Image classification tensor

Hello @ptrblck,

In fact, i want to concat image pixels (train_x) and their labels(train_y). Then I want to convert to tensor.
I don’t know how to do ?

Thank You

Some more details about your objective might help.

In order to concat 2 tensors, they need to be the same size, except in the dim being concatenated. You could do:

labels = torch.empty_like(train_x)
labels[:, 0, labels.size()[1], :] = train_y.view(-1, 1, labels.size()[1], 1)[train_x, labels], dim=1)

But, in most cases, you might find it easier to just use:

data_labels = [train_x, train_y]
train_x, train_y = data_labels[0], data_labels[1]

Thank you so much ! I will try it

But i have an error =

Call view on a PyTorch tensor not a numpy array:

x = torch.randn(2, 3, 4, 5)
x = x.view(-1, 1, 1, 1) # works

x = np.random.randn(2, 3, 4, 5)
x = x.view(-1, 1, 1, 1) # fails
# TypeError: view() takes from 0 to 2 positional arguments but 4 were given
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As @ptrblck said, if it’s a NumPy object, you’ll need to convert it into a Pytorch tensor, via torch.from_numpy():

labels[:, 0, labels.size()[1], :] = torch.from_numpy(train_y).view(-1, 1, labels.size()[1], 1)[train_x, labels], dim=1)

I have an error :

You changed train_y.view(-1,1,labels.size()[1], 1) to train_y.view(-1,1,labels.size()[0], 1). The -1 is intended to capture the batch size, as this can be variable, while the labels.size()[1] captures the number of labels per item. Change it back to dim 1 so the sizes match.

Additionally, it seems your images are removing a dim. Images should have 4 dimensions: batch size, channels, height, width.

If you are getting your images/labels from a numpy dataloader, it could be squeezing the channels dim. You’ll need that back in to pass to a model for inference, if it has convolutional layers. i.e. train_x=train_x.unsqueeze(1)

If the model is strictly linear layers, then you can resize the labels view accordingly.

But in train_x there are pixels for each images.

I changed because i got this error :

As I suspected, you’re missing the channels dim. Does your model have Conv2D layers?

I did not train the model. In fact, for the image classification, i have a folder with human faces and two csv files : train_csv contains the name of image (ex : 0001.jpg) and the label (1 or -1). So i start to collect then into datframe.

Then just remove the 2nd dim in the .view():

labels[:,0: labels.size()[1], 0:1] = train_y.view(-1, labels.size()[1], 1)

It did not change:

Can you print the sizes of train_x and train_y?

Yes :

import torch

train_x=torch.rand(200, 130, 130)

labels = torch.empty_like(train_x)
labels[:,:1, :1] = train_y.view(-1, 1, 1)[train_x, labels], dim=1)