Hi ,
I am doing multilabel image classification, I was prepared the data for CNN model then I am trying to show the images, but I was facing some errors. please help me out , how to fix it.
Note : my all images arein single folder.
Here my code
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
from tqdm import tqdm_notebook as tqdm
from sklearn.preprocessing import LabelEncoder
from PIL import Image
import matplotlib.pyplot as plt
import torch
# Neural networks can be constructed using the torch.nn package.
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import Dataset
import torchvision
import torchvision.transforms as transforms
image_folder = "dance\dataset\train"
train=pd.read_csv("dance/dataset/train.csv")
train.head()
lb = LabelEncoder()
train['target_labels'] = lb.fit_transform(train['target'])
train.head()
|Image|target|target_labels|
|0|96.jpg|manipuri|4|
|1|163.jpg|bharatanatyam|0|
|2|450.jpg|odissi|6|
|3|219.jpg|kathakali|2|
|4|455.jpg|odissi|6|
batch_size = 128
validation_split = .3
shuffle_dataset = True
random_seed= 42
dataset_size = len(train)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset :
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
class Dance_Dataset(Dataset):
def __init__(self, img_data,img_path,transform=None):
self.img_path = img_path
self.transform = transform
self.img_data = img_data
def __len__(self):
return len(self.img_data)
def __getitem__(self, index):
img_name = os.path.join(self.img_path,self.img_data.loc[index, 'target'],
self.img_data.loc[index, 'Image'])
image = Image.open(img_name)
#image = image.convert('RGB')
image = image.resize((300,300))
label = torch.tensor(self.img_data.loc[index, 'target_labels'])
if self.transform is not None:
image = self.transform(image)
return image, label
dataset = Dance_Dataset(train,image_folder,transform)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=valid_sampler)
def img_display(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
npimg = np.transpose(npimg, (1, 2, 0))
return npimg
dataiter = iter(train_loader)
images, labels = dataiter.next()
arthopod_types = {0: 'bharatanatyam', 1: 'kathak', 2: 'kathakali', 3: 'kuchipudi',4 :'manipuri', 5: 'mohiniyattam' ,6:'odissi', 7:'sattriya'}
# Viewing data examples used for training
fig, axis = plt.subplots(3, 5, figsize=(15, 10))
for i, ax in enumerate(axis.flat):
with torch.no_grad():
image, label = images[i], labels[i]
ax.imshow(img_display(image)) # add image
ax.set(title = f"{arthopod_types[label.item()]}") # add label
Error:
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-58-ad5957162f52> in <module>
1 # get some random training images
2 dataiter = iter(train_loader)
----> 3 images, labels = dataiter.next()
4 arthopod_types = {0: 'bharatanatyam', 1: 'kathak', 2: 'kathakali', 3: 'kuchipudi',4 :'manipuri', 5: 'mohiniyattam' ,6:'odissi', 7:'sattriya'}
5 # Viewing data examples used for training
~\anaconda3\envs\pytorch_b\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
558 if self.num_workers == 0: # same-process loading
559 indices = next(self.sample_iter) # may raise StopIteration
--> 560 batch = self.collate_fn([self.dataset[i] for i in indices])
561 if self.pin_memory:
562 batch = _utils.pin_memory.pin_memory_batch(batch)
~\anaconda3\envs\pytorch_b\lib\site-packages\torch\utils\data\dataloader.py in <listcomp>(.0)
558 if self.num_workers == 0: # same-process loading
559 indices = next(self.sample_iter) # may raise StopIteration
--> 560 batch = self.collate_fn([self.dataset[i] for i in indices])
561 if self.pin_memory:
562 batch = _utils.pin_memory.pin_memory_batch(batch)
<ipython-input-49-4a698726eaaa> in __getitem__(self, index)
11 img_name = os.path.join(self.img_path,self.img_data.loc[index, 'target'],
12 self.img_data.loc[index, 'Image'])
---> 13 image = Image.open(img_name)
14 #image = image.convert('RGB')
15 image = image.resize((300,300))
~\anaconda3\envs\pytorch_b\lib\site-packages\PIL\Image.py in open(fp, mode)
2841
2842 if filename:
-> 2843 fp = builtins.open(filename, "rb")
2844 exclusive_fp = True
2845
OSError: [Errno 22] Invalid argument: 'dance\\dataset\train\\kathakali\\190.jpg'
