Merge pull request #45587 from DEKHTIARJonathan:jdekhtiar/dynamic_shape/renaming_test_name
PiperOrigin-RevId: 347099335 Change-Id: I740168124dec6ac85ec1c46d8737e9ef053e6b83
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TensorFlower Gardener
commit
f805574892
@ -1893,27 +1893,28 @@ class ParameterizedOpConverterTestBase
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// how TRT handles the precision inside the TRT network, but should not matter
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// for the TF -> TRT conversion. Therefore it should be sufficient to test
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// for FP32.
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class OpConverterTest1 : public ParameterizedOpConverterTestBase {};
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class OpConverter_FP32_Test : public ParameterizedOpConverterTestBase {};
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// Base class for tests that need to be tested for both FP32 and FP16.
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class OpConverter_FP32_FP16_Test : public ParameterizedOpConverterTestBase {};
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// Base class for tests that need to be tested for FP32, FP16, and INT32
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class OpConverter_FP32_FP16_INT32_Test
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: public ParameterizedOpConverterTestBase {};
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// Instantiate parameter combinations to OpConverterTest1
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// Instantiate parameter combinations to OpConverter_<DT_X...>_Test
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INSTANTIATE_TEST_CASE_P(
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OpConvTestInstantiation, OpConverterTest1,
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OpConvTestInstantiation, OpConverter_FP32_Test,
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::testing::Combine(::testing::ValuesIn(ValidTrtModes),
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::testing::Values(DT_FLOAT),
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::testing::Values(TrtPrecisionMode::FP32)));
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// Base class for tests that need to be tested for both FP32 and FP16.
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class OpConverterTest2 : public ParameterizedOpConverterTestBase {};
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INSTANTIATE_TEST_CASE_P(
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OpConvTestInstantiation, OpConverterTest2,
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OpConvTestInstantiation, OpConverter_FP32_FP16_Test,
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::testing::Combine(::testing::ValuesIn(ValidTrtModes),
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::testing::Values(DT_FLOAT, DT_HALF),
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::testing::Values(TrtPrecisionMode::FP32)));
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// Base class for tests that need to be tested for FP32, FP16, and INT32
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class OpConverterTest3 : public ParameterizedOpConverterTestBase {};
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INSTANTIATE_TEST_CASE_P(
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OpConvTestInstantiation3, OpConverterTest3,
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OpConvTestInstantiation, OpConverter_FP32_FP16_INT32_Test,
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::testing::Combine(::testing::ValuesIn(ValidTrtModes),
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::testing::Values(DT_FLOAT, DT_HALF, DT_INT32),
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::testing::Values(TrtPrecisionMode::FP32)));
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@ -2078,7 +2079,7 @@ NodeDef CreateFusedBatchNormOp(DataType tf_type, std::string data_format,
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->def();
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}
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TEST_P(OpConverterTest1, ConvertFusedBatchNorm) {
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TEST_P(OpConverter_FP32_Test, ConvertFusedBatchNorm) {
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using OpFunc = std::function<NodeDef(DataType, std::string, bool, float)>;
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std::vector<OpFunc> get_node_def_vec{
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CreateFusedBatchNormOp<ops::FusedBatchNorm>,
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@ -2191,7 +2192,7 @@ TEST_P(OpConverterTest1, ConvertFusedBatchNorm) {
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}
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}
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TEST_P(OpConverterTest1, ConvertTranspose) {
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TEST_P(OpConverter_FP32_Test, ConvertTranspose) {
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// Get the NodeDef for Transpose.
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Scope s = Scope::NewRootScope();
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auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
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@ -2349,7 +2350,7 @@ TEST_F(OpConverterTest, ConvertReshape) {
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}
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}
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TEST_P(OpConverterTest1, ConvertShape) {
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TEST_P(OpConverter_FP32_Test, ConvertShape) {
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// Get the NodeDef for Shape op.
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Scope s = Scope::NewRootScope();
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auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
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@ -2637,7 +2638,7 @@ TEST_F(OpConverterTest, ConvertBatchMatMul) {
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TestMatMulHelper(this, get_batch_matmul_nodedef, "BatchMatMul");
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}
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TEST_P(OpConverterTest2, ConvertBiasAdd) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertBiasAdd) {
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// Note that kINT32 is not supported by IScaleLayer, so we don't test
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// DT_INT32 type here. DT_FLOAT and DT_HALF are tested.
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// Get the NodeDef for BiasAdd.
