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# 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/dense_heads/fce_head.py
"""
from paddle import nn
from paddle import ParamAttr
import paddle.nn.functional as F
from paddle.nn.initializer import Normal
import paddle
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 FCEHead(nn.Layer):
"""The class for implementing FCENet head.
FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text
Detection.
[https://arxiv.org/abs/2104.10442]
Args:
in_channels (int): The number of input channels.
scales (list[int]) : The scale of each layer.
fourier_degree (int) : The maximum Fourier transform degree k.
"""
def __init__(self, in_channels, fourier_degree=5):
super().__init__()
assert isinstance(in_channels, int)
self.downsample_ratio = 1.0
self.in_channels = in_channels
self.fourier_degree = fourier_degree
self.out_channels_cls = 4
self.out_channels_reg = (2 * self.fourier_degree + 1) * 2
self.out_conv_cls = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.out_channels_cls,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(
name='cls_weights',
initializer=Normal(
mean=0., std=0.01)),
bias_attr=True)
self.out_conv_reg = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.out_channels_reg,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(
name='reg_weights',
initializer=Normal(
mean=0., std=0.01)),
bias_attr=True)
def forward(self, feats, targets=None):
cls_res, reg_res = multi_apply(self.forward_single, feats)
level_num = len(cls_res)
outs = {}
if not self.training:
for i in range(level_num):
tr_pred = F.softmax(cls_res[i][:, 0:2, :, :], axis=1)
tcl_pred = F.softmax(cls_res[i][:, 2:, :, :], axis=1)
outs['level_{}'.format(i)] = paddle.concat(
[tr_pred, tcl_pred, reg_res[i]], axis=1)
else:
preds = [[cls_res[i], reg_res[i]] for i in range(level_num)]
outs['levels'] = preds
return outs
def forward_single(self, x):
cls_predict = self.out_conv_cls(x)
reg_predict = self.out_conv_reg(x)
return cls_predict, reg_predict
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