How to change DataLoader in PyTorch to read one image for prediction?
Currently, I have a pre-trained model that uses a DataLoader for reading a batch of images for training the model.
self.data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, 
   num_workers=1, pin_memory=True)

...

model.eval()
for step, inputs in enumerate(test_loader.data_loader):
   outputs = model(torch.cat([inputs], 1))

...

I want to process (make predictions) on images, as they arrive from a queue. It should be similar to a code that reads a single image and runs the model to make predictions on it. Something along the following lines:
from PIL import Image

new_input = Image.open(image_path)
model.eval()
outputs = model(torch.cat([new_input ], 1))

I was wondering if you could guide me how to do this and apply the same transformations in the DataLoader.

You can use do it with IterableDataset :

from torch.utils.data import IterableDataset

class MyDataset(IterableDataset):
    def __init__(self, image_queue):
      self.queue = image_queue

    def read_next_image(self):
        while self.queue.qsize() &gt; 0:
            # you can add transform here
            yield self.queue.get()
        return None

    def __iter__(self):
        return self.read_next_image()


and batch_size = 1 :

import queue
import torchvision.transforms.functional as TF

buffer = queue.Queue()
new_input = Image.open(image_path)
buffer.put(TF.to_tensor(new_input)) 
# ... Populate queue here

dataset = MyDataset(buffer)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1)
for data in dataloader:
   model(data) # data is one-image batch of size [1,3,H,W] where 3 - number of color channels

