474 lines
17 KiB
C++
474 lines
17 KiB
C++
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#include "gtest/gtest.h"
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/conv2d.h"
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#include "tim/vx/types.h"
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TEST(DepthwiseConv, shape_2_3_2_1_float32_SimpleTest) {
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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({2, 3, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{1, 2, weight_shape[2], input_shape[3]}); //whcn
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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 weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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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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// Input data nchw
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std::vector<float> input_data = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
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// weight data iohw
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std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
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3, -11, 7, 15, 4, 12, 8, -16};
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// bias data
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std::vector<float> bias_data = {1, 2, 3, 4};
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// nchw
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std::vector<float> golden = {71, 91, -34, -26, 99, 127, -20, -4};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({1, 1});
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int32_t multiplier = weight_shape[2] / input_shape[2];
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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padding, stride, dilation, multiplier);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_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(DepthwiseConv, shape_2_3_2_1_float32_StrideTest) {
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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({2, 3, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{1, 1, weight_shape[2], input_shape[3]}); //whcn
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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 weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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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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// Input data nchw
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std::vector<float> input_data = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
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// weight data iohw
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std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
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3, -11, 7, 15, 4, 12, 8, -16};
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// bias data
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std::vector<float> bias_data = {1, 2, 3, 4};
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// nchw
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std::vector<float> golden = {71, -34, 99, -20};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({1, 1});
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int32_t multiplier = weight_shape[2] / input_shape[2];
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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padding, stride, dilation, multiplier);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_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(DepthwiseConv, shape_2_3_2_1_float32_PaddingTest) {
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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({2, 3, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{1, 1, weight_shape[2], input_shape[3]}); //whcn
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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 weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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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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// Input data nchw
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std::vector<float> input_data = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
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// weight data iohw
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std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
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3, -11, 7, 15, 4, 12, 8, -16};
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// bias data
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std::vector<float> bias_data = {1, 2, 3, 4};
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// nchw
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std::vector<float> golden = {71, -34, 99, -20};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({1, 1});
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int32_t multiplier = weight_shape[2] / input_shape[2];
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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padding, stride, dilation, multiplier);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_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(DepthwiseConv, shape_9_9_1_1_float32_DilationValidTest) {
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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({9, 9, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{3, 3, weight_shape[2], input_shape[3]}); //whcn
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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 weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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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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// Input data nchw
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std::vector<float> input_data = {
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
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0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
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// weight data iohw
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std::vector<float> weight_data = {1, 2, 3, 4, 5, 6, 7, 8, 9};
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// bias data
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std::vector<float> bias_data = {0};
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// nchw
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std::vector<float> golden = {5, 5, 5, 5, 5, 5, 5, 5, 5};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({3, 3});
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int32_t multiplier = weight_shape[2] / input_shape[2];
