一、问题解决过程
为了实现目标检测模型fast_rcnn，准备使用coco数据集进行模型的训练，这里需要导入coco数据集，并且进行数据集处理。
import torch
import torchvision
import torch.utils.data
from pycocotools.coco import COCO
def convert_to_coco_api(ds):
coco_ds = COCO()
# annotation IDs need to start at 1, not 0
ann_id = 1
dataset = {'images': [], 'categories': [], 'annotations': []}
categories = set()
for img_idx in range(len(ds)):
# find better way to get target
hw, targets = ds.coco_index(img_idx)
image_id = targets["image_id"].item()
img_dict = {}
img_dict['id'] = image_id
img_dict['height'] = hw[0]
img_dict['width'] = hw[1]
dataset['images'].append(img_dict)
bboxes = targets["boxes"]
bboxes[:, 2:] -= bboxes[:, :2]
bboxes = bboxes.tolist()
labels = targets['labels'].tolist()
areas = targets['area'].tolist()
iscrowd = targets['iscrowd'].tolist()
num_objs = len(bboxes)
for i in range(num_objs):
ann = {}
ann['image_id'] = image_id
ann['bbox'] = bboxes[i]
ann['category_id'] = labels[i]
categories.add(labels[i])
ann['area'] = areas[i]
ann['iscrowd'] = iscrowd[i]
ann['id'] = ann_id
dataset['annotations'].append(ann)
ann_id += 1
dataset['categories'] = [{'id': i} for i in sorted(categories)]
coco_ds.dataset = dataset
coco_ds.createIndex()
return coco_ds
def get_coco_api_from_dataset(dataset):
for _ in range(10):
if isinstance(dataset, torchvision.datasets.CocoDetection):
break
if isinstance(dataset, torch.utils.data.Subset):
dataset = dataset.dataset
if isinstance(dataset, torchvision.datasets.CocoDetection):
return dataset.coco
return convert_to_coco_api(dataset)
运行出现 
可以得出当前环境没有 pycocotools 模块 故需要
pip install pycocotools
------报错了-------
然后查询有一种从gitee上下载的方式
pip install git+https://gitee.com/pursuit_zhangyu/cocoapi.git#subdirectory=PythonAPI
------报错了-------
上面报错信息发现是缺少Cython模块，那就下载Cython模块
pip install Cython
然后在进行上一步的操作
pip install git+https://gitee.com/pursuit_zhangyu/cocoapi.git#subdirectory=PythonAPI
------还是不行-------
最后发现linux下载是上面的操作，因为我是windous应该按照下面的指令
pip install pycocotools-windows
成功！！文章来源：https://www.toymoban.com/news/detail-536369.html
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