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PiperOrigin-RevId: 522287183
This commit is contained in:
MediaPipe Team 2023-04-06 02:47:33 -07:00 committed by Copybara-Service
parent 3c083f5d2b
commit a151e6485a
9 changed files with 26 additions and 25 deletions
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2
mediapipe/calculators/tensor/audio_to_tensor_calculator.cc
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@ -192,7 +192,7 @@ class AudioToTensorCalculator : public Node {
DftTensorFormat dft_tensor_format_; DftTensorFormat dft_tensor_format_;
Timestamp initial_timestamp_ = Timestamp::Unstarted(); Timestamp initial_timestamp_ = Timestamp::Unstarted();
int64 cumulative_input_samples_ = 0; int64_t cumulative_input_samples_ = 0;
Timestamp next_output_timestamp_ = Timestamp::Unstarted(); Timestamp next_output_timestamp_ = Timestamp::Unstarted();
double source_sample_rate_ = -1; double source_sample_rate_ = -1;

19
mediapipe/calculators/tensor/audio_to_tensor_calculator_test.cc
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@ -163,20 +163,21 @@ class AudioToTensorCalculatorNonStreamingModeTest : public ::testing::Test {
} }
void CheckTimestampsOutputPackets( void CheckTimestampsOutputPackets(
std::vector<int64> expected_timestamp_values) { std::vector<int64_t> expected_timestamp_values) {
ASSERT_EQ(num_iterations_, timestamps_packets_.size()); ASSERT_EQ(num_iterations_, timestamps_packets_.size());
for (int i = 0; i < timestamps_packets_.size(); ++i) { for (int i = 0; i < timestamps_packets_.size(); ++i) {
const auto& p = timestamps_packets_[i]; const auto& p = timestamps_packets_[i];
MP_ASSERT_OK(p.ValidateAsType<std::vector<Timestamp>>()); MP_ASSERT_OK(p.ValidateAsType<std::vector<Timestamp>>());
auto output_timestamps = p.Get<std::vector<Timestamp>>(); auto output_timestamps = p.Get<std::vector<Timestamp>>();
int64 base_timestamp = i * Timestamp::kTimestampUnitsPerSecond; int64_t base_timestamp = i * Timestamp::kTimestampUnitsPerSecond;
std::vector<Timestamp> expected_timestamps; std::vector<Timestamp> expected_timestamps;
expected_timestamps.resize(expected_timestamp_values.size()); expected_timestamps.resize(expected_timestamp_values.size());
std::transform( std::transform(expected_timestamp_values.begin(),
expected_timestamp_values.begin(), expected_timestamp_values.end(), expected_timestamp_values.end(),
expected_timestamps.begin(), [base_timestamp](int64 v) -> Timestamp { expected_timestamps.begin(),
return Timestamp(v + base_timestamp); [base_timestamp](int64_t v) -> Timestamp {
}); return Timestamp(v + base_timestamp);
});
EXPECT_EQ(expected_timestamps, output_timestamps); EXPECT_EQ(expected_timestamps, output_timestamps);
EXPECT_EQ(p.Timestamp(), expected_timestamps.back()); EXPECT_EQ(p.Timestamp(), expected_timestamps.back());
} }
@ -379,7 +380,7 @@ class AudioToTensorCalculatorStreamingModeTest : public ::testing::Test {
} }
void CheckTensorsOutputPackets(int sample_offset, int num_packets, void CheckTensorsOutputPackets(int sample_offset, int num_packets,
int64 timestamp_interval, int64_t timestamp_interval,
bool output_last_at_close) { bool output_last_at_close) {
ASSERT_EQ(num_packets, tensors_packets_.size()); ASSERT_EQ(num_packets, tensors_packets_.size());
for (int i = 0; i < num_packets; ++i) { for (int i = 0; i < num_packets; ++i) {
@ -550,7 +551,7 @@ class AudioToTensorCalculatorFftTest : public ::testing::Test {
protected: protected:
// Creates an audio matrix containing a single sample of 1.0 at a specified // Creates an audio matrix containing a single sample of 1.0 at a specified
// offset. // offset.
std::unique_ptr<Matrix> CreateImpulseSignalData(int64 num_samples, std::unique_ptr<Matrix> CreateImpulseSignalData(int64_t num_samples,
int impulse_offset_idx) { int impulse_offset_idx) {
Matrix impulse = Matrix::Zero(1, num_samples); Matrix impulse = Matrix::Zero(1, num_samples);
impulse(0, impulse_offset_idx) = 1.0; impulse(0, impulse_offset_idx) = 1.0;

