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# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
from caffe2.python import core, schema
from caffe2.python.modeling.net_modifier import NetModifier
import numpy as np
class GetEntryFromBlobs(NetModifier):
"""
This class modifies the net passed in by adding ops to get a certain entry
from certain blobs.
Args:
blobs: list of blobs to get entry from
logging_frequency: frequency for printing entry values to logs
i1, i2: the first, second dimension of the blob. (currently, we assume
the blobs to be 2-dimensional blobs). When i2 = -1, print all entries
in blob[i1]
"""
def __init__(self, blobs, logging_frequency, i1=0, i2=0):
self._blobs = blobs
self._logging_frequency = logging_frequency
self._i1 = i1
self._i2 = i2
self._field_name_suffix = '_{0}_{1}'.format(i1, i2) if i2 >= 0 \
else '_{0}_all'.format(i1)
def modify_net(self, net, init_net=None, grad_map=None, blob_to_device=None,
modify_output_record=False):
i1, i2 = [self._i1, self._i2]
if i1 < 0:
raise ValueError('index is out of range')
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_i1 = net.Slice([blob], starts=[i1, 0], ends=[i1 + 1, -1])
if self._i2 == -1:
blob_i1_i2 = net.Copy([blob_i1],
[net.NextScopedBlob(prefix=blob + '_{0}_all'.format(i1))])
else:
blob_i1_i2 = net.Slice([blob_i1],
net.NextScopedBlob(prefix=blob + '_{0}_{1}'.format(i1, i2)),
starts=[0, i2], ends=[-1, i2 + 1])
if self._logging_frequency >= 1:
net.Print(blob_i1_i2, [], every_n=self._logging_frequency)
if modify_output_record:
output_field_name = str(blob) + self._field_name_suffix
output_scalar = schema.Scalar((np.float), blob_i1_i2)
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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