Why TypeError: string indices must be integers

I’m trying to make object detection using Pytorch. I can’t solve the error in the title. The data file uses a csv file.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from PIL import Image
from pandas import Series, DataFrame
from tqdm import tqdm
import io
import glob
import os
import time
import torch
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as opt
import torch.backends.cudnn as cudnn
import torch.nn.init as init
import torch.utils.data as data
import torchvision.datasets as datasets
from torch import utils
from torch.autograd import Variable
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms as T
from torchvision.models import detection
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor

num_epoch = 100
num_classes = 6
batch_size = 5
info = 'info.json'
train = 'annotations/'
class PennFudanDataset(Dataset):
    def __init__(self, root, transform=None):
        self.root = root
        self.transform = transform
        self.imgs = list(sorted(os.listdir('images/')))
        self.my_data = list(sorted(pd.read_csv(os.path.join(root, 'annotations.csv'))))

    def __getitem__(self, index):
        my_data = pd.read_csv(os.path.join(self.root, 'annotations.csv'))
        img_path = os.path.join('images/', self.imgs[index])
        img = Image.open(img_path)
        mask = np.array(my_data)
        obj_ids = mask[1:]
        masks = mask == obj_ids[:, None, None]

        for i in range(num_classes):
            pos = np.where(masks[i])
            xmin = np.min(pos[1])
            xmax = np.max(pos[1])
            ymin = np.min(pos[0])
            ymax = np.max(pos[0])
        boxes = [] 
        boxes.append([xmin, ymin, xmax, ymax])
        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        labels = torch.ones((num_classes,), dtype=torch.int64)

        target = {}
        target["boxes"] = boxes
        target["labels"] = labels
        target["image_id"] = torch.tensor([index])

        if self.transform is not None:
            img = self.transform(img)

        return img, target

    def __len__(self):
        return len(self.my_data)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = opt.Adam(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
model = model.to(device)

train_loader = PennFudanDataset(train, transform=transforms)
train_loader = DataLoader(train_loader, batch_size=1)
for i, (img, target) in enumerate(train_loader):
    img = img.to(device)
    target = target

    outputs = model(img, target)
    loss = criterion(outputs)  # 損失を計算
    loss.backward()  # 逆伝播で勾配を計算
    optimizer.step()  # 最適化\

    for epoch in range(num_epoch):
        print('Epoch {}/{}'.format(epoch, num_epoch))
        print('-' * 10)

TypeError                                 Traceback (most recent call last)
/var/folders/yc/zkgdxm9j5yv1_zlyqb_djtjc0000gn/T/ipykernel_2905/2861585909.py in <module>
      5     target = target
----> 7     outputs = model(img, target)
      8     loss = criterion(outputs)  # 損失を計算
      9     loss.backward()  # 逆伝播で勾配を計算

/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

/opt/anaconda3/lib/python3.8/site-packages/torchvision/models/detection/generalized_rcnn.py in forward(self, images, targets)
     62             assert targets is not None
     63             for target in targets:
---> 64                 boxes = target["boxes"]
     65                 if isinstance(boxes, torch.Tensor):
     66                     if len(boxes.shape) != 2 or boxes.shape[-1] != 4:

TypeError: string indices must be integers

You are iterating a dict here:

for target in targets:

so target will be the key, not a dict item.

1 Like

TypeError: means that you are trying to perform an operation on a value whose type is not compatible with that operation. An Iterable is a collection of elements that can be accessed sequentially . In Python, iterable objects are indexed using numbers . When you try to access an iterable object using a string or a float as the index, an error will be returned as TypeError: string indices must be integers. This means that when you’re accessing an iterable object like a string or float value, you must do it using an integer value.

For example, str[hello"] and str[2.1] as indexes. As these are not integers, a TypeError exception is raised. This means that when you’re accessing an iterable object like a string or float value, you must do it using an integer value . If you are accessing items from a dictionary , make sure that you are accessing the dictionary itself and not a key in the dictionary.

Python supports slice notation for any sequential data type like lists, strings , tuples, bytes, bytearrays, and ranges. When working with strings and slice notation, it can happen that a TypeError: string indices must be integers is raised, pointing out that the indices must be integers, even if they obviously are.

Hi there, can anyone kindly help me with my issue.

I am having the issue where it says below.

[ERROR] TypeError: string indices must be integers

Traceback (most recent call last):

File “/var/task/custom_notification_lambda.py”, line 14, in lambda_handler

user = event[“detail”][“userIdentity”][“principalId”]

[ERROR] TypeError: string indices must be integers Traceback (most recent call last): File “/var/task/custom_notification_lambda.py”, line 14, in lambda_handler user = event[“detail”][“userIdentity”][“principalId”]

Here is my code:

import json

import boto3

import os

sns = boto3.client(‘sns’)

def lambda_handler(event, context):

#Extract details from JSON event

detailType = event[“detail-type”]

region = event[“region”]

accountId = event[“account”]

event = event[“detail”][“eventName”]

user = event[“detail”][“userIdentity”][“principalId”]

sns_topic = os.environ[‘SNS_TOPIC_ARN’]

message = “Alert: %s in %s for account: %s\n Event description: %s done by user: %s” % (detailType, region,accountId,event,user)

response = sns.publish(

TopicArn = sns_topic,

Message = message


return {

‘statusCode’: 200,

‘body’: json.dumps(‘Success!’)


What am I doing wrong here? Thanks

Your question doesn’t seem to be related to PyTorch so I would recommend posting it on e.g. StackOverflow for a faster and better support.
In any case, I would check the type of each intermediate result in event[“detail”][“userIdentity”][“principalId”] and make sure you can index this object with a string.