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