experiments/STT-tensorflow
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Use ASSERT_TRUE in TensorRT tests for is_tensor method calls

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This is to avoid crash later on in tensor method calls that fails if it is not
tensor.

PiperOrigin-RevId: 240917681
This commit is contained in:
Smit Hinsu 2019-03-28 21:43:33 -07:00 committed by TensorFlower Gardener
parent 9e467f4df3
commit 4dd52fbdc6
1 changed files with 31 additions and 31 deletions
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62
tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
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@ -346,7 +346,7 @@ TEST(TRT_TensorOrWeights_Test, Basic) {
assigned = *original_ptr;
for (auto ptr : {original_ptr, &copy, &assigned}) {
EXPECT_EQ(true, ptr->is_tensor());
ASSERT_TRUE(ptr->is_tensor());
EXPECT_EQ(false, ptr->is_weights());
if (original_ptr == &tw) {
EXPECT_EQ(-1, ptr->batch_size());
@ -369,7 +369,7 @@ TEST(TRT_TensorOrWeights_Test, Basic) {
assigned = tw;
for (auto ptr : {&tw, &copy, &assigned}) {
EXPECT_EQ(true, ptr->is_tensor());
ASSERT_TRUE(ptr->is_tensor());
EXPECT_EQ(false, ptr->is_weights());
EXPECT_EQ(1, ptr->batch_size());
EXPECT_NE(nullptr, ptr->tensor());
@ -469,7 +469,7 @@ TEST_F(ValidatorTest, ConvertToTensorOrWeights) {
TRT_TensorOrWeights output;
ExpectStatus(
convert_to_tensor_or_weights({batch_size, non_batch_dim}, &output));
EXPECT_EQ(true, output.is_tensor());
ASSERT_TRUE(output.is_tensor());
EXPECT_EQ(batch_size, output.batch_size());
EXPECT_NE(nullptr, output.tensor());
ExpectTrtDimsEqualsArray({non_batch_dim}, output.GetTrtDims());
@ -1495,7 +1495,7 @@ TEST_F(OpConverterTest, ConvertTranspose) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_transpose", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({3, 1, 2}, output.tensor()->getDimensions());
const DataVec input_data{
@ -1599,7 +1599,7 @@ TEST_F(OpConverterTest, ConvertReshape) {
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_reshape", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
const std::vector<int> expected_output_dims(shape.begin() + 1, shape.end());
const nvinfer1::Dims actual_output_dims = output.tensor()->getDimensions();
ExpectTrtDimsEqualsArray(expected_output_dims, actual_output_dims);
@ -1669,7 +1669,7 @@ TEST_F(OpConverterTest, ConvertMatMul) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_matmul", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({2}, output.tensor()->getDimensions());
const DataVec input_data{{"input", test::AsTensor<float>({0, 1})}};
@ -1725,7 +1725,7 @@ void TestConvertBiasAdd(OpConverterTest* test) {
test->RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_biasadd", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(dims_array, output.tensor()->getDimensions());
// Build and run the engine.
@ -1828,7 +1828,7 @@ void TestBinaryTensorOpWeightNoBroadcast(OpConverterTest* test) {
// Check the dims of the output ITensor.
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_binary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({1, 1, 2}, output.tensor()->getDimensions());
const DataVec input_data{
@ -1883,7 +1883,7 @@ void TestBinaryTensorOpWeightWithChannelWiseBroadcast(OpConverterTest* test) {
// Check the dims of the output ITensor.
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_binary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({2, 1, 2}, output.tensor()->getDimensions());
const DataVec input_data{{"input", test::AsTensor<CType>(input)}};
@ -1918,7 +1918,7 @@ void TestBinaryTensorOpWeightWithUniformlyBroadcast(OpConverterTest* test) {
// Check the dims of the output ITensor.
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_binary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({2, 1, 2}, output.tensor()->getDimensions());
const DataVec input_data{{"input", test::AsTensor<CType>(input)}};
@ -1957,7 +1957,7 @@ void TestBinaryTensorOpWeightFallback(OpConverterTest* test,
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_binary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
// Check the dims of the output ITensor.
std::vector<int> expected_output_dims = input_dims;
@ -2009,7 +2009,7 @@ void TestBinaryTensorOpTensor(OpConverterTest* test) {
// Check output dims.
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_binary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({2, 2}, output.tensor()->getDimensions());
const DataVec input_data{
@ -2171,7 +2171,7 @@ TEST_F(OpConverterTest, ConvertQuantize) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()]);
@ -2192,7 +2192,7 @@ TEST_F(OpConverterTest, ConvertQuantize) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()]);
@ -2213,7 +2213,7 @@ TEST_F(OpConverterTest, ConvertQuantize) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()]);
@ -2254,7 +2254,7 @@ TEST_F(OpConverterTest, ConvertQuantize) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()]);
@ -2276,7 +2276,7 @@ void TestConvertSquare(OpConverterTest* test) {
test->RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_square", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({1, 20}, output.tensor()->getDimensions());
const int num_inputs = 20;
@ -2400,10 +2400,10 @@ TEST_F(OpConverterTest, ConvertCombinedNMS) {
TF_EXPECT_OK(GetTensorOrWeights("my_nms:2", &nmsed_classes));
TF_EXPECT_OK(GetTensorOrWeights("my_nms:3", &valid_detections));
EXPECT_TRUE(nmsed_boxes.is_tensor());
EXPECT_TRUE(nmsed_scores.is_tensor());
EXPECT_TRUE(nmsed_classes.is_tensor());
EXPECT_TRUE(valid_detections.is_tensor());
ASSERT_TRUE(nmsed_boxes.is_tensor());
ASSERT_TRUE(nmsed_scores.is_tensor());
ASSERT_TRUE(nmsed_classes.is_tensor());
ASSERT_TRUE(valid_detections.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_nmsed_boxes_dims,
nmsed_boxes.tensor()->getDimensions());
@ -2506,7 +2506,7 @@ TEST_F(OpConverterTest, ConvertActivation) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_act", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions());
if (op_name == "Relu6") {
// Relu6 should set quantization range automatically.
@ -2625,7 +2625,7 @@ TEST_F(OpConverterTest, ConvertExpandDims) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -2755,7 +2755,7 @@ TEST_F(OpConverterTest, ConvertSqueeze) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -3271,7 +3271,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) {
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_strided_slice", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -3413,7 +3413,7 @@ TEST_F(OpConverterTest, ConvertSlice) {
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_slice", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -3682,7 +3682,7 @@ TEST_F(OpConverterTest, ConvertConv2D) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_conv2d", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -3733,7 +3733,7 @@ TEST_F(OpConverterTest, ConvertTopK) {
TF_EXPECT_OK(GetTensorOrWeights("my_topk", &outputs[0]));
TF_EXPECT_OK(GetTensorOrWeights("my_topk:1", &outputs[1]));
for (auto& output : outputs) {
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({1, 2, 2}, output.tensor()->getDimensions());
}
@ -3804,7 +3804,7 @@ void TestConvertGather(OpConverterTest* test) {
test->RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_gather", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
@ -4031,7 +4031,7 @@ TEST_F(OpConverterTest, ConvertUnary) {
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_unary", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions());
const std::vector<float> input = {-0.9f, 0.6f, 0.0f, -3.5f, 100.0f, 2.9f};
@ -4136,7 +4136,7 @@ void TestConvertConcat(OpConverterTest* test) {
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_concat", &output));
EXPECT_TRUE(output.is_tensor());
ASSERT_TRUE(output.is_tensor());
ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
output.tensor()->getDimensions());
// Create input data for tensors.