QuantumTransfer example inferece

PennyLane Help
1

Salve,

I am having a real difficulty breaking this apart. There is a lot pressed together.
Could someone help me to change this to a script that runs inference on an individual image? Rather than this subplot and grid thing?

def visualize_model(model, num_images=6, fig_name="Predictions"):
    images_so_far = 0
    _fig = plt.figure(fig_name)
    model.eval()
    with torch.no_grad():
        for _i, (inputs, labels) in enumerate(dataloaders["validation"]):
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images // 2, 2, images_so_far)
                ax.axis("off")
                ax.set_title("[{}]".format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])
                if images_so_far == num_images:
                    return

Just simple single image inference or loop over folder?

Merci!

2

Hi @quantumcoffeemonster, welcome to the forum!

In the code you can change the subplot line to ax = plt.subplot(1, 1, images_so_far), and you can execute this function with visualize_model(model_hybrid, num_images=1). This way you will get a single image being printed. Is this what you were looking for?