274 lines
13 KiB
C++
274 lines
13 KiB
C++
#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops.h"
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#include "tim/transform/layout_inference.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(LayoutInference, simple_conv2d) {
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auto ctx = tim::vx::Context::Create();
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auto src_graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({1, 3, 3, 1});
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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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auto input = src_graph->CreateTensor(input_spec);
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tim::vx::ShapeType kernel_shape({1, 2, 2, 1});
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tim::vx::TensorSpec kernel_spec(tim::vx::DataType::FLOAT32, kernel_shape,
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tim::vx::TensorAttribute::CONSTANT);
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std::vector<float> kernel_data = {
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0.25f, 0.25f, 0.25f, 0.25f};
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auto kernel = src_graph->CreateTensor(kernel_spec, kernel_data.data());
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tim::vx::ShapeType bias_shape({1});
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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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std::vector<float> bias_data = {0.0f};
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auto bias = src_graph->CreateTensor(bias_spec, bias_data.data());
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tim::vx::ShapeType output_shape({1, 2, 2, 1});
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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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auto output = src_graph->CreateTensor(output_spec);
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auto conv2d = src_graph->CreateOperation<tim::vx::ops::Conv2d>(
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kernel_shape[0], tim::vx::PadType::AUTO,
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std::array<uint32_t, 2>({kernel_shape[2], kernel_shape[1]}),
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std::array<uint32_t, 2>({1, 1}), std::array<uint32_t, 2>({0, 0}),
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std::array<uint32_t, 4>({0, 0, 0, 0}), 0, tim::vx::DataLayout::CWHN);
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(*conv2d).BindInputs({input, kernel, bias}).BindOutput(output);
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// Do layout inference
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auto transform = tim::transform::LayoutInference(src_graph, ctx);
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auto infer_graph = transform.first;
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auto graph_io_map = transform.second;
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infer_graph->Compile();
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std::vector<float> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f};
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auto infer_input = graph_io_map[src_graph->InputsTensor()[0]];
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auto infer_output = graph_io_map[src_graph->OutputsTensor()[0]];
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infer_input->CopyDataToTensor(input_data.data(), input_data.size() * sizeof(float));
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infer_graph->Run();
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std::vector<float> out_data;
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auto infer_out_shape = infer_output->GetShape();
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out_data.resize(infer_out_shape[0] * infer_out_shape[1] * infer_out_shape[2] *
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infer_out_shape[3]);
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infer_output->CopyDataFromTensor(out_data.data());
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std::vector<float> expect_output = {0.875f, 0.875f, 0.875f, 0.875f};
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EXPECT_TRUE(0 == memcmp((void*)out_data.data(), (void*)expect_output.data(),
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sizeof(float) * out_data.size()));
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tim::vx::ShapeType expect_shape({1, 2, 2, 1});
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EXPECT_EQ(infer_out_shape, expect_shape);
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}
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TEST(LayoutInference, weight_as_input_conv2d) {
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auto ctx = tim::vx::Context::Create();
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auto src_graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({3, 3, 1, 1}); //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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auto input = src_graph->CreateTensor(input_spec);
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tim::vx::ShapeType kernel_shape({1, 2, 2, 1}); //IWHO
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tim::vx::TensorSpec kernel_spec(tim::vx::DataType::FLOAT32, kernel_shape,
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tim::vx::TensorAttribute::INPUT);
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auto kernel = src_graph->CreateTensor(kernel_spec);
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tim::vx::ShapeType bias_shape({1});
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::INPUT);
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auto bias = src_graph->CreateTensor(bias_spec);
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tim::vx::ShapeType output_shape({2, 2, 1, 1}); //WHCN
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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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auto output = src_graph->CreateTensor(output_spec);
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auto conv2d = src_graph->CreateOperation<tim::vx::ops::Conv2d>(
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0, tim::vx::PadType::AUTO, std::array<uint32_t, 2>({0, 0}),
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std::array<uint32_t, 2>({1, 1}), std::array<uint32_t, 2>({1, 1}),
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std::array<uint32_t, 4>({0, 0, 0, 0}), 0, tim::vx::DataLayout::WHCN,
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tim::vx::DataLayout::IcWHOc);