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@ -2710,7 +2711,7 @@ NodeDef GetBinaryOpNodeDef(DataType dtype) {
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return op.operation.node()->def();
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}
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TEST_P(OpConverterTest2, ConvertBinary) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertBinary) {
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{
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AttrValue dtype;
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dtype.set_type(tf_type_);
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@ -2974,7 +2975,7 @@ TEST_F(OpConverterTest, ConvertQuantize) {
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}
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}
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TEST_P(OpConverterTest2, ConvertSquare) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertSquare) {
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{
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// Input is weights, should fail.
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Reset();
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@ -3127,7 +3128,7 @@ NodeDef CreateUnaryOp<ops::internal::LeakyRelu>(DataType tf_type) {
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->def();
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}
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TEST_P(OpConverterTest1, ConvertActivation) {
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TEST_P(OpConverter_FP32_Test, ConvertActivation) {
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{
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// Input is weights, should fail.
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Reset();
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@ -3213,7 +3214,7 @@ TEST_P(OpConverterTest1, ConvertActivation) {
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}
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}
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TEST_P(OpConverterTest1, ConvertExpandDims) {
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TEST_P(OpConverter_FP32_Test, ConvertExpandDims) {
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// Get the NodeDef for ExpandDims.
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Scope s = Scope::NewRootScope();
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auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
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@ -3290,7 +3291,7 @@ TEST_P(OpConverterTest1, ConvertExpandDims) {
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}
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}
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TEST_P(OpConverterTest1, ConvertSqueeze) {
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TEST_P(OpConverter_FP32_Test, ConvertSqueeze) {
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const bool use_implicit_batch = (trt_mode_ == TrtTestMode::kImplicitBatch);
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// Get the NodeDef for Squeeze.
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auto get_squeeze_nodedef = [](std::vector<int> axes,
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@ -4141,7 +4142,7 @@ TEST_F(OpConverterTest, ConvertSlice) {
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}
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}
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TEST_P(OpConverterTest1, ConvertConv2D) {
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TEST_P(OpConverter_FP32_Test, ConvertConv2D) {
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// Get nodedef for Conv2D layer.
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DataType tf_type = tf_type_;
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auto get_conv2d_nodedef =
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@ -4835,7 +4836,7 @@ NodeDef CreatePoolOp(DataType tf_type, std::vector<int> ksize,
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.operation.node()
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->def();
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}
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TEST_P(OpConverterTest1, ConvertPool) {
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TEST_P(OpConverter_FP32_Test, ConvertPool) {
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// Get nodedef for MaxPool and AvgPool layers (2D or 3D).
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auto get_pool_nodedef =
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[](DataType tf_type, int nDim, std::vector<int> ksize = {},
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@ -5049,7 +5050,7 @@ TEST_F(OpConverterTest, ConvertTopK) {
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}
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}
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TEST_P(OpConverterTest3, ConvertGather) {
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TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertGather) {
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// Get the NodeDef for GatherV2.
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Scope s = Scope::NewRootScope();
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auto params = ops::Placeholder(s.WithOpName("params"), tf_type_);
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@ -5302,7 +5303,7 @@ std::vector<float> CalcReduce(string op_name, std::vector<float> input, int m,
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}
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return output;
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}
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TEST_P(OpConverterTest1, ConvertReduce) {
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TEST_P(OpConverter_FP32_Test, ConvertReduce) {
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{
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// Input is weights, should fail.
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Reset();
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@ -5428,7 +5429,7 @@ NodeDef CreateCastOp(DataType tf_type) {
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->def();
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}
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TEST_P(OpConverterTest1, ConvertUnary) {
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TEST_P(OpConverter_FP32_Test, ConvertUnary) {
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{
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// Input is weights, should fail.
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Reset();
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@ -6041,9 +6042,9 @@ NodeDef GetPackNodeDef(DataType dtype, int num_inputs, int axis) {
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}
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#if IS_TRT_VERSION_GE(6, 0, 0, 0)
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TEST_P(OpConverterTest3, ConvertPack) {
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TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertPack) {
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#else
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TEST_P(OpConverterTest2, ConvertPack) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertPack) {
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#endif
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struct TestParams {
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std::vector<std::vector<int>> input_shapes;
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@ -6725,7 +6726,7 @@ NodeDef GetSquaredDifferenceNodeDef(DataType dtype) {
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return squared_diff.operation.node()->def();
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}
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TEST_P(OpConverterTest2, ConvertSquaredDifference) {
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TEST_P(OpConverter_FP32_FP16_Test, ConvertSquaredDifference) {
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{
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// Input is a weight, should fail.
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Reset();
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