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？那么恭喜您, 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类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)) 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) 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) 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)