287 lines
9.1 KiB
Python
287 lines
9.1 KiB
Python
#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
#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.
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
from .celoss import CELoss
|
|
from .dmlloss import DMLLoss
|
|
from .distanceloss import DistanceLoss
|
|
from .rkdloss import RKdAngle, RkdDistance
|
|
from .kldivloss import KLDivLoss
|
|
from .dkdloss import DKDLoss
|
|
from .multilabelloss import MultiLabelLoss
|
|
|
|
|
|
class DistillationCELoss(CELoss):
|
|
"""
|
|
DistillationCELoss
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_name_pairs=[],
|
|
epsilon=None,
|
|
key=None,
|
|
name="loss_ce"):
|
|
super().__init__(epsilon=epsilon)
|
|
assert isinstance(model_name_pairs, list)
|
|
self.key = key
|
|
self.model_name_pairs = model_name_pairs
|
|
self.name = name
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for idx, pair in enumerate(self.model_name_pairs):
|
|
out1 = predicts[pair[0]]
|
|
out2 = predicts[pair[1]]
|
|
if self.key is not None:
|
|
out1 = out1[self.key]
|
|
out2 = out2[self.key]
|
|
loss = super().forward(out1, out2)
|
|
for key in loss:
|
|
loss_dict["{}_{}_{}".format(key, pair[0], pair[1])] = loss[key]
|
|
return loss_dict
|
|
|
|
|
|
class DistillationGTCELoss(CELoss):
|
|
"""
|
|
DistillationGTCELoss
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_names=[],
|
|
epsilon=None,
|
|
key=None,
|
|
name="loss_gt_ce"):
|
|
super().__init__(epsilon=epsilon)
|
|
assert isinstance(model_names, list)
|
|
self.key = key
|
|
self.model_names = model_names
|
|
self.name = name
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for name in self.model_names:
|
|
out = predicts[name]
|
|
if self.key is not None:
|
|
out = out[self.key]
|
|
loss = super().forward(out, batch)
|
|
for key in loss:
|
|
loss_dict["{}_{}".format(key, name)] = loss[key]
|
|
return loss_dict
|
|
|
|
|
|
class DistillationDMLLoss(DMLLoss):
|
|
"""
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_name_pairs=[],
|
|
act="softmax",
|
|
weight_ratio=False,
|
|
sum_across_class_dim=False,
|
|
key=None,
|
|
name="loss_dml"):
|
|
super().__init__(act=act, sum_across_class_dim=sum_across_class_dim)
|
|
assert isinstance(model_name_pairs, list)
|
|
self.key = key
|
|
self.model_name_pairs = model_name_pairs
|
|
self.name = name
|
|
self.weight_ratio = weight_ratio
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for idx, pair in enumerate(self.model_name_pairs):
|
|
out1 = predicts[pair[0]]
|
|
out2 = predicts[pair[1]]
|
|
if self.key is not None:
|
|
out1 = out1[self.key]
|
|
out2 = out2[self.key]
|
|
if self.weight_ratio is True:
|
|
loss = super().forward(out1, out2, batch)
|
|
else:
|
|
loss = super().forward(out1, out2)
|
|
if isinstance(loss, dict):
|
|
for key in loss:
|
|
loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
|
|
idx)] = loss[key]
|
|
else:
|
|
loss_dict["{}_{}".format(self.name, idx)] = loss
|
|
return loss_dict
|
|
|
|
|
|
class DistillationDistanceLoss(DistanceLoss):
|
|
"""
|
|
"""
|
|
|
|
def __init__(self,
|
|
mode="l2",
|
|
model_name_pairs=[],
|
|
act=None,
|
|
key=None,
|
|
name="loss_",
|
|
**kargs):
|
|
super().__init__(mode=mode, **kargs)
|
|
assert isinstance(model_name_pairs, list)
|
|
self.key = key
|
|
self.model_name_pairs = model_name_pairs
|
|
self.name = name + mode
|
|
assert act in [None, "sigmoid", "softmax"]
|
|
if act == "sigmoid":
|
|
self.act = nn.Sigmoid()
|
|
elif act == "softmax":
|
|
self.act = nn.Softmax(axis=-1)
|
|
else:
|
|
self.act = None
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for idx, pair in enumerate(self.model_name_pairs):
|
|
out1 = predicts[pair[0]]
|
|
out2 = predicts[pair[1]]
|
|
