Add a string-to-bool test model to TextClassifier.

PiperOrigin-RevId: 479803799

mediapipe/calculators/tensor/BUILD

@@ -320,6 +320,8 @@ cc_library(
         "@com_google_absl//absl/status",
         "@com_google_absl//absl/status:statusor",
         "@org_tensorflow//tensorflow/lite:framework_stable",
+        "@org_tensorflow//tensorflow/lite:string_util",
+        "@org_tensorflow//tensorflow/lite/c:c_api_types",
         "@org_tensorflow//tensorflow/lite/core/api:op_resolver",
     ],
 )

mediapipe/calculators/tensor/inference_interpreter_delegate_runner.cc

@@ -21,8 +21,10 @@
 #include "absl/status/statusor.h"
 #include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/framework/port/ret_check.h"
+#include "tensorflow/lite/c/c_api_types.h"
 #include "tensorflow/lite/interpreter.h"
 #include "tensorflow/lite/interpreter_builder.h"
+#include "tensorflow/lite/string_util.h"
 
 namespace mediapipe {
 
@@ -39,6 +41,19 @@ void CopyTensorBufferToInterpreter(const Tensor& input_tensor,
   std::memcpy(local_tensor_buffer, input_tensor_buffer, input_tensor.bytes());
 }
 
+template <>
+void CopyTensorBufferToInterpreter<char>(const Tensor& input_tensor,
+                                         tflite::Interpreter* interpreter,
+                                         int input_tensor_index) {
+  const char* input_tensor_buffer =
+      input_tensor.GetCpuReadView().buffer<char>();
+  tflite::DynamicBuffer dynamic_buffer;
+  dynamic_buffer.AddString(input_tensor_buffer,
+                           input_tensor.shape().num_elements());
+  dynamic_buffer.WriteToTensorAsVector(
+      interpreter->tensor(interpreter->inputs()[input_tensor_index]));
+}
+
 template <typename T>
 void CopyTensorBufferFromInterpreter(tflite::Interpreter* interpreter,
                                      int output_tensor_index,
@@ -87,12 +102,12 @@ absl::StatusOr<std::vector<Tensor>> InferenceInterpreterDelegateRunner::Run(
         break;
       }
       case TfLiteType::kTfLiteUInt8: {
-        CopyTensorBufferToInterpreter<uint8>(input_tensors[i],
+        CopyTensorBufferToInterpreter<uint8_t>(input_tensors[i],
                                                interpreter_.get(), i);
         break;
       }
       case TfLiteType::kTfLiteInt8: {
-        CopyTensorBufferToInterpreter<int8>(input_tensors[i],
+        CopyTensorBufferToInterpreter<int8_t>(input_tensors[i],
                                               interpreter_.get(), i);
         break;
       }
@@ -101,6 +116,14 @@ absl::StatusOr<std::vector<Tensor>> InferenceInterpreterDelegateRunner::Run(
                                                interpreter_.get(), i);
         break;
       }
+      case TfLiteType::kTfLiteString: {
+        CopyTensorBufferToInterpreter<char>(input_tensors[i],
+                                            interpreter_.get(), i);
+        break;
+      }
+      case TfLiteType::kTfLiteBool:
+        // No current use-case for copying MediaPipe Tensors with bool type to
+        // TfLiteTensors.
       default:
         return absl::InvalidArgumentError(
             absl::StrCat("Unsupported input tensor type:", input_tensor_type));
@@ -146,6 +169,15 @@ absl::StatusOr<std::vector<Tensor>> InferenceInterpreterDelegateRunner::Run(
         CopyTensorBufferFromInterpreter<int32_t>(interpreter_.get(), i,
                                                  &output_tensors.back());
         break;
+      case TfLiteType::kTfLiteBool:
+        output_tensors.emplace_back(Tensor::ElementType::kBool, shape,
+                                    Tensor::QuantizationParameters{1.0f, 0});
+        CopyTensorBufferFromInterpreter<bool>(interpreter_.get(), i,
+                                              &output_tensors.back());
+        break;
+      case TfLiteType::kTfLiteString:
+        // No current use-case for copying TfLiteTensors with string type to
+        // MediaPipe Tensors.
       default:
         return absl::InvalidArgumentError(
             absl::StrCat("Unsupported output tensor type:",

