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