File size: 9,146 Bytes
a89d9fd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
# 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/fce_loss.py
"""
import numpy as np
from paddle import nn
import paddle
import paddle.nn.functional as F
from functools import partial
def multi_apply(func, *args, **kwargs):
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
return tuple(map(list, zip(*map_results)))
class FCELoss(nn.Layer):
"""The class for implementing FCENet loss
FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped
Text Detection
[https://arxiv.org/abs/2104.10442]
Args:
fourier_degree (int) : The maximum Fourier transform degree k.
num_sample (int) : The sampling points number of regression
loss. If it is too small, fcenet tends to be overfitting.
ohem_ratio (float): the negative/positive ratio in OHEM.
"""
def __init__(self, fourier_degree, num_sample, ohem_ratio=3.):
super().__init__()
self.fourier_degree = fourier_degree
self.num_sample = num_sample
self.ohem_ratio = ohem_ratio
def forward(self, preds, labels):
assert isinstance(preds, dict)
preds = preds['levels']
p3_maps, p4_maps, p5_maps = labels[1:]
assert p3_maps[0].shape[0] == 4 * self.fourier_degree + 5,\
'fourier degree not equal in FCEhead and FCEtarget'
# to tensor
gts = [p3_maps, p4_maps, p5_maps]
for idx, maps in enumerate(gts):
gts[idx] = paddle.to_tensor(np.stack(maps))
losses = multi_apply(self.forward_single, preds, gts)
loss_tr = paddle.to_tensor(0.).astype('float32')
loss_tcl = paddle.to_tensor(0.).astype('float32')
loss_reg_x = paddle.to_tensor(0.).astype('float32')
loss_reg_y = paddle.to_tensor(0.).astype('float32')
loss_all = paddle.to_tensor(0.).astype('float32')
for idx, loss in enumerate(losses):
loss_all += sum(loss)
if idx == 0:
loss_tr += sum(loss)
elif idx == 1:
loss_tcl += sum(loss)
elif idx == 2:
loss_reg_x += sum(loss)
else:
loss_reg_y += sum(loss)
results = dict(
loss=loss_all,
loss_text=loss_tr,
loss_center=loss_tcl,
loss_reg_x=loss_reg_x,
loss_reg_y=loss_reg_y, )
return results
def forward_single(self, pred, gt):
cls_pred = paddle.transpose(pred[0], (0, 2, 3, 1))
reg_pred = paddle.transpose(pred[1], (0, 2, 3, 1))
gt = paddle.transpose(gt, (0, 2, 3, 1))
k = 2 * self.fourier_degree + 1
tr_pred = paddle.reshape(cls_pred[:, :, :, :2], (-1, 2))
tcl_pred = paddle.reshape(cls_pred[:, :, :, 2:], (-1, 2))
x_pred = paddle.reshape(reg_pred[:, :, :, 0:k], (-1, k))
y_pred = paddle.reshape(reg_pred[:, :, :, k:2 * k], (-1, k))
tr_mask = gt[:, :, :, :1].reshape([-1])
tcl_mask = gt[:, :, :, 1:2].reshape([-1])
train_mask = gt[:, :, :, 2:3].reshape([-1])
x_map = paddle.reshape(gt[:, :, :, 3:3 + k], (-1, k))
y_map = paddle.reshape(gt[:, :, :, 3 + k:], (-1, k))
tr_train_mask = (train_mask * tr_mask).astype('bool')
tr_train_mask2 = paddle.concat(
[tr_train_mask.unsqueeze(1), tr_train_mask.unsqueeze(1)], axis=1)
# tr loss
loss_tr = self.ohem(tr_pred, tr_mask, train_mask)
# tcl loss
loss_tcl = paddle.to_tensor(0.).astype('float32')
tr_neg_mask = tr_train_mask.logical_not()
tr_neg_mask2 = paddle.concat(
[tr_neg_mask.unsqueeze(1), tr_neg_mask.unsqueeze(1)], axis=1)
if tr_train_mask.sum().item() > 0:
loss_tcl_pos = F.cross_entropy(
tcl_pred.masked_select(tr_train_mask2).reshape([-1, 2]),
tcl_mask.masked_select(tr_train_mask).astype('int64'))
loss_tcl_neg = F.cross_entropy(
