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from caffe2.python import core, schema
from caffe2.python.modeling.net_modifier import NetModifier
import numpy as np
class ComputeHistogramForBlobs(NetModifier):
"""
This class modifies the net passed in by adding ops to compute histogram for
certain blobs.
Args:
blobs: list of blobs to compute histogram for
logging_frequency: frequency for printing
lower_bound: left boundary of histogram values
upper_bound: right boundary of histogram values
num_buckets: number of buckets to use in [lower_bound, upper_bound)
accumulate: boolean to output accumulate or per-batch histogram
"""
def __init__(self, blobs, logging_frequency, num_buckets=30,
lower_bound=0.0, upper_bound=1.0, accumulate=False):
self._blobs = blobs
self._logging_frequency = logging_frequency
self._accumulate = accumulate
if self._accumulate:
self._field_name_suffix = '_acc_normalized_hist'
else:
self._field_name_suffix = '_curr_normalized_hist'
self._num_buckets = int(num_buckets)
assert self._num_buckets > 0, (
"num_buckets need to be greater than 0, got {}".format(num_buckets))
self._lower_bound = float(lower_bound)
self._upper_bound = float(upper_bound)
def modify_net(self, net, init_net=None, grad_map=None, blob_to_device=None,
modify_output_record=False):
for blob_name in self._blobs:
blob = core.BlobReference(blob_name)
assert net.BlobIsDefined(blob), 'blob {} is not defined in net {} whose proto is {}'.format(blob, net.Name(), net.Proto())
blob_float = net.Cast(blob, net.NextScopedBlob(prefix=blob +
'_float'), to=core.DataType.FLOAT)
curr_hist, acc_hist = net.AccumulateHistogram(
[blob_float],
[net.NextScopedBlob(prefix=blob + '_curr_hist'),
net.NextScopedBlob(prefix=blob + '_acc_hist')],
num_buckets=self._num_buckets,
lower_bound=self._lower_bound,
upper_bound=self._upper_bound)
if self._accumulate:
hist = net.Cast(
acc_hist,
net.NextScopedBlob(prefix=blob + '_cast_hist'),
to=core.DataType.FLOAT)
else:
hist = net.Cast(
curr_hist,
net.NextScopedBlob(prefix=blob + '_cast_hist'),
to=core.DataType.FLOAT)
normalized_hist = net.NormalizeL1(
hist,
net.NextScopedBlob(prefix=blob + self._field_name_suffix)
)
if self._logging_frequency >= 1:
net.Print(normalized_hist, [], every_n=self._logging_frequency)
if modify_output_record:
output_field_name = str(blob) + self._field_name_suffix
output_scalar = schema.Scalar((np.float32, (self._num_buckets + 2,)),
normalized_hist)
if net.output_record() is None:
net.set_output_record(
schema.Struct((output_field_name, output_scalar))
)
else:
net.AppendOutputRecordField(
output_field_name,
output_scalar)
def field_name_suffix(self):
return self._field_name_suffix
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