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(*conv2d).BindInputs({input, kernel, bias}).BindOutput(output);
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// Do layout inference
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auto transform = tim::transform::LayoutInference(src_graph, ctx);
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auto infer_graph = transform.first;
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auto graph_io_map = transform.second;
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infer_graph->Compile();
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std::vector<float> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f};
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std::vector<float> kernel_data = {0.25f, 0.25f, 0.25f, 0.25f};
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std::vector<float> bias_data = {0.0f};
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auto infer_input = graph_io_map[src_graph->InputsTensor()[0]];
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auto infer_weight = graph_io_map[src_graph->InputsTensor()[1]];
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auto infer_bias = graph_io_map[src_graph->InputsTensor()[2]];
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auto infer_output = graph_io_map[src_graph->OutputsTensor()[0]];
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infer_input->CopyDataToTensor(input_data.data(), input_data.size() * sizeof(float));
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infer_weight->CopyDataToTensor(kernel_data.data(), kernel_data.size() * sizeof(float));
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infer_bias->CopyDataToTensor(bias_data.data(), bias_data.size() * sizeof(float));
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infer_graph->Run();
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std::vector<float> out_data;
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auto infer_out_shape = infer_output->GetShape();
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out_data.resize(infer_out_shape[0] * infer_out_shape[1] * infer_out_shape[2] *
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infer_out_shape[3]);
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infer_output->CopyDataFromTensor(out_data.data());
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std::vector<float> expect_output = {0.875f, 0.875f, 0.875f, 0.875f};
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EXPECT_TRUE(0 == memcmp((void*)out_data.data(), (void*)expect_output.data(),
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sizeof(float) * out_data.size()));
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tim::vx::ShapeType expect_shape({2, 2, 1, 1});
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EXPECT_EQ(infer_out_shape, expect_shape);
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}
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TEST(GroupedConv2d, kernel_bigger_than_input_SAME) {
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auto ctx = tim::vx::Context::Create();
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auto src_graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
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tim::vx::ShapeType kernel_shape({1, 3, 2, 2}); //iwho, i*groups=c
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tim::vx::ShapeType bias_shape({2});
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tim::vx::ShapeType output_shape({2, 3, 2, 1});
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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 kernel_spec(tim::vx::DataType::FLOAT32, kernel_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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std::vector<float> in_data = {1.0f, 3.0f, 4.0f, 2.0f, 2.0f, 3.0f,
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2.0f, 4.0f, 3.0f, 1.0f, 3.0f, 3.0f};
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std::vector<float> weight = {100.0f, 20.0f, 1.0f, 200.0f, 10.0f, 2.0f,
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200.0f, 30.0f, 1.0f, 100.0f, 20.0f, 3.0f};
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std::vector<float> bias = {500.0f, -1000.0f};
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std::vector<float> golden = {567.0f, 1480.0f, 608.0f, 1370.0f,
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543.0f, 760.0f, -873.0f, -160.0f,
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-840.0f, -10.0f, -907.0f, -310.0f};
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auto input_tensor = src_graph->CreateTensor(input_spec);
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auto weight_tensor = src_graph->CreateTensor(kernel_spec, weight.data());
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auto bias_tensor = src_graph->CreateTensor(bias_spec, bias.data());
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auto output_tensor = src_graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> dilations = {0, 0};
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std::array<uint32_t, 2> strides = {1, 1};
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auto op = src_graph->CreateOperation<tim::vx::ops::GroupedConv2d>(
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tim::vx::PadType::SAME, strides, dilations, 2, tim::vx::DataLayout::WHCN,
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tim::vx::DataLayout::IcWHOc);
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(*op).BindInputs({input_tensor, weight_tensor, bias_tensor}).BindOutputs({output_tensor});
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// Do layout inference
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auto transform = tim::transform::LayoutInference(src_graph, ctx);
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auto infer_graph = transform.first;
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auto graph_io_map = transform.second;
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infer_graph->Compile();
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auto infer_input = graph_io_map[src_graph->InputsTensor()[0]];
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auto infer_output = graph_io_map[src_graph->OutputsTensor()[0]];
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infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
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infer_graph->Run();
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std::vector<float> output(golden.size());
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EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(FC, share_const_tensor) {
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auto ctx = tim::vx::Context::Create();
