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add unit test for conv2d #681

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Feb 20, 2024
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add unit test for conv2d #681

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150 changes: 149 additions & 1 deletion src/tim/vx/ops/conv2d_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1975,4 +1975,152 @@ TEST(Conv2d, kernel_bigger_than_input_SAME) {
std::vector<float> output(output_size);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
}

TEST(Conv2d, float16_kernel11_stride22) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
using namespace half_float::literal;

tim::vx::ShapeType input_shape({1, 1, 8, 1}); //whcn
tim::vx::ShapeType weight_shape({1, 1, 8, 2}); //whio
tim::vx::ShapeType bias_shape({weight_shape[3]});
tim::vx::ShapeType output_shape(
{1, 1, weight_shape[3], input_shape[3]}); //whcn

tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT16, input_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT16, weight_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT16, bias_shape,
tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT16, output_shape,
tim::vx::TensorAttribute::OUTPUT);

// Input data nchw

std::vector<half_float::half> input_data = {
// 0.0461, 0.4024, -1.0115, 0.2167, -0.6123, 0.5036, 0.2310, 0.6931
0.0461_h, 0.4024_h, -1.0115_h, 0.2167_h, -0.6123_h, 0.5036_h, 0.2310_h, 0.6931_h
};

// weight data oihw
std::vector<half_float::half> weight_data = {
-0.1530_h, 0.1108_h, -0.1847_h, 0.1636_h, 0.0716_h, -0.1383_h, -0.1735_h, 0.0915_h,
0.3298_h, 0.1697_h, -0.0341_h, -0.0172_h, 0.2009_h, -0.2457_h, 0.1176_h, -0.1171_h
};

// bias data
std::vector<half_float::half> bias_data = {0.0_h, 0.0_h, 0.0_h};

std::vector<half_float::half> golden = {
// first channel
0.1697_h, -0.1865_h};

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::AUTO;
std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({1, 1});
std::array<uint32_t, 2> ksize({1,1});
std::array<uint32_t, 4> conv_pad = {0, 0, 0, 0};

auto conv2d =
graph->CreateOperation<tim::vx::ops::Conv2d>(2, padding, ksize, stride, dilation, conv_pad);
(*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<half_float::half> output(output_size);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, (half_float::half)0.1));
}

TEST(Conv2d, float16_kernel11_stride22_2) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
using namespace half_float::literal;

tim::vx::ShapeType input_shape({4, 4, 1, 1}); //whcn
tim::vx::ShapeType weight_shape({1, 1, 1, 1}); //whio
tim::vx::ShapeType bias_shape({weight_shape[3]});
tim::vx::ShapeType output_shape(
{2, 2, weight_shape[3], input_shape[3]}); //whcn

tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT16, input_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT16, weight_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT16, bias_shape,
tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT16, output_shape,
tim::vx::TensorAttribute::OUTPUT);

// Input data nchw

std::vector<half_float::half> input_data = {
0.0461_h, 0.4024_h, -0.0115_h, 0.2167_h,
-0.6123_h, 0.5036_h, 0.2310_h, 0.6931_h,
1.0461_h, 1.4024_h, -1.0115_h, 1.2167_h,
-1.6123_h, 1.5036_h, 1.2310_h, 1.6931_h
};

// weight data oihw
std::vector<half_float::half> weight_data = {-1.0_h};

// bias data
std::vector<half_float::half> bias_data = {0.0_h};

std::vector<half_float::half> golden = {
-0.0461_h, 0.0115_h,
-1.0461_h, 1.0115_h,
};

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::AUTO;
std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({1, 1});
std::array<uint32_t, 2> ksize({1,1});
std::array<uint32_t, 4> conv_pad = {0, 0, 0, 0};

auto conv2d =
graph->CreateOperation<tim::vx::ops::Conv2d>(1, padding, ksize, stride, dilation, conv_pad);
(*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<half_float::half> output(output_size);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, (half_float::half)0.1));
}
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