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[onert] Revisit DepthwiseConv2D nnfw_api test #13559

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Aug 1, 2024
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[onert] Revisit DepthwiseConv2D nnfw_api test #13559

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[onert] Revisit DepthwiseConv2D nnfw_api test
jyoungyun Jul 30, 2024
fe93805
Update wrong weight data
jyoungyun Aug 1, 2024
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71 changes: 60 additions & 11 deletions tests/nnfw_api/src/GenModelTests/one_op_tests/DepthwiseConv2D.test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -19,27 +19,51 @@
TEST_F(GenModelTest, OneOp_DepthwiseConv2D)
{
CircleGen cgen;
std::vector<float> weight_data{1, 2, 3, 4, -9, 10, -11, 12, 5, 6, 7, 8, 13, -14, 15, -16};
std::vector<float> weight_data{1.0f, 2.0f, 3.0f, 4.0f, -9.0f, 10.0f, -11.0f, 12.0f,
5.0f, 6.0f, 7.0f, 8.0f, 13.0f, -14.0f, 15.0f, -16};
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Why weight shape is {1, 2, 2, 2} and data size is 16?

Suggested change
std::vector<float> weight_data{1.0f, 2.0f, 3.0f, 4.0f, -9.0f, 10.0f, -11.0f, 12.0f,
5.0f, 6.0f, 7.0f, 8.0f, 13.0f, -14.0f, 15.0f, -16};
std::vector<float> weight_data{1.0f, 2.0f, 3.0f, 4.0f, -9.0f, 10.0f, -11.0f, 12.0f};

uint32_t weight_buf = cgen.addBuffer(weight_data);
std::vector<float> bias_data{1, 2, 3, 4};
std::vector<float> bias_data{0.0f, 0.0f};
uint32_t bias_buf = cgen.addBuffer(bias_data);
int in = cgen.addTensor({{1, 3, 2, 2}, circle::TensorType::TensorType_FLOAT32});
int weight = cgen.addTensor({{1, 2, 2, 4}, circle::TensorType::TensorType_FLOAT32, weight_buf});
int bias = cgen.addTensor({{4}, circle::TensorType::TensorType_FLOAT32, bias_buf});
int out = cgen.addTensor({{1, 2, 1, 4}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_VALID, 1, 1, 2,
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int weight = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf});
int bias = cgen.addTensor({{2}, circle::TensorType::TensorType_FLOAT32, bias_buf});
int out = cgen.addTensor({{1, 2, 1, 2}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_VALID, 1, 1, 1,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({in}, {out});

_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(uniformTCD<float>({{1, 2, 7, 8, 3, 4, 9, 10, 5, 6, 11, 12}},
{{71, -34, 99, -20, 91, -26, 127, -4}}));
_context->setBackends({"acl_cl", "acl_neon", "cpu", "xnnpack"});
_context->addTestCase(
uniformTCD<float>({{1.0f, 2.0f, 7.0f, 8.0f, 3.0f, 4.0f, 9.0f, 10.0f, 5.0f, 6.0f, 11.0f, 12.0f}},
{{-104.f, 196.0f, -136.0f, 252.0f}}));
_context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"});
SUCCEED();
}

TEST_F(GenModelTest, OneOp_DepthwiseConv2D_Padding_SAME)
{
CircleGen cgen;
std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
uint32_t weight_buf = cgen.addBuffer(weight_data);
std::vector<float> bias_data{0.0f, 0.0f};
uint32_t bias_buf = cgen.addBuffer(bias_data);
int in = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32});
int weight = cgen.addTensor({{1, 3, 1, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf});
int bias = cgen.addTensor({{2}, circle::TensorType::TensorType_FLOAT32, bias_buf});
int out = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_SAME, 1, 1, 1,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({in}, {out});

_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(
uniformTCD<float>({{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f}},
{{16.0f, 28.0f, 28.0f, 44.0f, 8.0f, 16.0f, 12.0f, 24.0f}}));
_context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"});
SUCCEED();
}

TEST_F(GenModelTest, OneOp_DepthwiseConv2D_No_Multiplier)
TEST_F(GenModelTest, OneOp_DepthwiseConv2D_Bias)
{
CircleGen cgen;
std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
Expand All @@ -62,7 +86,32 @@ TEST_F(GenModelTest, OneOp_DepthwiseConv2D_No_Multiplier)
SUCCEED();
}

TEST_F(GenModelTest, OneOp_DepthwiseConv2D_No_Multiplier_RELU6)
TEST_F(GenModelTest, OneOp_DepthwiseConv2D_Muliplier)
{
CircleGen cgen;
std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-5.0f, -4.0f, -3.0f, -2.0f, -1.0f, 0.0};
uint32_t weight_buf = cgen.addBuffer(weight_data);
std::vector<float> bias_data{0.5f, -0.5f, 0.3f, -0.3f};
uint32_t bias_buf = cgen.addBuffer(bias_data);
int in = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32});
int weight = cgen.addTensor({{1, 3, 1, 4}, circle::TensorType::TensorType_FLOAT32, weight_buf});
int bias = cgen.addTensor({{4}, circle::TensorType::TensorType_FLOAT32, bias_buf});
int out = cgen.addTensor({{1, 2, 2, 4}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_SAME, 1, 1, 2,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({in}, {out});

_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(
uniformTCD<float>({{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f}},
{{-11.5f, -8.5f, -9.7f, -4.3f, -9.5f, -2.5f, -21.7f, -12.3f, 16.5f, 19.5f,
-22.7f, -17.3f, 24.5f, 31.5f, -28.7f, -19.3f}}));
_context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"});
SUCCEED();
}

TEST_F(GenModelTest, OneOp_DepthwiseConv2D_RELU6)
{
CircleGen cgen;
std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
Expand Down