Internal change
PiperOrigin-RevId: 523306493
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@ -26,7 +26,7 @@ namespace mediapipe {
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namespace {
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struct MultiScaleAnchorInfo {
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int32 level;
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int32_t level;
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std::vector<float> aspect_ratios;
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std::vector<float> scales;
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std::pair<float, float> base_anchor_size;
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@ -337,9 +337,9 @@ absl::Status TfLiteConverterCalculator::ProcessCPU(CalculatorContext* cc) {
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if (use_quantized_tensors_) {
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const int width_padding =
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image_frame.WidthStep() / image_frame.ByteDepth() - width * channels;
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const uint8* image_buffer =
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reinterpret_cast<const uint8*>(image_frame.PixelData());
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uint8* tensor_buffer = tensor->data.uint8;
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const uint8_t* image_buffer =
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reinterpret_cast<const uint8_t*>(image_frame.PixelData());
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uint8_t* tensor_buffer = tensor->data.uint8;
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RET_CHECK(tensor_buffer);
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for (int row = 0; row < height; ++row) {
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for (int col = 0; col < width; ++col) {
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@ -354,8 +354,8 @@ absl::Status TfLiteConverterCalculator::ProcessCPU(CalculatorContext* cc) {
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float* tensor_buffer = tensor->data.f;
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RET_CHECK(tensor_buffer);
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if (image_frame.ByteDepth() == 1) {
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MP_RETURN_IF_ERROR(NormalizeImage<uint8>(image_frame, flip_vertically_,
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tensor_buffer));
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MP_RETURN_IF_ERROR(NormalizeImage<uint8_t>(
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image_frame, flip_vertically_, tensor_buffer));
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} else if (image_frame.ByteDepth() == 4) {
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MP_RETURN_IF_ERROR(NormalizeImage<float>(image_frame, flip_vertically_,
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tensor_buffer));
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@ -42,7 +42,7 @@ constexpr char kTransposeOptionsString[] =
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using RandomEngine = std::mt19937_64;
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using testing::Eq;
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const uint32 kSeed = 1234;
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const uint32_t kSeed = 1234;
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const int kNumSizes = 8;
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const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9}, {2, 2},
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{5, 3}, {7, 13}, {16, 32}, {101, 2}};
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@ -50,7 +50,7 @@ const int sizes[kNumSizes][2] = {{1, 1}, {12, 1}, {1, 9}, {2, 2},
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class TfLiteConverterCalculatorTest : public ::testing::Test {
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protected:
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// Adds a packet with a matrix filled with random values in [0,1].
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void AddRandomMatrix(int num_rows, int num_columns, uint32 seed,
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void AddRandomMatrix(int num_rows, int num_columns, uint32_t seed,
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bool row_major_matrix = false) {
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RandomEngine random(kSeed);
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std::uniform_real_distribution<> uniform_dist(0, 1.0);
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@ -228,7 +228,7 @@ TEST_F(TfLiteConverterCalculatorTest, CustomDivAndSub) {
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MP_ASSERT_OK(graph.StartRun({}));
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auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
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cv::Mat mat = mediapipe::formats::MatView(input_image.get());
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mat.at<uint8>(0, 0) = 200;
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mat.at<uint8_t>(0, 0) = 200;
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MP_ASSERT_OK(graph.AddPacketToInputStream(
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"input_image", Adopt(input_image.release()).At(Timestamp(0))));
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@ -284,7 +284,7 @@ TEST_F(TfLiteConverterCalculatorTest, SetOutputRange) {
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MP_ASSERT_OK(graph.StartRun({}));
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auto input_image = absl::make_unique<ImageFrame>(ImageFormat::GRAY8, 1, 1);
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cv::Mat mat = mediapipe::formats::MatView(input_image.get());
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mat.at<uint8>(0, 0) = 200;
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mat.at<uint8_t>(0, 0) = 200;
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MP_ASSERT_OK(graph.AddPacketToInputStream(
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"input_image", Adopt(input_image.release()).At(Timestamp(0))));
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@ -535,8 +535,9 @@ absl::Status TfLiteInferenceCalculator::ProcessInputsCpu(
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const TfLiteTensor* input_tensor = &input_tensors[i];
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RET_CHECK(input_tensor->data.raw);
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if (use_quantized_tensors_) {
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const uint8* input_tensor_buffer = input_tensor->data.uint8;
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uint8* local_tensor_buffer = interpreter_->typed_input_tensor<uint8>(i);
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const uint8_t* input_tensor_buffer = input_tensor->data.uint8;
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uint8_t* local_tensor_buffer =
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interpreter_->typed_input_tensor<uint8_t>(i);
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std::memcpy(local_tensor_buffer, input_tensor_buffer,
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input_tensor->bytes);
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} else {
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@ -60,7 +60,7 @@ class TfLiteTensorsToClassificationCalculatorTest : public ::testing::Test {
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auto tensors = absl::make_unique<std::vector<TfLiteTensor>>();
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tensors->emplace_back(*tensor);
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int64 stream_timestamp = 0;
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int64_t stream_timestamp = 0;
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auto& input_stream_packets =
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runner->MutableInputs()->Tag("TENSORS").packets;
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