mirror of
https://github.com/clearml/clearml
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103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
import sys
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import six
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from pathlib2 import Path
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from trains.binding.frameworks.base_bind import PatchBaseModelIO
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from ..frameworks import _patched_call, WeightsFileHandler, _Empty
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from ..import_bind import PostImportHookPatching
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from ...config import running_remotely
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from ...model import Framework
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class PatchPyTorchModelIO(PatchBaseModelIO):
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__main_task = None
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__patched = None
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@staticmethod
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def update_current_task(task, **_):
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PatchPyTorchModelIO.__main_task = task
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PatchPyTorchModelIO._patch_model_io()
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PostImportHookPatching.add_on_import('torch', PatchPyTorchModelIO._patch_model_io)
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@staticmethod
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def _patch_model_io():
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if PatchPyTorchModelIO.__patched:
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return
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if 'torch' not in sys.modules:
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return
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PatchPyTorchModelIO.__patched = True
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# noinspection PyBroadException
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try:
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# hack: make sure tensorflow.__init__ is called
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import torch
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torch.save = _patched_call(torch.save, PatchPyTorchModelIO._save)
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torch.load = _patched_call(torch.load, PatchPyTorchModelIO._load)
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except ImportError:
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pass
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except Exception:
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pass # print('Failed patching pytorch')
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@staticmethod
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def _save(original_fn, obj, f, *args, **kwargs):
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ret = original_fn(obj, f, *args, **kwargs)
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if not PatchPyTorchModelIO.__main_task:
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return ret
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if isinstance(f, six.string_types):
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filename = f
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elif hasattr(f, 'name'):
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filename = f.name
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# noinspection PyBroadException
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try:
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f.flush()
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except Exception:
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pass
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else:
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filename = None
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# give the model a descriptive name based on the file name
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# noinspection PyBroadException
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try:
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model_name = Path(filename).stem
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except Exception:
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model_name = None
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WeightsFileHandler.create_output_model(obj, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task,
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singlefile=True, model_name=model_name)
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return ret
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@staticmethod
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def _load(original_fn, f, *args, **kwargs):
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if isinstance(f, six.string_types):
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filename = f
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elif hasattr(f, 'name'):
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filename = f.name
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else:
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filename = None
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if not PatchPyTorchModelIO.__main_task:
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return original_fn(f, *args, **kwargs)
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# register input model
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empty = _Empty()
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if running_remotely():
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filename = WeightsFileHandler.restore_weights_file(empty, filename, Framework.pytorch,
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PatchPyTorchModelIO.__main_task)
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model = original_fn(filename or f, *args, **kwargs)
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else:
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# try to load model before registering, in case we fail
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model = original_fn(filename or f, *args, **kwargs)
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WeightsFileHandler.restore_weights_file(empty, filename, Framework.pytorch,
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PatchPyTorchModelIO.__main_task)
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if empty.trains_in_model:
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# noinspection PyBroadException
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try:
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model.trains_in_model = empty.trains_in_model
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except Exception:
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pass
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return model
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