object_detection.utils.test_utilsMockBoxPredictor()在Python中的用法及相关测试案例

The test_utils.MockBoxPredictor() function is a utility function provided by the object_detection.utils module in the TensorFlow Object Detection API. It is primarily used for testing purposes to create a mock implementation of a box predictor, which can be useful for unit testing or creating test cases for object detection models.

The MockBoxPredictor() function returns an instance of the BoxPredictor class, which is used to predict bounding box coordinates and object classes for detected objects. The mocked box predictor behaves similarly to a real box predictor but instead returns predefined results for testing purposes.

Here is an example of how to use MockBoxPredictor():

from object_detection.utils import test_utils
from object_detection.predictors import box_predictor

# Define the number of classes
num_classes = 2

# Create a mock box predictor instance
mock_box_predictor = test_utils.MockBoxPredictor(num_classes)

# Create a dummy input tensor
input_tensor = tf.placeholder(tf.float32, shape=[None, 224, 224, 3])

# Create a dummy image shape tensor
image_shape = tf.constant([1, 224, 224, 3])

# Create a box predictor object
box_predictor_object = box_predictor.BoxPredictor(mock_box_predictor)

# Perform inference using the box predictor
predictions = box_predictor_object.predict(
    input_tensor, image_shape=image_shape)

# Print the predicted bounding boxes and object classes
print(predictions['detection_boxes'])
print(predictions['detection_classes'])

In this example, we first import the necessary modules and classes. We then create an instance of MockBoxPredictor by passing the number of classes as an argument. We create a dummy input tensor and a dummy image shape tensor.

Next, we create an instance of BoxPredictor by passing the MockBoxPredictor instance as an argument. We then use this box predictor object to perform inference on the input tensor by calling the predict() method, passing the input tensor and image shape tensor as arguments.

Finally, we print the predicted bounding boxes and object classes from the predictions dictionary.

Note that this is just a simple example to demonstrate the usage of MockBoxPredictor(). In a real scenario, you would typically have more complex input tensors, pre-trained models, and additional post-processing steps for object detection.

To unit test your code or create test cases using MockBoxPredictor(), you can use assertions to validate the expected results against the predictions returned by the mock box predictor.