我从here下载了完整的COCO数据集。我需要修改instances_train2017.json文件以获得以下内容：

• 修改注解文件，使其将整个训练数据集平均划分为所有类的1/3
• 意思是假设我有100个class_1的图像/注解，所以我希望修改后的注解文件在json文件中保存图像对象/dict的100/3

我已经写了这样的代码，但它需要太多的时间和结果/修改的文件是错误的：

import json
from collections import defaultdict
import random
# Path to your local COCO-format JSON annotation file
original_annotation_file = 'coco/annotations/instances_train2017.json'
output_annotation_file = 'evenly.json'
# Load the local COCO-format dataset from your JSON file
with open(original_annotation_file, 'r') as f:
    coco_data = json.load(f)

class_counts = defaultdict(int)
target_class_counts = defaultdict(int)

# Calculate the target count for each class
for ann in coco_data['annotations']:
    class_id = ann['category_id']
    class_counts[class_id] += 1

for class_id, count in class_counts.items():
    target_class_counts[class_id] = count // 3

# Create a list to hold the selected annotations
selected_annotations = []

# Iterate through the annotations and select the subset
for ann in coco_data['annotations']:
    class_id = ann['category_id']

    # Only include this annotation if we haven't reached the target count for this class
    if class_counts[class_id] <= target_class_counts[class_id]:
        selected_annotations.append(ann)

        # Update the count for this class
        class_counts[class_id] += 1

# Create a new COCO-format JSON data structure for the subset
subset_data = {
    'info': coco_data['info'],
    'licenses': coco_data['licenses'],
    'categories': coco_data['categories'],
    'images': coco_data['images'],
    'annotations': selected_annotations
}

# Shuffle the selected annotations to mix up the classes if desired
random.shuffle(subset_data['annotations'])

# Write the subset data to a new JSON file
with open(output_annotation_file, 'w') as f:
    json.dump(subset_data, f)