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Added a case for resize_bilinear layoutinfer (#595) 

Signed-off-by: Chen <jack.chen@verisilicon.com>
Co-authored-by: Chen <jack.chen@verisilicon.com>
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chxin66 2023-06-02 07:52:06 +08:00 committed by GitHub
parent aa7b3a6f8f
commit a64a0f7379
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1 changed files with 80 additions and 0 deletions
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80
src/tim/transform/layout_inference_test.cc
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@ -271,4 +271,84 @@ TEST(InstanceNorm, nhwc) {
std::vector<float> output(golden.size()); std::vector<float> output(golden.size());
EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data())); EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(Resize, bilinear_factor) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({1,2,2, 1});
tim::vx::ShapeType output_shape({1,3,3, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {1.0f, 1.0f, 2.0f, 2.0f};
std::vector<float> golden = {1.0f, 1.0f, 1.0f, 1.6666666269302368f, 1.6666666269302368f,
1.6666666269302368f, 2.0f, 2.0f, 2.0f};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::Resize>(tim::vx::ResizeType::BILINEAR,
1.7999999523162842f, false, false, 0, 0, tim::vx::DataLayout::CWHN);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto transform = tim::transform::LayoutInference(graph, ctx);
auto infer_graph = transform.first;
auto graph_io_map = transform.second;
infer_graph->Compile();
auto infer_input = graph_io_map[graph->InputsTensor()[0]];
auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
infer_graph->Run();
std::vector<float> output(golden.size());
EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(Resize, bilinear_outputsize) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({1,2,2, 1});
tim::vx::ShapeType output_shape({1,3,3, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {1.0f, 1.0f, 2.0f, 2.0f};
std::vector<float> golden = {1.0f, 1.0f, 1.0f, 1.6666666269302368f, 1.6666666269302368f,
1.6666666269302368f, 2.0f, 2.0f, 2.0f};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::Resize>(tim::vx::ResizeType::BILINEAR,
0, false, false, 3, 3, tim::vx::DataLayout::CWHN);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto transform = tim::transform::LayoutInference(graph, ctx);
auto infer_graph = transform.first;
auto graph_io_map = transform.second;
infer_graph->Compile();
auto infer_input = graph_io_map[graph->InputsTensor()[0]];
auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
infer_graph->Run();
std::vector<float> output(golden.size());
EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
} }