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提交 7fbe7361 编写于 作者: L Liangliang He
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Refactor ops test util

上级 cd6092ab
隐藏空白更改
内联 并排
Showing with 162 addition and 127 deletion
mace/kernels/conv_pool_2d_util.cc
浏览文件 @ 7fbe7361
......@@ -12,20 +12,23 @@ void CalcPaddingAndOutputSize(const index_t* input_shape, // NCHW
const int* dilations,
const int* strides,
Padding padding,
std::vector<index_t>* output_shape,
std::vector<int>* padding_size) {
index_t* output_shape,
int* padding_size) {
MACE_CHECK(dilations[0] > 0 && dilations[1] > 0,
"Invalid dilations, must >= 1");
MACE_CHECK((dilations[0] == 1 || strides[0] == 1) &&
(dilations[1] == 1 || strides[1] == 1),
"If dilations > 1, strides should be 1");
MACE_CHECK_NOTNULL(output_shape);
MACE_CHECK_NOTNULL(padding_size);
/*
* Convlution/pooling arithmetic:
* o = (i + 2 * p - k - (k - 1) * (d - 1)) / s + 1
* For details, see https://arxiv.org/pdf/1603.07285.pdf or
* http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html
*/
*padding_size = {0, 0};
padding_size[0] = 0;
padding_size[1] = 0;
index_t output_height, output_width;
index_t kernel_height = filter_shape[2];
......@@ -57,16 +60,15 @@ void CalcPaddingAndOutputSize(const index_t* input_shape, // NCHW
// utilize the more centered features. We need to benchmark
// based on the model accuracy.
(*padding_size)[0] = (output_height - 1) * strides[0] +
padding_size[0] = (output_height - 1) * strides[0] +
k_extent_height - input_shape[2];
(*padding_size)[1] = (output_width - 1) * strides[1] +
padding_size[1] = (output_width - 1) * strides[1] +
k_extent_width - input_shape[3];
*output_shape = std::vector<index_t>(4); // NCHW
(*output_shape)[0] = input_shape[0];
(*output_shape)[1] = output_channels;
(*output_shape)[2] = output_height;
(*output_shape)[3] = output_width;
output_shape[0] = input_shape[0];
output_shape[1] = output_channels;
output_shape[2] = output_height;
output_shape[3] = output_width;
}
} // namespace kernels
......
mace/kernels/conv_pool_2d_util.h
浏览文件 @ 7fbe7361
......@@ -22,8 +22,8 @@ void CalcPaddingAndOutputSize(const index_t* input_shape, // NCHW
const int* dilations,
const int* strides,
Padding padding,
std::vector<index_t>* output_shape,
std::vector<int>* padding_size);
index_t* output_shape,
int* padding_size);
} // namespace kernels
} // namespace mace
......
mace/ops/batch_norm_test.cc
浏览文件 @ 7fbe7361
......@@ -11,6 +11,7 @@ class BatchNormOpTest : public OpsTestBase {};
TEST_F(BatchNormOpTest, Simple) {
// Construct graph
auto net = test_net();
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("Input")
.Input("Scale")
......@@ -18,25 +19,25 @@ TEST_F(BatchNormOpTest, Simple) {
.Input("Mean")
.Input("Var")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add input data
AddInputFromArray<float>("Input", {1, 1, 6, 2},
net->AddInputFromArray<float>("Input", {1, 1, 6, 2},
{5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15});
AddInputFromArray<float>("Scale", {1}, {4.0f});
AddInputFromArray<float>("Offset", {1}, {2.0});
AddInputFromArray<float>("Mean", {1}, {10});
AddInputFromArray<float>("Var", {1}, {11.67f});
net->AddInputFromArray<float>("Scale", {1}, {4.0f});
net->AddInputFromArray<float>("Offset", {1}, {2.0});
net->AddInputFromArray<float>("Mean", {1}, {10});
net->AddInputFromArray<float>("Var", {1}, {11.67f});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 1, 6, 2},
{-3.86, -3.86, -1.51, -1.51, 0.83, 0.83,
3.17, 3.17, 5.51, 5.51, 7.86, 7.86});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.01);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.01);
}
}
mace/ops/conv_2d.h
浏览文件 @ 7fbe7361
......@@ -25,15 +25,15 @@ class Conv2dOp : public ConvPool2dOpBase<D, T> {
const Tensor* bias = this->Input(BIAS);
Tensor* output = this->Output(OUTPUT);
std::vector<index_t> output_shape;
std::vector<int> paddings;
std::vector<index_t> output_shape(4);
std::vector<int> paddings(2);
kernels::CalcPaddingAndOutputSize(input->shape().data(),
filter->shape().data(),
this->dilations_.data(),
this->strides_.data(),
this->padding_,
&output_shape,
&paddings);
output_shape.data(),
paddings.data());
output->Resize(output_shape);
auto conv2d = kernels::Conv2dFunctor<D, T>(this->strides_.data(),
......
