Add MobileNetV2_I320 and MobileNetMultiHWAVG_I384 to support larger input image sizes.

PiperOrigin-RevId: 544393692

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

@@ -74,8 +74,8 @@ class ObjectDetectorModel(tf.keras.Model):
       generator_config: configs.retinanet.DetectionGenerator = configs.retinanet.DetectionGenerator(),
   ) -> configs.retinanet.RetinaNet:
     model_config = configs.retinanet.RetinaNet(
-        min_level=3,
-        max_level=7,
+        min_level=self._model_spec.min_level,
+        max_level=self._model_spec.max_level,
         num_classes=self._num_classes,
         input_size=self._model_spec.input_image_shape,
         anchor=configs.retinanet.Anchor(

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

@@ -20,18 +20,30 @@ from typing import List
 from mediapipe.model_maker.python.core.utils import file_util
 
 
-MOBILENET_V2_FILES = file_util.DownloadedFiles(
-    'object_detector/mobilenetv2',
+MOBILENET_V2_I256_FILES = file_util.DownloadedFiles(
+    'object_detector/mobilenetv2_i256',
     'https://storage.googleapis.com/tf_model_garden/vision/qat/mobilenetv2_ssd_coco/mobilenetv2_ssd_i256_ckpt.tar.gz',
     is_folder=True,
 )
 
+MOBILENET_V2_I320_FILES = file_util.DownloadedFiles(
+    'object_detector/mobilenetv2_i320',
+    'https://storage.googleapis.com/tf_model_garden/vision/qat/mobilenetv2_ssd_coco/mobilenetv2_ssd_i320_ckpt.tar.gz',
+    is_folder=True,
+)
+
 MOBILENET_MULTI_AVG_FILES = file_util.DownloadedFiles(
     'object_detector/mobilenetmultiavg',
     'https://storage.googleapis.com/tf_model_garden/vision/qat/mobilenetv3.5_ssd_coco/mobilenetv3.5_ssd_i256_ckpt.tar.gz',
     is_folder=True,
 )
 
+MOBILENET_MULTI_AVG_I384_FILES = file_util.DownloadedFiles(
+    'object_detector/mobilenetmultiavg_i384',
+    'https://storage.googleapis.com/tf_model_garden/vision/qat/mobilenetv2_ssd_coco/mobilenetv3.5_ssd_i384_ckpt.tar.gz',
+    is_folder=True,
+)
+
 
 @dataclasses.dataclass
 class ModelSpec(object):
@@ -48,30 +60,66 @@ class ModelSpec(object):
   input_image_shape: List[int]
   model_id: str
 
+  # Model Config values
+  min_level: int
+  max_level: int
 
-mobilenet_v2_spec = functools.partial(
+
+mobilenet_v2_i256_spec = functools.partial(
     ModelSpec,
-    downloaded_files=MOBILENET_V2_FILES,
+    downloaded_files=MOBILENET_V2_I256_FILES,
     checkpoint_name='ckpt-277200',
     input_image_shape=[256, 256, 3],
     model_id='MobileNetV2',
+    min_level=3,
+    max_level=7,
 )
 
-mobilenet_multi_avg_spec = functools.partial(
+mobilenet_v2_i320_spec = functools.partial(
+    ModelSpec,
+    downloaded_files=MOBILENET_V2_I320_FILES,
+    checkpoint_name='ckpt-277200',
+    input_image_shape=[320, 320, 3],
+    model_id='MobileNetV2',
+    min_level=3,
+    max_level=6,
+)
+
+mobilenet_multi_avg_i256_spec = functools.partial(
     ModelSpec,
     downloaded_files=MOBILENET_MULTI_AVG_FILES,
     checkpoint_name='ckpt-277200',
     input_image_shape=[256, 256, 3],
     model_id='MobileNetMultiAVG',
+    min_level=3,
+    max_level=7,
+)
+
+mobilenet_multi_avg_i384_spec = functools.partial(
+    ModelSpec,
+    downloaded_files=MOBILENET_MULTI_AVG_I384_FILES,
+    checkpoint_name='ckpt-277200',
+    input_image_shape=[384, 384, 3],
+    model_id='MobileNetMultiAVG',
+    min_level=3,
+    max_level=7,
 )
 
 
 @enum.unique
 class SupportedModels(enum.Enum):
-  """Predefined object detector model specs supported by Model Maker."""
+  """Predefined object detector model specs supported by Model Maker.
 
-  MOBILENET_V2 = mobilenet_v2_spec
-  MOBILENET_MULTI_AVG = mobilenet_multi_avg_spec
+  Supported models include the following:
+  - MOBILENET_V2: MobileNetV2 256x256 input
+  - MOBILENET_V2_I320: MobileNetV2 320x320 input
+  - MOBILENET_MULTI_AVG: MobileNet-MultiHW-AVG 256x256 input
+  - MOBILENET_MULTI_AVG_I384: MobileNet-MultiHW-AVG 384x384 input
+  """
+  MOBILENET_V2 = mobilenet_v2_i256_spec
+  MOBILENET_V2_I320 = mobilenet_v2_i320_spec
+  MOBILENET_MULTI_AVG = mobilenet_multi_avg_i256_spec
+  MOBILENET_MULTI_AVG_I384 = mobilenet_multi_avg_i384_spec
 
   @classmethod
   def get(cls, spec: 'SupportedModels') -> 'ModelSpec':

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

@@ -395,7 +395,7 @@ class ObjectDetector(classifier.Classifier):
   ) -> tf.keras.optimizers.Optimizer:
     """Creates an optimizer with learning rate schedule for regular training.
 
-    Uses Keras PiecewiseConstantDecay schedule by default.
+    Uses Keras CosineDecay schedule by default.
 
     Args:
       steps_per_epoch: Steps per epoch to calculate the step boundaries from the
@@ -404,6 +404,8 @@ class ObjectDetector(classifier.Classifier):
     Returns:
       A tf.keras.optimizer.Optimizer for model training.
     """
+    total_steps = steps_per_epoch * self._hparams.epochs
+    warmup_steps = int(total_steps * 0.1)
     init_lr = self._hparams.learning_rate * self._hparams.batch_size / 256
     decay_epochs = (
         self._hparams.cosine_decay_epochs
@@ -415,6 +417,11 @@ class ObjectDetector(classifier.Classifier):
         steps_per_epoch * decay_epochs,
         self._hparams.cosine_decay_alpha,
     )
+    learning_rate = model_util.WarmUp(
+        initial_learning_rate=init_lr,
+        decay_schedule_fn=learning_rate,
+        warmup_steps=warmup_steps,
+    )
     return tf.keras.optimizers.experimental.SGD(
         learning_rate=learning_rate, momentum=0.9
     )

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

@@ -32,8 +32,8 @@ class Preprocessor(object):
     self._mean_norm = model_spec.mean_norm
     self._stddev_norm = model_spec.stddev_norm
     self._output_size = model_spec.input_image_shape[:2]
-    self._min_level = 3
-    self._max_level = 7
+    self._min_level = model_spec.min_level
+    self._max_level = model_spec.max_level
     self._num_scales = 3
     self._aspect_ratios = [0.5, 1, 2]
     self._anchor_size = 3