mediapipe/calculators/tensor/tensors_dequantization_calculator.cc

@@ -87,6 +87,9 @@ absl::Status TensorsDequantizationCalculator::Process(CalculatorContext* cc) {
       case Tensor::ElementType::kInt8:
         Dequantize<int8>(input_tensor, &output_tensors->back());
         break;
+      case Tensor::ElementType::kBool:
+        Dequantize<bool>(input_tensor, &output_tensors->back());
+        break;
       default:
         return absl::InvalidArgumentError(absl::StrCat(
             "Unsupported input tensor type: ", input_tensor.element_type()));

mediapipe/calculators/tensor/tensors_dequantization_calculator_test.cc

@@ -124,5 +124,15 @@ TEST_F(TensorsDequantizationCalculatorTest, SucceedsWithInt8Tensors) {
   ValidateResult(GetOutput(), {-1.007874, 0, 1});
 }
 
+TEST_F(TensorsDequantizationCalculatorTest, SucceedsWithBoolTensors) {
+  std::vector<bool> tensor = {true, false, true};
+  PushTensor(Tensor::ElementType::kBool, tensor,
+             Tensor::QuantizationParameters{1.0f, 0});
+
+  MP_ASSERT_OK(runner_.Run());
+
+  ValidateResult(GetOutput(), {1, 0, 1});
+}
+
 }  // namespace
 }  // namespace mediapipe

mediapipe/framework/formats/tensor.h

@@ -97,8 +97,8 @@ class Tensor {
     kUInt8,
     kInt8,
     kInt32,
-    // TODO: Update the inference runner to handle kTfLiteString.
+    kChar,
-    kChar
+    kBool
   };
   struct Shape {
     Shape() = default;
@@ -330,6 +330,8 @@ class Tensor {
         return sizeof(int32_t);
       case ElementType::kChar:
         return sizeof(char);
+      case ElementType::kBool:
+        return sizeof(bool);
     }
   }
   int bytes() const { return shape_.num_elements() * element_size(); }

mediapipe/framework/formats/tensor_test.cc

@@ -29,6 +29,9 @@ TEST(General, TestDataTypes) {
 
   Tensor t_char(Tensor::ElementType::kChar, Tensor::Shape{4});
   EXPECT_EQ(t_char.bytes(), t_char.shape().num_elements() * sizeof(char));
+
+  Tensor t_bool(Tensor::ElementType::kBool, Tensor::Shape{2, 3});
+  EXPECT_EQ(t_bool.bytes(), t_bool.shape().num_elements() * sizeof(bool));
 }
 
 TEST(Cpu, TestMemoryAllocation) {

mediapipe/tasks/cc/components/processors/classification_postprocessing_graph.cc

@@ -123,15 +123,17 @@ absl::StatusOr<ClassificationHeadsProperties> GetClassificationHeadsProperties(
     const auto* tensor =
         primary_subgraph->tensors()->Get(primary_subgraph->outputs()->Get(i));
     if (tensor->type() != tflite::TensorType_FLOAT32 &&
-        tensor->type() != tflite::TensorType_UINT8) {
+        tensor->type() != tflite::TensorType_UINT8 &&
+        tensor->type() != tflite::TensorType_BOOL) {
       return CreateStatusWithPayload(
           absl::StatusCode::kInvalidArgument,
           absl::StrFormat("Expected output tensor at index %d to have type "
-                          "UINT8 or FLOAT32, found %s instead.",
+                          "UINT8 or FLOAT32 or BOOL, found %s instead.",
                           i, tflite::EnumNameTensorType(tensor->type())),
           MediaPipeTasksStatus::kInvalidOutputTensorTypeError);
     }
-    if (tensor->type() == tflite::TensorType_UINT8) {
+    if (tensor->type() == tflite::TensorType_UINT8 ||
+        tensor->type() == tflite::TensorType_BOOL) {
       num_quantized_tensors++;
     }
   }

