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#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 numpy as np
import cv2
from .rec_ctc_loss import CTCLoss
from .rec_sar_loss import SARLoss
from .basic_loss import DMLLoss
from .basic_loss import DistanceLoss
from .basic_loss import LossFromOutput
from .det_db_loss import DBLoss
from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss
def _sum_loss(loss_dict):
if "loss" in loss_dict.keys():
return loss_dict
else:
loss_dict["loss"] = 0.
for k, value in loss_dict.items():
if k == "loss":
continue
else:
loss_dict["loss"] += value
return loss_dict
class DistillationDMLLoss(DMLLoss):
"""
"""
def __init__(self,
model_name_pairs=[],
act=None,
use_log=False,
key=None,
multi_head=False,
dis_head='ctc',
maps_name=None,
name="dml"):
super().__init__(act=act, use_log=use_log)
assert isinstance(model_name_pairs, list)
self.key = key
self.multi_head = multi_head
self.dis_head = dis_head
self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
self.name = name
self.maps_name = self._check_maps_name(maps_name)
def _check_model_name_pairs(self, model_name_pairs):
if not isinstance(model_name_pairs, list):
return []
elif isinstance(model_name_pairs[0], list) and isinstance(
model_name_pairs[0][0], str):
return model_name_pairs
else:
return [model_name_pairs]
def _check_maps_name(self, maps_name):
if maps_name is None:
return None
elif type(maps_name) == str:
return [maps_name]
elif type(maps_name) == list:
return [maps_name]
else:
return None
def _slice_out(self, outs):
new_outs = {}
for k in self.maps_name:
if k == "thrink_maps":
new_outs[k] = outs[:, 0, :, :]
elif k == "threshold_maps":
new_outs[k] = outs[:, 1, :, :]
elif k == "binary_maps":
new_outs[k] = outs[:, 2, :, :]
else:
continue
return new_outs
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.maps_name is None:
if self.multi_head:
loss = super().forward(out1[self.dis_head],
out2[self.dis_head])
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
else:
outs1 = self._slice_out(out1)
outs2 = self._slice_out(out2)
for _c, k in enumerate(outs1.keys()):
loss = super().forward(outs1[k], outs2[k])
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}_{}_{}_{}".format(key, pair[
0], pair[1], self.maps_name, idx)] = loss[key]
else:
loss_dict["{}_{}_{}".format(self.name, self.maps_name[
_c], idx)] = loss
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationCTCLoss(CTCLoss):
def __init__(self,
model_name_list=[],
key=None,
multi_head=False,
name="loss_ctc"):
super().__init__()
self.model_name_list = model_name_list
self.key = key
self.name = name
self.multi_head = multi_head
def forward(self, predicts, batch):
loss_dict = dict()
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.key is not None:
out = out[self.key]
if self.multi_head:
assert 'ctc' in out, 'multi head has multi out'
loss = super().forward(out['ctc'], batch[:2] + batch[3:])
else:
loss = super().forward(out, batch)
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}_{}".format(self.name, model_name,
idx)] = loss[key]
else:
loss_dict["{}_{}".format(self.name, model_name)] = loss
return loss_dict
class DistillationSARLoss(SARLoss):
def __init__(self,
model_name_list=[],
key=None,
multi_head=False,
name="loss_sar",
**kwargs):
ignore_index = kwargs.get('ignore_index', 92)
super().__init__(ignore_index=ignore_index)
self.model_name_list = model_name_list
self.key = key
self.name = name
self.multi_head = multi_head
def forward(self, predicts, batch):
loss_dict = dict()
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.key is not None:
out = out[self.key]
if self.multi_head:
assert 'sar' in out, 'multi head has multi out'
loss = super().forward(out['sar'], batch[:1] + batch[2:])
else:
loss = super().forward(out, batch)
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}_{}".format(self.name, model_name,
idx)] = loss[key]
else:
loss_dict["{}_{}".format(self.name, model_name)] = loss
return loss_dict
class DistillationDBLoss(DBLoss):
def __init__(self,
model_name_list=[],
balance_loss=True,
main_loss_type='DiceLoss',
alpha=5,
beta=10,
ohem_ratio=3,
eps=1e-6,
name="db",
**kwargs):
super().__init__()
self.model_name_list = model_name_list
self.name = name
self.key = None
def forward(self, predicts, batch):
loss_dict = {}
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.key is not None:
out = out[self.key]
loss = super().forward(out, batch)
if isinstance(loss, dict):
for key in loss.keys():
if key == "loss":
continue
name = "{}_{}_{}".format(self.name, model_name, key)
loss_dict[name] = loss[key]
else:
loss_dict["{}_{}".format(self.name, model_name)] = loss
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationDilaDBLoss(DBLoss):
def __init__(self,
model_name_pairs=[],
key=None,
balance_loss=True,
main_loss_type='DiceLoss',
