100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/dense_heads/fce_head.py
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"""
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from paddle import nn
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from paddle import ParamAttr
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import paddle.nn.functional as F
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from paddle.nn.initializer import Normal
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import paddle
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from functools import partial
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def multi_apply(func, *args, **kwargs):
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pfunc = partial(func, **kwargs) if kwargs else func
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map_results = map(pfunc, *args)
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return tuple(map(list, zip(*map_results)))
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class FCEHead(nn.Layer):
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"""The class for implementing FCENet head.
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FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text
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Detection.
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[https://arxiv.org/abs/2104.10442]
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Args:
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in_channels (int): The number of input channels.
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scales (list[int]) : The scale of each layer.
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fourier_degree (int) : The maximum Fourier transform degree k.
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"""
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def __init__(self, in_channels, fourier_degree=5):
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super().__init__()
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assert isinstance(in_channels, int)
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self.downsample_ratio = 1.0
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self.in_channels = in_channels
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self.fourier_degree = fourier_degree
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self.out_channels_cls = 4
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self.out_channels_reg = (2 * self.fourier_degree + 1) * 2
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self.out_conv_cls = nn.Conv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels_cls,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=1,
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weight_attr=ParamAttr(
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name='cls_weights',
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initializer=Normal(
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mean=0., std=0.01)),
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bias_attr=True)
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self.out_conv_reg = nn.Conv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels_reg,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=1,
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weight_attr=ParamAttr(
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name='reg_weights',
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initializer=Normal(
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mean=0., std=0.01)),
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bias_attr=True)
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def forward(self, feats, targets=None):
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cls_res, reg_res = multi_apply(self.forward_single, feats)
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level_num = len(cls_res)
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outs = {}
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if not self.training:
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for i in range(level_num):
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tr_pred = F.softmax(cls_res[i][:, 0:2, :, :], axis=1)
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tcl_pred = F.softmax(cls_res[i][:, 2:, :, :], axis=1)
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outs['level_{}'.format(i)] = paddle.concat(
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[tr_pred, tcl_pred, reg_res[i]], axis=1)
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else:
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preds = [[cls_res[i], reg_res[i]] for i in range(level_num)]
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outs['levels'] = preds
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return outs
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def forward_single(self, x):
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cls_predict = self.out_conv_cls(x)
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reg_predict = self.out_conv_reg(x)
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return cls_predict, reg_predict
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