Open source EfficientNetV2.

PiperOrigin-RevId: 373612565

Author: mingxingtan

tensorflow_examples/lite/model_maker/third_party/efficientdet/Det-AdvProp.md

@@ -1,4 +1,6 @@
 # Det-AdvProp
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.sandbox.google.com/github/google/automl/blob/master/efficientdet
+/det_advprop_tutorial.ipynb)
 
 [1] Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing
 Gong. CVPR 2021. Arxiv link: https://arxiv.org/abs/2103.13886

tensorflow_examples/lite/model_maker/third_party/efficientdet/utils.py

@@ -57,6 +57,9 @@ def activation_fn(features: tf.Tensor, act_type: Text):
 def cross_replica_mean(t, num_shards_per_group=None):
   """Calculates the average value of input tensor across TPU replicas."""
   num_shards = tpu_function.get_tpu_context().number_of_shards
+  if not num_shards:
+    return t
+
   if not num_shards_per_group:
     return tf.tpu.cross_replica_sum(t) / tf.cast(num_shards, t.dtype)
 
@@ -75,8 +78,9 @@ def cross_replica_mean(t, num_shards_per_group=None):
 
 def get_ema_vars():
   """Get all exponential moving average (ema) variables."""
-  ema_vars = tf.trainable_variables() + \
-             tf.get_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES)
+  ema_vars = (
+      tf.trainable_variables() +
+      tf.get_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES))
   for v in tf.global_variables():
     # We maintain mva for batch norm moving mean and variance as well.
     if 'moving_mean' in v.name or 'moving_variance' in v.name:
@@ -127,6 +131,8 @@ def get_ckpt_var_map(ckpt_path, ckpt_scope, var_scope, skip_mismatch=None):
       logging.info('skip {} -- does not match scope {}'.format(
           var_op_name, var_scope))
     ckpt_var = ckpt_scope + var_op_name[len(var_scope):]
+    if 'global_step' in ckpt_var:
+      continue
 
     if (ckpt_var not in ckpt_var_names and
         var_op_name.endswith('/ExponentialMovingAverage')):