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https://github.com/clearml/clearml
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Improve pytorch ignite integration
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parent
a5b1ed0330
commit
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@ -32,10 +32,23 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
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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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# no need to worry about recursive calls, _patched_call takes care of that
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if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_save'):
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torch.serialization._save = _patched_call(
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torch.serialization._save, PatchPyTorchModelIO._save)
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if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_load'):
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torch.serialization._load = _patched_call(
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torch.serialization._load, PatchPyTorchModelIO._load)
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if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_legacy_save'):
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torch.serialization._legacy_save = _patched_call(
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torch.serialization._legacy_save, PatchPyTorchModelIO._save)
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if hasattr(torch, 'serialization') and hasattr(torch.serialization, '_legacy_load'):
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torch.serialization._legacy_load = _patched_call(
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torch.serialization._legacy_load, PatchPyTorchModelIO._load)
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except ImportError:
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pass
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except Exception:
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@ -44,6 +57,7 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
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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 there is no main task or this is a nested call
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if not PatchPyTorchModelIO.__main_task:
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return ret
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@ -73,17 +87,18 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
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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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model_name = Path(filename).stem if filename is not None else None
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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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WeightsFileHandler.create_output_model(
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obj, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task, 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 there is no main task or this is a nested call
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if not PatchPyTorchModelIO.__main_task:
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return original_fn(f, *args, **kwargs)
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@ -104,14 +119,14 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
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empty = _Empty()
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# Hack: disabled
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if False and 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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filename = WeightsFileHandler.restore_weights_file(
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empty, filename, Framework.pytorch, 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(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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WeightsFileHandler.restore_weights_file(
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empty, filename, Framework.pytorch, PatchPyTorchModelIO.__main_task)
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if empty.trains_in_model:
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# noinspection PyBroadException
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@ -119,4 +134,5 @@ class PatchPyTorchModelIO(PatchBaseModelIO):
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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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