forked from PaddlePaddle/PaddleOCR
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdet_drrg_loss.py
224 lines (193 loc) · 8.36 KB
/
det_drrg_loss.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
212
213
214
215
216
217
218
219
220
221
222
223
224
# copyright (c) 2022 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.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/losses/drrg_loss.py
"""
import paddle
import paddle.nn.functional as F
from paddle import nn
class DRRGLoss(nn.Layer):
def __init__(self, ohem_ratio=3.0):
super().__init__()
self.ohem_ratio = ohem_ratio
self.downsample_ratio = 1.0
def balance_bce_loss(self, pred, gt, mask):
"""Balanced Binary-CrossEntropy Loss.
Args:
pred (Tensor): Shape of :math:`(1, H, W)`.
gt (Tensor): Shape of :math:`(1, H, W)`.
mask (Tensor): Shape of :math:`(1, H, W)`.
Returns:
Tensor: Balanced bce loss.
"""
assert pred.shape == gt.shape == mask.shape
assert paddle.all(pred >= 0) and paddle.all(pred <= 1)
assert paddle.all(gt >= 0) and paddle.all(gt <= 1)
positive = gt * mask
negative = (1 - gt) * mask
positive_count = int(positive.sum())
if positive_count > 0:
loss = F.binary_cross_entropy(pred, gt, reduction='none')
positive_loss = paddle.sum(loss * positive)
negative_loss = loss * negative
negative_count = min(
int(negative.sum()), int(positive_count * self.ohem_ratio))
else:
positive_loss = paddle.to_tensor(0.0)
loss = F.binary_cross_entropy(pred, gt, reduction='none')
negative_loss = loss * negative
negative_count = 100
negative_loss, _ = paddle.topk(
negative_loss.reshape([-1]), negative_count)
balance_loss = (positive_loss + paddle.sum(negative_loss)) / (
float(positive_count + negative_count) + 1e-5)
return balance_loss
def gcn_loss(self, gcn_data):
"""CrossEntropy Loss from gcn module.
Args:
gcn_data (tuple(Tensor, Tensor)): The first is the
prediction with shape :math:`(N, 2)` and the
second is the gt label with shape :math:`(m, n)`
where :math:`m * n = N`.
Returns:
Tensor: CrossEntropy loss.
"""
gcn_pred, gt_labels = gcn_data
gt_labels = gt_labels.reshape([-1])
loss = F.cross_entropy(gcn_pred, gt_labels)
return loss
def bitmasks2tensor(self, bitmasks, target_sz):
"""Convert Bitmasks to tensor.
Args:
bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is
for one img.
target_sz (tuple(int, int)): The target tensor of size
:math:`(H, W)`.
Returns:
list[Tensor]: The list of kernel tensors. Each element stands for
one kernel level.
"""
batch_size = len(bitmasks)
results = []
kernel = []
for batch_inx in range(batch_size):
mask = bitmasks[batch_inx]
# hxw
mask_sz = mask.shape
# left, right, top, bottom
pad = [0, target_sz[1] - mask_sz[1], 0, target_sz[0] - mask_sz[0]]
mask = F.pad(mask, pad, mode='constant', value=0)
kernel.append(mask)
kernel = paddle.stack(kernel)
results.append(kernel)
return results
def forward(self, preds, labels):
"""Compute Drrg loss.
