def _all_gather(engine):
scores = engine.state.metric_details["metric3"]
engine.state.metric_details[
"metric3"] = evenly_divisible_all_gather(data=scores,
concat=True)
scores = engine.state.metric_details["metric4"]
engine.state.metric_details[
"metric4"] = evenly_divisible_all_gather(data=scores,
concat=True)
def _finalize(self, _engine: Engine) -> None:
"""
All gather classification results from ranks and save to CSV file.
Args:
_engine: Ignite Engine, unused argument.
"""
ws = idist.get_world_size()
if self.save_rank >= ws:
raise ValueError(
"target save rank is greater than the distributed group size.")
outputs = torch.stack(self._outputs, dim=0)
filenames = self._filenames
if ws > 1:
outputs = evenly_divisible_all_gather(outputs, concat=True)
filenames = string_list_all_gather(filenames)
if len(filenames) == 0:
meta_dict = None
else:
if len(filenames) != len(outputs):
warnings.warn(
f"filenames length: {len(filenames)} doesn't match outputs length: {len(outputs)}."
)
meta_dict = {Key.FILENAME_OR_OBJ: filenames}
# save to CSV file only in the expected rank
if idist.get_rank() == self.save_rank:
saver = self.saver or CSVSaver(output_dir=self.output_dir,
filename=self.filename,
overwrite=self.overwrite,
delimiter=self.delimiter)
saver.save_batch(outputs, meta_dict)
saver.finalize()
def _finalize(self, engine: Engine) -> None:
"""
All gather classification results from ranks and save to CSV file.
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
ws = idist.get_world_size()
if self.save_rank >= ws:
raise ValueError(
"target save rank is greater than the distributed group size."
)
outputs = torch.stack(self._outputs, dim=0)
filenames = self._filenames
if ws > 1:
outputs = evenly_divisible_all_gather(outputs, concat=True)
filenames = string_list_all_gather(filenames)
if len(filenames) == 0:
meta_dict = None
else:
if len(filenames) != len(outputs):
warnings.warn(
f"filenames length: {len(filenames)} doesn't match outputs length: {len(outputs)}."
)
meta_dict = {Key.FILENAME_OR_OBJ: filenames}
# save to CSV file only in the expected rank
if idist.get_rank() == self.save_rank:
# print('Output:', type(outputs), len(outputs), type(outputs[0]), len(outputs[0]))
# print('Labels:', type(self._labels), len(self._labels), type(self._labels[0]), len(self._labels[0]))
# print('Meta:', type(meta_dict[Key.FILENAME_OR_OBJ]), len(meta_dict[Key.FILENAME_OR_OBJ]))
self.saver.save_batch(outputs, self._labels, meta_dict)
self.saver.finalize()
def _sync(self):
"""
All gather the buffers across distributed ranks for aggregating.
Every buffer will be concatenated as a PyTorch Tensor.
"""
self._synced_tensors = [
evenly_divisible_all_gather(torch.cat(b, dim=0), concat=True)
for b in self._buffers
]
self._synced = True
def _run(self):
if dist.get_rank() == 0:
data1 = torch.tensor([[1, 2], [3, 4]])
data2 = torch.tensor([[1.0, 2.0]])
data3 = torch.tensor(7)
if dist.get_rank() == 1:
data1 = torch.tensor([[5, 6]])
data2 = torch.tensor([[3.0, 4.0], [5.0, 6.0]])
data3 = torch.tensor(8)
result1 = evenly_divisible_all_gather(data=data1, concat=True)
torch.testing.assert_allclose(result1,
torch.tensor([[1, 2], [3, 4], [5, 6]]))
result2 = evenly_divisible_all_gather(data=data2, concat=False)
for r, e in zip(result2, [
torch.tensor([[1.0, 2.0]]),
torch.tensor([[3.0, 4.0], [5.0, 6.0]])
]):
torch.testing.assert_allclose(r, e)
result3 = evenly_divisible_all_gather(data=data3, concat=False)
for r in result3:
self.assertEqual(r.ndimension(), 0)
def _sync(self):
"""
All gather the buffers across distributed ranks for aggregating.
Each buffer will be concatenated as a PyTorch Tensor.
"""
if self._synced or self._buffers is None:
return
try:
self._synced_tensors = [
evenly_divisible_all_gather(torch.stack(b, dim=0), concat=True) for b in self._buffers
]
except (RuntimeError, TypeError, ValueError) as e:
raise TypeError(f"{e}. unable to sync buffer contents: {self._buffers}.") from e
self._synced = True