8
mediapipe/calculators/tensor/image_to_tensor_calculator_test.cc
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@ -163,12 +163,12 @@ void RunTestWithInputImagePacket(
EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kInt8); EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kInt8);
tensor_mat = cv::Mat(expected_result.rows, expected_result.cols, tensor_mat = cv::Mat(expected_result.rows, expected_result.cols,
channels == 1 ? CV_8SC1 : CV_8SC3, channels == 1 ? CV_8SC1 : CV_8SC3,
const_cast<int8*>(view.buffer<int8>())); const_cast<int8_t*>(view.buffer<int8_t>()));
} else { } else {
EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kUInt8); EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kUInt8);
tensor_mat = cv::Mat(expected_result.rows, expected_result.cols, tensor_mat = cv::Mat(expected_result.rows, expected_result.cols,
channels == 1 ? CV_8UC1 : CV_8UC3, channels == 1 ? CV_8UC1 : CV_8UC3,
const_cast<uint8*>(view.buffer<uint8>())); const_cast<uint8_t*>(view.buffer<uint8_t>()));
} }
} else { } else {
EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kFloat32); EXPECT_EQ(tensor.element_type(), Tensor::ElementType::kFloat32);
@ -210,14 +210,14 @@ mediapipe::ImageFormat::Format GetImageFormat(int image_channels) {
Packet MakeImageFramePacket(cv::Mat input) { Packet MakeImageFramePacket(cv::Mat input) {
ImageFrame input_image(GetImageFormat(input.channels()), input.cols, ImageFrame input_image(GetImageFormat(input.channels()), input.cols,
input.rows, input.step, input.data, [](uint8*) {}); input.rows, input.step, input.data, [](uint8_t*) {});
return MakePacket<ImageFrame>(std::move(input_image)).At(Timestamp(0)); return MakePacket<ImageFrame>(std::move(input_image)).At(Timestamp(0));
} }
Packet MakeImagePacket(cv::Mat input) { Packet MakeImagePacket(cv::Mat input) {
mediapipe::Image input_image(std::make_shared<mediapipe::ImageFrame>( mediapipe::Image input_image(std::make_shared<mediapipe::ImageFrame>(
GetImageFormat(input.channels()), input.cols, input.rows, input.step, GetImageFormat(input.channels()), input.cols, input.rows, input.step,
input.data, [](uint8*) {})); input.data, [](uint8_t*) {}));
return MakePacket<mediapipe::Image>(std::move(input_image)).At(Timestamp(0)); return MakePacket<mediapipe::Image>(std::move(input_image)).At(Timestamp(0));
} }

8
mediapipe/calculators/tensor/inference_interpreter_delegate_runner.cc
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@ -160,16 +160,16 @@ absl::StatusOr<std::vector<Tensor>> InferenceInterpreterDelegateRunner::Run(
Tensor::ElementType::kUInt8, shape, Tensor::ElementType::kUInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale, Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point}); tensor->params.zero_point});
CopyTensorBufferFromInterpreter<uint8>(interpreter_.get(), i, CopyTensorBufferFromInterpreter<uint8_t>(interpreter_.get(), i,
&output_tensors.back()); &output_tensors.back());
break; break;
case TfLiteType::kTfLiteInt8: case TfLiteType::kTfLiteInt8:
output_tensors.emplace_back( output_tensors.emplace_back(
Tensor::ElementType::kInt8, shape, Tensor::ElementType::kInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale, Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point}); tensor->params.zero_point});
CopyTensorBufferFromInterpreter<int8>(interpreter_.get(), i, CopyTensorBufferFromInterpreter<int8_t>(interpreter_.get(), i,
&output_tensors.back()); &output_tensors.back());
break; break;
case TfLiteType::kTfLiteInt32: case TfLiteType::kTfLiteInt32:
output_tensors.emplace_back(Tensor::ElementType::kInt32, shape); output_tensors.emplace_back(Tensor::ElementType::kInt32, shape);