mace/ops/conv_2d_benchmark.cc
浏览文件 @ 7fbe7361
......@@ -2,10 +2,14 @@
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include <algorithm>
#include "mace/core/testing/test_benchmark.h"
#include "mace/ops/conv_2d.h"
#include "mace/kernels/conv_2d.h"
#include "mace/kernels/conv_pool_2d_util.h"
namespace mace {
namespace kernels {
template <DeviceType D, typename T>
static void Conv2d(int iters, int batch, int channels, int height, int width,
......@@ -34,4 +38,5 @@ static void Conv2d(int iters, int batch, int channels, int height, int width,
BM_CONV_2D(1, 64, 32, 32, 1, 1, 1, VALID, 128, float);
} // namespace kernels
} // namespace mace
mace/ops/conv_2d_test.cc
浏览文件 @ 7fbe7361
......@@ -12,71 +12,73 @@ class Conv2dOpTest : public OpsTestBase {};
TEST_F(Conv2dOpTest, Simple_VALID) {
// Construct graph
auto net = test_net();
OpDefBuilder("Conv2d", "Conv2dTest")
.Input("Input")
.Input("Filter")
.Input("Bias")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("strides", {1, 1});
AddIntArg("padding", Padding::VALID);
AddIntsArg("dilations", {1, 1});
net->AddIntsArg("strides", {1, 1});
net->AddIntArg("padding", Padding::VALID);
net->AddIntsArg("dilations", {1, 1});
// Add input data
AddInputFromArray<float>("Input", {1, 2, 3, 3},
net->AddInputFromArray<float>("Input", {1, 2, 3, 3},
{1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1});
AddInputFromArray<float>("Filter", {1, 2, 3, 3},
net->AddInputFromArray<float>("Filter", {1, 2, 3, 3},
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
AddInputFromArray<float>("Bias", {1}, {0.1f});
net->AddInputFromArray<float>("Bias", {1}, {0.1f});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 1, 1, 1}, {18.1f});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(Conv2dOpTest, Simple_SAME) {
// Construct graph
auto net = test_net();
OpDefBuilder("Conv2d", "Conv2dTest")
.Input("Input")
.Input("Filter")
.Input("Bias")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("strides", {1, 1});
AddIntArg("padding", Padding::SAME);
AddIntsArg("dilations", {1, 1});
net->AddIntsArg("strides", {1, 1});
net->AddIntArg("padding", Padding::SAME);
net->AddIntsArg("dilations", {1, 1});
// Add input data
AddInputFromArray<float>("Input", {1, 2, 3, 3},
net->AddInputFromArray<float>("Input", {1, 2, 3, 3},
{1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1});
AddInputFromArray<float>("Filter", {1, 2, 3, 3},
net->AddInputFromArray<float>("Filter", {1, 2, 3, 3},
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
AddInputFromArray<float>("Bias", {1}, {0.1f});
net->AddInputFromArray<float>("Bias", {1}, {0.1f});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 1, 3, 3},
......@@ -84,25 +86,26 @@ TEST_F(Conv2dOpTest, Simple_SAME) {
12.1f, 18.1f, 12.1f,
8.1f, 12.1f, 8.1f});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(Conv2dOpTest, Combined) {
// Construct graph
auto net = test_net();
OpDefBuilder("Conv2d", "Conv2dTest")
.Input("Input")
.Input("Filter")
.Input("Bias")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("strides", {2, 2});
AddIntArg("padding", Padding::SAME);
AddIntsArg("dilations", {1, 1});
net->AddIntsArg("strides", {2, 2});
net->AddIntArg("padding", Padding::SAME);
net->AddIntsArg("dilations", {1, 1});
// Add input data
AddInputFromArray<float>("Input", {1, 2, 5, 5},
net->AddInputFromArray<float>("Input", {1, 2, 5, 5},
{1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
......@@ -113,15 +116,15 @@ TEST_F(Conv2dOpTest, Combined) {
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1});
AddInputFromArray<float>("Filter", {2, 2, 3, 3},
