I am writing a binary classification model that consists of audio files of 40 participants and classifies them according to whether they have a speech disorder or not. The audio files have been divided into 5 second segments and to avoid subject bias, I have split the training/testing/validation sets such that a subject only appears in one set (i.e. participant ID02 does not appear in both the training and testing sets). The following error appears when I attempt to enumerate over the DataLoader validLoader in the code below and I’m not entirely sure why this error is occurring. Does anyone have any advice?
KeyError Traceback (most recent call last)
<ipython-input-69-55be99283cf7> in <module>()
----> 1 for i, data in enumerate(valid_loader, 0):
2 images, labels = data
3 print("Batch", i, "size:", len(images))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
987 else:
988 del self._task_info[idx]
--> 989 return self._process_data(data)
990
991 def _try_put_index(self):
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _process_data(self, data)
1012 self._try_put_index()
1013 if isinstance(data, ExceptionWrapper):
-> 1014 data.reraise()
1015 return data
1016
/usr/local/lib/python3.6/dist-packages/torch/_utils.py in reraise(self)
393 # (https://bugs.python.org/issue2651), so we work around it.
394 msg = KeyErrorMessage(msg)
--> 395 raise self.exc_type(msg)
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "<ipython-input-44-245be0a1e978>", line 19, in __getitem__
x = Image.open(self.df['path'][index])
File "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py", line 871, in __getitem__
result = self.index.get_value(self, key)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 4405, in get_value
return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
File "pandas/_libs/index.pyx", line 80, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 90, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 998, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1005, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 36
Can anyone advise why this is happening?
from google.colab import drive
drive.mount('/content/drive')
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import utils
from torch.utils.data import Dataset
from sklearn.metrics import confusion_matrix
from skimage import io, transform, data
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
import pandas as pd
import numpy as np
import csv
import os
import math
import cv2
root_dir = "/content/drive/My Drive/Read_Text/5_Second_Segments/"
class_names = [
"Parkinsons_Disease",
"Healthy_Control"
]
def get_meta(root_dir, dirs):
""" Fetches the meta data for all the images and assigns labels.
"""
paths, classes = [], []
for i, dir_ in enumerate(dirs):
for entry in os.scandir(root_dir + dir_):
if (entry.is_file()):
paths.append(entry.path)
classes.append(i)
return paths, classes
paths, classes = get_meta(root_dir, class_names)
data = {
'path': paths,
'class': classes
}
data_df = pd.DataFrame(data, columns=['path', 'class'])
data_df = data_df.sample(frac=1).reset_index(drop=True) # Shuffles the data
from pandas import option_context
print("Found", len(data_df), "images.")
with option_context('display.max_colwidth', 400):
display(data_df.head(100))
class Audio(Dataset):
def __init__(self, df, transform=None):
"""
Args:
image_dir (string): Directory with all the images
df (DataFrame object): Dataframe containing the images, paths and classes
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.df = df
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, index):
# Load image from path and get label
x = Image.open(self.df['path'][index])
try:
x = x.convert('RGB') # To deal with some grayscale images in the data
except:
pass
y = torch.tensor(int(self.df['class'][index]))
if self.transform:
x = self.transform(x)
return x, y
def compute_img_mean_std(image_paths):
"""
Author: @xinruizhuang. Computing the mean and std of three channel on the whole dataset,
first we should normalize the image from 0-255 to 0-1
"""
img_h, img_w = 224, 224
imgs = []
means, stdevs = [], []
for i in tqdm(range(len(image_paths))):
img = cv2.imread(image_paths[i])
img = cv2.resize(img, (img_h, img_w))
imgs.append(img)
imgs = np.stack(imgs, axis=3)
print(imgs.shape)
imgs = imgs.astype(np.float32) / 255.
for i in range(3):
pixels = imgs[:, :, i, :].ravel() # resize to one row
means.append(np.mean(pixels))
stdevs.append(np.std(pixels))
means.reverse() # BGR --> RGB
stdevs.reverse()
print("normMean = {}".format(means))
print("normStd = {}".format(stdevs))
return means, stdevs
norm_mean, norm_std = compute_img_mean_std(paths)
data_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
unique_users = data_df['path'].str[-20:-16].unique()
train_users, test_users = np.split(np.random.permutation(unique_users), [int(0.8*len(unique_users))])
df_train = data_df[data_df['path'].str[-20:-16].isin(train_users)]
test_data_df = data_df[data_df['path'].str[-20:-16].isin(test_users)]
train_unique_users = df_train['path'].str[-20:-16].unique()
train_users, validate_users = np.split(np.random.permutation(train_unique_users), [int(0.875*len(train_unique_users))])
train_data_df = df_train[df_train['path'].str[-20:-16].isin(train_users)]
valid_data_df = df_train[df_train['path'].str[-20:-16].isin(validate_users)]
ins_dataset_train = Audio(
df=train_data_df,
transform=data_transform,
)
ins_dataset_valid = Audio(
df=valid_data_df,
transform=data_transform,
)
ins_dataset_test = Audio(
df=test_data_df,
transform=data_transform,
)
train_loader = torch.utils.data.DataLoader(
ins_dataset_train,
batch_size=8,
shuffle=True,
num_workers=2
)
test_loader = torch.utils.data.DataLoader(
ins_dataset_test,
batch_size=16,
shuffle=True,
num_workers=2
)
valid_loader = torch.utils.data.DataLoader(
ins_dataset_valid,
batch_size=16,
shuffle=True,
num_workers=2
)
//(This is where the error is occurring.)
for i, data in enumerate(valid_loader, 0):
images, labels = data
print("Batch", i, "size:", len(images))