"""
assert isinstance(preds, tuple)
gt_text_mask, gt_center_region_mask, gt_mask, gt_top_height_map, gt_bot_height_map, gt_sin_map, gt_cos_map = labels[
1:8]
downsample_ratio = self.downsample_ratio
pred_maps, gcn_data = preds
pred_text_region = pred_maps[:, 0, :, :]
pred_center_region = pred_maps[:, 1, :, :]
pred_sin_map = pred_maps[:, 2, :, :]
pred_cos_map = pred_maps[:, 3, :, :]
pred_top_height_map = pred_maps[:, 4, :, :]
pred_bot_height_map = pred_maps[:, 5, :, :]
feature_sz = pred_maps.shape
# bitmask 2 tensor
mapping = {
'gt_text_mask': paddle.cast(gt_text_mask, 'float32'),
'gt_center_region_mask':
paddle.cast(gt_center_region_mask, 'float32'),
'gt_mask': paddle.cast(gt_mask, 'float32'),
'gt_top_height_map': paddle.cast(gt_top_height_map, 'float32'),
'gt_bot_height_map': paddle.cast(gt_bot_height_map, 'float32'),
'gt_sin_map': paddle.cast(gt_sin_map, 'float32'),
'gt_cos_map': paddle.cast(gt_cos_map, 'float32')
}
gt = {}
for key, value in mapping.items():
gt[key] = value
if abs(downsample_ratio - 1.0) < 1e-2:
gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
else:
gt[key] = [item.rescale(downsample_ratio) for item in gt[key]]
gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
if key in ['gt_top_height_map', 'gt_bot_height_map']:
gt[key] = [item * downsample_ratio for item in gt[key]]
gt[key] = [item for item in gt[key]]
scale = paddle.sqrt(1.0 / (pred_sin_map**2 + pred_cos_map**2 + 1e-8))
pred_sin_map = pred_sin_map * scale
pred_cos_map = pred_cos_map * scale
loss_text = self.balance_bce_loss(
F.sigmoid(pred_text_region), gt['gt_text_mask'][0],
gt['gt_mask'][0])
text_mask = (gt['gt_text_mask'][0] * gt['gt_mask'][0])
negative_text_mask = ((1 - gt['gt_text_mask'][0]) * gt['gt_mask'][0])
loss_center_map = F.binary_cross_entropy(
F.sigmoid(pred_center_region),
gt['gt_center_region_mask'][0],
reduction='none')
if int(text_mask.sum()) > 0:
loss_center_positive = paddle.sum(loss_center_map *
text_mask) / paddle.sum(text_mask)
else:
loss_center_positive = paddle.to_tensor(0.0)
loss_center_negative = paddle.sum(
loss_center_map *
negative_text_mask) / paddle.sum(negative_text_mask)
loss_center = loss_center_positive + 0.5 * loss_center_negative
center_mask = (gt['gt_center_region_mask'][0] * gt['gt_mask'][0])
if int(center_mask.sum()) > 0:
map_sz = pred_top_height_map.shape
ones = paddle.ones(map_sz, dtype='float32')
loss_top = F.smooth_l1_loss(
pred_top_height_map / (gt['gt_top_height_map'][0] + 1e-2),
ones,
reduction='none')
loss_bot = F.smooth_l1_loss(
pred_bot_height_map / (gt['gt_bot_height_map'][0] + 1e-2),
ones,
reduction='none')
gt_height = (
gt['gt_top_height_map'][0] + gt['gt_bot_height_map'][0])
loss_height = paddle.sum(
(paddle.log(gt_height + 1) *
(loss_top + loss_bot)) * center_mask) / paddle.sum(center_mask)
loss_sin = paddle.sum(
F.smooth_l1_loss(
pred_sin_map, gt['gt_sin_map'][0],
reduction='none') * center_mask) / paddle.sum(center_mask)
loss_cos = paddle.sum(
F.smooth_l1_loss(
pred_cos_map, gt['gt_cos_map'][0],
reduction='none') * center_mask) / paddle.sum(center_mask)
else:
loss_height = paddle.to_tensor(0.0)
loss_sin = paddle.to_tensor(0.0)
loss_cos = paddle.to_tensor(0.0)
loss_gcn = self.gcn_loss(gcn_data)
loss = loss_text + loss_center + loss_height + loss_sin + loss_cos + loss_gcn
results = dict(
loss=loss,
loss_text=loss_text,
loss_center=loss_center,
loss_height=loss_height,
loss_sin=loss_sin,
loss_cos=loss_cos,
loss_gcn=loss_gcn)
return results