tcl_pred.masked_select(tr_neg_mask2).reshape([-1, 2]),
tcl_mask.masked_select(tr_neg_mask).astype('int64'))
loss_tcl = loss_tcl_pos + 0.5 * loss_tcl_neg
# regression loss
loss_reg_x = paddle.to_tensor(0.).astype('float32')
loss_reg_y = paddle.to_tensor(0.).astype('float32')
if tr_train_mask.sum().item() > 0:
weight = (tr_mask.masked_select(tr_train_mask.astype('bool'))
.astype('float32') + tcl_mask.masked_select(
tr_train_mask.astype('bool')).astype('float32')) / 2
weight = weight.reshape([-1, 1])
ft_x, ft_y = self.fourier2poly(x_map, y_map)
ft_x_pre, ft_y_pre = self.fourier2poly(x_pred, y_pred)
dim = ft_x.shape[1]
tr_train_mask3 = paddle.concat(
[tr_train_mask.unsqueeze(1) for i in range(dim)], axis=1)
loss_reg_x = paddle.mean(weight * F.smooth_l1_loss(
ft_x_pre.masked_select(tr_train_mask3).reshape([-1, dim]),
ft_x.masked_select(tr_train_mask3).reshape([-1, dim]),
reduction='none'))
loss_reg_y = paddle.mean(weight * F.smooth_l1_loss(
ft_y_pre.masked_select(tr_train_mask3).reshape([-1, dim]),
ft_y.masked_select(tr_train_mask3).reshape([-1, dim]),
reduction='none'))
return loss_tr, loss_tcl, loss_reg_x, loss_reg_y
def ohem(self, predict, target, train_mask):
pos = (target * train_mask).astype('bool')
neg = ((1 - target) * train_mask).astype('bool')
pos2 = paddle.concat([pos.unsqueeze(1), pos.unsqueeze(1)], axis=1)
neg2 = paddle.concat([neg.unsqueeze(1), neg.unsqueeze(1)], axis=1)
n_pos = pos.astype('float32').sum()
if n_pos.item() > 0:
loss_pos = F.cross_entropy(
predict.masked_select(pos2).reshape([-1, 2]),
target.masked_select(pos).astype('int64'),
reduction='sum')
loss_neg = F.cross_entropy(
predict.masked_select(neg2).reshape([-1, 2]),
target.masked_select(neg).astype('int64'),
reduction='none')
n_neg = min(
int(neg.astype('float32').sum().item()),
int(self.ohem_ratio * n_pos.astype('float32')))
else:
loss_pos = paddle.to_tensor(0.)
loss_neg = F.cross_entropy(
predict.masked_select(neg2).reshape([-1, 2]),
target.masked_select(neg).astype('int64'),
reduction='none')
n_neg = 100
if len(loss_neg) > n_neg:
loss_neg, _ = paddle.topk(loss_neg, n_neg)
return (loss_pos + loss_neg.sum()) / (n_pos + n_neg).astype('float32')
def fourier2poly(self, real_maps, imag_maps):
"""Transform Fourier coefficient maps to polygon maps.
Args:
real_maps (tensor): A map composed of the real parts of the
Fourier coefficients, whose shape is (-1, 2k+1)
imag_maps (tensor):A map composed of the imag parts of the
Fourier coefficients, whose shape is (-1, 2k+1)
Returns
x_maps (tensor): A map composed of the x value of the polygon
represented by n sample points (xn, yn), whose shape is (-1, n)
y_maps (tensor): A map composed of the y value of the polygon
represented by n sample points (xn, yn), whose shape is (-1, n)
"""
k_vect = paddle.arange(
-self.fourier_degree, self.fourier_degree + 1,
dtype='float32').reshape([-1, 1])
i_vect = paddle.arange(
0, self.num_sample, dtype='float32').reshape([1, -1])
transform_matrix = 2 * np.pi / self.num_sample * paddle.matmul(k_vect,
i_vect)
x1 = paddle.einsum('ak, kn-> an', real_maps,
paddle.cos(transform_matrix))
x2 = paddle.einsum('ak, kn-> an', imag_maps,
paddle.sin(transform_matrix))
y1 = paddle.einsum('ak, kn-> an', real_maps,
paddle.sin(transform_matrix))
y2 = paddle.einsum('ak, kn-> an', imag_maps,
paddle.cos(transform_matrix))
x_maps = x1 - x2
y_maps = y1 + y2
return x_maps, y_maps
|