alpha=5,
beta=10,
ohem_ratio=3,
eps=1e-6,
name="dila_dbloss"):
super().__init__()
self.model_name_pairs = model_name_pairs
self.name = name
self.key = key
def forward(self, predicts, batch):
loss_dict = dict()
for idx, pair in enumerate(self.model_name_pairs):
stu_outs = predicts[pair[0]]
tch_outs = predicts[pair[1]]
if self.key is not None:
stu_preds = stu_outs[self.key]
tch_preds = tch_outs[self.key]
stu_shrink_maps = stu_preds[:, 0, :, :]
stu_binary_maps = stu_preds[:, 2, :, :]
# dilation to teacher prediction
dilation_w = np.array([[1, 1], [1, 1]])
th_shrink_maps = tch_preds[:, 0, :, :]
th_shrink_maps = th_shrink_maps.numpy() > 0.3 # thresh = 0.3
dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
for i in range(th_shrink_maps.shape[0]):
dilate_maps[i] = cv2.dilate(
th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
th_shrink_maps = paddle.to_tensor(dilate_maps)
label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
1:]
# calculate the shrink map loss
bce_loss = self.alpha * self.bce_loss(
stu_shrink_maps, th_shrink_maps, label_shrink_mask)
loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
label_shrink_mask)
# k = f"{self.name}_{pair[0]}_{pair[1]}"
k = "{}_{}_{}".format(self.name, pair[0], pair[1])
loss_dict[k] = bce_loss + loss_binary_maps
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationDistanceLoss(DistanceLoss):
"""
"""
def __init__(self,
mode="l2",
model_name_pairs=[],
key=None,
name="loss_distance",
**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 + "_l2"
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)
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[
key]
else:
loss_dict["{}_{}_{}_{}".format(self.name, pair[0], pair[1],
idx)] = loss
return loss_dict
class DistillationVQASerTokenLayoutLMLoss(VQASerTokenLayoutLMLoss):
def __init__(self,
num_classes,
model_name_list=[],
key=None,
name="loss_ser"):
super().__init__(num_classes=num_classes)
self.model_name_list = model_name_list
self.key = key
self.name = name
def forward(self, predicts, batch):
loss_dict = dict()
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.key is not None:
out = out[self.key]
loss = super().forward(out, batch)
loss_dict["{}_{}".format(self.name, model_name)] = loss["loss"]
return loss_dict
class DistillationLossFromOutput(LossFromOutput):
def __init__(self,
reduction="none",
model_name_list=[],
dist_key=None,
key="loss",
name="loss_re"):
super().__init__(key=key, reduction=reduction)
self.model_name_list = model_name_list
self.name = name
self.dist_key = dist_key
def forward(self, predicts, batch):
loss_dict = dict()
for idx, model_name in enumerate(self.model_name_list):
out = predicts[model_name]
if self.dist_key is not None:
out = out[self.dist_key]
loss = super().forward(out, batch)
loss_dict["{}_{}".format(self.name, model_name)] = loss["loss"]
return loss_dict
class DistillationSERDMLLoss(DMLLoss):
"""
"""
def __init__(self,
act="softmax",
use_log=True,
num_classes=7,
model_name_pairs=[],
key=None,
name="loss_dml_ser"):
super().__init__(act=act, use_log=use_log)
assert isinstance(model_name_pairs, list)
self.key = key
self.name = name
self.num_classes = num_classes
self.model_name_pairs = model_name_pairs
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]
out1 = out1.reshape([-1, out1.shape[-1]])
out2 = out2.reshape([-1, out2.shape[-1]])
attention_mask = batch[2]
if attention_mask is not None:
active_output = attention_mask.reshape([-1, ]) == 1
out1 = out1[active_output]
out2 = out2[active_output]
loss_dict["{}_{}".format(self.name, idx)] = super().forward(out1,
out2)
return loss_dict
class DistillationVQADistanceLoss(DistanceLoss):
def __init__(self,
mode="l2",
model_name_pairs=[],
key=None,
index=None,
name="loss_distance",
**kargs):
super().__init__(mode=mode, **kargs)
assert isinstance(model_name_pairs, list)
self.key = key
self.index = index
self.model_name_pairs = model_name_pairs
self.name = name + "_l2"
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]]
attention_mask = batch[2]
if self.key is not None:
out1 = out1[self.key]
out2 = out2[self.key]
if self.index is not None:
out1 = out1[:, self.index, :, :]
out2 = out2[:, self.index, :, :]
if attention_mask is not None:
max_len = attention_mask.shape[-1]
out1 = out1[:, :max_len]
out2 = out2[:, :max_len]
out1 = out1.reshape([-1, out1.shape[-1]])
out2 = out2.reshape([-1, out2.shape[-1]])
if attention_mask is not None:
active_output = attention_mask.reshape([-1, ]) == 1
out1 = out1[active_output]
out2 = out2[active_output]
loss = super().forward(out1, out2)
if isinstance(loss, dict):
for key in loss:
loss_dict["{}_{}nohu_{}".format(self.name, key,
idx)] = loss[key]
else:
loss_dict["{}_{}_{}_{}".format(self.name, pair[0], pair[1],
idx)] = loss
return loss_dict
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