使用Google drive中的文件时出现Google colab错误

Question

因此，要在google colab中使用google drive中的文件，我使用以下代码：

from google.colab import drive
drive.mount('/content/gdrive')
!ln -s /content/gdrive/My\ Drive/ /mydrive
!ls /mydrive

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
 app  'Colab Notebooks'   lixo  'My Drive'   pixellib   yolov3

在pixellib内部，我有文件夹meat和文件pretraining.h5

我也安装了这个：

!pip3 install pixellib

直到这里，好的，但当我运行此代码时：

import pixellib
from pixellib.custom_train import instance_custom_training

    train_maskrcnn = instance_custom_training()
    train_maskrcnn.modelConfig(network_backbone = "resnet101", num_classes= 2, batch_size = 4)
    train_maskrcnn.load_pretrained_model("/mydrive/pixellib/pretraining.h5")
    train_maskrcnn.load_dataset("/mydrive/pixellib/meat")
    train_maskrcnn.train_model(num_epochs = 20, augmentation=True,  path_trained_models = "/mydrive/pixellib/mask_rcnn_models")

出现以下错误消息：

Using resnet101 as network backbone For Mask R-CNN model
---------------------------------------------------------------------------
IsADirectoryError                         Traceback (most recent call last)
<ipython-input-44-8a3b66f50c89> in <module>()
      7 train_maskrcnn.modelConfig(network_backbone = "resnet101", num_classes= 2, batch_size = 4)
      8 train_maskrcnn.load_pretrained_model("/mydrive/pixellib/pretraining.h5")
----> 9 train_maskrcnn.load_dataset("/mydrive/pixellib/meat")
     10 train_maskrcnn.train_model(num_epochs = 20, augmentation=True,  path_trained_models = "/mydrive/pixellib/mask_rcnn_models")

7 frames
/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
   2807 
   2808     if filename:
-> 2809         fp = builtins.open(filename, "rb")
   2810         exclusive_fp = True
   2811 

IsADirectoryError: [Errno 21] Is a directory: '/mydrive/pixellib/meat/train/78-3.jpg'

那是什么样的错误？ 我的代码有问题吗

Answer 1

如果这个目录中的每个图像都有一个json（他在评论中这样说），那么在我看来，您的一个json文件的结尾可能是.jpg。要么下载json和jpg并确认它们是正确的文件类型，要么尝试绘制图像以查看它是否是图像