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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padding, stride, dilation, multiplier);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_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(DepthwiseConv, shape_3_3_1_1_float32_DilationSameTest) {
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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, 3, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 1, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{3, 3, weight_shape[2], input_shape[3]}); //whcn
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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 weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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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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// Input data nchw
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std::vector<float> input_data = {1, 1, 1, 1, 1, 1, 1, 1, 1};
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// weight data iohw
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std::vector<float> weight_data = {1, 2, 3, 4};
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// bias data
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std::vector<float> bias_data = {0};
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// nchw
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std::vector<float> golden = {4, 7, 3, 6, 10, 4, 2, 3, 1};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({2, 2});
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int32_t multiplier = weight_shape[2] / input_shape[2];
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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padding, stride, dilation, multiplier);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
|
||
|
|
std::vector<float> output(output_size);
|
||
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||
|
|
EXPECT_EQ(golden, output);
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(DepthwiseConv, shape_3_3_4_2_float32_BatchValidTest) {
|
||
|
|
auto ctx = tim::vx::Context::Create();
|
||
|
|
auto graph = ctx->CreateGraph();
|
||
|
|
|
||
|
|
tim::vx::ShapeType input_shape({3, 3, 4, 2}); //whcn
|
||
|
|
tim::vx::ShapeType weight_shape({3, 3, 4, 1}); //whoi
|
||
|
|
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||
|
|
tim::vx::ShapeType output_shape(
|
||
|
|
{1, 1, weight_shape[2], input_shape[3]}); //whcn
|
||
|
|
|
||
|
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||
|
|
tim::vx::TensorAttribute::INPUT);
|
||
|
|
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||
|
|
tim::vx::TensorAttribute::CONSTANT);
|
||
|
|
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||
|
|
tim::vx::TensorAttribute::CONSTANT);
|
||
|
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||
|
|
tim::vx::TensorAttribute::OUTPUT);
|
||
|
|
|
||
|
|
// Input data nchw
|
||
|
|
std::vector<float> input_data = {
|
||
|
|
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
||
|
|
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||
|
|
|
||
|
|
// weight data iohw
|
||
|
|
std::vector<float> weight_data = {1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,
|
||
|
|
2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
|
||
|
|
3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4};
|
||
|
|
|
||
|
|
// bias data
|
||
|
|
std::vector<float> bias_data = {0, 0, 0, 0};
|
||
|
|
|
||
|
|
// nchw
|
||
|
|
std::vector<float> golden = {9, 18, 0, 0, 9, 18, 0, 0};
|
||
|
|
|
||
|
|
auto input_tensor = graph->CreateTensor(input_spec);
|
||
|
|
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||
|
|
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||
|
|
|
||
|
|
auto output_tensor = graph->CreateTensor(output_spec);
|
||
|
|
|
||
|
|
auto padding = tim::vx::PadType::VALID;
|
||
|
|
std::array<uint32_t, 2> stride({1, 1});
|
||
|
|
std::array<uint32_t, 2> dilation({1, 1});
|
||
|
|
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||
|
|
|
||
|
|
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||
|
|
padding, stride, dilation, multiplier);
|
||
|
|
(*conv2d)
|
||
|
|
.BindInput(input_tensor)
|
||
|
|
.BindInput(weight_tensor)
|
||
|
|
.BindInput(bias_tensor)
|
||
|
|
.BindOutput(output_tensor);
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Compile());
|
||
|
|
|
||
|
|
input_tensor->CopyDataToTensor(input_data.data());
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Run());
|
||
|
|
|
||
|
|
uint32_t output_size = 1;
|
||
|
|
for (auto i : output_tensor->GetShape()) {
|
||
|
|
output_size *= i;
|
||
|
|
}
|
||
|
|
std::vector<float> output(output_size);
|
||
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||
|
|
EXPECT_EQ(golden, output);
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(DepthwiseConv, shape_2_2_1_4_float32_BatchSameTest) {
|
||
|
|
auto ctx = tim::vx::Context::Create();
|
||
|
|
auto graph = ctx->CreateGraph();
|
||
|
|
|
||
|
|
tim::vx::ShapeType input_shape({2, 2, 1, 4}); //whcn
|
||
|
|
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whoi
|
||
|
|
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||
|
|
tim::vx::ShapeType output_shape(
|
||
|
|
{2, 2, weight_shape[2], input_shape[3]}); //whcn
|
||
|
|
|
||
|
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||
|
|
tim::vx::TensorAttribute::INPUT);
|
||
|
|
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||
|
|
tim::vx::TensorAttribute::CONSTANT);
|
||
|
|
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||
|
|
tim::vx::TensorAttribute::CONSTANT);
|
||
|
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||
|
|
tim::vx::TensorAttribute::OUTPUT);
|
||
|
|
|
||
|
|
// Input data nchw
|
||
|
|
std::vector<float> input_data = {1, 1, 1, 1, 0, 0, 0, 0,
|
||
|
|
1, 1, 2, 2, 2, 2, 2, 2};
|
||
|
|
|
||
|
|
// weight data iohw
|
||
|
|
std::vector<float> weight_data = {1, 1, 1, 0, 2, 0, 1, 1, 1};
|
||
|
|
|
||
|
|
// bias data
|
||
|
|
std::vector<float> bias_data = {0};
|
||
|
|
|
||
|
|
// nchw
|
||
|
|
std::vector<float> golden = {4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 6, 8, 8, 8, 8};
|
||
|
|
|
||
|
|
auto input_tensor = graph->CreateTensor(input_spec);
|
||
|
|
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||
|
|
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||
|
|
|
||
|
|
auto output_tensor = graph->CreateTensor(output_spec);
|
||
|
|
|
||
|
|
auto padding = tim::vx::PadType::SAME;
|
||
|
|
std::array<uint32_t, 2> stride({1, 1});
|
||
|
|
std::array<uint32_t, 2> dilation({1, 1});
|
||
|
|
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||
|
|
|
||
|
|
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||
|
|
padding, stride, dilation, multiplier);
|
||
|
|
(*conv2d)
|
||
|
|
.BindInput(input_tensor)
|
||
|
|
.BindInput(weight_tensor)
|
||
|
|
.BindInput(bias_tensor)
|
||
|
|
.BindOutput(output_tensor);
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Compile());
|
||
|
|
|
||
|
|
input_tensor->CopyDataToTensor(input_data.data());
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Run());
|
||
|
|
|
||
|
|
uint32_t output_size = 1;
|
||
|
|
for (auto i : output_tensor->GetShape()) {
|
||
|
|
output_size *= i;
|
||
|
|
}
|
||
|
|
std::vector<float> output(output_size);
|
||
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||
|
|
EXPECT_EQ(golden, output);
|
||
|
|
}
|