add unit test for get_proposal_feature_extractor_model

research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor_tf2_test.py

+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for models.faster_rcnn_resnet_v1_fpn_keras_feature_extractor."""
+import unittest
+import tensorflow.compat.v1 as tf
+
+from google.protobuf import text_format
+
+from object_detection.builders import hyperparams_builder
+from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_res_fpn
+from object_detection.utils import tf_version
+from object_detection.protos import hyperparams_pb2
+
+
+@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
+class FasterRCNNResnetV1FPNKerasFeatureExtractorTest(tf.test.TestCase):
+
+  def _build_conv_hyperparams(self):
+    conv_hyperparams = hyperparams_pb2.Hyperparams()
+    conv_hyperparams_text_proto = """
+      regularizer {
+        l2_regularizer {
+        }
+      }
+      initializer {
+        truncated_normal_initializer {
+        }
+      }
+    """
+    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
+    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
+
+  def _build_feature_extractor(self, architecture='resnet_v1_50'):
+    return frcnn_res_fpn.FasterRCNNResnet50FPNKerasFeatureExtractor(
+        is_training=False
+        ,
+        conv_hyperparams=self._build_conv_hyperparams(),
+        first_stage_features_stride=16,
+        batch_norm_trainable=False,
+        weight_decay=0.0)
+  
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor()
+    preprocessed_inputs = tf.random_uniform(
+        [2, 160, 160, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_maps = feature_extractor.get_proposal_feature_extractor_model(
+        name='TestScope')(preprocessed_inputs)
+    features_shapes = [tf.shape(rpn_feature_map) 
+        for name, rpn_feature_map in rpn_feature_maps.items()]
+    
+    self.assertAllEqual(features_shapes[0].numpy(), [2, 20, 20, 256])
+    self.assertAllEqual(features_shapes[1].numpy(), [2, 10, 10, 256])
+    self.assertAllEqual(features_shapes[2].numpy(), [2, 5, 5, 256])