Change object detector learning rate decay to cosine decay.

PiperOrigin-RevId: 527337105

docs/framework_concepts/graphs.md

@@ -273,7 +273,7 @@ defined in the enclosing protobuf in order to be traversed using
 
 ## Cycles
 
-<!-- TODO -->
+<!-- TODO: add discussion of PreviousLoopbackCalculator -->
 
 By default, MediaPipe requires calculator graphs to be acyclic and treats cycles
 in a graph as errors. If a graph is intended to have cycles, the cycles need to

mediapipe/framework/api2/contract.h

@@ -164,7 +164,7 @@ class Contract {
 
   std::tuple<T...> items;
 
-  // TODO -, check for conflicts.
+  // TODO: when forwarding nested items (e.g. ports), check for conflicts.
   decltype(ExtractNestedItems(items)) all_items{ExtractNestedItems(items)};
 
   constexpr auto inputs() const {

mediapipe/framework/calculator_base.h

@@ -150,7 +150,7 @@ class CalculatorBase {
   // Packets may be output during a call to Close().  However, output packets
   // are silently discarded if Close() is called after a graph run has ended.
   //
-  // NOTE - needs to perform an action only when processing is
+  // NOTE: If Close() needs to perform an action only when processing is
   // complete, Close() must check if cc->GraphStatus() is OK.
   virtual absl::Status Close(CalculatorContext* cc) { return absl::OkStatus(); }
 

mediapipe/framework/calculator_context.h

@@ -111,7 +111,7 @@ class CalculatorContext {
 
   // Returns the status of the graph run.
   //
-  // NOTE -.
+  // NOTE: This method should only be called during CalculatorBase::Close().
   absl::Status GraphStatus() const { return graph_status_; }
 
   ProfilingContext* GetProfilingContext() const {

mediapipe/framework/calculator_runner.h

@@ -66,7 +66,7 @@ class CalculatorRunner {
   explicit CalculatorRunner(const std::string& node_config_string);
   // Convenience constructor to initialize a calculator which uses indexes
   // (not tags) for all its fields.
-  // NOTE -, which
+  // NOTE: This constructor calls proto_ns::TextFormat::ParseFromString(), which
   // is not available when using lite protos.
   CalculatorRunner(const std::string& calculator_type,
                    const std::string& options_string, int num_inputs,

mediapipe/framework/output_side_packet.h

@@ -30,7 +30,7 @@ class OutputSidePacket {
 
   // Sets the output side packet. The Packet must contain the data.
   //
-  // NOTE - cannot report errors via the return value. It uses an error
+  // NOTE: Set() cannot report errors via the return value. It uses an error
   // callback function to report errors.
   virtual void Set(const Packet& packet) = 0;
 };

mediapipe/framework/output_side_packet_impl.h

@@ -48,7 +48,7 @@ class OutputSidePacketImpl : public OutputSidePacket {
 
   // Sets the output side packet. The Packet must contain the data.
   //
-  // NOTE - cannot report errors via the return value. It uses an error
+  // NOTE: Set() cannot report errors via the return value. It uses an error
   // callback function to report errors.
   void Set(const Packet& packet) override;
 

mediapipe/framework/output_stream.h

@@ -50,7 +50,7 @@ class OutputStream {
   //   the only packet in the stream.
   // Violation of any of these conditions causes a CHECK-failure.
   //
-  // NOTE - cannot report errors via the return value. Instead of a
+  // NOTE: AddPacket() cannot report errors via the return value. Instead of a
   // CHECK-failure, a subclass of OutputStream should use a callback function
   // to report errors.
   virtual void AddPacket(const Packet& packet) = 0;

mediapipe/framework/scheduler_queue.h

@@ -102,7 +102,7 @@ class SchedulerQueue : public TaskQueue {
   // Implements the TaskQueue interface.
   void RunNextTask() override;
 
-  // NOTE -, the caller must call
+  // NOTE: After calling SetRunning(true), the caller must call
   // SubmitWaitingTasksToExecutor since tasks may have been added while the
   // queue was not running.
   void SetRunning(bool running) ABSL_LOCKS_EXCLUDED(mutex_);

mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc

@@ -25,7 +25,7 @@ constexpr char kAnchorsTag[] = "ANCHORS";
 constexpr char kBoxesInputTag[] = "BOXES";
 constexpr char kBoxesOutputTag[] = "START_POS";
 constexpr char kCancelTag[] = "CANCEL_ID";
-// TODO -
+// TODO: Find optimal Height/Width (0.1-0.3)
 constexpr float kBoxEdgeSize =
     0.2f;  // Used to establish tracking box dimensions
 constexpr float kUsToMs =

mediapipe/java/com/google/mediapipe/glutil/ExternalTextureRenderer.java

@@ -106,7 +106,7 @@ public class ExternalTextureRenderer {
    *
    * <p>Before calling this, {@link #setup} must have been called.
    *
-   * <p>NOTE -} on passed surface texture.
+   * <p>NOTE: Calls {@link SurfaceTexture#updateTexImage()} on passed surface texture.
    */
   public void render(SurfaceTexture surfaceTexture) {
     GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT);

mediapipe/model_maker/python/vision/object_detector/hyperparameters.py

@@ -14,7 +14,7 @@
 """Hyperparameters for training object detection models."""
 
