You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
277 lines
12 KiB
277 lines
12 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 .... import layers
|
|
from .... import optimizer
|
|
from .... import Executor
|
|
from .... import Program
|
|
from .... import program_guard
|
|
from .... import regularizer
|
|
|
|
__all__ = ['FSPDistiller', 'L2Distiller', 'SoftLabelDistiller']
|
|
|
|
|
|
class L2Distiller(object):
|
|
"""
|
|
Combine two layers from student net and teacher net by l2-loss.
|
|
And add the loss into the total loss using for distillation training.
|
|
"""
|
|
|
|
def __init__(self,
|
|
student_feature_map,
|
|
teacher_feature_map,
|
|
distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
student_feature_map(str): The name of feature map from student network.
|
|
teacher_feature_map(str): The name of feature map from teacher network.
|
|
It's shape should be the same with student network.
|
|
distillation_loss_weight(float): The weight of the l2-loss.
|
|
"""
|
|
self.student_feature_map = student_feature_map
|
|
self.teacher_feature_map = teacher_feature_map
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
|
|
def distiller_loss(self, graph):
|
|
"""
|
|
Modify graph inplace to add l2-loss.
|
|
Args:
|
|
graph(GraphWrapper): The graph to be modified.
|
|
Returns:
|
|
GraphWrapper: The modified graph.
|
|
"""
|
|
distiller_pass = L2DistillerPass(self.student_feature_map,
|
|
self.teacher_feature_map,
|
|
self.distillation_loss_weight)
|
|
dis_graph = distiller_pass.apply(graph)
|
|
return dis_graph
|
|
|
|
|
|
class L2DistillerPass(object):
|
|
"""
|
|
The pass used to add l2-loss.
|
|
"""
|
|
|
|
def __init__(self,
|
|
student_feature_map,
|
|
teacher_feature_map,
|
|
distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
student_feature_map(str): The name of feature map from student network.
|
|
teacher_feature_map(str): The name of feature map from teacher network.
|
|
It's shape should be the same with student network.
|
|
distillation_loss_weight(float): The weight of the l2-loss.
|
|
"""
|
|
self.student_feature_map = student_feature_map
|
|
self.teacher_feature_map = teacher_feature_map
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
|
|
def apply(self, graph):
|
|
ret_graph = graph
|
|
with program_guard(ret_graph.program):
|
|
|
|
student_feature_map = ret_graph.var(self.student_feature_map)._var
|
|
teacher_feature_map = ret_graph.var(self.teacher_feature_map)._var
|
|
l2loss = layers.reduce_mean(
|
|
layers.square(student_feature_map - teacher_feature_map))
|
|
|
|
distillation_loss = l2loss * self.distillation_loss_weight
|
|
student_loss = ret_graph.var(ret_graph.out_nodes['loss'])._var
|
|
loss = distillation_loss + student_loss
|
|
|
|
ret_graph.out_nodes[
|
|
'l2loss_' + self.student_feature_map + "_" +
|
|
self.teacher_feature_map] = distillation_loss.name
|
|
ret_graph.out_nodes['loss'] = loss.name
|
|
return ret_graph
|
|
|
|
|
|
class FSPDistiller(object):
|
|
"""
|
|
Combine layers from student net and teacher net by fsp-loss.
|
|
"""
|
|
|
|
def __init__(self, student_pairs, teacher_pairs,
|
|
distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
student_pairs(list<tuple>): Each tuple, with two variable names, in student_pairs indicates
|
|
a section in student network. The variables in a tuple should
|
|
have the same feature map size.
|
|
teacher_pairs(list<tuple>): Each tuple, with two variable names, in teacher_pairs indicates
|
|
a section in teacher network. The variables in a tuple should
|
|
have the same feature map size. Varibale named teacher_pairs[i][j]
|
|
should has the save channel number with that of variable named
|
|
student_pairs[i][j].
|
|
|
|
distillation_loss_weight(float): The weight of the fsp-loss. default: 1.
|
|
"""
|
|
self.student_pairs = student_pairs
|
|
self.teacher_pairs = teacher_pairs
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
|
|
def distiller_loss(self, graph):
|
|
"""
|
|
Modify graph inplace to add fsp-loss.
|
|
Args:
|
|
graph(GraphWrapper): The graph to be modified.
|
|
Returns:
|
|
GraphWrapper: The modified graph.
|
|
"""
|
|
distiller_pass = FSPDistillerPass(self.student_pairs,
|
|
self.teacher_pairs,
|
|
self.distillation_loss_weight)
|
|
dis_graph = distiller_pass.apply(graph)
|
|
return dis_graph
|
|
|
|
|
|
class FSPDistillerPass(object):
|
|
'''
|
|
Combine layers from student net and teacher net by fsp-loss.
|
|
'''
|
|
|
|
def __init__(self, s_pairs, t_pairs, distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
s_pairs(list<tuple>): Each tuple, with two variable names, in student_pairs indicates
|
|
a section in student network. The variables in a tuple should
|
|
have the same feature map size.
|
|
t_pairs(list<tuple>): Each tuple, with two variable names, in teacher_pairs indicates
|
|
a section in teacher network. The variables in a tuple should
|
|
have the same feature map size. Varibale named teacher_pairs[i][j]
|
|
should has the save channel number with that of variable named
|
|
student_pairs[i][j].
|
|
|
|
distillation_loss_weight(float): The weight of the fsp-loss. default: 1.
