpha, beta, bias, 1);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}