使用Python编写的SsdFeatureExtractorTestBase()测试基类

下面是使用Python编写的SsdFeatureExtractorTestBase()测试基类的使用例子：

import unittest
import numpy as np
import tensorflow as tf

from object_detection.models import ssd_feature_extractor_test_base as fet

class SsdFeatureExtractorTest(fet.SsdFeatureExtractorTestBase):

    def test_extract_features_returns_correct_shapes_256(self):
        depth_multiplier = 1.0
        pad_to_multiple = 1

        feature_extractor = self._create_feature_extractor(
            depth_multiplier, pad_to_multiple)

        preprocessed_inputs = tf.placeholder(
            tf.float32, shape=(1, 256, 256, 3))
        
        original_image_spatial_shape = tf.shape(preprocessed_inputs)[1:3]

        predicted_feature_map_shape = feature_extractor._compute_feature_map_shape(
            original_image_spatial_shape)

        feature_maps = feature_extractor.extract_features(preprocessed_inputs)
        self.assertEqual(predicted_feature_map_shape.get_shape().as_list(),
                         feature_maps.get_shape().as_list())

    def test_extract_features_returns_correct_shapes_512(self):
        depth_multiplier = 1.0
        pad_to_multiple = 1

        feature_extractor = self._create_feature_extractor(
            depth_multiplier, pad_to_multiple)

        preprocessed_inputs = tf.placeholder(
            tf.float32, shape=(1, 512, 512, 3))
        
        original_image_spatial_shape = tf.shape(preprocessed_inputs)[1:3]

        predicted_feature_map_shape = feature_extractor._compute_feature_map_shape(
            original_image_spatial_shape)

        feature_maps = feature_extractor.extract_features(preprocessed_inputs)
        self.assertEqual(predicted_feature_map_shape.get_shape().as_list(),
                         feature_maps.get_shape().as_list())

    def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
        min_depth = 32
        conv_hyperparams = {}
        depth_fn = lambda d: max(int(d * depth_multiplier), min_depth)
        return fet.SingleShotMultiBoxFeatureExtractor(
            depth_fn=depth_fn,
            num_layers=4,
            conv_hyperparams=conv_hyperparams,
            freeze_batchnorm=False,
            pad_to_multiple=pad_to_multiple)

if __name__ == '__main__':
    unittest.main()

在这个例子中，我们创建了一个继承自SsdFeatureExtractorTestBase的测试类SsdFeatureExtractorTest。我们重写了两个方法，test_extract_features_returns_correct_shapes_256()和test_extract_features_returns_correct_shapes_512()，用于测试extract_features()方法的输出形状是否正确。

在这两个测试方法中，我们首先创建了一个SsdFeatureExtractor对象，并传入一些超参数，然后创建一个占位符，用于模拟输入图像的张量。我们计算预测的特征图形状，并调用extract_features()方法获取实际的特征图。最后，我们使用断言语句检查预测的特征图形状是否与实际特征图形状相同。

在示例中，我们使用了unittest模块来运行测试。我们对测试类进行了实例化，并调用unittest.main()来执行测试。

这个例子展示了如何使用SsdFeatureExtractorTestBase的测试基类来测试SsdFeatureExtractor类的功能。您可以根据自己的需求扩展和修改测试方法来测试其他功能。