net->AddInputFromArray<float>("Filter", {2, 2, 3, 3},
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f});
AddInputFromArray<float>("Bias", {2}, {0.1f, 0.2f});
net->AddInputFromArray<float>("Bias", {2}, {0.1f, 0.2f});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 2, 3, 3},
......@@ -133,25 +136,26 @@ TEST_F(Conv2dOpTest, Combined) {
4.2f, 6.2f, 4.2f});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(Conv2dOpTest, Conv1x1) {
// Construct graph
auto net = test_net();
OpDefBuilder("Conv2d", "Conv2dTest")
.Input("Input")
.Input("Filter")
.Input("Bias")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("strides", {1, 1});
AddIntArg("padding", Padding::VALID);
AddIntsArg("dilations", {1, 1});
net->AddIntsArg("strides", {1, 1});
net->AddIntArg("padding", Padding::VALID);
net->AddIntsArg("dilations", {1, 1});
// Add input data
AddInputFromArray<float>("Input", {1, 5, 3, 10},
net->AddInputFromArray<float>("Input", {1, 5, 3, 10},
{1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
......@@ -167,13 +171,13 @@ TEST_F(Conv2dOpTest, Conv1x1) {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
AddInputFromArray<float>("Filter", {2, 5, 1, 1},
net->AddInputFromArray<float>("Filter", {2, 5, 1, 1},
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
2.0f, 2.0f, 2.0f, 2.0f, 2.0f});
AddInputFromArray<float>("Bias", {2}, {0.1f, 0.2f});
net->AddInputFromArray<float>("Bias", {2}, {0.1f, 0.2f});
// Run
RunOp(DeviceType::NEON);
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 2, 3, 10},
......@@ -184,7 +188,7 @@ TEST_F(Conv2dOpTest, Conv1x1) {
10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f,
10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f, 10.2f});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
// TODO we need more tests
mace/ops/ops_test_util.h
浏览文件 @ 7fbe7361
......@@ -35,17 +35,12 @@ class OpDefBuilder {
OperatorDef op_def_;
};
class OpsTestBase : public ::testing::Test {
protected:
virtual void TearDown() {
auto tensor_names = ws_.Tensors();
for (auto& name : tensor_names) {
ws_.RemoveTensor(name);
}
}
class OpsTestNet {
public:
template <typename T>
void AddInputFromArray(const char* name, const std::vector<index_t>& shape, const std::vector<T>& data) {
void AddInputFromArray(const char* name,
const std::vector<index_t>& shape,
const std::vector<T>& data) {
Tensor* input = ws_.CreateTensor(name, cpu_allocator(), DataTypeToEnum<T>::v());
input->Resize(shape);
float* input_data = input->mutable_data<float>();
......@@ -97,12 +92,16 @@ class OpsTestBase : public ::testing::Test {
OperatorDef* operator_def() { return &op_def_; }
Workspace* ws() { return &ws_; }
bool RunOp(DeviceType device) {
NetDef net_def;
net_def.add_op()->CopyFrom(op_def_);
VLOG(0) << net_def.DebugString();
auto net = CreateNet(net_def, &ws_, device);
return net->Run();
if (!net_) {
NetDef net_def;
net_def.add_op()->CopyFrom(op_def_);
VLOG(0) << net_def.DebugString();
net_ = CreateNet(net_def, &ws_, device);
}
return net_->Run();
}
bool RunOp() {
......@@ -113,9 +112,27 @@ class OpsTestBase : public ::testing::Test {
return ws_.GetTensor(output_name);
}
private:
public:
Workspace ws_;
OperatorDef op_def_;
std::unique_ptr<NetBase> net_;
};
class OpsTestBase : public ::testing::Test {
public:
OpsTestNet* test_net() { return &test_net_; };
protected:
virtual void TearDown() {
auto ws = test_net_.ws();
auto tensor_names = ws->Tensors();
for (auto& name : tensor_names) {
ws->RemoveTensor(name);
}
}
private:
OpsTestNet test_net_;
};
template <typename T>
......
