4f14cb629462c9e685ea92a961a80a8c5dcda83a,object_detection/meta_architectures/ssd_meta_arch_test.py,SsdMetaArchTest,test_postprocess_results_are_correct,#SsdMetaArchTest#,143

Before Change

    self.assertTrue("num_detections" in detections)

    init_op = tf.global_variables_initializer()
    with self.test_session() as sess:
      sess.run(init_op)
      detections_out = sess.run(detections)
      self.assertAllClose(detections_out["detection_boxes"], expected_boxes)
      self.assertAllClose(detections_out["detection_scores"], expected_scores)
      self.assertAllClose(detections_out["detection_classes"], expected_classes)
      self.assertAllClose(detections_out["num_detections"],
                          expected_num_detections)

  def test_loss_results_are_correct(self):
    batch_size = 2
    preprocessed_input = tf.random_uniform((batch_size, 2, 2, 3),
                                           dtype=tf.float32)

After Change

                                 [0, 0, 0, 0, 0]])
    expected_num_detections = np.array([4, 4])

    for input_shape in input_shapes:
      tf_graph = tf.Graph()
      with tf_graph.as_default():
        preprocessed_input_placeholder = tf.placeholder(tf.float32,
                                                        shape=input_shape)
        prediction_dict = self._model.predict(preprocessed_input_placeholder)
        detections = self._model.postprocess(prediction_dict)
        self.assertTrue("detection_boxes" in detections)
        self.assertTrue("detection_scores" in detections)
        self.assertTrue("detection_classes" in detections)
        self.assertTrue("num_detections" in detections)
        init_op = tf.global_variables_initializer()
      with self.test_session(graph=tf_graph) as sess:
        sess.run(init_op)
        detections_out = sess.run(detections,
                                  feed_dict={
                                      preprocessed_input_placeholder:
                                      np.random.uniform(
                                          size=(batch_size, 2, 2, 3))})
      self.assertAllClose(detections_out["detection_boxes"], expected_boxes)
      self.assertAllClose(detections_out["detection_scores"], expected_scores)
      self.assertAllClose(detections_out["detection_classes"], expected_classes)
      self.assertAllClose(detections_out["num_detections"],
                          expected_num_detections)

  def test_loss_results_are_correct(self):
    batch_size = 2
    preprocessed_input = tf.random_uniform((batch_size, 2, 2, 3),
                                           dtype=tf.float32)

Instances