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Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
## @package elementwise_linear
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# Module caffe2.python.helpers.elementwise_linear
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from caffe2.python import core
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from caffe2.python.modeling.parameter_info import ParameterTags
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def _elementwise_linear(
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model, op_call, blob_in, blob_out, dim,
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weight_init=None, bias_init=None, **kwargs
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):
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"""Elementwise_Linear"""
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weight_init = weight_init or ('ConstantFill', {'value': 1.0})
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bias_init = bias_init or ('ConstantFill', {'value': 0.0})
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blob_out = blob_out or model.net.NextName()
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if model.init_params:
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weight = model.param_init_net.__getattr__(weight_init[0])(
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[],
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blob_out + '_w',
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shape=[dim],
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**weight_init[1]
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)
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bias = model.param_init_net.__getattr__(bias_init[0])(
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[],
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blob_out + '_b',
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shape=[dim],
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**bias_init[1]
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)
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else:
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weight = core.ScopedBlobReference(
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blob_out + '_w', model.param_init_net)
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bias = core.ScopedBlobReference(
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blob_out + '_b', model.param_init_net)
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model.AddParameter(weight, ParameterTags.WEIGHT)
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model.AddParameter(bias, ParameterTags.BIAS)
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return op_call([blob_in, weight, bias], blob_out, **kwargs)
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def elementwise_linear(model, *args, **kwargs):
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return _elementwise_linear(
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model, model.net.ElementwiseLinear, *args, **kwargs)
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