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27c7158166
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
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
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from caffe2.python import core, schema
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from caffe2.python.modeling.net_modifier import NetModifier
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import numpy as np
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class ComputeStatisticsForBlobs(NetModifier):
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"""
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This class modifies the net passed in by adding ops to compute statistics
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for certain blobs. For each blob in the list, its min, max, mean and standard
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deviation will be computed.
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Args:
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blobs: list of blobs to compute norm for
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logging_frequency: frequency for printing norms to logs
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"""
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def __init__(self, blobs, logging_frequency):
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self._blobs = blobs
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self._logging_frequency = logging_frequency
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self._field_name_suffix = '_summary'
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def modify_net(self, net, init_net=None, grad_map=None, blob_to_device=None,
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modify_output_record=False):
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for blob_name in self._blobs:
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blob = core.BlobReference(blob_name)
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assert net.BlobIsDefined(blob), 'blob {} is not defined in net {} whose proto is {}'.format(blob, net.Name(), net.Proto())
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cast_blob = net.Cast(blob, to=core.DataType.FLOAT)
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stats_name = net.NextScopedBlob(prefix=blob + self._field_name_suffix)
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stats = net.Summarize(cast_blob, stats_name, to_file=0)
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net.Print(stats, [], every_n=self._logging_frequency)
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if modify_output_record:
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output_field_name = str(blob) + self._field_name_suffix
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output_scalar = schema.Scalar((np.float, (1,)), stats)
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if net.output_record() is None:
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net.set_output_record(
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schema.Struct((output_field_name, output_scalar))
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)
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else:
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net.AppendOutputRecordField(
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output_field_name,
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output_scalar)
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def field_name_suffix(self):
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return self._field_name_suffix
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