add unit test for conv2d (#681)
conv2d unit test: kernel 1x1, stride 2x2, dtype=fp16 Type: Unit Test Signed-off-by: Tang Jing <jing.tang@verisilicon.com>
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@ -1975,4 +1975,152 @@ TEST(Conv2d, kernel_bigger_than_input_SAME) {
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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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}
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TEST(Conv2d, float16_kernel11_stride22) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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using namespace half_float::literal;
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tim::vx::ShapeType input_shape({1, 1, 8, 1}); //whcn
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tim::vx::ShapeType weight_shape({1, 1, 8, 2}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{1, 1, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT16, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT16, weight_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT16, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT16, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<half_float::half> input_data = {
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// 0.0461, 0.4024, -1.0115, 0.2167, -0.6123, 0.5036, 0.2310, 0.6931
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0.0461_h, 0.4024_h, -1.0115_h, 0.2167_h, -0.6123_h, 0.5036_h, 0.2310_h, 0.6931_h
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};
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// weight data oihw
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std::vector<half_float::half> weight_data = {
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-0.1530_h, 0.1108_h, -0.1847_h, 0.1636_h, 0.0716_h, -0.1383_h, -0.1735_h, 0.0915_h,
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0.3298_h, 0.1697_h, -0.0341_h, -0.0172_h, 0.2009_h, -0.2457_h, 0.1176_h, -0.1171_h
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};
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// bias data
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std::vector<half_float::half> bias_data = {0.0_h, 0.0_h, 0.0_h};
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std::vector<half_float::half> golden = {
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// first channel
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0.1697_h, -0.1865_h};
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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::AUTO;
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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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std::array<uint32_t, 2> ksize({1,1});
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std::array<uint32_t, 4> conv_pad = {0, 0, 0, 0};
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auto conv2d =
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graph->CreateOperation<tim::vx::ops::Conv2d>(2, padding, ksize, stride, dilation, conv_pad);
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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<half_float::half> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, (half_float::half)0.1));
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}
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TEST(Conv2d, float16_kernel11_stride22_2) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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using namespace half_float::literal;
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tim::vx::ShapeType input_shape({4, 4, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({1, 1, 1, 1}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 2, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT16, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT16, weight_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT16, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT16, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<half_float::half> input_data = {
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0.0461_h, 0.4024_h, -0.0115_h, 0.2167_h,
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-0.6123_h, 0.5036_h, 0.2310_h, 0.6931_h,
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1.0461_h, 1.4024_h, -1.0115_h, 1.2167_h,
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-1.6123_h, 1.5036_h, 1.2310_h, 1.6931_h
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};
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// weight data oihw
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std::vector<half_float::half> weight_data = {-1.0_h};
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// bias data
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std::vector<half_float::half> bias_data = {0.0_h};
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std::vector<half_float::half> golden = {
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-0.0461_h, 0.0115_h,
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-1.0461_h, 1.0115_h,
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};
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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::AUTO;
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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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std::array<uint32_t, 2> ksize({1,1});
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std::array<uint32_t, 4> conv_pad = {0, 0, 0, 0};
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auto conv2d =
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graph->CreateOperation<tim::vx::ops::Conv2d>(1, padding, ksize, stride, dilation, conv_pad);
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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<half_float::half> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, (half_float::half)0.1));
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}
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