def dataset_to_stream(dataset, input_name): """Takes a tf.Dataset and creates a numpy stream of ready batches.""" # All input-pipeline processing should be on CPU. for example in fastmath.dataset_as_numpy(dataset): features = example[0] inp, out = features[input_name], example[1] mask = features['mask'] if 'mask' in features else None # Some accelerators don't handle uint8 well, cast to int. if isinstance(inp, np.uint8): inp = inp.astype(np.int32) if isinstance(out, np.uint8): out = out.astype(np.int32) yield (inp, out) if mask is None else (inp, out, mask)
def gen(generator=None): del generator for example in fastmath.dataset_as_numpy(dataset): yield example
def gen(unused_arg): for example in fastmath.dataset_as_numpy(dataset): yield example