add mobilenet experiment setup template

official/vision/beta/configs/experiments/image_classification/imagenet_mobilenetv2_1.0_gpu.yaml

+runtime:
+  distribution_strategy: 'mirrored'
+  mixed_precision_dtype: 'float32'
+  loss_scale: 'dynamic'
+task:
+  model:
+    num_classes: 1001
+    input_size: [224, 224, 3]
+    backbone:
+      type: 'mobilenet'
+      mobilenet:
+        model_id: 'MobileNetV2'
+        width_multiplier: 1.0
+    dropout_rate: 0.2
+  losses:
+    l2_weight_decay: 0.0001
+    one_hot: True
+    label_smoothing: 0.1
+  train_data:
+    input_path: 'imagenet-2012-tfrecord/train*'
+    is_training: True
+    global_batch_size: 96
+    dtype: 'float32'
+  validation_data:
+    input_path: 'imagenet-2012-tfrecord/valid*'
+    is_training: False
+    global_batch_size: 96
+    dtype: 'float32'
+    drop_remainder: False
+trainer:
+  train_steps: 1201050 # 90 * steps_per_epoch
+  validation_steps: 520
+  validation_interval: 13345
+  steps_per_loop: 13345 # NUM_EXAMPLES (1281167) // global_batch_size
+  summary_interval: 13345
+  checkpoint_interval: 13345
+  optimizer_config:
+    optimizer:
+      type: 'rmsprop'
+      rmsprop:
+        decay: 0.9,
+        momentum: 0.9,
+        epsilon: 0.002,
+    learning_rate:
+      type: 'exponential'
+      exponential:
+        initial_learning_rate: 0.045,
+        decay_steps: 33362, # 2.5 * steps_per_epoch
+        decay_rate: 0.97,
+        staircase: True
+    warmup:
+      type: 'linear'
+      linear:
+        warmup_steps: 66725 # 5 * steps_per_epoch
+    ema:
+      average_decay: 0.9999
\ No newline at end of file

official/vision/beta/configs/image_classification.py

@@ -126,10 +126,10 @@ def image_classification_imagenet() -> cfg.ExperimentConfig:
                           80 * steps_per_epoch
                       ],
                       'values': [
-                          0.1 *  train_batch_size / 256,
-                          0.01 *  train_batch_size / 256,
-                          0.001 *  train_batch_size / 256,
-                          0.0001 *  train_batch_size / 256,
+                          0.1 * train_batch_size / 256,
+                          0.01 * train_batch_size / 256,
+                          0.001 * train_batch_size / 256,
+                          0.0001 * train_batch_size / 256,
                       ]
                   }
               },
@@ -213,3 +213,74 @@ def image_classification_imagenet_revnet() -> cfg.ExperimentConfig:
       ])
 
   return config
+
+
+@exp_factory.register_config_factory('mobilenet_imagenet')
+def image_classification_imagenet_mobilenet() -> cfg.ExperimentConfig:
+  """Image classification on imagenet with mobilenet."""
+  train_batch_size = 192
+  eval_batch_size = 192
+  steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
+  config = cfg.ExperimentConfig(
+      task=ImageClassificationTask(
+          model=ImageClassificationModel(
+              num_classes=1001,
+              dropout_rate=0.2,
+              input_size=[224, 224, 3],
+              backbone=backbones.Backbone(
+                  type='mobilenet',
+                  mobilenet=backbones.MobileNet(
+                      model_id='MobileNetV2', width_multiplier=1.0)),
+              norm_activation=common.NormActivation(
+                  norm_momentum=0.9997, norm_epsilon=1e-3)),
+          losses=Losses(l2_weight_decay=1e-4, label_smoothing=0.1),
+          train_data=DataConfig(
+              input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'train*'),
+              is_training=True,
+              global_batch_size=train_batch_size),
+          validation_data=DataConfig(
+              input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'valid*'),
+              is_training=False,
+              global_batch_size=eval_batch_size)),
+      trainer=cfg.TrainerConfig(
+          steps_per_loop=steps_per_epoch,
+          summary_interval=steps_per_epoch,
+          checkpoint_interval=steps_per_epoch,
+          train_steps=90 * steps_per_epoch,
+          validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
+          validation_interval=steps_per_epoch,
+          optimizer_config=optimization.OptimizationConfig({
+              'optimizer': {
+                  'type': 'rmsprop',
+                  'rmsprop': {
+                      'decay': 0.9,
+                      'momentum': 0.9,
+                      'epsilon': 0.002,
+                  }
+              },
+              'learning_rate': {
+                  'type': 'exponential',
+                  'exponential': {
+                      'initial_learning_rate': 0.045,
+                      'decay_steps': int(2.4 * steps_per_epoch),
+                      'decay_rate': 0.97,
+                      'staircase': True
+                  }
+              },
+              'warmup': {
+                  'type': 'linear',
+                  'linear': {
+                      'warmup_steps': 5 * steps_per_epoch,
+                      'warmup_learning_rate': 0
+                  }
+              },
+              'ema': {
+                  'average_decay': 0.9999
+              }
+          })),
+      restrictions=[
+          'task.train_data.is_training != None',
+          'task.validation_data.is_training != None'
+      ])
+
+  return config

official/vision/beta/configs/image_classification_test.py

@@ -28,7 +28,8 @@ from official.vision.beta.configs import image_classification as exp_cfg
 class ImageClassificationConfigTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.parameters(('resnet_imagenet',),
-                            ('revnet_imagenet',))
+                            ('revnet_imagenet',),
+                            ('mobilenet_imagenet'),)
   def test_image_classification_configs(self, config_name):
     config = exp_factory.get_exp_config(config_name)
     self.assertIsInstance(config, cfg.ExperimentConfig)

official/vision/beta/modeling/backbones/mobilenet.py

@@ -593,6 +593,7 @@ class MobileNet(tf.keras.Model):
     """A dict of {level: TensorShape} pairs for the model output."""
     return self._output_specs
 
+
 @factory.register_backbone_builder('mobilenet')
 def build_mobilenet(
     input_specs: tf.keras.layers.InputSpec,