Project import generated by Copybara.

GitOrigin-RevId: f4b1fe3f15810450fb6539e733f6a260d3ee082c

mediapipe/calculators/tensor/inference_calculator_cpu.cc

@@ -73,6 +73,7 @@ class InferenceCalculatorCpuImpl
  private:
   absl::Status LoadModel(CalculatorContext* cc);
   absl::Status LoadDelegate(CalculatorContext* cc);
+  absl::Status LoadDelegateAndAllocateTensors(CalculatorContext* cc);
 
   // TfLite requires us to keep the model alive as long as the interpreter is.
   Packet<TfLiteModelPtr> model_packet_;
@@ -91,8 +92,7 @@ absl::Status InferenceCalculatorCpuImpl::UpdateContract(
 
 absl::Status InferenceCalculatorCpuImpl::Open(CalculatorContext* cc) {
   MP_RETURN_IF_ERROR(LoadModel(cc));
-  MP_RETURN_IF_ERROR(LoadDelegate(cc));
-  return absl::OkStatus();
+  return LoadDelegateAndAllocateTensors(cc);
 }
 
 absl::Status InferenceCalculatorCpuImpl::Process(CalculatorContext* cc) {
@@ -156,11 +156,19 @@ absl::Status InferenceCalculatorCpuImpl::LoadModel(CalculatorContext* cc) {
       cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread());
 #endif  // __EMSCRIPTEN__
 
+  return absl::OkStatus();
+}
+
+absl::Status InferenceCalculatorCpuImpl::LoadDelegateAndAllocateTensors(
+    CalculatorContext* cc) {
+  MP_RETURN_IF_ERROR(LoadDelegate(cc));
+
+  // AllocateTensors() can be called only after ModifyGraphWithDelegate.
   RET_CHECK_EQ(interpreter_->AllocateTensors(), kTfLiteOk);
   // TODO: Support quantized tensors.
-  CHECK(interpreter_->tensor(interpreter_->inputs()[0])->quantization.type !=
-        kTfLiteAffineQuantization);
-
+  RET_CHECK_NE(
+      interpreter_->tensor(interpreter_->inputs()[0])->quantization.type,
+      kTfLiteAffineQuantization);
   return absl::OkStatus();
 }
 

mediapipe/calculators/tensor/inference_calculator_gl.cc

@@ -53,6 +53,7 @@ class InferenceCalculatorGlImpl
   absl::Status WriteKernelsToFile();
   absl::Status LoadModel(CalculatorContext* cc);
   absl::Status LoadDelegate(CalculatorContext* cc);
+  absl::Status LoadDelegateAndAllocateTensors(CalculatorContext* cc);
   absl::Status InitTFLiteGPURunner(CalculatorContext* cc);
 
   // TfLite requires us to keep the model alive as long as the interpreter is.
@@ -119,10 +120,11 @@ absl::Status InferenceCalculatorGlImpl::Open(CalculatorContext* cc) {
   }
 
   MP_RETURN_IF_ERROR(gpu_helper_.Open(cc));
-  MP_RETURN_IF_ERROR(gpu_helper_.RunInGlContext([this,
-                                                 &cc]() -> ::mediapipe::Status {
-    return use_advanced_gpu_api_ ? InitTFLiteGPURunner(cc) : LoadDelegate(cc);
-  }));
+  MP_RETURN_IF_ERROR(
+      gpu_helper_.RunInGlContext([this, &cc]() -> ::mediapipe::Status {
+        return use_advanced_gpu_api_ ? InitTFLiteGPURunner(cc)
+                                     : LoadDelegateAndAllocateTensors(cc);
+      }));
   return absl::OkStatus();
 }
 
@@ -324,11 +326,19 @@ absl::Status InferenceCalculatorGlImpl::LoadModel(CalculatorContext* cc) {
       cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread());
 #endif  // __EMSCRIPTEN__
 
