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Python test_utils.first_rows_close_as_set方法代码示例

本文整理汇总了Python中object_detection.utils.test_utils.first_rows_close_as_set方法的典型用法代码示例。如果您正苦于以下问题:Python test_utils.first_rows_close_as_set方法的具体用法?Python test_utils.first_rows_close_as_set怎么用?Python test_utils.first_rows_close_as_set使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在object_detection.utils.test_utils的用法示例。


在下文中一共展示了test_utils.first_rows_close_as_set方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_first_rows_close_as_set

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import first_rows_close_as_set [as 别名]
def test_first_rows_close_as_set(self):
    a = [1, 2, 3, 0, 0]
    b = [3, 2, 1, 0, 0]
    k = 3
    self.assertTrue(test_utils.first_rows_close_as_set(a, b, k))

    a = [[1, 2], [1, 4], [0, 0]]
    b = [[1, 4 + 1e-9], [1, 2], [0, 0]]
    k = 2
    self.assertTrue(test_utils.first_rows_close_as_set(a, b, k))

    a = [[1, 2], [1, 4], [0, 0]]
    b = [[1, 4 + 1e-9], [2, 2], [0, 0]]
    k = 2
    self.assertFalse(test_utils.first_rows_close_as_set(a, b, k)) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:17,代码来源:test_utils_test.py

示例2: test_postprocess_results_are_correct

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import first_rows_close_as_set [as 别名]
def test_postprocess_results_are_correct(self, use_keras):
    batch_size = 2
    image_size = 2
    input_shapes = [(batch_size, image_size, image_size, 3),
                    (None, image_size, image_size, 3),
                    (batch_size, None, None, 3),
                    (None, None, None, 3)]

    expected_boxes = [
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0],  # pruned prediction
            [0, 0, 0, 0]
        ],  # padding
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0],  # pruned prediction
            [0, 0, 0, 0]
        ]
    ]  # padding
    expected_scores = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
    expected_classes = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
    expected_num_detections = np.array([3, 3])

    for input_shape in input_shapes:
      tf_graph = tf.Graph()
      with tf_graph.as_default():
        model, _, _, _ = self._create_model(use_keras=use_keras)
        input_placeholder = tf.placeholder(tf.float32, shape=input_shape)
        preprocessed_inputs, true_image_shapes = model.preprocess(
            input_placeholder)
        prediction_dict = model.predict(preprocessed_inputs,
                                        true_image_shapes)
        detections = model.postprocess(prediction_dict, true_image_shapes)
        self.assertIn('detection_boxes', detections)
        self.assertIn('detection_scores', detections)
        self.assertIn('detection_classes', detections)
        self.assertIn('num_detections', 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={
                                      input_placeholder:
                                      np.random.uniform(
                                          size=(batch_size, 2, 2, 3))})
      for image_idx in range(batch_size):
        self.assertTrue(
            test_utils.first_rows_close_as_set(
                detections_out['detection_boxes'][image_idx].tolist(),
                expected_boxes[image_idx]))
      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) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:61,代码来源:ssd_meta_arch_test.py

示例3: test_postprocess_results_are_correct

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import first_rows_close_as_set [as 别名]
def test_postprocess_results_are_correct(self):
    batch_size = 2
    image_size = 2
    input_shapes = [(batch_size, image_size, image_size, 3),
                    (None, image_size, image_size, 3),
                    (batch_size, None, None, 3),
                    (None, None, None, 3)]

    expected_boxes = [
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0],  # pruned prediction
            [0, 0, 0, 0]
        ],  # padding
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0],  # pruned prediction
            [0, 0, 0, 0]
        ]
    ]  # padding
    expected_scores = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
    expected_classes = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
    expected_num_detections = np.array([3, 3])

    for input_shape in input_shapes:
      tf_graph = tf.Graph()
      with tf_graph.as_default():
        model, _, _, _ = self._create_model()
        input_placeholder = tf.placeholder(tf.float32, shape=input_shape)
        preprocessed_inputs, true_image_shapes = model.preprocess(
            input_placeholder)
        prediction_dict = model.predict(preprocessed_inputs,
                                        true_image_shapes)
        detections = model.postprocess(prediction_dict, true_image_shapes)
        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={
                                      input_placeholder:
                                      np.random.uniform(
                                          size=(batch_size, 2, 2, 3))})
      for image_idx in range(batch_size):
        self.assertTrue(
            test_utils.first_rows_close_as_set(
                detections_out['detection_boxes'][image_idx].tolist(),
                expected_boxes[image_idx]))
      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) 
开发者ID:ambakick,项目名称:Person-Detection-and-Tracking,代码行数:61,代码来源:ssd_meta_arch_test.py

示例4: test_postprocess_results_are_correct_static

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import first_rows_close_as_set [as 别名]
def test_postprocess_results_are_correct_static(self):
    with test_utils.GraphContextOrNone() as g:
      model, _, _, _ = self._create_model(use_static_shapes=True,
                                          nms_max_size_per_class=4)

    def graph_fn(input_image):
      preprocessed_inputs, true_image_shapes = model.preprocess(input_image)
      prediction_dict = model.predict(preprocessed_inputs,
                                      true_image_shapes)
      detections = model.postprocess(prediction_dict, true_image_shapes)
      return (detections['detection_boxes'], detections['detection_scores'],
              detections['detection_classes'], detections['num_detections'],
              detections['detection_multiclass_scores'])

    expected_boxes = [
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0]
        ],  # padding
        [
            [0, 0, .5, .5],
            [0, .5, .5, 1],
            [.5, 0, 1, .5],
            [0, 0, 0, 0]
        ]
    ]  # padding
    expected_scores = [[0, 0, 0, 0], [0, 0, 0, 0]]
    expected_multiclass_scores = [[[0, 0], [0, 0], [0, 0], [0, 0]],
                                  [[0, 0], [0, 0], [0, 0], [0, 0]]]
    expected_classes = [[0, 0, 0, 0], [0, 0, 0, 0]]
    expected_num_detections = np.array([3, 3])
    batch_size = 2
    image_size = 2
    channels = 3
    input_image = np.random.rand(batch_size, image_size, image_size,
                                 channels).astype(np.float32)
    (detection_boxes, detection_scores, detection_classes,
     num_detections, detection_multiclass_scores) = self.execute(graph_fn,
                                                                 [input_image],
                                                                 graph=g)
    for image_idx in range(batch_size):
      self.assertTrue(test_utils.first_rows_close_as_set(
          detection_boxes[image_idx][
              0:expected_num_detections[image_idx]].tolist(),
          expected_boxes[image_idx][0:expected_num_detections[image_idx]]))
      self.assertAllClose(
          detection_scores[image_idx][0:expected_num_detections[image_idx]],
          expected_scores[image_idx][0:expected_num_detections[image_idx]])
      self.assertAllClose(
          detection_multiclass_scores[image_idx]
          [0:expected_num_detections[image_idx]],
          expected_multiclass_scores[image_idx]
          [0:expected_num_detections[image_idx]])
      self.assertAllClose(
          detection_classes[image_idx][0:expected_num_detections[image_idx]],
          expected_classes[image_idx][0:expected_num_detections[image_idx]])
    self.assertAllClose(num_detections,
                        expected_num_detections) 
开发者ID:tensorflow,项目名称:models,代码行数:62,代码来源:ssd_meta_arch_test.py


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