《Keras 2 ：使用 RetinaNet 进行对象检测》：此文为AI自动翻译

import os
import re
import zipfile

import numpy as np
import tensorflow as tf
from tensorflow import keras

import matplotlib.pyplot as plt
import tensorflow_datasets as tfds

url = "https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip"
filename = os.path.join(os.getcwd(), "data.zip")
keras.utils.get_file(filename, url)


with zipfile.ZipFile("data.zip", "r") as z_fp:
    z_fp.extractall("./")

def swap_xy(boxes):
    """Swaps order the of x and y coordinates of the boxes.

    Arguments:
      boxes: A tensor with shape `(num_boxes, 4)` representing bounding boxes.

    Returns:
      swapped boxes with shape same as that of boxes.
    """
    return tf.stack([boxes[:, 1], boxes[:, 0], boxes[:, 3], boxes[:, 2]], axis=-1)


def convert_to_xywh(boxes):
    """Changes the box format to center, width and height.

    Arguments:
      boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`
        representing bounding boxes where each box is of the format
        `[xmin, ymin, xmax, ymax]`.

    Returns:
      converted boxes with shape same as that of boxes.
    """
    return tf.concat(
        [(boxes[..., :2] + boxes[..., 2:]) / 2.0, boxes[..., 2:] - boxes[..., :2]],
        axis=-1,
    )


def convert_to_corners(boxes):
    """Changes the box format to corner coordinates

    Arguments:
      boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`
        representing bounding boxes where each box is of the format
        `[x, y, width, height]`.

    Returns:
      converted boxes with shape same as that of boxes.
    """
    return tf.concat(
        [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],
        axis=-1,
    )

def compute_iou(boxes1, boxes2):
    """Computes pairwise IOU matrix for given two sets of boxes

    Arguments:
      boxes1: A tensor with shape `(N, 4)` representing bounding boxes
        where each box is of the format `[x, y, width, height]`.
        boxes2: A tensor with shape `(M, 4)` representing bounding boxes
        where each box is of the format `[x, y, width, height]`.

    Returns:
      pairwise IOU matrix with shape `(N, M)`, where the value at ith row
        jth column holds the IOU between ith box and jth box from
        boxes1 and boxes2 respectively.
    """
    boxes1_corners = convert_to_corners(boxes1)
    boxes2_corners = convert_to_corners(boxes2)
    lu = tf.maximum(boxes1_corners[:, None, :2], boxes2_corners[:, :2])
    rd = tf.minimum(boxes1_corners[:, None, 2:], boxes2_corners[:, 2:])
    intersection = tf.maximum(0.0, rd - lu)
    intersection_area = intersection[:, :, 0] * intersection[:, :, 1]
    boxes1_area = boxes1[:, 2] * boxes1[:, 3]
    boxes2_area = boxes2[:, 2] * boxes2[:, 3]
    union_area = tf.maximum(
        boxes1_area[:, None] + boxes2_area - intersection_area, 1e-8
    )
    return tf.clip_by_value(intersection_area / union_area, 0.0, 1.0)


def visualize_detections(
    image, boxes, classes, scores, figsize=(7, 7), linewidth=1, color=[0, 0, 1]
):
    """Visualize Detections"""
    image = np.array(image, dtype=np.uint8)
    plt.figure(figsize=figsize)
    plt.axis("off")
    plt.imshow(image)
    ax = plt.gca()
    for box, _cls, score in zip(boxes, classes, scores):
        text = "{}: {:.2f}".format(_cls, score)
        x1, y1, x2, y2 = box
        w, h = x2 - x1, y2 - y1
        patch = plt.Rectangle(
            [x1, y1], w, h, fill=False, edgecolor=color, linewidth=linewidth
        )
        ax.add_patch(patch)
        ax.text(
            x1,
            y1,
            text,
            bbox={
       "facecolor": color, "alpha": 0.4},
            clip_box=ax.clipbox,
            clip_on=True,
        )
    plt.show()
    return ax

class AnchorBox:
    """Generates anchor boxes.

    This class has operations to generate anchor boxes for feature maps at
    strides `[8, 16, 32, 64, 128]`. Where each anchor each box is of the
    format `[x, y, width, height]`.

    Attributes:
      aspect_ratios: A list of float values representing the aspect ratios of
        the anchor boxes at each location on the feature map
      scales: A list of float values representing the scale of the anchor boxes
        at each location on the feature map.
      num_anchors: The number of anchor boxes at each location on feature map
      areas: A list of float values representing the areas of the anchor
        boxes for each feature map in the feature pyramid.
      strides: A list of float value representing the strides for each feature
        map in the feature pyramid.
    """

    def __init__(self):
        self.aspect_ratios = [0.5, 1.0, 2.0]
        self.scales = [2 ** x for x in [0, 1 / 3, 2 / 3]]

        self._num_anchors = len(self.aspect_ratios) * len(self.scales)
        self._strides = [2 ** i for i in range(3, 8)]
        self._areas = [x ** 2 for x in [32.0, 64.0, 128.0, 256.0, 512.0]]
        self._anchor_dims = self._compute_dims()

    def _compute_dims(self):
        """Computes anchor box dimensions for all ratios and scales at all levels
        of the feature pyramid.
        """
        anchor_dims_all = []
        for area in self._areas:
            anchor_dims = []
            for ratio in self.aspect_ratios:
                anchor_height = tf.math.sqrt(area / ratio)
                anchor_width = area / anchor_height
                dims = tf.reshape(
                    tf.stack([anchor_width, anchor_height], axis=-1), [1, 1, 2]
                )
                for scale in self.scales:
                    anchor_dims.append(scale * dims)
            anchor_dims_all.append(tf.stack(anchor_dims, axis=-2))
        return anchor_dims_all

    def _get_anchors(