mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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671415bbd8
* add rec vitstr algorithm. * fix cpu_thread and precision * fix svtr tipc * modify vitstr name * modify vitstr config batchsize * [New Rec] add vitstr and ABINet * add rec_resnet45 * svtr ch large model * [application] svtr ch model * [application] svtr ch model * [application] svtr ch model * add abinet_rec_aug and trained model * aug p infe * fix ci export bug * fix abinet ci bug
148 lines
4.4 KiB
Python
148 lines
4.4 KiB
Python
# copyright (c) 2021 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/FangShancheng/ABINet/tree/main/modules
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import ParamAttr
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from paddle.nn.initializer import KaimingNormal
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import paddle.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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import math
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__all__ = ["ResNet45"]
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def conv1x1(in_planes, out_planes, stride=1):
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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=1,
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stride=1,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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def conv3x3(in_channel, out_channel, stride=1):
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return nn.Conv2D(
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in_channel,
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out_channel,
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kernel_size=3,
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stride=stride,
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padding=1,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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class BasicBlock(nn.Layer):
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expansion = 1
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def __init__(self, in_channels, channels, stride=1, downsample=None):
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super().__init__()
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self.conv1 = conv1x1(in_channels, channels)
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self.bn1 = nn.BatchNorm2D(channels)
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self.relu = nn.ReLU()
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self.conv2 = conv3x3(channels, channels, stride)
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self.bn2 = nn.BatchNorm2D(channels)
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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 ResNet45(nn.Layer):
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def __init__(self, block=BasicBlock, layers=[3, 4, 6, 6, 3], in_channels=3):
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self.inplanes = 32
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super(ResNet45, self).__init__()
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self.conv1 = nn.Conv2D(
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3,
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32,
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kernel_size=3,
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stride=1,
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padding=1,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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self.bn1 = nn.BatchNorm2D(32)
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self.relu = nn.ReLU()
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self.layer1 = self._make_layer(block, 32, layers[0], stride=2)
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self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
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self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
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self.layer5 = self._make_layer(block, 512, layers[4], stride=1)
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self.out_channels = 512
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# for m in self.modules():
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# if isinstance(m, nn.Conv2D):
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# n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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# m.weight.data.normal_(0, math.sqrt(2. / n))
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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# downsample = True
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downsample = nn.Sequential(
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nn.Conv2D(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False),
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nn.BatchNorm2D(planes * block.expansion), )
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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# print(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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# print(x)
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x = self.layer4(x)
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x = self.layer5(x)
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return x
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