Release iNaturalist Species-trained models, refactor of evaluation, box predictor for object detection.

research/object_detection/README.md

@@ -99,6 +99,16 @@ reporting an issue.
 
 ## Release information
 
+### Sep 17, 2018
+
+We have released Faster R-CNN detectors with ResNet-50 / ResNet-101 feature
+extractors trained on the [iNaturalist Species Detection Dataset](https://github.com/visipedia/inat_comp/blob/master/2017/README.md#bounding-boxes).
+The models are trained on the training split of the iNaturalist data for 4M
+iterations, they achieve 55% and 58% mean AP@.5 over 2854 classes respectively.
+For more details please refer to this [paper](https://arxiv.org/abs/1707.06642).
+
+<b>Thanks to contributors</b>: Chen Sun
+
 ### July 13, 2018
 
 There are many new updates in this release, extending the functionality and

research/object_detection/builders/box_predictor_builder_test.py

@@ -14,13 +14,16 @@
 # ==============================================================================
 
 """Tests for box_predictor_builder."""
+
 import mock
 import tensorflow as tf
 
 from google.protobuf import text_format
 from object_detection.builders import box_predictor_builder
 from object_detection.builders import hyperparams_builder
+from object_detection.predictors import convolutional_box_predictor
 from object_detection.predictors import mask_rcnn_box_predictor
+from object_detection.predictors.heads import mask_head
 from object_detection.protos import box_predictor_pb2
 from object_detection.protos import hyperparams_pb2
 
@@ -155,6 +158,73 @@ def test_construct_default_conv_box_predictor(self):
     self.assertTrue(box_predictor._is_training)
     self.assertFalse(class_head._use_depthwise)
 
+  def test_construct_default_conv_box_predictor_with_default_mask_head(self):
+    box_predictor_text_proto = """
+      convolutional_box_predictor {
+        mask_head {
+        }
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
+                    box_predictor._other_heads)
+    mask_prediction_head = (
+        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
+    )
+    self.assertEqual(mask_prediction_head._mask_height, 15)
+    self.assertEqual(mask_prediction_head._mask_width, 15)
+    self.assertTrue(mask_prediction_head._masks_are_class_agnostic)
+
+  def test_construct_default_conv_box_predictor_with_custom_mask_head(self):
+    box_predictor_text_proto = """
+      convolutional_box_predictor {
+        mask_head {
+          mask_height: 7
+          mask_width: 7
+          masks_are_class_agnostic: false
+        }
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
+                    box_predictor._other_heads)
+    mask_prediction_head = (
+        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
+    )
+    self.assertEqual(mask_prediction_head._mask_height, 7)
+    self.assertEqual(mask_prediction_head._mask_width, 7)
+    self.assertFalse(mask_prediction_head._masks_are_class_agnostic)
+
 
 class WeightSharedConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
 
@@ -240,7 +310,51 @@ def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
     class_head = box_predictor._class_prediction_head
     self.assertEqual(box_predictor._depth, 2)
     self.assertEqual(box_predictor._num_layers_before_predictor, 2)
+    self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
+    self.assertEqual(box_predictor.num_classes, 10)
+    self.assertFalse(box_predictor._is_training)
     self.assertEqual(box_predictor._apply_batch_norm, False)
+
+  def test_construct_non_default_depthwise_conv_box_predictor(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        depth: 2
+        num_layers_before_predictor: 2
+        kernel_size: 7
+        box_code_size: 3
+        class_prediction_bias_init: 4.0
+        use_depthwise: true
+      }
+    """
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l1_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    hyperparams_proto = hyperparams_pb2.Hyperparams()
+    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
+    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
+      return (conv_hyperparams_arg, is_training)
+
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    (box_predictor_proto.weight_shared_convolutional_box_predictor.
+     conv_hyperparams.CopyFrom(hyperparams_proto))
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=mock_conv_argscope_builder,
+        box_predictor_config=box_predictor_proto,
+        is_training=False,
+        num_classes=10)
+    class_head = box_predictor._class_prediction_head
+    self.assertEqual(box_predictor._depth, 2)
+    self.assertEqual(box_predictor._num_layers_before_predictor, 2)
+    self.assertEqual(box_predictor._apply_batch_norm, False)
+    self.assertEqual(box_predictor._use_depthwise, True)
     self.assertAlmostEqual(class_head._class_prediction_bias_init, 4.0)
     self.assertEqual(box_predictor.num_classes, 10)
     self.assertFalse(box_predictor._is_training)
@@ -302,6 +416,79 @@ def test_construct_default_conv_box_predictor_with_batch_norm(self):
     self.assertTrue(box_predictor._is_training)
     self.assertEqual(box_predictor._apply_batch_norm, True)
 
+  def test_construct_weight_shared_predictor_with_default_mask_head(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        mask_head {
+        }
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
+                    box_predictor._other_heads)
+    weight_shared_convolutional_mask_head = (
+        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
+    )
+    self.assertIsInstance(weight_shared_convolutional_mask_head,
+                          mask_head.WeightSharedConvolutionalMaskHead)
+    self.assertEqual(weight_shared_convolutional_mask_head._mask_height, 15)
+    self.assertEqual(weight_shared_convolutional_mask_head._mask_width, 15)
+    self.assertTrue(
+        weight_shared_convolutional_mask_head._masks_are_class_agnostic)
+
+  def test_construct_weight_shared_predictor_with_custom_mask_head(self):
+    box_predictor_text_proto = """
+      weight_shared_convolutional_box_predictor {
+        mask_head {
+          mask_height: 7
+          mask_width: 7
+          masks_are_class_agnostic: false
+        }
+        conv_hyperparams {
+          regularizer {
+            l1_regularizer {
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+            }
+          }
+        }
+      }"""
+    box_predictor_proto = box_predictor_pb2.BoxPredictor()
+    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
+    box_predictor = box_predictor_builder.build(
+        argscope_fn=hyperparams_builder.build,
+        box_predictor_config=box_predictor_proto,
+        is_training=True,
+        num_classes=90)
+    self.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
+                    box_predictor._other_heads)
+    weight_shared_convolutional_mask_head = (
+        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
+    )
+    self.assertIsInstance(weight_shared_convolutional_mask_head,
+                          mask_head.WeightSharedConvolutionalMaskHead)
+    self.assertEqual(weight_shared_convolutional_mask_head._mask_height, 7)
+    self.assertEqual(weight_shared_convolutional_mask_head._mask_width, 7)
+    self.assertFalse(
+        weight_shared_convolutional_mask_head._masks_are_class_agnostic)
+
 
 class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
 

research/object_detection/builders/dataset_builder.py

@@ -132,6 +132,8 @@ def process_fn(value):
     dataset = read_dataset(
         functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
         config.input_path[:], input_reader_config)
+    if input_reader_config.sample_1_of_n_examples > 1:
+      dataset = dataset.shard(input_reader_config.sample_1_of_n_examples, 0)
     # TODO(rathodv): make batch size a required argument once the old binaries
     # are deleted.
     if batch_size: