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https://github.com/saymrwulf/pytorch.git
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771fcb3455
* [easy] allow empty tensor in cuda relu op The diff has not enabled unit test of empty tensor, because MLKVersion of ReluOp need extra work to support * Make blob norm plotting work with distributed trainer when the old framework is used
56 lines
2 KiB
Python
56 lines
2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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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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if not net.BlobIsDefined(blob):
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raise Exception('blob {0} is not defined in net {1}'.format(
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blob, net.Name()))
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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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