86 lines
3.3 KiB
C++
86 lines
3.3 KiB
C++
|
#include "tim/vx/context.h"
|
||
|
|
#include "tim/vx/graph.h"
|
||
|
|
#include "tim/vx/ops/deconv.h"
|
||
|
|
|
||
|
|
#include "gtest/gtest.h"
|
||
|
|
|
||
|
|
namespace {
|
||
|
|
|
||
|
|
size_t element_count(const tim::vx::ShapeType& shape) {
|
||
|
|
size_t sz = 1;
|
||
|
|
for (auto d : shape) {
|
||
|
|
sz *= d;
|
||
|
|
}
|
||
|
|
|
||
|
|
return sz;
|
||
|
|
}
|
||
|
|
|
||
|
|
} // namespace
|
||
|
|
|
||
|
|
TEST(OP, deconv_group) {
|
||
|
|
auto ctx = tim::vx::Context::Create();
|
||
|
|
auto graph = ctx->CreateGraph();
|
||
|
|
|
||
|
|
tim::vx::ShapeType input_shape ({3, 3, 2, 1}); //whcn
|
||
|
|
tim::vx::ShapeType kernel_shape({3, 3, 2, 1}); //whc1 same as depthwise convolution
|
||
|
|
tim::vx::ShapeType output_shape({5, 5, 2, 1}); //whcn
|
||
|
|
|
||
|
|
tim::vx::TensorSpec input_spec (tim::vx::DataType::FLOAT32, input_shape, tim::vx::TensorAttribute::INPUT);
|
||
|
|
tim::vx::TensorSpec kernel_spec (tim::vx::DataType::FLOAT32, kernel_shape, tim::vx::TensorAttribute::CONSTANT);
|
||
|
|
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);
|
||
|
|
auto kernel_tensor = graph->CreateTensor(kernel_spec);
|
||
|
|
|
||
|
|
std::vector<float> input_data = {3.0f, 8.0f, 1.0f,
|
||
|
|
9.0f, 5.0f, 7.0f,
|
||
|
|
3.0f, 2.0f, 3.0f,
|
||
|
|
|
||
|
|
7.0f, 9.0f, 1.0f,
|
||
|
|
5.0f, 2.0f, 3.0f,
|
||
|
|
9.0f, 0.0f, 2.0f};
|
||
|
|
std::vector<float> kernel_data =
|
||
|
|
{9.0f, 0.0f, 3.0f,
|
||
|
|
0.0f, 0.0f, 0.0f,
|
||
|
|
1.0f, 0.0f, 2.0f,
|
||
|
|
|
||
|
|
3.0f, 0.0f, 7.0f,
|
||
|
|
0.0f, 0.0f, 0.0f,
|
||
|
|
0.0f, 0.0f, 8.0f,
|
||
|
|
};
|
||
|
|
|
||
|
|
std::vector<float> output_data(element_count(output_shape));
|
||
|
|
|
||
|
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
|
||
|
|
EXPECT_TRUE(kernel_tensor->CopyDataToTensor(kernel_data.data(), kernel_data.size()*4));
|
||
|
|
|
||
|
|
auto add = graph->CreateOperation<tim::vx::ops::DeConv2d>(
|
||
|
|
2,
|
||
|
|
tim::vx::PadType::SAME,
|
||
|
|
std::array<uint32_t, 2>({3, 3}), /*ksize*/
|
||
|
|
std::array<uint32_t, 2>({1, 1}), /*stride*/
|
||
|
|
std::array<uint32_t, 2>({1, 1}), /*dilation*/
|
||
|
|
std::array<uint32_t, 4>({0, 0, 0, 0}), /*pad*/
|
||
|
|
2/*group*/);
|
||
|
|
(*add).BindInputs({input_tensor, kernel_tensor}).BindOutputs({output_tensor});
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Compile());
|
||
|
|
EXPECT_TRUE(graph->Run());
|
||
|
|
|
||
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_data.data()));
|
||
|
|
std::vector<float> golden = {
|
||
|
|
27.0f, 72.0f, 18.0f, 24.0f, 3.0f,
|
||
|
|
81.0f, 45.0f, 90.0f, 15.0f, 21.0f,
|
||
|
|
30.0f, 26.0f, 43.0f, 22.0f, 11.0f,
|
||
|
|
9.0f, 5.0f, 25.0f, 10.0f, 14.0f,
|
||
|
|
3.0f, 2.0f, 9.0f, 4.0f, 6.0f,
|
||
|
|
|
||
|
|
21.0f, 27.0f, 52.0f, 63.0f, 7.0f,
|
||
|
|
15.0f, 6.0f, 44.0f, 14.0f, 21.0f,
|
||
|
|
27.0f, 0.0f, 125.0f, 72.0f, 22.0f,
|
||
|
|
0.0f, 0.0f, 40.0f, 16.0f, 24.0f,
|
||
|
|
0.0f, 0.0f, 72.0f, 0.0f, 16.0f};
|
||
|
|
|
||
|
|
EXPECT_EQ(golden, output_data) << "Result mismatch";
|
||
|
|
}
|