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load multi-modal data with pytorch

Model summary in pytorchmulti-variable linear regression with pytorchHow do I load up an image and convert it to a proper tensor for PyTorch?MemoryError when attempting to create a docker image with Torch/PyTorchMulti label classification in pytorchUpdating pre-trained Deep Learning model with respect to new data pointsPyTorch - loading images without sub foldersLoad data from CSV using DataLoaderPytorch torchvision MNIST downloadinteger argument expected, got float Pytorch : transform

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1

I'm trying to load multi-modal data (e.g. text and image) in pytorch for image classification. I do not know how to load them simultaneously, like the following code.

 def __init__(self, img_path, txt_path, transform=None, loader=default_loader):

def __len__(self):
 return len(self.img_name)

def __getitem__(self, item):

Can anyone help me?

pytorch

share|improve this question

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

add a comment | 

1

I'm trying to load multi-modal data (e.g. text and image) in pytorch for image classification. I do not know how to load them simultaneously, like the following code.

 def __init__(self, img_path, txt_path, transform=None, loader=default_loader):

def __len__(self):
 return len(self.img_name)

def __getitem__(self, item):

Can anyone help me?

pytorch

share|improve this question

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

add a comment | 

I'm trying to load multi-modal data (e.g. text and image) in pytorch for image classification. I do not know how to load them simultaneously, like the following code.

 def __init__(self, img_path, txt_path, transform=None, loader=default_loader):

def __len__(self):
 return len(self.img_name)

def __getitem__(self, item):

Can anyone help me?

pytorch

share|improve this question

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

 def __init__(self, img_path, txt_path, transform=None, loader=default_loader):

def __len__(self):
 return len(self.img_name)

def __getitem__(self, item):

share|improve this question

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

share|improve this question

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

asked Mar 22 at 15:11

xiaodan zhangxiaodan zhang

61

2 Answers
2

active

oldest

votes

0

In __getitem__, you can use a dictionary or a tuple to represent one sample of your data. Later during training when you create a dataloader using the dataset, pytorch will automatically create batches of dictonary or tuples.

If you want to create samples in a much more different way, check out collate_fn in pytorch.

share|improve this answer

answered Mar 23 at 10:42

shivam2298shivam2298

184

add a comment | 

0

The method getitem(self, item) would help you do this.

For example:

def __getitem__(self, item): # item can be thought as an index

 text = textList[item] # textList would be a list containing the text you want to input into the model for element 'item'
 img = imgList[image] # imgList would be a list containing the images you want to input into the model for element 'item'

 input = [text, img] 
 y = labels[item] # labels would be a list containing the label for the input of the text and img. This is your target.

 return input, y

share|improve this answer

answered Mar 24 at 4:10

asong24asong24

234

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your reply. I will try it.
  
  – xiaodan zhang
  Mar 24 at 8:48
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  if it is fixed please mark my answer as correct
  
  – asong24
  Mar 24 at 14:31
  

  
  
  
  

add a comment | 

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0

In __getitem__, you can use a dictionary or a tuple to represent one sample of your data. Later during training when you create a dataloader using the dataset, pytorch will automatically create batches of dictonary or tuples.

If you want to create samples in a much more different way, check out collate_fn in pytorch.

share|improve this answer

answered Mar 23 at 10:42

shivam2298shivam2298

184

add a comment | 

0

In __getitem__, you can use a dictionary or a tuple to represent one sample of your data. Later during training when you create a dataloader using the dataset, pytorch will automatically create batches of dictonary or tuples.

If you want to create samples in a much more different way, check out collate_fn in pytorch.

share|improve this answer

answered Mar 23 at 10:42

shivam2298shivam2298

184

add a comment | 

In __getitem__, you can use a dictionary or a tuple to represent one sample of your data. Later during training when you create a dataloader using the dataset, pytorch will automatically create batches of dictonary or tuples.

If you want to create samples in a much more different way, check out collate_fn in pytorch.

share|improve this answer

answered Mar 23 at 10:42

shivam2298shivam2298

184

share|improve this answer

answered Mar 23 at 10:42

shivam2298shivam2298

184

answered Mar 23 at 10:42

shivam2298shivam2298

184

answered Mar 23 at 10:42

shivam2298shivam2298

184

0

The method getitem(self, item) would help you do this.

For example:

def __getitem__(self, item): # item can be thought as an index

 text = textList[item] # textList would be a list containing the text you want to input into the model for element 'item'
 img = imgList[image] # imgList would be a list containing the images you want to input into the model for element 'item'

 input = [text, img] 
 y = labels[item] # labels would be a list containing the label for the input of the text and img. This is your target.

 return input, y

share|improve this answer

answered Mar 24 at 4:10

asong24asong24

234

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your reply. I will try it.
  
  – xiaodan zhang
  Mar 24 at 8:48
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  if it is fixed please mark my answer as correct
  
  – asong24
  Mar 24 at 14:31
  

  
  
  
  

add a comment | 

0

The method getitem(self, item) would help you do this.

For example:

def __getitem__(self, item): # item can be thought as an index

 text = textList[item] # textList would be a list containing the text you want to input into the model for element 'item'
 img = imgList[image] # imgList would be a list containing the images you want to input into the model for element 'item'

 input = [text, img] 
 y = labels[item] # labels would be a list containing the label for the input of the text and img. This is your target.

 return input, y

share|improve this answer

answered Mar 24 at 4:10

asong24asong24

234

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thanks for your reply. I will try it.
  
  – xiaodan zhang
  Mar 24 at 8:48
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  if it is fixed please mark my answer as correct
  
  – asong24
  Mar 24 at 14:31
  

  
  
  
  

add a comment | 

The method getitem(self, item) would help you do this.

For example:

def __getitem__(self, item): # item can be thought as an index

 text = textList[item] # textList would be a list containing the text you want to input into the model for element 'item'
 img = imgList[image] # imgList would be a list containing the images you want to input into the model for element 'item'

 input = [text, img] 
 y = labels[item] # labels would be a list containing the label for the input of the text and img. This is your target.

 return input, y

share|improve this answer

answered Mar 24 at 4:10

asong24asong24

234

def __getitem__(self, item): # item can be thought as an index

 text = textList[item] # textList would be a list containing the text you want to input into the model for element 'item'
 img = imgList[image] # imgList would be a list containing the images you want to input into the model for element 'item'

 input = [text, img] 
 y = labels[item] # labels would be a list containing the label for the input of the text and img. This is your target.

 return input, y

share|improve this answer

answered Mar 24 at 4:10

asong24asong24

234

answered Mar 24 at 4:10

asong24asong24

234

answered Mar 24 at 4:10

asong24asong24

234