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add float32 unit_test for depthwise convolution 

Signed-off-by: Jing.Deng <Jing.Deng@verisilicon.com>
This commit is contained in:
Jing.Deng 2021-06-11 09:42:39 +08:00 committed by Kainan Cha
parent e2c52d2d8a
commit c77217745f
1 changed files with 473 additions and 0 deletions
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src/tim/vx/ops/depthwiseConv_test.cc Normal file
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#include "gtest/gtest.h"
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/conv2d.h"
#include "tim/vx/types.h"
TEST(DepthwiseConv, shape_2_3_2_1_float32_SimpleTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
tim::vx::ShapeType bias_shape({weight_shape[2]});
tim::vx::ShapeType output_shape(
{1, 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, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
// weight data iohw
std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
3, -11, 7, 15, 4, 12, 8, -16};
// bias data
std::vector<float> bias_data = {1, 2, 3, 4};
// nchw
std::vector<float> golden = {71, 91, -34, -26, 99, 127, -20, -4};
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_3_2_1_float32_StrideTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
tim::vx::ShapeType weight_shape({2, 2, 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, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
// weight data iohw
std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
3, -11, 7, 15, 4, 12, 8, -16};
// bias data
std::vector<float> bias_data = {1, 2, 3, 4};
// nchw
std::vector<float> golden = {71, -34, 99, -20};
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({2, 2});
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_3_2_1_float32_PaddingTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
tim::vx::ShapeType weight_shape({2, 2, 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, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
// weight data iohw
std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
3, -11, 7, 15, 4, 12, 8, -16};
// bias data
std::vector<float> bias_data = {1, 2, 3, 4};
// nchw
std::vector<float> golden = {71, -34, 99, -20};
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({2, 2});
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_9_9_1_1_float32_DilationValidTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({9, 9, 1, 1}); //whcn
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whoi
tim::vx::ShapeType bias_shape({weight_shape[2]});
tim::vx::ShapeType output_shape(
{3, 3, 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 = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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, 2, 3, 4, 5, 6, 7, 8, 9};
// bias data
std::vector<float> bias_data = {0};
// nchw
std::vector<float> golden = {5, 5, 5, 5, 5, 5, 5, 5, 5};
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({3, 3});
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_3_3_1_1_float32_DilationSameTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3, 3, 1, 1}); //whcn
tim::vx::ShapeType weight_shape({2, 2, 1, 1}); //whoi
tim::vx::ShapeType bias_shape({weight_shape[2]});
tim::vx::ShapeType output_shape(
{3, 3, 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};
// weight data iohw
std::vector<float> weight_data = {1, 2, 3, 4};
// bias data
std::vector<float> bias_data = {0};
// nchw
std::vector<float> golden = {4, 7, 3, 6, 10, 4, 2, 3, 1};
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({2, 2});
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_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);
}