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PaddleOCR
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40bf3b10
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体验新版 GitCode,发现更多精彩内容 >>
提交
40bf3b10
编写于
7月 06, 2021
作者:
L
LDOUBLEV
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差异文件
add DistillationDilaDBLoss loss
上级
a411c825
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1
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1 changed file
with
2 addition
and
88 deletion
+2
-88
ppocr/losses/distillation_loss.py
ppocr/losses/distillation_loss.py
+2
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未找到文件。
ppocr/losses/distillation_loss.py
浏览文件 @
40bf3b10
...
@@ -34,7 +34,8 @@ def _sum_loss(loss_dict):
...
@@ -34,7 +34,8 @@ def _sum_loss(loss_dict):
loss_dict
[
"loss"
]
+=
value
loss_dict
[
"loss"
]
+=
value
return
loss_dict
return
loss_dict
# class DistillationDMLLoss(DMLLoss):
class
DistillationDMLLoss
(
DMLLoss
):
"""
"""
"""
"""
...
@@ -131,93 +132,6 @@ class DistillationCTCLoss(CTCLoss):
...
@@ -131,93 +132,6 @@ class DistillationCTCLoss(CTCLoss):
return
loss_dict
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_loss",
**kwargs):
super().__init__()
self.model_name_list = model_name_list
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)
if isinstance(loss, dict):
for key in loss.keys():
if key == "loss":
continue
loss_dict[f"{self.name}_{model_name}_{key}"] = loss[key]
else:
loss_dict[f"{self.name}_{model_name}"] = loss
loss_dict = _sum_loss(loss_dict)
return loss_dict
class DistillationDilaDBLoss(DBLoss):
def __init__(self, model_name_pairs=[],
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
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]}"
loss_dict[k] = bce_loss + loss_binary_maps
loss_dict = _sum_loss(loss_dict)
return loss
"""
class
DistillationDistanceLoss
(
DistanceLoss
):
class
DistillationDistanceLoss
(
DistanceLoss
):
"""
"""
"""
"""
...
...
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