add NASnet feature extractor

research/object_detection/builders/model_builder.py

@@ -47,6 +47,8 @@
 
 # A map of names to Faster R-CNN feature extractors.
 FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP = {
+ 'faster_rcnn_nas':
+ frcnn_nas.FasterRCNNNASFeatureExtractor,
  'faster_rcnn_inception_resnet_v2':
  frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor,
  'faster_rcnn_inception_v2':

research/object_detection/builders/model_builder_test.py

@@ -24,6 +24,7 @@
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
 from object_detection.models import faster_rcnn_inception_v2_feature_extractor as frcnn_inc_v2
+from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
 from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
 from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
 from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
@@ -412,6 +413,73 @@ def test_create_faster_rcnn_resnet101_with_mask_prediction_enabled(self):
  model = model_builder.build(model_proto, is_training=True)
  self.assertAlmostEqual(model._second_stage_mask_loss_weight, 3.0)
 
+ def test_create_faster_rcnn_nas_model_from_config(self):
+ model_text_proto = """
+ faster_rcnn {
+ num_classes: 3
+ image_resizer {
+ keep_aspect_ratio_resizer {
+ min_dimension: 600
+ max_dimension: 1024
+ }
+ }
+ feature_extractor {
+ type: 'faster_rcnn_nas'
+ }
+ first_stage_anchor_generator {
+ grid_anchor_generator {
+ scales: [0.25, 0.5, 1.0, 2.0]
+ aspect_ratios: [0.5, 1.0, 2.0]
+ height_stride: 16
+ width_stride: 16
+ }
+ }
+ first_stage_box_predictor_conv_hyperparams {
+ regularizer {
+ l2_regularizer {
+ }
+ }
+ initializer {
+ truncated_normal_initializer {
+ }
+ }
+ }
+ initial_crop_size: 17
+ maxpool_kernel_size: 1
+ maxpool_stride: 1
+ second_stage_box_predictor {
+ mask_rcnn_box_predictor {
+ fc_hyperparams {
+ op: FC
+ regularizer {
+ l2_regularizer {
+ }
+ }
+ initializer {
+ truncated_normal_initializer {
+ }
+ }
+ }
+ }
+ }
+ second_stage_post_processing {
+ batch_non_max_suppression {
+ score_threshold: 0.01
+ iou_threshold: 0.6
+ max_detections_per_class: 100
+ max_total_detections: 300
+ }
+ score_converter: SOFTMAX
+ }
+ }"""
+ model_proto = model_pb2.DetectionModel()
+ text_format.Merge(model_text_proto, model_proto)
+ model = model_builder.build(model_proto, is_training=True)
+ self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
+ self.assertIsInstance(
+ model._feature_extractor,
+ frcnn_nas.FasterRCNNNASFeatureExtractor)
+
  def test_create_faster_rcnn_inception_resnet_v2_model_from_config(self):
  model_text_proto = """
  faster_rcnn {

research/object_detection/models/BUILD

@@ -135,6 +135,29 @@ py_test(
  ],
 )
 
+py_test(
+ name = "faster_rcnn_nas_feature_extractor_test",
+ srcs = [
+ "faster_rcnn_nas_feature_extractor_test.py",
+ ],
+ deps = [
+ ":faster_rcnn_nas_feature_extractor",
+ "//tensorflow",
+ ],
+)
+
+py_library(
+ name = "faster_rcnn_nas_feature_extractor",
+ srcs = [
+ "faster_rcnn_nas_feature_extractor.py",
+ ],
+ deps = [
+ "//tensorflow",
+ "//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch",
+ "//tensorflow_models/slim:nasnet",
+ ],
+)
+
 py_library(
  name = "faster_rcnn_inception_resnet_v2_feature_extractor",
  srcs = [