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| # copyright (c) 2020 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/ayumiymk/aster.pytorch/blob/master/lib/models/resnet_aster.py | |
| """ | |
| import paddle | |
| import paddle.nn as nn | |
| import sys | |
| import math | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2D( | |
| in_planes, | |
| out_planes, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=1, | |
| bias_attr=False) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2D( | |
| in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False) | |
| def get_sinusoid_encoding(n_position, feat_dim, wave_length=10000): | |
| # [n_position] | |
| positions = paddle.arange(0, n_position) | |
| # [feat_dim] | |
| dim_range = paddle.arange(0, feat_dim) | |
| dim_range = paddle.pow(wave_length, 2 * (dim_range // 2) / feat_dim) | |
| # [n_position, feat_dim] | |
| angles = paddle.unsqueeze( | |
| positions, axis=1) / paddle.unsqueeze( | |
| dim_range, axis=0) | |
| angles = paddle.cast(angles, "float32") | |
| angles[:, 0::2] = paddle.sin(angles[:, 0::2]) | |
| angles[:, 1::2] = paddle.cos(angles[:, 1::2]) | |
| return angles | |
| class AsterBlock(nn.Layer): | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(AsterBlock, self).__init__() | |
| self.conv1 = conv1x1(inplanes, planes, stride) | |
| self.bn1 = nn.BatchNorm2D(planes) | |
| self.relu = nn.ReLU() | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm2D(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet_ASTER(nn.Layer): | |
| """For aster or crnn""" | |
| def __init__(self, with_lstm=True, n_group=1, in_channels=3): | |
| super(ResNet_ASTER, self).__init__() | |
| self.with_lstm = with_lstm | |
| self.n_group = n_group | |
| self.layer0 = nn.Sequential( | |
| nn.Conv2D( | |
| in_channels, | |
| 32, | |
| kernel_size=(3, 3), | |
| stride=1, | |
| padding=1, | |
| bias_attr=False), | |
| nn.BatchNorm2D(32), | |
| nn.ReLU()) | |
| self.inplanes = 32 | |
| self.layer1 = self._make_layer(32, 3, [2, 2]) # [16, 50] | |
| self.layer2 = self._make_layer(64, 4, [2, 2]) # [8, 25] | |
| self.layer3 = self._make_layer(128, 6, [2, 1]) # [4, 25] | |
| self.layer4 = self._make_layer(256, 6, [2, 1]) # [2, 25] | |
| self.layer5 = self._make_layer(512, 3, [2, 1]) # [1, 25] | |
| if with_lstm: | |
| self.rnn = nn.LSTM(512, 256, direction="bidirect", num_layers=2) | |
| self.out_channels = 2 * 256 | |
| else: | |
| self.out_channels = 512 | |
| def _make_layer(self, planes, blocks, stride): | |
| downsample = None | |
| if stride != [1, 1] or self.inplanes != planes: | |
| downsample = nn.Sequential( | |
| conv1x1(self.inplanes, planes, stride), nn.BatchNorm2D(planes)) | |
| layers = [] | |
| layers.append(AsterBlock(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes | |
| for _ in range(1, blocks): | |
| layers.append(AsterBlock(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x0 = self.layer0(x) | |
| x1 = self.layer1(x0) | |
| x2 = self.layer2(x1) | |
| x3 = self.layer3(x2) | |
| x4 = self.layer4(x3) | |
| x5 = self.layer5(x4) | |
| cnn_feat = x5.squeeze(2) # [N, c, w] | |
| cnn_feat = paddle.transpose(cnn_feat, perm=[0, 2, 1]) | |
| if self.with_lstm: | |
| rnn_feat, _ = self.rnn(cnn_feat) | |
| return rnn_feat | |
| else: | |
| return cnn_feat |