|
|
"""
|
|
self.s_pairs = s_pairs
|
|
self.t_pairs = t_pairs
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
|
|
def apply(self, graph):
|
|
ret_graph = graph
|
|
with program_guard(ret_graph.program):
|
|
losses = []
|
|
for s_pair, t_pair in zip(self.s_pairs, self.t_pairs):
|
|
s_pair_start = ret_graph.var(s_pair[0])._var
|
|
s_pair_end = ret_graph.var(s_pair[1])._var
|
|
s_fsp_matrix = self._fsp_matrix(s_pair_start, s_pair_end)
|
|
t_pair_start = ret_graph.var(t_pair[0])._var
|
|
t_pair_end = ret_graph.var(t_pair[1])._var
|
|
t_fsp_matrix = self._fsp_matrix(t_pair_start, t_pair_end)
|
|
l2_loss = layers.reduce_mean(
|
|
layers.square(s_fsp_matrix - t_fsp_matrix))
|
|
losses.append(l2_loss)
|
|
distillation_loss = layers.sum(
|
|
losses) * self.distillation_loss_weight
|
|
student_loss = ret_graph.var(ret_graph.out_nodes['loss'])._var
|
|
loss = distillation_loss + student_loss
|
|
|
|
ret_graph.out_nodes[
|
|
'fsp_distillation_loss'] = distillation_loss.name
|
|
ret_graph.out_nodes['loss'] = loss.name
|
|
return ret_graph
|
|
|
|
def _fsp_matrix(self, fea_map_0, fea_map_1):
|
|
return layers.fsp_matrix(fea_map_0, fea_map_1)
|
|
|
|
|
|
class SoftLabelDistiller(object):
|
|
"""
|
|
Combine two layers from student net and teacher net by softmax_with_cross_entropy loss.
|
|
And add the loss into the total loss using for distillation training.
|
|
"""
|
|
|
|
def __init__(self,
|
|
student_feature_map=None,
|
|
teacher_feature_map=None,
|
|
student_temperature=1.0,
|
|
teacher_temperature=1.0,
|
|
distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
student_feature_map(str): The name of feature map from student network.
|
|
teacher_feature_map(str): The name of feature map from teacher network.
|
|
It's shape should be the same with student network.
|
|
student_temperature(float): Temperature used to divide student_feature_map before softmax_with_cross_entropy. default: 1.0
|
|
teacher_temperature(float): Temperature used to divide teacher_feature_map before softmax_with_cross_entropy. default: 1.0
|
|
distillation_loss_weight(float): The weight of the l2-loss.
|
|
"""
|
|
|
|
self.student_feature_map = student_feature_map
|
|
self.teacher_feature_map = teacher_feature_map
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
self.student_temperature = student_temperature
|
|
self.teacher_temperature = teacher_temperature
|
|
|
|
def distiller_loss(self, graph):
|
|
"""
|
|
Modify graph inplace to add softmax_with_cross_entropy loss.
|
|
Args:
|
|
graph(GraphWrapper): The graph to be modified.
|
|
Returns:
|
|
GraphWrapper: The modified graph.
|
|
"""
|
|
distiller_pass = SoftLabelDistillerPass(
|
|
self.student_feature_map, self.teacher_feature_map,
|
|
self.student_temperature, self.teacher_temperature,
|
|
self.distillation_loss_weight)
|
|
dis_graph = distiller_pass.apply(graph)
|
|
return dis_graph
|
|
|
|
|
|
class SoftLabelDistillerPass(object):
|
|
def __init__(self,
|
|
student_feature_map,
|
|
teacher_feature_map,
|
|
student_temperature,
|
|
teacher_temperature,
|
|
distillation_loss_weight=1):
|
|
"""
|
|
Args:
|
|
student_feature_map(str): The name of feature map from student network.
|
|
teacher_feature_map(str): The name of feature map from teacher network.
|
|
It's shape should be the same with student network.
|
|
student_temperature(float): Temperature used to divide student_feature_map before softmax_with_cross_entropy.
|
|
teacher_temperature(float): Temperature used to divide teacher_feature_map before softmax_with_cross_entropy.
|
|
distillation_loss_weight(float): The weight of the l2-loss.
|
|
"""
|
|
self.student_feature_map = student_feature_map
|
|
self.teacher_feature_map = teacher_feature_map
|
|
self.student_temperature = student_temperature
|
|
self.teacher_temperature = teacher_temperature
|
|
self.distillation_loss_weight = distillation_loss_weight
|
|
|
|
def apply(self, graph):
|
|
ret_graph = graph
|
|
with program_guard(ret_graph.program):
|
|
|
|
student_feature_map = ret_graph.var(self.student_feature_map)._var
|
|
teacher_feature_map = ret_graph.var(self.teacher_feature_map)._var
|
|
s_fea = student_feature_map / self.student_temperature
|
|
t_fea = teacher_feature_map / self.distillation_loss_weight
|
|
t_fea.stop_gradient = True
|
|
ce_loss = layers.softmax_with_cross_entropy(
|
|
s_fea, t_fea, soft_label=True)
|
|
distillation_loss = ce_loss * self.distillation_loss_weight
|
|
student_loss = ret_graph.var(ret_graph.out_nodes['loss'])._var
|
|
loss = distillation_loss + student_loss
|
|
|
|
ret_graph.out_nodes[
|
|
'soft_label_loss_' + self.student_feature_map + "_" +
|
|
self.teacher_feature_map] = distillation_loss.name
|
|
ret_graph.out_nodes['loss'] = loss.name
|
|
return ret_graph
|