Merge branch 'master' of https://github.com/tensorflow/models into latest

research/object_detection/CONTRIBUTING.md

-# Contributing to the Tensorflow Object Detection API
+# Contributing to the TensorFlow Object Detection API
 
-Patches to Tensorflow Object Detection API are welcome!
+Patches to TensorFlow Object Detection API are welcome!
 
 We require contributors to fill out either the individual or corporate
 Contributor License Agreement (CLA).
@@ -9,5 +9,5 @@ Contributor License Agreement (CLA).
   * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
 
 Please follow the
-[Tensorflow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)
+[TensorFlow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)
 when submitting pull requests.

research/object_detection/builders/box_predictor_builder_test.py

@@ -17,9 +17,8 @@
 """Tests for box_predictor_builder."""
 
 import unittest
-import mock
+from unittest import mock  # pylint: disable=g-importing-member
 import tensorflow.compat.v1 as tf
-
 from google.protobuf import text_format
 from object_detection.builders import box_predictor_builder
 from object_detection.builders import hyperparams_builder

research/object_detection/builders/graph_rewriter_builder_tf1_test.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 """Tests for graph_rewriter_builder."""
 import unittest
-import mock
+from unittest import mock  # pylint: disable=g-importing-member
 import tensorflow.compat.v1 as tf
 import tf_slim as slim
 

research/object_detection/builders/model_builder.py

@@ -16,6 +16,7 @@
 """A function to build a DetectionModel from configuration."""
 
 import functools
+import sys
 from object_detection.builders import anchor_generator_builder
 from object_detection.builders import box_coder_builder
 from object_detection.builders import box_predictor_builder
@@ -58,6 +59,8 @@ if tf_version.is_tf2():
   from object_detection.models.ssd_mobilenet_v2_fpn_keras_feature_extractor import SSDMobileNetV2FpnKerasFeatureExtractor
   from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
   from object_detection.predictors import rfcn_keras_box_predictor
+  if sys.version_info[0] >= 3:
+    from object_detection.models import ssd_efficientnet_bifpn_feature_extractor as ssd_efficientnet_bifpn
 
 if tf_version.is_tf1():
   from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
@@ -99,6 +102,22 @@ if tf_version.is_tf2():
           ssd_resnet_v1_fpn_keras.SSDResNet101V1FpnKerasFeatureExtractor,
       'ssd_resnet152_v1_fpn_keras':
           ssd_resnet_v1_fpn_keras.SSDResNet152V1FpnKerasFeatureExtractor,
+      'ssd_efficientnet-b0_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB0BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b1_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB1BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b2_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB2BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b3_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB3BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b4_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB4BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b5_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB5BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b6_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB6BiFPNKerasFeatureExtractor,
+      'ssd_efficientnet-b7_bifpn_keras':
+          ssd_efficientnet_bifpn.SSDEfficientNetB7BiFPNKerasFeatureExtractor,
   }
 
   FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
@@ -310,6 +329,14 @@ def _build_ssd_feature_extractor(feature_extractor_config,
             feature_extractor_config.fpn.additional_layer_depth,
     })
 
+  if feature_extractor_config.HasField('bifpn'):
+    kwargs.update({
+        'bifpn_min_level': feature_extractor_config.bifpn.min_level,
+        'bifpn_max_level': feature_extractor_config.bifpn.max_level,
+        'bifpn_num_iterations': feature_extractor_config.bifpn.num_iterations,
+        'bifpn_num_filters': feature_extractor_config.bifpn.num_filters,
+        'bifpn_combine_method': feature_extractor_config.bifpn.combine_method,
+    })
 
   return feature_extractor_class(**kwargs)
 
@@ -843,6 +870,22 @@ def mask_proto_to_params(mask_config):
       heatmap_bias_init=mask_config.heatmap_bias_init)
 
