You could use from_numpy to transform the type from a numpy array to a PyTorch tensor without any normalization:
# create or load PIL.Image
tmp = np.random.randint(0, 255, (224, 224, 3)).astype(np.uint8)
img = PIL.Image.fromarray(tmp)

# get numpy array
arr = np.array(img)
tensor = torch.from_numpy(arr)
print(tensor)

You might additionally want to permute the axes to make the tensor channels-first, which is the expected default format.