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auto src_graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({2, 1});
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tim::vx::ShapeType kernel_shape({2, 2});
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tim::vx::ShapeType bias_shape({2});
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tim::vx::ShapeType output_shape({2, 1});
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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 kernel_spec(tim::vx::DataType::FLOAT32, kernel_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 tran_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::TRANSIENT);
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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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std::vector<float> in_data = {1,4,};
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std::vector<float> weight = {-3,3,2,1,};
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std::vector<float> bias = {0.1, 0.4,};
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std::vector<float> golden = {-8, 25};
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auto input_tensor = src_graph->CreateTensor(input_spec);
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auto weight_tensor = src_graph->CreateTensor(kernel_spec, weight.data());
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auto bias_tensor = src_graph->CreateTensor(bias_spec, bias.data());
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auto tran_tensor = src_graph->CreateTensor(tran_spec);
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auto output_tensor = src_graph->CreateTensor(output_spec);
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auto op1 = src_graph->CreateOperation<tim::vx::ops::FullyConnected>(0,2);
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(*op1).BindInputs({input_tensor, weight_tensor, bias_tensor}).BindOutputs({tran_tensor});
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auto op2 = src_graph->CreateOperation<tim::vx::ops::FullyConnected>(0,2);
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(*op2).BindInputs({tran_tensor, weight_tensor, bias_tensor}).BindOutputs({output_tensor});
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// Do layout inference
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auto transform = tim::transform::LayoutInference(src_graph, ctx);
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auto infer_graph = transform.first;
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auto graph_io_map = transform.second;
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infer_graph->Compile();
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auto infer_input = graph_io_map[src_graph->InputsTensor()[0]];
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auto infer_output = graph_io_map[src_graph->OutputsTensor()[0]];
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infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
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infer_graph->Run();
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std::vector<float> output(golden.size());
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EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(InstanceNorm, nhwc) {
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auto ctx = tim::vx::Context::Create();
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auto src_graph = ctx->CreateGraph();
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tim::vx::ShapeType io_shape({2, 2, 2, 2}); //nhwc
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tim::vx::ShapeType param_shape({1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = src_graph->CreateTensor(input_spec);
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auto beta_tensor = src_graph->CreateTensor(param_spec);
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auto gamma_tensor = src_graph->CreateTensor(param_spec);
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auto output_tensor = src_graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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0.0f, 1.0f, 0.0f, 2.0f, 0.0f, 2.0f, 0.0f, 4.0f, 1.0f, -1.0f, -1.0f, 2.0f, -1.0f, -2.0f, 1.0f, 4.0f
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};
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std::vector<float> beta = {0};
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std::vector<float> gamma = {1.0f};
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std::vector<float> golden = {
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0.0f, -1.1470304f, 0.0f, -0.22940612f, 0.0f, -0.22940612f, 0.0f, 1.6058424f, 0.99995005f,
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-0.7337929f, -0.99995005f, 0.52413774f, -0.99995005f, -1.1531031f, 0.99995005f, 1.3627582f,
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};
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auto op = src_graph->CreateOperation<tim::vx::ops::InstanceNormalization>(1e-4f, tim::vx::DataLayout::CWHN);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_tensor}).BindOutputs({output_tensor});
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// Do layout inference
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auto transform = tim::transform::LayoutInference(src_graph, ctx);
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auto infer_graph = transform.first;
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auto graph_io_map = transform.second;
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infer_graph->Compile();
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auto infer_input = graph_io_map[src_graph->InputsTensor()[0]];
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auto infer_beta = graph_io_map[src_graph->InputsTensor()[1]];
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auto infer_gamma = graph_io_map[src_graph->InputsTensor()[2]];
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auto infer_output = graph_io_map[src_graph->OutputsTensor()[0]];
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infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
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infer_beta->CopyDataToTensor(beta.data(), beta.size() * sizeof(float));
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infer_gamma->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float));
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infer_graph->Run();
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std::vector<float> output(golden.size());
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EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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} |