4
mediapipe/calculators/tensor/tensor_converter_calculator.cc
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@ -251,8 +251,8 @@ absl::Status TensorConverterCalculator::ProcessCPU(CalculatorContext* cc) {
// Copy image data into tensor. // Copy image data into tensor.
if (image_frame.ByteDepth() == 1) { if (image_frame.ByteDepth() == 1) {
MP_RETURN_IF_ERROR(NormalizeImage<uint8>(image_frame, flip_vertically_, MP_RETURN_IF_ERROR(NormalizeImage<uint8_t>(image_frame, flip_vertically_,
cpu_view.buffer<float>())); cpu_view.buffer<float>()));
} else if (image_frame.ByteDepth() == 4) { } else if (image_frame.ByteDepth() == 4) {
MP_RETURN_IF_ERROR(NormalizeImage<float>(image_frame, flip_vertically_, MP_RETURN_IF_ERROR(NormalizeImage<float>(image_frame, flip_vertically_,
cpu_view.buffer<float>())); cpu_view.buffer<float>()));

4
mediapipe/calculators/tensor/tensors_dequantization_calculator.cc
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@ -82,10 +82,10 @@ absl::Status TensorsDequantizationCalculator::Process(CalculatorContext* cc) {
input_tensor.shape()); input_tensor.shape());
switch (input_tensor.element_type()) { switch (input_tensor.element_type()) {
case Tensor::ElementType::kUInt8: case Tensor::ElementType::kUInt8:
Dequantize<uint8>(input_tensor, &output_tensors->back()); Dequantize<uint8_t>(input_tensor, &output_tensors->back());
break; break;
case Tensor::ElementType::kInt8: case Tensor::ElementType::kInt8:
Dequantize<int8>(input_tensor, &output_tensors->back()); Dequantize<int8_t>(input_tensor, &output_tensors->back());
break; break;
case Tensor::ElementType::kBool: case Tensor::ElementType::kBool:
Dequantize<bool>(input_tensor, &output_tensors->back()); Dequantize<bool>(input_tensor, &output_tensors->back());

2
mediapipe/calculators/tensor/tensors_to_audio_calculator_test.cc
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@ -36,7 +36,7 @@ class TensorsToAudioCalculatorFftTest : public ::testing::Test {
protected: protected:
// Creates an audio matrix containing a single sample of 1.0 at a specified // Creates an audio matrix containing a single sample of 1.0 at a specified
// offset. // offset.
Matrix CreateImpulseSignalData(int64 num_samples, int impulse_offset_idx) { Matrix CreateImpulseSignalData(int64_t num_samples, int impulse_offset_idx) {
Matrix impulse = Matrix::Zero(1, num_samples); Matrix impulse = Matrix::Zero(1, num_samples);
impulse(0, impulse_offset_idx) = 1.0; impulse(0, impulse_offset_idx) = 1.0;
return impulse; return impulse;

2
mediapipe/calculators/tensor/tensors_to_classification_calculator_test.cc
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@ -47,7 +47,7 @@ class TensorsToClassificationCalculatorTest : public ::testing::Test {
tensor_buffer[i] = scores[i]; tensor_buffer[i] = scores[i];
} }
int64 stream_timestamp = 0; int64_t stream_timestamp = 0;
auto& input_stream_packets = auto& input_stream_packets =
runner->MutableInputs()->Tag("TENSORS").packets; runner->MutableInputs()->Tag("TENSORS").packets;

2
mediapipe/calculators/tensor/tensors_to_floats_calculator_test.cc
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@ -46,7 +46,7 @@ class TensorsToFloatsCalculatorTest : public ::testing::Test {
tensor_buffer[i] = values[i]; tensor_buffer[i] = values[i];
} }
int64 stream_timestamp = 0; int64_t stream_timestamp = 0;
auto& input_stream_packets = auto& input_stream_packets =
runner->MutableInputs()->Tag("TENSORS").packets; runner->MutableInputs()->Tag("TENSORS").packets;