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