Hey there I am new to PyTorch. I have a inference code that predicts and classify images. I can predict and classify images one by one, can anyone please help me to classify all the images of a folder in a batch.
Directory structure:
images
… img1.jpg
… img2.jpg
… img3.jpg
Output:
elephant
lion
tiger
How can I load all the image in the folder and predict one by one.
I am using the prediction code as follows:
def predict_image(image_path):
    print("prediciton in progress")
    image = Image.open(image_path)

    transformation = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    image_tensor = transformation(image).float()
    image_tensor = image_tensor.unsqueeze_(0)

    if cuda:
        image_tensor.cuda()

    input = Variable(image_tensor)
    output = model(input)

    index = output.data.numpy().argmax()
    return index

This works for single images, if called again and again the time of execution will increase. Also I am doing inference on a CPU machine.