mediapipe/tasks/cc/components/proto/text_preprocessing_graph_options.proto

@@ -31,6 +31,8 @@ message TextPreprocessingGraphOptions {
     BERT_PREPROCESSOR = 1;
     // Used for the RegexPreprocessorCalculator.
     REGEX_PREPROCESSOR = 2;
+    // Used for the TextToTensorCalculator.
+    STRING_PREPROCESSOR = 3;
   }
   optional PreprocessorType preprocessor_type = 1;
 

mediapipe/tasks/cc/components/text_preprocessing_graph.cc

@@ -65,6 +65,8 @@ absl::StatusOr<std::string> GetCalculatorNameFromPreprocessorType(
       return "BertPreprocessorCalculator";
     case TextPreprocessingGraphOptions::REGEX_PREPROCESSOR:
       return "RegexPreprocessorCalculator";
+    case TextPreprocessingGraphOptions::STRING_PREPROCESSOR:
+      return "TextToTensorCalculator";
   }
 }
 
@@ -91,11 +93,7 @@ GetPreprocessorType(const ModelResources& model_resources) {
         MediaPipeTasksStatus::kInvalidInputTensorTypeError);
   }
   if (all_string_tensors) {
-    // TODO: Support a TextToTensor calculator for string tensors.
+    return TextPreprocessingGraphOptions::STRING_PREPROCESSOR;
-    return CreateStatusWithPayload(
-        absl::StatusCode::kInvalidArgument,
-        "String tensors are not supported yet",
-        MediaPipeTasksStatus::kInvalidInputTensorTypeError);
   }
 
   // Otherwise, all tensors should have type int32
@@ -185,10 +183,19 @@ absl::Status ConfigureTextPreprocessingSubgraph(
       TextPreprocessingGraphOptions::PreprocessorType preprocessor_type,
       GetPreprocessorType(model_resources));
   options.set_preprocessor_type(preprocessor_type);
+  switch (preprocessor_type) {
+    case TextPreprocessingGraphOptions::UNSPECIFIED_PREPROCESSOR:
+    case TextPreprocessingGraphOptions::STRING_PREPROCESSOR: {
+      break;
+    }
+    case TextPreprocessingGraphOptions::BERT_PREPROCESSOR:
+    case TextPreprocessingGraphOptions::REGEX_PREPROCESSOR: {
       ASSIGN_OR_RETURN(
           int max_seq_len,
           GetMaxSeqLen(*(*model_resources.GetTfLiteModel()->subgraphs())[0]));
       options.set_max_seq_len(max_seq_len);
+    }
+  }
 
   return absl::OkStatus();
 }
@@ -236,7 +243,8 @@ class TextPreprocessingSubgraph : public mediapipe::Subgraph {
         GetCalculatorNameFromPreprocessorType(options.preprocessor_type()));
     auto& text_preprocessor = graph.AddNode(preprocessor_name);
     switch (options.preprocessor_type()) {
-      case TextPreprocessingGraphOptions::UNSPECIFIED_PREPROCESSOR: {
+      case TextPreprocessingGraphOptions::UNSPECIFIED_PREPROCESSOR:
+      case TextPreprocessingGraphOptions::STRING_PREPROCESSOR: {
         break;
       }
       case TextPreprocessingGraphOptions::BERT_PREPROCESSOR: {

mediapipe/tasks/testdata/text/BUILD

@@ -27,6 +27,7 @@ mediapipe_files(srcs = [
     "albert_with_metadata.tflite",
     "bert_text_classifier.tflite",
     "mobilebert_with_metadata.tflite",
+    "test_model_text_classifier_bool_output.tflite",
     "test_model_text_classifier_with_regex_tokenizer.tflite",
 ])
 

third_party/external_files.bzl

@@ -562,6 +562,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/test_model_add_op.tflite?generation=1661875950076192"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_test_model_text_classifier_bool_output_tflite",
+        sha256 = "09877ac6d718d78da6380e21fe8179854909d116632d6d770c12f8a51792e310",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/test_model_text_classifier_bool_output.tflite?generation=1664904110313163"],
+    )
+
     http_file(
         name = "com_google_mediapipe_test_model_text_classifier_with_regex_tokenizer_tflite",
         sha256 = "cb12618d084b813cb7b90ceb39c9fe4b18dae4de9880b912cdcd4b577cd65b4f",