mace/ops/pooling.h
浏览文件 @ 7fbe7361
......@@ -26,8 +26,8 @@ public:
Tensor* output = this->Output(OUTPUT);
std::vector<index_t> in_shape = input->shape();
std::vector<index_t> output_shape;
std::vector<int> paddings;
std::vector<index_t> output_shape(4);
std::vector<int> paddings(2);
std::vector<index_t> filter_shape = std::vector<index_t>(4);
filter_shape[0] = in_shape[1];
filter_shape[1] = in_shape[0];
......@@ -38,8 +38,8 @@ public:
this->dilations_.data(),
this->strides_.data(),
this->padding_,
&output_shape,
&paddings);
output_shape.data(),
paddings.data());
output->Resize(output_shape);
auto pooling_func = kernels::PoolingFunctor<D, T>(pooling_type_,
......
mace/ops/pooling_test.cc
浏览文件 @ 7fbe7361
......@@ -15,20 +15,21 @@ class PoolingOpTest : public OpsTestBase {};
TEST_F(PoolingOpTest, MAX_VALID) {
// Construct graph
auto net = test_net();
OpDefBuilder("Pooling", "PoolingTest")
.Input("Input")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("kernels", {2, 2});
AddIntsArg("strides", {2, 2});
AddIntArg("padding", Padding::VALID);
AddIntsArg("dilations", {1, 1});
AddIntArg("pooling_type", PoolingType::MAX);
net->AddIntsArg("kernels", {2, 2});
net->AddIntsArg("strides", {2, 2});
net->AddIntArg("padding", Padding::VALID);
net->AddIntsArg("dilations", {1, 1});
net->AddIntArg("pooling_type", PoolingType::MAX);
// Add input data
AddInputFromArray<float>("Input", {1, 2, 4, 4},
net->AddInputFromArray<float>("Input", {1, 2, 4, 4},
{0, 1, 2, 3,
4, 5, 6, 7,
8, 9, 10, 11,
......@@ -39,32 +40,33 @@ TEST_F(PoolingOpTest, MAX_VALID) {
28, 29, 30, 31});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 2, 2, 2},
{5, 7, 13, 15, 21, 23, 29, 31});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(PoolingOpTest, AVG_VALID) {
// Construct graph
auto net = test_net();
OpDefBuilder("Pooling", "PoolingTest")
.Input("Input")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("kernels", {2, 2});
AddIntsArg("strides", {2, 2});
AddIntArg("padding", Padding::VALID);
AddIntsArg("dilations", {1, 1});
AddIntArg("pooling_type", PoolingType::AVG);
net->AddIntsArg("kernels", {2, 2});
net->AddIntsArg("strides", {2, 2});
net->AddIntArg("padding", Padding::VALID);
net->AddIntsArg("dilations", {1, 1});
net->AddIntArg("pooling_type", PoolingType::AVG);
// Add input data
AddInputFromArray<float>("Input", {1, 2, 4, 4},
net->AddInputFromArray<float>("Input", {1, 2, 4, 4},
{0, 1, 2, 3,
4, 5, 6, 7,
8, 9, 10, 11,
......@@ -75,72 +77,74 @@ TEST_F(PoolingOpTest, AVG_VALID) {
28, 29, 30, 31});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 2, 2, 2},
{2.5, 4.5, 10.5, 12.5, 18.5, 20.5, 26.5, 28.5});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(PoolingOpTest, MAX_SAME) {
// Construct graph
auto net = test_net();
OpDefBuilder("Pooling", "PoolingTest")
.Input("Input")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("kernels", {2, 2});
AddIntsArg("strides", {2, 2});
AddIntArg("padding", Padding::SAME);
AddIntsArg("dilations", {1, 1});
AddIntArg("pooling_type", PoolingType::MAX);
net->AddIntsArg("kernels", {2, 2});
net->AddIntsArg("strides", {2, 2});
