-
Notifications
You must be signed in to change notification settings - Fork 3
/
classifier_interface.py
213 lines (179 loc) · 8.09 KB
/
classifier_interface.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
#!/usr/bin/env python3
# Copyright 2019 Christian Henning
#
# 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.
#
# @title :mnets/classifier_interface.py
# @author :ch
# @contact :[email protected]
# @created :09/20/2019
# @version :1.0
# @python_version :3.6.8
"""
Interface for Classifiers
-------------------------
A general interface for main networks used in classification tasks. This
abstract base class also provides a collection of static helper functions that
are useful in classification problems.
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from warnings import warn
from mnets.mnet_interface import MainNetInterface
class Classifier(nn.Module, MainNetInterface):
"""A general interface for classification networks.
Attributes:
num_classes: Number of output neurons.
"""
def __init__(self, num_classes, verbose):
"""Initialize the network.
Args:
num_classes: The number of output neurons.
verbose: Allow printing of general information about the generated
network (such as number of weights).
"""
# FIXME find a way using super to handle multiple inheritance.
#super(Classifier, self).__init__()
nn.Module.__init__(self)
MainNetInterface.__init__(self)
assert(num_classes > 0)
self._num_classes = num_classes
self._verbose = verbose
@property
def num_classes(self):
"""Getter for read-only attribute num_classes."""
return self._num_classes
@staticmethod
def logit_cross_entropy_loss(h, t, reduction='mean'):
"""Compute cross-entropy loss for given predictions and targets.
Note, we assume that the argmax of the target vectors results in the
correct label.
Args:
h: Unscaled outputs from the main network, i.e., activations of the
last hidden layer (unscaled logits).
t: Targets in form os soft labels or 1-hot encodings.
reduction (str): The reduction method to be passed to
:func:`torch.nn.functional.cross_entropy`.
Returns:
Cross-entropy loss computed on logits h and labels extracted
from target vector t.
"""
assert(t.shape[1] == h.shape[1])
targets = t.argmax(dim=1, keepdim=False)
return F.cross_entropy(h, targets, reduction=reduction)
@staticmethod
def knowledge_distillation_loss(logits, target_logits, target_mapping=None,
device=None, T=2.):
"""Compute the knowledge distillation loss as proposed by
Hinton et al., "Distilling the Knowledge in a Neural Network",
NIPS Deep Learning and Representation Learning Workshop, 2015.
http://arxiv.org/abs/1503.02531
Args:
logits: Unscaled outputs from the main network, i.e., activations of
the last hidden layer (unscaled logits).
target_logits: Target logits, i.e., activations of the last hidden
layer (unscaled logits) from the target model.
Note, we won't detach "target_logits" from the graph. Make sure,
that you do this before calling this method.
target_mapping: In continual learning, it might be that the output
layer size of a model is growing. Thus, it could be that the
model providing the ``target_logits`` has a smaller output size
than the current model providing the ``logits``. Therefore, one
has to provide a mapping, which is a list of indices for
``logits`` that state which activations in ``logits`` have a
corresponding target in ``target_logits``.
For instance, if the output layer size just increased by 1
through appending a new output neuron to the current model, the
mapping would simply be:
:code:`target_mapping = list(range(target_logits.shape[1]))`.
device: Current PyTorch device. Only needs to be specified if
"target_mapping" is given.
T: Softmax temperature.
Returns:
Knowledge Distillation (KD) loss.
"""
assert target_mapping is None or device is not None
targets = F.softmax(target_logits / T, dim=1)
n_classes = logits.shape[1]
n_targets = targets.shape[1]
if target_mapping is None:
if n_classes != n_targets:
raise ValueError('If sizes of "logits" and "target_logits" ' +
'differ, "target_mapping" must be specified.')
else:
new_targets = torch.zeros_like(logits).to(device)
new_targets[:, target_mapping] = targets
targets = new_targets
# Note, I think the multiplication with T^2 here is not necessary. The
# original paper prescribes it, but on a gradient analysis where the
# MSE between tempered softmax targets and predictions is minimized
# (assuming the logits are zero-mean). Here, the gradient should consist
# of two terms where the first is scaled by 1/T and the second can be
# considered as scaled by 1/T^2 if making the same assumption as the
# distillation paper.
# Though, I wouldn't change any of this, since the loss function has
# been used and I don't think it matter for reasonable temperature
# choices.
return -(targets * F.log_softmax(logits / T,dim=1)).sum(dim=1).mean()*\
T**2
@staticmethod
def softmax_and_cross_entropy(h, t, reduction_sum=False):
"""Compute the cross entropy from logits, allowing smoothed labels
(i.e., this function does not require 1-hot targets).
Args:
h: Unscaled outputs from the main network, i.e., activations of the
last hidden layer (unscaled logits).
t: Targets in form os soft labels or 1-hot encodings.
Returns:
Cross-entropy loss computed on logits h and given targets t.
"""
assert(t.shape[1] == h.shape[1])
loss = -(t * torch.nn.functional.log_softmax(h, dim=1)).sum(dim=1)
if reduction_sum:
return loss.sum()
else:
return loss.mean()
@staticmethod
def accuracy(y, t):
"""Computing the accuracy between predictions y and targets t. We
assume that the argmax of t results in labels as described in the
docstring of method "cross_entropy_loss".
Args:
y: Outputs from the main network.
t: Targets in form of soft labels or 1-hot encodings.
Returns:
Relative prediction accuracy on the given batch.
"""
assert(t.shape[1] == y.shape[1])
predictions = y.argmax(dim=1, keepdim=False)
targets = t.argmax(dim=1, keepdim=False)
return (predictions == targets).float().mean()
@staticmethod
def num_hyper_weights(dims):
"""The number of weights that have to be predicted by a hypernetwork.
.. deprecated:: 1.0
Please use method
:meth:`mnets.mnet_interface.MainNetInterface.shapes_to_num_weights`
instead.
Args:
dims: For instance, the attribute :attr:`hyper_shapes`.
Returns:
(int)
"""
warn('Please use class "mnets.mnet_interface.MainNetInterface.' +
'shapes_to_num_weights" instead.', DeprecationWarning)
return np.sum([np.prod(l) for l in dims])
if __name__ == '__main__':
pass