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8d45f555d7
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94587 Approved by: https://github.com/ezyang
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
# @package onnx
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# Module caffe2.python.onnx.backend_rep
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from caffe2.python import core
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from caffe2.proto import caffe2_pb2
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from onnx.backend.base import BackendRep, namedtupledict
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class Caffe2Rep(BackendRep):
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def __init__(self, init_net, predict_net, workspace, uninitialized):
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super().__init__()
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self.init_net = init_net
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self.predict_net = predict_net
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self.workspace = workspace
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# The list of uninitialized external_inputs in workspace, we need this to
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# pair the name with given sequence inputs.
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self.uninitialized = uninitialized
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self.nets_created = False
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self.ran_init_net = False
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@property
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def _name_scope(self):
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if self.predict_net.device_option.device_type == caffe2_pb2.CUDA:
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return 'gpu_{}'.format(self.predict_net.device_option.device_id)
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return ''
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def run(self, inputs, **kwargs):
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super().run(inputs, **kwargs)
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with core.DeviceScope(self.predict_net.device_option):
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if isinstance(inputs, dict):
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with core.NameScope(self._name_scope):
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for key, value in inputs.items():
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self.workspace.FeedBlob(key, value)
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elif isinstance(inputs, list) or isinstance(inputs, tuple):
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if len(self.uninitialized) != len(inputs):
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raise RuntimeError('Expected {} values for uninitialized '
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'graph inputs ({}), but got {}.'.format(
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len(self.uninitialized),
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', '.join(self.uninitialized),
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len(inputs)))
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for i, value in enumerate(inputs):
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# namescope already baked into protobuf
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self.workspace.FeedBlob(self.uninitialized[i], value)
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else:
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# single input
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self.workspace.FeedBlob(self.uninitialized[0], inputs)
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if not self.nets_created:
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self.workspace.CreateNet(self.init_net)
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self.workspace.CreateNet(self.predict_net)
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self.nets_created = True
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if not self.ran_init_net:
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self.workspace.RunNet(self.init_net.name)
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self.ran_init_net = True
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self.workspace.RunNet(self.predict_net.name)
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output_values = []
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for name in self.predict_net.external_output:
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try:
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output_values.append(self.workspace.FetchBlob(name))
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except Exception:
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output_values.append(self.workspace.FetchInt8Blob(name))
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return namedtupledict('Outputs',
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self.predict_net.external_output)(*output_values)
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