net->AddIntArg("padding", Padding::SAME);
net->AddIntsArg("dilations", {1, 1});
net->AddIntArg("pooling_type", PoolingType::MAX);
// Add input data
AddInputFromArray<float>("Input", {1, 1, 3, 3},
net->AddInputFromArray<float>("Input", {1, 1, 3, 3},
{0, 1, 2,
3, 4, 5,
6, 7, 8});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 1, 2, 2},
{4, 5, 7, 8});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
// Construct graph
auto net = test_net();
OpDefBuilder("Pooling", "PoolingTest")
.Input("Input")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add args
AddIntsArg("kernels", {2, 2});
AddIntsArg("strides", {1, 1});
AddIntArg("padding", Padding::VALID);
AddIntsArg("dilations", {2, 2});
AddIntArg("pooling_type", PoolingType::MAX);
net->AddIntsArg("kernels", {2, 2});
net->AddIntsArg("strides", {1, 1});
net->AddIntArg("padding", Padding::VALID);
net->AddIntsArg("dilations", {2, 2});
net->AddIntArg("pooling_type", PoolingType::MAX);
// Add input data
AddInputFromArray<float>("Input", {1, 1, 4, 4},
net->AddInputFromArray<float>("Input", {1, 1, 4, 4},
{0, 1, 2, 3,
4, 5, 6, 7,
8, 9, 10, 11,
12, 13, 14, 15});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 1, 2, 2},
{10, 11, 14, 15});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
mace/ops/resize_bilinear_test.cc
浏览文件 @ 7fbe7361
......@@ -13,49 +13,51 @@ class ResizeBilinearTest : public OpsTestBase {};
TEST_F(ResizeBilinearTest, ResizeBilinearWOAlignCorners) {
testing::internal::LogToStderr();
// Construct graph
auto net = test_net();
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("Input")
.Input("OutSize")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
// Add input data
vector<float> input(24);
std::iota(begin(input), end(input), 0);
AddInputFromArray<float>("Input", {1, 3, 2, 4}, input);
AddInputFromArray<index_t>("OutSize", {2}, {1, 2});
net->AddInputFromArray<float>("Input", {1, 3, 2, 4}, input);
net->AddInputFromArray<index_t>("OutSize", {2}, {1, 2});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 3, 1, 2}, {0, 2, 8, 10, 16, 18});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
TEST_F(ResizeBilinearTest, ResizeBilinearWAlignCorners) {
testing::internal::LogToStderr();
// Construct graph
auto net = test_net();
OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
.Input("Input")
.Input("OutSize")
.Output("Output")
.Finalize(operator_def());
.Finalize(net->operator_def());
AddIntArg("align_corners", 1);
net->AddIntArg("align_corners", 1);
// Add input data
vector<float> input(24);
std::iota(begin(input), end(input), 0);
AddInputFromArray<float>("Input", {1, 3, 2, 4}, input);
AddInputFromArray<index_t>("OutSize", {2}, {1, 2});
net->AddInputFromArray<float>("Input", {1, 3, 2, 4}, input);
net->AddInputFromArray<index_t>("OutSize", {2}, {1, 2});
// Run
RunOp();
net->RunOp();
// Check
Tensor expected = CreateTensor<float>({1, 3, 1, 2}, {0, 3, 8, 11, 16, 19});
ExpectTensorNear<float>(expected, *GetOutput("Output"), 0.001);
ExpectTensorNear<float>(expected, *net->GetOutput("Output"), 0.001);
}
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