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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/1.x/mmocr/models/textrecog/backbones/shallow_cnn.py | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import math | |
| import numpy as np | |
| import paddle | |
| from paddle import ParamAttr | |
| import paddle.nn as nn | |
| import paddle.nn.functional as F | |
| from paddle.nn import MaxPool2D | |
| from paddle.nn.initializer import KaimingNormal, Uniform, Constant | |
| class ConvBNLayer(nn.Layer): | |
| def __init__(self, | |
| num_channels, | |
| filter_size, | |
| num_filters, | |
| stride, | |
| padding, | |
| num_groups=1): | |
| super(ConvBNLayer, self).__init__() | |
| self.conv = nn.Conv2D( | |
| in_channels=num_channels, | |
| out_channels=num_filters, | |
| kernel_size=filter_size, | |
| stride=stride, | |
| padding=padding, | |
| groups=num_groups, | |
| weight_attr=ParamAttr(initializer=KaimingNormal()), | |
| bias_attr=False) | |
| self.bn = nn.BatchNorm2D( | |
| num_filters, | |
| weight_attr=ParamAttr(initializer=Uniform(0, 1)), | |
| bias_attr=ParamAttr(initializer=Constant(0))) | |
| self.relu = nn.ReLU() | |
| def forward(self, inputs): | |
| y = self.conv(inputs) | |
| y = self.bn(y) | |
| y = self.relu(y) | |
| return y | |
| class ShallowCNN(nn.Layer): | |
| def __init__(self, in_channels=1, hidden_dim=512): | |
| super().__init__() | |
| assert isinstance(in_channels, int) | |
| assert isinstance(hidden_dim, int) | |
| self.conv1 = ConvBNLayer( | |
| in_channels, 3, hidden_dim // 2, stride=1, padding=1) | |
| self.conv2 = ConvBNLayer( | |
| hidden_dim // 2, 3, hidden_dim, stride=1, padding=1) | |
| self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) | |
| self.out_channels = hidden_dim | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.pool(x) | |
| x = self.conv2(x) | |
| x = self.pool(x) | |
| return x | |