If you want to visualize the PIL.Image after each transformation, you could create a custom transformation and call e.g. img.show() (or use any other library to plot the image):
class Visualize(object):
    def __call__(self, img):
        img.show()
        return img
        
spatial_transform = transforms.Compose([transforms.RandomRotation(20),
                                        Visualize(),
                                        transforms.RandomResizedCrop(224),
                                        Visualize(),
                                        transforms.RandomHorizontalFlip(),
                                        Visualize(),
                                        transforms.ColorJitter(hue=.05, saturation=.05),
                                        Visualize(),
                                        transforms.ToTensor(),
                                        transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])


to_pil_image = transforms.ToPILImage()
x = torch.randn(3, 256, 256)
img = to_pil_image(x)
out = spatial_transform(img)