使用Python编写的SsdFeatureExtractorTestBase()类测试基类

下面是使用Python编写的SsdFeatureExtractorTestBase()类的基类测试代码示例，该示例演示了如何使用该类进行特征提取器的测试。

import numpy as np
import tensorflow as tf
from object_detection.models import ssd_feature_extractor_testbase

class SsdFeatureExtractorTest(ssd_feature_extractor_testbase.SsdFeatureExtractorTestBase):
    def test_extract_features_returns_correct_shapes_3x3_paddings(self):
        feature_extractor = self._create_feature_extractor(
            pad_to_multiple=1, use_depthwise=True)

        preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
        expected_feature_map_shape = np.array([4, 3, 3, 512])

        feature_maps = feature_extractor.extract_features(preprocessed_inputs)
        init_op = tf.global_variables_initializer()
        with self.test_session() as sess:
            sess.run(init_op)
            feature_map_actual_shape = sess.run(feature_maps,
                                                feed_dict={preprocessed_inputs: np.random.rand(4, 6, 6, 3)}).shape

            self.assertAllEqual(feature_map_actual_shape, expected_feature_map_shape)

    def _create_feature_extractor(self, pad_to_multiple, use_depthwise):
        # Create the feature extractor object
        feature_extractor = MyFeatureExtractor(use_depthwise=use_depthwise)

        # Set the parameters for the feature extractor
        feature_extractor.set_parameters({'pad_to_multiple': pad_to_multiple})

        return feature_extractor


class MyFeatureExtractor(ssd_feature_extractor_testbase.SsdFeatureExtractorTestBase):
    def __init__(self, use_depthwise=False):
        self._use_depthwise = use_depthwise

    def set_parameters(self, params):
        self._params = params

    def _build(self, preprocessed_inputs):
        # Create the feature extraction layers
        conv1 = tf.layers.conv2d(preprocessed_inputs, filters=64, kernel_size=(3, 3), strides=(2, 2), padding='same')
        conv2 = tf.layers.conv2d(conv1, filters=128, kernel_size=(3, 3), strides=(2, 2), padding='same')
        conv3 = tf.layers.conv2d(conv2, filters=256, kernel_size=(3, 3), strides=(2, 2), padding='same')
        conv4 = tf.layers.conv2d(conv3, filters=512, kernel_size=(3, 3), strides=(2, 2), padding='same')

        return conv4


if __name__ == '__main__':
    tf.test.main()

在上述示例中，我们首先定义了一个SsdFeatureExtractorTest类，该类继承了SsdFeatureExtractorTestBase类。在SsdFeatureExtractorTest类中，我们定义了一个test_extract_features_returns_correct_shapes_3x3_paddings方法，用于测试提取特征的功能。该方法通过创建MyFeatureExtractor类的实例来创建特征提取器，并设置一些参数。然后，我们使用feature_extractor.extract_features()方法传入输入数据进行特征提取，并通过assertAllEqual方法来验证提取得到的特征图形状与期望的特征图形状是否一致。最后，我们使用tf.test.main()方法运行测试。

在MyFeatureExtractor类中，我们实现了_build方法，该方法用于构建特征提取的层。我们创建了几个卷积层，并将其连接起来以构建特征提取器。然后，我们可以通过调用feature_extractor.set_parameters()方法来设置特征提取器的参数。

以上是一个基于SsdFeatureExtractorTestBase类的特征提取器测试的示例代码，你可以根据自己的需求和特征提取器的实现来进行修改和拓展。