 import dataclasses
-from typing import List
+from typing import Optional
 
 from mediapipe.model_maker.python.core import hyperparameters as hp
 
@@ -29,12 +29,13 @@ class HParams(hp.BaseHParams):
     epochs: Number of training iterations over the dataset.
     do_fine_tuning: If true, the base module is trained together with the
       classification layer on top.
-    learning_rate_epoch_boundaries: List of epoch boundaries where
+    cosine_decay_epochs: The number of epochs for cosine decay learning rate.
-      learning_rate_epoch_boundaries[i] is the epoch where the learning rate
+      See
-      will decay to learning_rate * learning_rate_decay_multipliers[i].
+      https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules/CosineDecay
-    learning_rate_decay_multipliers: List of learning rate multipliers which
+        for more info.
-      calculates the learning rate at the ith boundary as learning_rate *
+    cosine_decay_alpha: The alpha value for cosine decay learning rate. See
-      learning_rate_decay_multipliers[i].
+      https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules/CosineDecay
+        for more info.
   """
 
   # Parameters from BaseHParams class.
@@ -42,41 +43,9 @@ class HParams(hp.BaseHParams):
   batch_size: int = 32
   epochs: int = 10
 
-  # Parameters for learning rate decay
+  # Parameters for cosine learning rate decay
-  learning_rate_epoch_boundaries: List[int] = dataclasses.field(
+  cosine_decay_epochs: Optional[int] = None
-      default_factory=lambda: []
+  cosine_decay_alpha: float = 0.0
-  )
-  learning_rate_decay_multipliers: List[float] = dataclasses.field(
-      default_factory=lambda: []
-  )
-
-  def __post_init__(self):
-    # Validate stepwise learning rate parameters
-    lr_boundary_len = len(self.learning_rate_epoch_boundaries)
-    lr_decay_multipliers_len = len(self.learning_rate_decay_multipliers)
-    if lr_boundary_len != lr_decay_multipliers_len:
-      raise ValueError(
-          "Length of learning_rate_epoch_boundaries and ",
-          "learning_rate_decay_multipliers do not match: ",
-          f"{lr_boundary_len}!={lr_decay_multipliers_len}",
-      )
-    # Validate learning_rate_epoch_boundaries
-    if (
-        sorted(self.learning_rate_epoch_boundaries)
-        != self.learning_rate_epoch_boundaries
-    ):
-      raise ValueError(
-          "learning_rate_epoch_boundaries is not in ascending order: ",
-          self.learning_rate_epoch_boundaries,
-      )
-    if (
-        self.learning_rate_epoch_boundaries
-        and self.learning_rate_epoch_boundaries[-1] > self.epochs
-    ):
-      raise ValueError(
-          "Values in learning_rate_epoch_boundaries cannot be greater ",
-          "than epochs",
-      )
 
 
 @dataclasses.dataclass

mediapipe/model_maker/python/vision/object_detector/object_detector.py

@@ -354,19 +354,16 @@ class ObjectDetector(classifier.Classifier):
       A tf.keras.optimizer.Optimizer for model training.
     """
     init_lr = self._hparams.learning_rate * self._hparams.batch_size / 256
-    if self._hparams.learning_rate_epoch_boundaries:
+    decay_epochs = (
-      lr_values = [init_lr] + [
+        self._hparams.cosine_decay_epochs
-          init_lr * m for m in self._hparams.learning_rate_decay_multipliers
+        if self._hparams.cosine_decay_epochs
-      ]
+        else self._hparams.epochs
-      lr_step_boundaries = [
+    )
-          steps_per_epoch * epoch_boundary
+    learning_rate = tf.keras.optimizers.schedules.CosineDecay(
-          for epoch_boundary in self._hparams.learning_rate_epoch_boundaries
+        init_lr,
-      ]
+        steps_per_epoch * decay_epochs,
-      learning_rate = tf.keras.optimizers.schedules.PiecewiseConstantDecay(
+        self._hparams.cosine_decay_alpha,
-          lr_step_boundaries, lr_values
     )
-    else:
-      learning_rate = init_lr
     return tf.keras.optimizers.experimental.SGD(
         learning_rate=learning_rate, momentum=0.9
     )

mediapipe/tasks/cc/vision/image_segmenter/calculators/segmentation_postprocessor_gl.cc

@@ -581,7 +581,7 @@ SegmentationPostprocessorGl::GetSegmentationResultGpu(
       // Step 2.5: For SOFTMAX, apply softmax shaders (max, transformAndSum, and
       // normalization) to create softmax-transformed chunks before channel
       // extraction.
-      // NOTE - / sum_over_x(exp(x-C)) = exp(x) / sum_over_x(exp(x)). So
+      // NOTE: exp(x-C) / sum_over_x(exp(x-C)) = exp(x) / sum_over_x(exp(x)). So
       //   theoretically we can skip the max shader step entirely. However,
       //   applying it does bring all our values into a nice (0, 1] range, so it
       //   will likely be better for precision, especially when dealing with an

mediapipe/util/tracking/box_tracker.h

@@ -200,7 +200,7 @@ class BoxTracker {
 
   // Cancels all ongoing tracks. To avoid race conditions all NewBoxTrack's in
   // flight will also be canceled. Future NewBoxTrack's will be canceled.
-  // NOTE - before
+  // NOTE: To resume execution, you have to call ResumeTracking() before
   //       issuing more NewBoxTrack calls.
   void CancelAllOngoingTracks() ABSL_LOCKS_EXCLUDED(status_mutex_);
   void ResumeTracking() ABSL_LOCKS_EXCLUDED(status_mutex_);
@@ -208,7 +208,7 @@ class BoxTracker {
   // Waits for all ongoing tracks to complete.
   // Optionally accepts a timeout in microseconds (== 0 for infinite wait).
   // Returns true on success, false if timeout is reached.
-  // NOTE - must
+  // NOTE: If WaitForAllOngoingTracks timed out, CancelAllOngoingTracks() must
   // be called before destructing the BoxTracker object or dangeling running
   // threads might try to access invalid data.
   bool WaitForAllOngoingTracks(int timeout_us = 0)