remove ema for optimizer since it throw errors

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

@@ -38,19 +38,17 @@ trainer:
     optimizer:
       type: 'rmsprop'
       rmsprop:
-        decay: 0.9,
-        momentum: 0.9,
-        epsilon: 0.002,
+        decay: 0.9
+        momentum: 0.9
+        epsilon: 0.002
     learning_rate:
       type: 'exponential'
       exponential:
-        initial_learning_rate: 0.36,  # 0.045 * NUM_GPUS
-        decay_steps: 4170,  # 2.5 * steps_per_epoch
-        decay_rate: 0.97,
+        initial_learning_rate: 0.36  # 0.045 * NUM_GPUS
+        decay_steps: 4170  # 2.5 * steps_per_epoch
+        decay_rate: 0.97
         staircase: True
     warmup:
       type: 'linear'
       linear:
         warmup_steps: 8340  # 5 * steps_per_epoch
\ No newline at end of file
-    ema:
-      average_decay: 0.9999
\ No newline at end of file

official/vision/beta/configs/image_classification.py

@@ -277,9 +277,6 @@ def image_classification_imagenet_mobilenet() -> cfg.ExperimentConfig:
                       'warmup_learning_rate': 0
                   }
               },
-              'ema': {
-                  'average_decay': 0.9999
-              }
           })),
       restrictions=[
           'task.train_data.is_training != None',

official/vision/beta/modeling/layers/nn_blocks.py

@@ -490,9 +490,7 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
       # First 1x1 conv for channel expansion.
       expand_filters = nn_layers.make_divisible(
           self._in_filters * self._expand_ratio, self._divisible_by)
-      logging.info('expand_filter: {}, divisible_version {}'.format(
-          self._in_filters * self._expand_ratio, expand_filters
-      ))
+
       expand_kernel = 1 if self._use_depthwise else self._kernel_size
       expand_stride = 1 if self._use_depthwise else self._strides