mirror of
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
94 lines
3.6 KiB
Python
94 lines
3.6 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 ComputeHistogramForBlobs(NetModifier):
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"""
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This class modifies the net passed in by adding ops to compute histogram for
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certain blobs.
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Args:
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blobs: list of blobs to compute histogram for
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logging_frequency: frequency for printing
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lower_bound: left boundary of histogram values
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upper_bound: right boundary of histogram values
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num_buckets: number of buckets to use in [lower_bound, upper_bound)
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accumulate: boolean to output accumulate or per-batch histogram
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"""
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def __init__(self, blobs, logging_frequency, num_buckets=30,
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lower_bound=0.0, upper_bound=1.0, accumulate=False):
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self._blobs = blobs
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self._logging_frequency = logging_frequency
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self._accumulate = accumulate
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if self._accumulate:
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self._field_name_suffix = '_acc_normalized_hist'
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else:
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self._field_name_suffix = '_curr_normalized_hist'
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self._num_buckets = int(num_buckets)
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assert self._num_buckets > 0, (
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"num_buckets need to be greater than 0, got {}".format(num_buckets))
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self._lower_bound = float(lower_bound)
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self._upper_bound = float(upper_bound)
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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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blob_float = net.Cast(blob, net.NextScopedBlob(prefix=blob +
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'_float'), to=core.DataType.FLOAT)
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curr_hist, acc_hist = net.AccumulateHistogram(
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[blob_float],
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[net.NextScopedBlob(prefix=blob + '_curr_hist'),
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net.NextScopedBlob(prefix=blob + '_acc_hist')],
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num_buckets=self._num_buckets,
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lower_bound=self._lower_bound,
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upper_bound=self._upper_bound)
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if self._accumulate:
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hist = net.Cast(
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acc_hist,
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net.NextScopedBlob(prefix=blob + '_cast_hist'),
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to=core.DataType.FLOAT)
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else:
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hist = net.Cast(
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curr_hist,
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net.NextScopedBlob(prefix=blob + '_cast_hist'),
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to=core.DataType.FLOAT)
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normalized_hist = net.NormalizeL1(
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hist,
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net.NextScopedBlob(prefix=blob + self._field_name_suffix)
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)
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if self._logging_frequency >= 1:
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net.Print(normalized_hist, [], 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.float32, (self._num_buckets + 2,)),
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normalized_hist)
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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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