def _get_outputs(
self) -> List[Tuple[List[VisualizationOutput], SampleCache]]:
batch_data = next(self._dataset_iter)
vis_outputs = []
# Type ignore for issue with passing union to function taking generic
# https://github.com/python/mypy/issues/1533
for (
inputs,
additional_forward_args,
label,
) in _batched_generator( # type: ignore
inputs=batch_data.inputs,
additional_forward_args=batch_data.additional_args,
target_ind=batch_data.labels,
internal_batch_size=
1, # should be 1 until we have batch label support
):
output = self._calculate_vis_output(inputs,
additional_forward_args, label)
if output is not None:
cache = SampleCache(inputs, additional_forward_args, label)
vis_outputs.append((output, cache))
return vis_outputs
def _get_outputs(self) -> List[VisualizationOutput]:
batch_data = next(self.dataset)
vis_outputs = []
for inputs, additional_forward_args, label in _batched_generator(
inputs=batch_data.inputs,
additional_forward_args=batch_data.additional_args,
target_ind=batch_data.labels,
internal_batch_size=1, # should be 1 until we have batch label support
):
output = self._calculate_vis_output(inputs, additional_forward_args, label)
if output is not None:
cache = SampleCache(inputs, additional_forward_args, label)
vis_outputs.append((output, cache))
return vis_outputs
def _get_outputs(
self) -> List[Tuple[List[VisualizationOutput], SampleCache]]:
# If we run out of new batches, then we need to
# display data which was already shown before.
# However, since the dataset given to us is a generator,
# we can't reset it to return to the beginning.
# Because of this, we store a small cache of stale
# data, and iterate on it after the main generator
# stops returning new batches.
try:
batch_data = next(self._dataset_iter)
self._dataset_cache.append(batch_data)
if len(self._dataset_cache) > self._config.num_examples:
self._dataset_cache.pop(0)
except StopIteration:
self._dataset_iter = cycle(self._dataset_cache)
batch_data = next(self._dataset_iter)
vis_outputs = []
# Type ignore for issue with passing union to function taking generic
# https://github.com/python/mypy/issues/1533
for (
inputs,
additional_forward_args,
label,
) in _batched_generator( # type: ignore
inputs=batch_data.inputs,
additional_forward_args=batch_data.additional_args,
target_ind=batch_data.labels,
internal_batch_size=
1, # should be 1 until we have batch label support
):
output = self._calculate_vis_output(inputs,
additional_forward_args, label)
if output is not None:
cache = SampleCache(inputs, additional_forward_args, label)
vis_outputs.append((output, cache))
return vis_outputs
def test_batched_generator(self):
def sample_operator(inputs, additional_forward_args, target_ind,
scale):
return (
scale * (sum(inputs)),
scale * sum(additional_forward_args),
target_ind,
)
array1 = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
array2 = [[6, 7, 8], [0, 1, 2], [3, 4, 5]]
array3 = [[0, 1, 2], [0, 0, 0], [0, 0, 0]]
inp1, inp2, inp3 = (
torch.tensor(array1),
torch.tensor(array2),
torch.tensor(array3),
)
for index, (inp, add, targ) in enumerate(
_batched_generator((inp1, inp2), (inp3, 5), 7, 1)):
assertTensorAlmostEqual(self, inp[0], array1[index])
assertTensorAlmostEqual(self, inp[1], array2[index])
assertTensorAlmostEqual(self, add[0], array3[index])
self.assertEqual(add[1], 5)
self.assertEqual(targ, 7)