 
+def densepose_proto_to_params(densepose_config):
+  """Converts CenterNet.DensePoseEstimation proto to parameter namedtuple."""
+  classification_loss, localization_loss, _, _, _, _, _ = (
+      losses_builder.build(densepose_config.loss))
+  return center_net_meta_arch.DensePoseParams(
+      class_id=densepose_config.class_id,
+      classification_loss=classification_loss,
+      localization_loss=localization_loss,
+      part_loss_weight=densepose_config.part_loss_weight,
+      coordinate_loss_weight=densepose_config.coordinate_loss_weight,
+      num_parts=densepose_config.num_parts,
+      task_loss_weight=densepose_config.task_loss_weight,
+      upsample_to_input_res=densepose_config.upsample_to_input_res,
+      heatmap_bias_init=densepose_config.heatmap_bias_init)
+
+
 def _build_center_net_model(center_net_config, is_training, add_summaries):
   """Build a CenterNet detection model.
 
@@ -895,6 +938,11 @@ def _build_center_net_model(center_net_config, is_training, add_summaries):
   if center_net_config.HasField('mask_estimation_task'):
     mask_params = mask_proto_to_params(center_net_config.mask_estimation_task)
 
+  densepose_params = None
+  if center_net_config.HasField('densepose_estimation_task'):
+    densepose_params = densepose_proto_to_params(
+        center_net_config.densepose_estimation_task)
+
   return center_net_meta_arch.CenterNetMetaArch(
       is_training=is_training,
       add_summaries=add_summaries,
@@ -904,7 +952,8 @@ def _build_center_net_model(center_net_config, is_training, add_summaries):
       object_center_params=object_center_params,
       object_detection_params=object_detection_params,
       keypoint_params_dict=keypoint_params_dict,
-      mask_params=mask_params)
+      mask_params=mask_params,
+      densepose_params=densepose_params)
 
 
 def _build_center_net_feature_extractor(

research/object_detection/builders/model_builder_test.py

@@ -39,6 +39,9 @@ class ModelBuilderTest(test_case.TestCase, parameterized.TestCase):
   def ssd_feature_extractors(self):
     raise NotImplementedError
 
+  def get_override_base_feature_extractor_hyperparams(self, extractor_type):
+    raise NotImplementedError
+
   def faster_rcnn_feature_extractors(self):
     raise NotImplementedError
 
@@ -70,7 +73,6 @@ class ModelBuilderTest(test_case.TestCase, parameterized.TestCase):
                 }
               }
           }
-          override_base_feature_extractor_hyperparams: true
         }
         box_coder {
           faster_rcnn_box_coder {
@@ -205,6 +207,8 @@ class ModelBuilderTest(test_case.TestCase, parameterized.TestCase):
     for extractor_type, extractor_class in self.ssd_feature_extractors().items(
     ):
       model_proto.ssd.feature_extractor.type = extractor_type
+      model_proto.ssd.feature_extractor.override_base_feature_extractor_hyperparams = (
+          self.get_override_base_feature_extractor_hyperparams(extractor_type))
       model = model_builder.build(model_proto, is_training=True)
       self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
       self.assertIsInstance(model._feature_extractor, extractor_class)

research/object_detection/builders/model_builder_tf1_test.py

@@ -38,6 +38,9 @@ class ModelBuilderTF1Test(model_builder_test.ModelBuilderTest):
   def ssd_feature_extractors(self):
     return model_builder.SSD_FEATURE_EXTRACTOR_CLASS_MAP
 
+  def get_override_base_feature_extractor_hyperparams(self, extractor_type):
+    return extractor_type in {'ssd_inception_v2', 'ssd_inception_v3'}
+
   def faster_rcnn_feature_extractors(self):
     return model_builder.FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP
 

research/object_detection/builders/preprocessor_builder.py

@@ -417,4 +417,12 @@ def build(preprocessor_step_config):
         'num_scales': config.num_scales
     }
 
+  if step_type == 'random_scale_crop_and_pad_to_square':
+    config = preprocessor_step_config.random_scale_crop_and_pad_to_square
+    return preprocessor.random_scale_crop_and_pad_to_square, {
+        'scale_min': config.scale_min,
+        'scale_max': config.scale_max,
+        'output_size': config.output_size,
+    }
+
   raise ValueError('Unknown preprocessing step.')