if self.key is not None:
|
|
out1 = out1[self.key]
|
|
out2 = out2[self.key]
|
|
if self.act is not None:
|
|
out1 = self.act(out1)
|
|
out2 = self.act(out2)
|
|
loss = super().forward(out1, out2)
|
|
for key in loss:
|
|
loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[key]
|
|
return loss_dict
|
|
|
|
|
|
class DistillationRKDLoss(nn.Layer):
|
|
def __init__(self,
|
|
target_size=None,
|
|
model_name_pairs=(["Student", "Teacher"], ),
|
|
student_keepkeys=[],
|
|
teacher_keepkeys=[]):
|
|
super().__init__()
|
|
self.student_keepkeys = student_keepkeys
|
|
self.teacher_keepkeys = teacher_keepkeys
|
|
self.model_name_pairs = model_name_pairs
|
|
assert len(self.student_keepkeys) == len(self.teacher_keepkeys)
|
|
|
|
self.rkd_angle_loss = RKdAngle(target_size=target_size)
|
|
self.rkd_dist_loss = RkdDistance(target_size=target_size)
|
|
|
|
def __call__(self, predicts, batch):
|
|
loss_dict = {}
|
|
for m1, m2 in self.model_name_pairs:
|
|
for idx, (
|
|
student_name, teacher_name
|
|
) in enumerate(zip(self.student_keepkeys, self.teacher_keepkeys)):
|
|
student_out = predicts[m1][student_name]
|
|
teacher_out = predicts[m2][teacher_name]
|
|
|
|
loss_dict[f"loss_angle_{idx}_{m1}_{m2}"] = self.rkd_angle_loss(
|
|
student_out, teacher_out)
|
|
loss_dict[f"loss_dist_{idx}_{m1}_{m2}"] = self.rkd_dist_loss(
|
|
student_out, teacher_out)
|
|
|
|
return loss_dict
|
|
|
|
|
|
class DistillationKLDivLoss(KLDivLoss):
|
|
"""
|
|
DistillationKLDivLoss
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_name_pairs=[],
|
|
temperature=4,
|
|
key=None,
|
|
name="loss_kl"):
|
|
super().__init__(temperature=temperature)
|
|
assert isinstance(model_name_pairs, list)
|
|
self.key = key
|
|
self.model_name_pairs = model_name_pairs
|
|
self.name = name
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for idx, pair in enumerate(self.model_name_pairs):
|
|
out1 = predicts[pair[0]]
|
|
out2 = predicts[pair[1]]
|
|
if self.key is not None:
|
|
out1 = out1[self.key]
|
|
out2 = out2[self.key]
|
|
loss = super().forward(out1, out2)
|
|
for key in loss:
|
|
loss_dict["{}_{}_{}".format(key, pair[0], pair[1])] = loss[key]
|
|
return loss_dict
|
|
|
|
|
|
class DistillationDKDLoss(DKDLoss):
|
|
"""
|
|
DistillationDKDLoss
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_name_pairs=[],
|
|
key=None,
|
|
temperature=1.0,
|
|
alpha=1.0,
|
|
beta=1.0,
|
|
name="loss_dkd"):
|
|
super().__init__(temperature=temperature, alpha=alpha, beta=beta)
|
|
self.key = key
|
|
self.model_name_pairs = model_name_pairs
|
|
self.name = name
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for idx, pair in enumerate(self.model_name_pairs):
|
|
out1 = predicts[pair[0]]
|
|
out2 = predicts[pair[1]]
|
|
if self.key is not None:
|
|
out1 = out1[self.key]
|
|
out2 = out2[self.key]
|
|
loss = super().forward(out1, out2, batch)
|
|
loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
|
|
return loss_dict
|
|
|
|
|
|
class DistillationMultiLabelLoss(MultiLabelLoss):
|
|
"""
|
|
DistillationMultiLabelLoss
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_names=[],
|
|
epsilon=None,
|
|
size_sum=False,
|
|
weight_ratio=False,
|
|
key=None,
|
|
name="loss_mll"):
|
|
super().__init__(
|
|
epsilon=epsilon, size_sum=size_sum, weight_ratio=weight_ratio)
|
|
assert isinstance(model_names, list)
|
|
self.key = key
|
|
self.model_names = model_names
|
|
self.name = name
|
|
|
|
def forward(self, predicts, batch):
|
|
loss_dict = dict()
|
|
for name in self.model_names:
|
|
out = predicts[name]
|
|
if self.key is not None:
|
|
out = out[self.key]
|
|
loss = super().forward(out, batch)
|
|
for key in loss:
|
|
loss_dict["{}_{}".format(key, name)] = loss[key]
|
|
return loss_dict
|