Merge pull request #45589 from DEKHTIARJonathan:jdekhtiar/dynamic_shape/ConvertClipByValue
PiperOrigin-RevId: 347719835 Change-Id: I1b3b9bfa0ac0268e8d394c9a19ac27ad126b38ed
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TensorFlower Gardener
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66f670ded6
@ -6607,83 +6607,26 @@ TEST_F(OpConverterTest, ConvertSpaceToDepth) {
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}
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#if IS_TRT_VERSION_GE(5, 1, 2, 0)
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// Get the NodeDef for ClipByValue.
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NodeDef GetClipByValueNodeDef(DataType dtype) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertClipByValue) {
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Scope s = Scope::NewRootScope();
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auto t = ops::Placeholder(s.WithOpName("t"), dtype);
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auto clip_value_min = ops::Placeholder(s.WithOpName("clip_value_min"), dtype);
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auto clip_value_max = ops::Placeholder(s.WithOpName("clip_value_max"), dtype);
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auto t = ops::Placeholder(s.WithOpName("t"), tf_type_);
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auto clip_value_min =
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ops::Placeholder(s.WithOpName("clip_value_min"), tf_type_);
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auto clip_value_max =
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ops::Placeholder(s.WithOpName("clip_value_max"), tf_type_);
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auto clip = ops::ClipByValue(s.WithOpName("my_clip"), t, clip_value_min,
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clip_value_max);
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return clip.operation.node()->def();
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}
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const NodeDef& node_def = clip.operation.node()->def();
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template <DataType dtype>
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void TestConvertClipByValue(OpConverterTest* test) {
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typedef typename EnumToDataType<dtype>::Type CType;
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nvinfer1::DataType trt_type_;
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TF_ASSERT_OK(TfTypeToTrtType(tf_type_, &trt_type_));
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struct TestParams {
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std::vector<int> dims;
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std::vector<CType> input_value;
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CType clip_value_min;
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CType clip_value_max;
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std::vector<CType> expected_output;
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};
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const std::vector<CType> common_input = InitTestVector<CType>(6);
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std::vector<TestParams> params = {
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{
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/*dims=*/{1, 2, 3},
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/*input_value=*/common_input,
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/*clip_value_min=*/CType(2),
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/*clip_value_max=*/CType(5),
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/*expected_output=*/
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{CType(2), CType(2), CType(2), CType(3), CType(4), CType(5)},
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},
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{
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/*dims=*/{2, 1, 3},
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/*input_value=*/common_input,
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/*clip_value_min=*/CType(-1),
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/*clip_value_max=*/CType(8),
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/*expected_output=*/common_input,
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},
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};
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for (int i = 0; i < params.size(); ++i) {
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test->Reset();
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NodeDef node_def = GetClipByValueNodeDef(dtype);
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nvinfer1::DataType trt_type;
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TF_ASSERT_OK(TfTypeToTrtType(dtype, &trt_type));
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test->AddTestTensor("t", params[i].dims, 1, trt_type);
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test->AddTestWeights<CType>("clip_value_min", {1},
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{params[i].clip_value_min});
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test->AddTestWeights<CType>("clip_value_max", {1},
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{params[i].clip_value_max});
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test->RunValidationAndConversion(node_def);
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TRT_TensorOrWeights output;
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TF_EXPECT_OK(test->GetTensorOrWeights("my_clip", &output));
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EXPECT_TRUE(output.is_tensor());
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ExpectTrtDimsEqualsArray(params[i].dims, output.tensor()->getDimensions());
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DataVec input_data{{"t", test->AsTensor<CType>(params[i].input_value)}};
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DataVec output_data{{"my_clip", test->ConstructTensor<CType>(
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params[i].expected_output.size())}};
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TF_EXPECT_OK(test->BuildAndRun(input_data, &output_data));
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EXPECT_THAT(GetSpanForData<CType>(output_data[0]),
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ElementsAreArray(params[i].expected_output));
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}
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}
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TEST_F(OpConverterTest, ConvertClipByValue) {
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{
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// Input is a weight, should fail.
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Reset();
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NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
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AddTestWeights<float>("t", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
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AddTestWeights<float>("clip_value_min", {1}, {1});
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AddTestWeights<float>("clip_value_max", {1}, {5});
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AddTestWeights("t", {1, 2, 3}, {1, 2, 3, 4, 5, 6}, tf_type_);
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AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
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AddTestWeights("clip_value_max", {1}, {5}, tf_type_);
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RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
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"The input \"t\" for ClipByValue must be a "
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"tensor, at my_clip");
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@ -6691,10 +6634,9 @@ TEST_F(OpConverterTest, ConvertClipByValue) {
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{
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// Clip min is a tensor, should fail.
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Reset();
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NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
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AddTestTensor("t", {1, 2, 3});
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AddTestTensor("clip_value_min", {1});
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AddTestWeights<float>("clip_value_max", {1}, {1});
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AddTestWeights("clip_value_max", {1}, {1}, tf_type_);
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RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
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"The input \"clip_value_min\" for ClipByValue "
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"must be a constant, at my_clip");
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@ -6702,17 +6644,78 @@ TEST_F(OpConverterTest, ConvertClipByValue) {
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{
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// Clip max is a tensor, should fail.
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Reset();
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NodeDef node_def = GetClipByValueNodeDef(DT_FLOAT);
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AddTestTensor("t", {1, 2, 3});
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AddTestWeights<float>("clip_value_min", {1}, {1});
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AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
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AddTestTensor("clip_value_max", {1});
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RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
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"The input \"clip_value_max\" for ClipByValue "
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"must be a constant, at my_clip");
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}
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TestConvertClipByValue<DT_FLOAT>(this);
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TestConvertClipByValue<DT_HALF>(this);
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struct TestParams {
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std::vector<int> dims;
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int clip_value_min;
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int clip_value_max;
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std::vector<float> expected_output;
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};
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const std::vector<float> common_input = InitTestVector<float>(6);
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std::vector<TestParams> params = {{
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/*dims=*/{6},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{1, 6},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{1, 2, 3},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{1, 2, 3, 1},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{1, 1, 3, 1, 2},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{1, 1, 3, 1, 2, 1},
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/*clip_value_min=*/2,
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/*clip_value_max=*/4,
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/*expected_output=*/{2, 2, 2, 3, 4, 4},
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},
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{
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/*dims=*/{2, 1, 3},
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/*clip_value_min=*/-1,
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/*clip_value_max=*/8,
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/*expected_output=*/common_input,
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}};
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for (auto p : params) {
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Reset();
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AddTestTensor("t", p.dims, tf_type_, common_input);
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AddTestWeights("clip_value_min", {1}, {p.clip_value_min}, tf_type_);
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AddTestWeights("clip_value_max", {1}, {p.clip_value_max}, tf_type_);
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TestOpConverter("my_clip", node_def, p.dims,
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/*expected_conversion_status=*/Status::OK(),
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/*expected_runtime_status=*/Status::OK(),
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/*matcher=*/ElementsAreArray(p.expected_output));
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}
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}
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#endif // IS_TRT_VERSION_GE(5, 1, 2, 0)
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