+  return absl::OkStatus();
+}
+
+absl::Status InferenceCalculatorGlImpl::LoadDelegateAndAllocateTensors(
+    CalculatorContext* cc) {
+  MP_RETURN_IF_ERROR(LoadDelegate(cc));
+
+  // AllocateTensors() can be called only after ModifyGraphWithDelegate.
   RET_CHECK_EQ(interpreter_->AllocateTensors(), kTfLiteOk);
   // TODO: Support quantized tensors.
-  CHECK(interpreter_->tensor(interpreter_->inputs()[0])->quantization.type !=
-        kTfLiteAffineQuantization);
-
+  RET_CHECK_NE(
+      interpreter_->tensor(interpreter_->inputs()[0])->quantization.type,
+      kTfLiteAffineQuantization);
   return absl::OkStatus();
 }
 

mediapipe/calculators/tensor/inference_calculator_metal.cc

@@ -92,6 +92,7 @@ class InferenceCalculatorMetalImpl
  private:
   absl::Status LoadModel(CalculatorContext* cc);
   absl::Status LoadDelegate(CalculatorContext* cc);
+  absl::Status LoadDelegateAndAllocateTensors(CalculatorContext* cc);
 
   // TfLite requires us to keep the model alive as long as the interpreter is.
   Packet<TfLiteModelPtr> model_packet_;
@@ -130,8 +131,7 @@ absl::Status InferenceCalculatorMetalImpl::Open(CalculatorContext* cc) {
 
   gpu_helper_ = [[MPPMetalHelper alloc] initWithCalculatorContext:cc];
   RET_CHECK(gpu_helper_);
-  MP_RETURN_IF_ERROR(LoadDelegate(cc));
-  return absl::OkStatus();
+  return LoadDelegateAndAllocateTensors(cc);
 }
 
 absl::Status InferenceCalculatorMetalImpl::Process(CalculatorContext* cc) {
@@ -212,11 +212,19 @@ absl::Status InferenceCalculatorMetalImpl::LoadModel(CalculatorContext* cc) {
   interpreter_->SetNumThreads(
       cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread());
 
+  return absl::OkStatus();
+}
+
+absl::Status InferenceCalculatorMetalImpl::LoadDelegateAndAllocateTensors(
+    CalculatorContext* cc) {
+  MP_RETURN_IF_ERROR(LoadDelegate(cc));
+
+  // AllocateTensors() can be called only after ModifyGraphWithDelegate.
   RET_CHECK_EQ(interpreter_->AllocateTensors(), kTfLiteOk);
   // TODO: Support quantized tensors.
-  CHECK(interpreter_->tensor(interpreter_->inputs()[0])->quantization.type !=
-        kTfLiteAffineQuantization);
-
+  RET_CHECK_NE(
+      interpreter_->tensor(interpreter_->inputs()[0])->quantization.type,
+      kTfLiteAffineQuantization);
   return absl::OkStatus();
 }
 
@@ -236,6 +244,7 @@ absl::Status InferenceCalculatorMetalImpl::LoadDelegate(CalculatorContext* cc) {
       TfLiteDelegatePtr(TFLGpuDelegateCreate(&options), &TFLGpuDelegateDelete);
   RET_CHECK_EQ(interpreter_->ModifyGraphWithDelegate(delegate_.get()),
                kTfLiteOk);
+
   id<MTLDevice> device = gpu_helper_.mtlDevice;
 
   // Get input image sizes.

mediapipe/calculators/tensor/tensors_to_detections_calculator.cc

@@ -670,7 +670,8 @@ absl::Status TensorsToDetectionsCalculator::ConvertToDetections(
         detection_boxes[box_offset + 2], detection_boxes[box_offset + 3],
         detection_scores[i], detection_classes[i], options_.flip_vertically());
     const auto& bbox = detection.location_data().relative_bounding_box();
-    if (bbox.width() < 0 || bbox.height() < 0) {
+    if (bbox.width() < 0 || bbox.height() < 0 || std::isnan(bbox.width()) ||
+        std::isnan(bbox.height())) {
       // Decoded detection boxes could have negative values for width/height due
       // to model prediction. Filter out those boxes since some downstream
       // calculators may assume non-negative values. (b/171391719)

mediapipe/calculators/tensor/tensors_to_segmentation_calculator.cc

@@ -138,7 +138,6 @@ using ::tflite::gpu::gl::GlShader;
 //   }
 // }
 //
-// Currently only OpenGLES 3.1 and CPU backends supported.
 // TODO Refactor and add support for other backends/platforms.
 //
 class TensorsToSegmentationCalculator : public CalculatorBase {