Merged commit includes the following changes: (#8829)

320618558  by rathodv:

    Internal Change.

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320597532  by ronnyvotel:

    Exposing DensePose visualizations to model_lib_v2.py.

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320533669  by ronnyvotel:

    Adding utilities to visualize DensePose outputs.

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320529647  by lzc:

    Fix saved_model issue in object_detection_tutorial notebook.

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320510127  by aom:

    Internal change.

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320490236  by derekjchow:

    Update Dockerfiles to use setup.py

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320443572  by rathodv:

    Update `Tensorflow` to `TensorFlow` in documentation.

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320426460  by ronnyvotel:

    DensePose proto and model builder.

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320352611  by rathodv:

    Update documentation to reflect the support for TF1 and TF2. Provide separate sets of instructions to reduce confusion.

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320350724  by rathodv:

    Internal Change.

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PiperOrigin-RevId: 320618558

Co-authored-by: kmindspark <[email protected]>

Author: tombstone

Diff for: research/object_detection/.gitignore

@@ -1,6 +1,6 @@
-# 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.

Diff for: research/object_detection/CONTRIBUTING.md

@@ -870,6 +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.
 
@@ -922,6 +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,
@@ -931,7 +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(

Diff for: research/object_detection/builders/model_builder.py

@@ -164,6 +164,28 @@ def get_fake_mask_proto(self):
     return text_format.Merge(proto_txt,
                              center_net_pb2.CenterNet.MaskEstimation())
 
+  def get_fake_densepose_proto(self):
+    proto_txt = """
+      task_loss_weight: 0.5
+      class_id: 0
+      loss {
+        classification_loss {
+          weighted_softmax {}
+        }
+        localization_loss {
+          l1_localization_loss {
+          }
+        }
+      }
+      num_parts: 24
+      part_loss_weight: 1.0
+      coordinate_loss_weight: 2.0
+      upsample_to_input_res: true
+      heatmap_bias_init: -2.0
+    """
+    return text_format.Merge(proto_txt,
+                             center_net_pb2.CenterNet.DensePoseEstimation())
+
   def test_create_center_net_model(self):
     """Test building a CenterNet model from proto txt."""
     proto_txt = """
@@ -195,6 +217,8 @@ def test_create_center_net_model(self):
         self.get_fake_label_map_file_path())
     config.center_net.mask_estimation_task.CopyFrom(
         self.get_fake_mask_proto())
+    config.center_net.densepose_estimation_task.CopyFrom(
+        self.get_fake_densepose_proto())
 
     # Build the model from the configuration.
     model = model_builder.build(config, is_training=True)
@@ -251,6 +275,21 @@ def test_create_center_net_model(self):
     self.assertAlmostEqual(
         model._mask_params.heatmap_bias_init, -2.0, places=4)
 
+    # Check DensePose related parameters.
+    self.assertEqual(model._densepose_params.class_id, 0)
+    self.assertIsInstance(model._densepose_params.classification_loss,
+                          losses.WeightedSoftmaxClassificationLoss)
+    self.assertIsInstance(model._densepose_params.localization_loss,
+                          losses.L1LocalizationLoss)
+    self.assertAlmostEqual(model._densepose_params.part_loss_weight, 1.0)
+    self.assertAlmostEqual(model._densepose_params.coordinate_loss_weight, 2.0)
+    self.assertEqual(model._densepose_params.num_parts, 24)
+    self.assertAlmostEqual(model._densepose_params.task_loss_weight, 0.5)
+    self.assertTrue(model._densepose_params.upsample_to_input_res)
+    self.assertEqual(model._densepose_params.upsample_method, 'bilinear')
+    self.assertAlmostEqual(
+        model._densepose_params.heatmap_bias_init, -2.0, places=4)
+
     # Check feature extractor parameters.
     self.assertIsInstance(
         model._feature_extractor,