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[TFTRT] Add Dynamic Shape Testing for ConvertClipByValue
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@DEKHTIARJonathan
DEKHTIARJonathan committed Dec 15, 2020
1 parent 110199c commit 2f368c1
Showing 1 changed file with 78 additions and 75 deletions.
153 changes: 78 additions & 75 deletions tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -6607,112 +6607,115 @@ TEST_F(OpConverterTest, ConvertSpaceToDepth) {
}

#if IS_TRT_VERSION_GE(5, 1, 2, 0)
// Get the NodeDef for ClipByValue.
NodeDef GetClipByValueNodeDef(DataType dtype) {
TEST_P(OpConverter_FP32_FP16_Test, ConvertClipByValue) {
Scope s = Scope::NewRootScope();
auto t = ops::Placeholder(s.WithOpName("t"), dtype);
auto clip_value_min = ops::Placeholder(s.WithOpName("clip_value_min"), dtype);
auto clip_value_max = ops::Placeholder(s.WithOpName("clip_value_max"), dtype);
auto t = ops::Placeholder(s.WithOpName("t"), tf_type_);
auto clip_value_min = ops::Placeholder(s.WithOpName("clip_value_min"), tf_type_);
auto clip_value_max = ops::Placeholder(s.WithOpName("clip_value_max"), tf_type_);
auto clip = ops::ClipByValue(s.WithOpName("my_clip"), t, clip_value_min,
clip_value_max);
return clip.operation.node()->def();
}

template <DataType dtype>
void TestConvertClipByValue(OpConverterTest* test) {
typedef typename EnumToDataType<dtype>::Type CType;

struct TestParams {
std::vector<int> dims;
std::vector<CType> input_value;
CType clip_value_min;
CType clip_value_max;
std::vector<CType> expected_output;
};

const std::vector<CType> common_input = InitTestVector<CType>(6);
std::vector<TestParams> params = {
{
/*dims=*/{1, 2, 3},
/*input_value=*/common_input,
/*clip_value_min=*/CType(2),
/*clip_value_max=*/CType(5),
/*expected_output=*/
{CType(2), CType(2), CType(2), CType(3), CType(4), CType(5)},
},
{
/*dims=*/{2, 1, 3},
/*input_value=*/common_input,
/*clip_value_min=*/CType(-1),
/*clip_value_max=*/CType(8),
/*expected_output=*/common_input,
},
};

for (int i = 0; i < params.size(); ++i) {
test->Reset();
const NodeDef& node_def = clip.operation.node()->def();

NodeDef node_def = GetClipByValueNodeDef(dtype);
nvinfer1::DataType trt_type;
TF_ASSERT_OK(TfTypeToTrtType(dtype, &trt_type));
test->AddTestTensor("t", params[i].dims, 1, trt_type);
test->AddTestWeights<CType>("clip_value_min", {1},
{params[i].clip_value_min});
test->AddTestWeights<CType>("clip_value_max", {1},
{params[i].clip_value_max});
test->RunValidationAndConversion(node_def);
nvinfer1::DataType trt_type_;
TF_ASSERT_OK(TfTypeToTrtType(tf_type_, &trt_type_));

TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_clip", &output));
EXPECT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(params[i].dims, output.tensor()->getDimensions());

DataVec input_data{{"t", test->AsTensor<CType>(params[i].input_value)}};
DataVec output_data{{"my_clip", test->ConstructTensor<CType>(
params[i].expected_output.size())}};
TF_EXPECT_OK(test->BuildAndRun(input_data, &output_data));
EXPECT_THAT(GetSpanForData<CType>(output_data[0]),
ElementsAreArray(params[i].expected_output));
}
}

TEST_F(OpConverterTest, ConvertClipByValue) {
{
// Input is a weight, should fail.
Reset();
NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
AddTestWeights<float>("t", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestWeights<float>("clip_value_min", {1}, {1});
AddTestWeights<float>("clip_value_max", {1}, {5});
AddTestWeights("t", {1, 2, 3}, {1, 2, 3, 4, 5, 6}, tf_type_);
AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
AddTestWeights("clip_value_max", {1}, {5}, tf_type_);
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"t\" for ClipByValue must be a "
"tensor, at my_clip");
}
{
// Clip min is a tensor, should fail.
Reset();
NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
AddTestTensor("t", {1, 2, 3});
AddTestTensor("clip_value_min", {1});
AddTestWeights<float>("clip_value_max", {1}, {1});
AddTestWeights("clip_value_max", {1}, {1}, tf_type_);
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"clip_value_min\" for ClipByValue "
"must be a constant, at my_clip");
}
{
// Clip max is a tensor, should fail.
Reset();
NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
AddTestTensor("t", {1, 2, 3});
AddTestWeights<float>("clip_value_min", {1}, {1});
AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
AddTestTensor("clip_value_max", {1});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"clip_value_max\" for ClipByValue "
"must be a constant, at my_clip");
}

TestConvertClipByValue<DT_FLOAT>(this);
TestConvertClipByValue<DT_HALF>(this);
struct TestParams {
std::vector<int> dims;
float clip_value_min;
float clip_value_max;
std::vector<float> expected_output;
};

const std::vector<float> common_input = InitTestVector<float>(6);

std::vector<TestParams> params = {
{
/*dims=*/{6},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 6},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 2, 3},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 2, 3, 1},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 1, 3, 1, 2},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 1, 3, 1, 2, 1},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{2, 1, 3},
/*clip_value_min=*/-1,
/*clip_value_max=*/8,
/*expected_output=*/common_input,
}
};

for (auto p : params) {
Reset();

AddTestTensor("t", p.dims, tf_type_, common_input);
AddTestWeights("clip_value_min", {1}, {p.clip_value_min}, tf_type_);
AddTestWeights("clip_value_max", {1}, {p.clip_value_max}, tf_type_);

TestOpConverter("my_clip", node_def, p.dims,
/*expected_conversion_status=*/Status::OK(),
/*expected_runtime_status=*/Status::OK(),
/*matcher=*/ElementsAreArray(p.expected_output));
}
}
#endif // IS_TRT_VERSION_GE(5, 1, 2, 0)

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