python 使用Yolact训练自己的数据集

import os
import json
import numpy as np
import glob
import shutil
from sklearn.model_selection import train_test_split
np.random.seed(41)

#0为背景，此处根据你数据集的类别来修改key
classname_to_id = {"1": 1}

class Lableme2CoCo:

 def __init__(self):
  self.images = []
  self.annotations = []
  self.categories = []
  self.img_id = 0
  self.ann_id = 0

 def save_coco_json(self, instance, save_path):
  json.dump(instance, open(save_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=1) # indent=2 更加美观显示

 # 由json文件构建COCO
 def to_coco(self, json_path_list):
  self._init_categories()
  for json_path in json_path_list:
   obj = self.read_jsonfile(json_path)
   self.images.append(self._image(obj, json_path))
   shapes = obj['shapes']
   for shape in shapes:
    annotation = self._annotation(shape)
    self.annotations.append(annotation)
    self.ann_id += 1
   self.img_id += 1
  instance = {}
  instance['info'] = 'spytensor created'
  instance['license'] = ['license']
  instance['images'] = self.images
  instance['annotations'] = self.annotations
  instance['categories'] = self.categories
  return instance

 # 构建类别
 def _init_categories(self):
  for k, v in classname_to_id.items():
   category = {}
   category['id'] = v
   category['name'] = k
   self.categories.append(category)

 # 构建COCO的image字段
 def _image(self, obj, path):
  image = {}
  from labelme import utils
  img_x = utils.img_b64_to_arr(obj['imageData'])
  h, w = img_x.shape[:-1]
  image['height'] = h
  image['width'] = w
  image['id'] = self.img_id
  image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
  return image

 # 构建COCO的annotation字段
 def _annotation(self, shape):
  label = shape['label']
  points = shape['points']
  annotation = {}
  annotation['id'] = self.ann_id
  annotation['image_id'] = self.img_id
  annotation['category_id'] = int(classname_to_id[label])
  annotation['segmentation'] = [np.asarray(points).flatten().tolist()]
  annotation['bbox'] = self._get_box(points)
  annotation['iscrowd'] = 0
  annotation['area'] = 1.0
  return annotation

 # 读取json文件，返回一个json对象
 def read_jsonfile(self, path):
  with open(path, "r", encoding='utf-8') as f:
   return json.load(f)

 # COCO的格式： [x1,y1,w,h] 对应COCO的bbox格式
 def _get_box(self, points):
  min_x = min_y = np.inf
  max_x = max_y = 0
  for x, y in points:
   min_x = min(min_x, x)
   min_y = min(min_y, y)
   max_x = max(max_x, x)
   max_y = max(max_y, y)
  return [min_x, min_y, max_x - min_x, max_y - min_y]


if __name__ == '__main__':
 labelme_path = "labelme/" # 此处根据你的数据集地址来修改
 saved_coco_path = "./"
 # 创建文件
 if not os.path.exists("%scoco/annotations/"%saved_coco_path):
  os.makedirs("%scoco/annotations/"%saved_coco_path)
 if not os.path.exists("%scoco/images/train2017/"%saved_coco_path):
  os.makedirs("%scoco/images/train2017"%saved_coco_path)
 if not os.path.exists("%scoco/images/val2017/"%saved_coco_path):
  os.makedirs("%scoco/images/val2017"%saved_coco_path)
 # 获取images目录下所有的joson文件列表
 json_list_path = glob.glob(labelme_path + "/*.json")
 # 数据划分,这里没有区分val2017和tran2017目录，所有图片都放在images目录下
 train_path, val_path = train_test_split(json_list_path, test_size=0.12)
 print("train_n:", len(train_path), 'val_n:', len(val_path))

 # 把训练集转化为COCO的json格式
 l2c_train = Lableme2CoCo()
 train_instance = l2c_train.to_coco(train_path)
 l2c_train.save_coco_json(train_instance, '%scoco/annotations/instances_train2017.json'%saved_coco_path)
 for file in train_path:
  shutil.copy(file.replace("json","jpg"),"%scoco/images/train2017/"%saved_coco_path)
 for file in val_path:
  shutil.copy(file.replace("json","jpg"),"%scoco/images/val2017/"%saved_coco_path)

 # 把验证集转化为COCO的json格式
 l2c_val = Lableme2CoCo()
 val_instance = l2c_val.to_coco(val_path)
 l2c_val.save_coco_json(val_instance, '%scoco/annotations/instances_val2017.json'%saved_coco_path)

python train.py --config=yolact_base_config

PytorchStreamReader failed reading zip archive: failed finding central directory

RuntimeError: unexpected EOF, expected *** more bytes. The file might be corruptrd