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Improve pytorch ignite integration

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This commit is contained in:
allegroai 2020-06-13 22:10:59 +03:00
parent a5b1ed0330
commit aa61fa3f06
1 changed files with 24 additions and 8 deletions
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32
trains/binding/frameworks/pytorch_bind.py
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@ -32,10 +32,23 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
# noinspection PyBroadException # noinspection PyBroadException
try: try:
# hack: make sure tensorflow.__init__ is called
import torch import torch
torch.save = _patched_call(torch.save, PatchPyTorchModelIO._save) torch.save = _patched_call(torch.save, PatchPyTorchModelIO._save)
torch.load = _patched_call(torch.load, PatchPyTorchModelIO._load) torch.load = _patched_call(torch.load, PatchPyTorchModelIO._load)
# no need to worry about recursive calls, _patched_call takes care of that
if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_save'):
torch.serialization._save = _patched_call(
torch.serialization._save, PatchPyTorchModelIO._save)
if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_load'):
torch.serialization._load = _patched_call(
torch.serialization._load, PatchPyTorchModelIO._load)
if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_legacy_save'):
torch.serialization._legacy_save = _patched_call(
torch.serialization._legacy_save, PatchPyTorchModelIO._save)
if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_legacy_load'):
torch.serialization._legacy_load = _patched_call(
torch.serialization._legacy_load, PatchPyTorchModelIO._load)
except ImportError: except ImportError:
pass pass
except Exception: except Exception:
@ -44,6 +57,7 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
@staticmethod @staticmethod
def _save(original_fn, obj, f, *args, **kwargs): def _save(original_fn, obj, f, *args, **kwargs):
ret = original_fn(obj, f, *args, **kwargs) ret = original_fn(obj, f, *args, **kwargs)
# if there is no main task or this is a nested call
if not PatchPyTorchModelIO.__main_task: if not PatchPyTorchModelIO.__main_task:
return ret return ret
@ -73,17 +87,18 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
# give the model a descriptive name based on the file name # give the model a descriptive name based on the file name
# noinspection PyBroadException # noinspection PyBroadException
try: try:
model_name = Path(filename).stem model_name = Path(filename).stem if filename is not None else None
except Exception: except Exception:
model_name = None model_name = None
WeightsFileHandler.create_output_model(obj, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task, WeightsFileHandler.create_output_model(
singlefile=True, model_name=model_name) obj, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task, singlefile=True, model_name=model_name)
return ret return ret
@staticmethod @staticmethod
def _load(original_fn, f, *args, **kwargs): def _load(original_fn, f, *args, **kwargs):
# if there is no main task or this is a nested call
if not PatchPyTorchModelIO.__main_task: if not PatchPyTorchModelIO.__main_task:
return original_fn(f, *args, **kwargs) return original_fn(f, *args, **kwargs)
@ -104,14 +119,14 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
empty = _Empty() empty = _Empty()
# Hack: disabled # Hack: disabled
if False and running_remotely(): if False and running_remotely():
filename = WeightsFileHandler.restore_weights_file(empty, filename, Framework.pytorch, filename = WeightsFileHandler.restore_weights_file(
PatchPyTorchModelIO.__main_task) empty, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task)
model = original_fn(filename or f, *args, **kwargs) model = original_fn(filename or f, *args, **kwargs)
else: else:
# try to load model before registering, in case we fail # try to load model before registering, in case we fail
model = original_fn(f, *args, **kwargs) model = original_fn(f, *args, **kwargs)
WeightsFileHandler.restore_weights_file(empty, filename, Framework.pytorch, WeightsFileHandler.restore_weights_file(
PatchPyTorchModelIO.__main_task) empty, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task)
if empty.trains_in_model: if empty.trains_in_model:
# noinspection PyBroadException # noinspection PyBroadException
@ -119,4 +134,5 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
model.trains_in_model = empty.trains_in_model model.trains_in_model = empty.trains_in_model
except Exception: except Exception:
pass pass
return model return model