319 lines
9.6 KiB
Python
319 lines
9.6 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/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/layers/conv_layer.py
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https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/backbones/resnet31_ocr.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 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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import numpy as np
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__all__ = ["ResNet31"]
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def conv3x3(in_channel, out_channel, stride=1, conv_weight_attr=None):
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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=conv_weight_attr,
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bias_attr=False,
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)
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class BasicBlock(nn.Layer):
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expansion = 1
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def __init__(
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self,
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in_channels,
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channels,
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stride=1,
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downsample=False,
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conv_weight_attr=None,
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bn_weight_attr=None,
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):
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super().__init__()
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self.conv1 = conv3x3(
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in_channels, channels, stride, conv_weight_attr=conv_weight_attr
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)
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self.bn1 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
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self.relu = nn.ReLU()
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self.conv2 = conv3x3(channels, channels, conv_weight_attr=conv_weight_attr)
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self.bn2 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
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self.downsample = downsample
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if downsample:
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self.downsample = nn.Sequential(
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nn.Conv2D(
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in_channels,
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channels * self.expansion,
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1,
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stride,
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weight_attr=conv_weight_attr,
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bias_attr=False,
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),
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nn.BatchNorm2D(channels * self.expansion, weight_attr=bn_weight_attr),
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)
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else:
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self.downsample = nn.Sequential()
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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:
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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 ResNet31(nn.Layer):
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"""
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Args:
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in_channels (int): Number of channels of input image tensor.
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layers (list[int]): List of BasicBlock number for each stage.
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channels (list[int]): List of out_channels of Conv2d layer.
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out_indices (None | Sequence[int]): Indices of output stages.
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last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
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init_type (None | str): the config to control the initialization.
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"""
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def __init__(
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self,
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in_channels=3,
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layers=[1, 2, 5, 3],
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channels=[64, 128, 256, 256, 512, 512, 512],
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out_indices=None,
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last_stage_pool=False,
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init_type=None,
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):
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super(ResNet31, self).__init__()
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assert isinstance(in_channels, int)
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assert isinstance(last_stage_pool, bool)
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self.out_indices = out_indices
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self.last_stage_pool = last_stage_pool
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conv_weight_attr = None
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bn_weight_attr = None
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if init_type is not None:
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support_dict = ["KaimingNormal"]
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assert init_type in support_dict, Exception(
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"resnet31 only support {}".format(support_dict)
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)
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conv_weight_attr = nn.initializer.KaimingNormal()
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bn_weight_attr = ParamAttr(
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initializer=nn.initializer.Uniform(), learning_rate=1
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)
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# conv 1 (Conv Conv)
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self.conv1_1 = nn.Conv2D(
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in_channels,
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channels[0],
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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=conv_weight_attr,
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)
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self.bn1_1 = nn.BatchNorm2D(channels[0], weight_attr=bn_weight_attr)
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self.relu1_1 = nn.ReLU()
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self.conv1_2 = nn.Conv2D(
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channels[0],
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channels[1],
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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=conv_weight_attr,
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)
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self.bn1_2 = nn.BatchNorm2D(channels[1], weight_attr=bn_weight_attr)
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self.relu1_2 = nn.ReLU()
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# conv 2 (Max-pooling, Residual block, Conv)
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self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self.block2 = self._make_layer(
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channels[1],
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channels[2],
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layers[0],
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conv_weight_attr=conv_weight_attr,
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bn_weight_attr=bn_weight_attr,
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)
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self.conv2 = nn.Conv2D(
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channels[2],
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channels[2],
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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=conv_weight_attr,
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)
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self.bn2 = nn.BatchNorm2D(channels[2], weight_attr=bn_weight_attr)
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self.relu2 = nn.ReLU()
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# conv 3 (Max-pooling, Residual block, Conv)
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self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self.block3 = self._make_layer(
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channels[2],
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channels[3],
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layers[1],
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conv_weight_attr=conv_weight_attr,
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bn_weight_attr=bn_weight_attr,
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)
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self.conv3 = nn.Conv2D(
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channels[3],
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channels[3],
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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=conv_weight_attr,
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)
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self.bn3 = nn.BatchNorm2D(channels[3], weight_attr=bn_weight_attr)
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self.relu3 = nn.ReLU()
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# conv 4 (Max-pooling, Residual block, Conv)
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self.pool4 = nn.MaxPool2D(
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kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True
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)
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self.block4 = self._make_layer(
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channels[3],
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channels[4],
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layers[2],
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conv_weight_attr=conv_weight_attr,
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bn_weight_attr=bn_weight_attr,
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)
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self.conv4 = nn.Conv2D(
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channels[4],
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channels[4],
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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=conv_weight_attr,
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)
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self.bn4 = nn.BatchNorm2D(channels[4], weight_attr=bn_weight_attr)
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self.relu4 = nn.ReLU()
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# conv 5 ((Max-pooling), Residual block, Conv)
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self.pool5 = None
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if self.last_stage_pool:
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self.pool5 = nn.MaxPool2D(
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kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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self.block5 = self._make_layer(
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channels[4],
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channels[5],
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layers[3],
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conv_weight_attr=conv_weight_attr,
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bn_weight_attr=bn_weight_attr,
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)
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self.conv5 = nn.Conv2D(
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channels[5],
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channels[5],
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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=conv_weight_attr,
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)
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self.bn5 = nn.BatchNorm2D(channels[5], weight_attr=bn_weight_attr)
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self.relu5 = nn.ReLU()
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self.out_channels = channels[-1]
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def _make_layer(
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self,
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input_channels,
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output_channels,
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blocks,
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conv_weight_attr=None,
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bn_weight_attr=None,
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):
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layers = []
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for _ in range(blocks):
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downsample = None
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if input_channels != output_channels:
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downsample = nn.Sequential(
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nn.Conv2D(
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input_channels,
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output_channels,
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kernel_size=1,
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stride=1,
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weight_attr=conv_weight_attr,
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bias_attr=False,
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),
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nn.BatchNorm2D(output_channels, weight_attr=bn_weight_attr),
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)
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layers.append(
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BasicBlock(
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input_channels,
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output_channels,
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downsample=downsample,
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conv_weight_attr=conv_weight_attr,
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bn_weight_attr=bn_weight_attr,
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)
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)
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input_channels = output_channels
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1_1(x)
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x = self.bn1_1(x)
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x = self.relu1_1(x)
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x = self.conv1_2(x)
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x = self.bn1_2(x)
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x = self.relu1_2(x)
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outs = []
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for i in range(4):
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layer_index = i + 2
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pool_layer = getattr(self, f"pool{layer_index}")
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block_layer = getattr(self, f"block{layer_index}")
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conv_layer = getattr(self, f"conv{layer_index}")
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bn_layer = getattr(self, f"bn{layer_index}")
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relu_layer = getattr(self, f"relu{layer_index}")
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if pool_layer is not None:
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x = pool_layer(x)
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x = block_layer(x)
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x = conv_layer(x)
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x = bn_layer(x)
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x = relu_layer(x)
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outs.append(x)
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if self.out_indices is not None:
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return tuple([outs[i] for i in self.out_indices])
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return x
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