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Merge pull request #4547 from andyjpaddle/add_ref_for_sar

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add refer to backbone and head of sar
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MissPenguin 2021-11-04 19:39:23 +08:00 committed by GitHub
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2 changed files with 88 additions and 35 deletions
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104
ppocr/modeling/backbones/rec_resnet_31.py
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@ -1,3 +1,22 @@
# copyright (c) 2021 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/main/mmocr/models/textrecog/layers/conv_layer.py
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/backbones/resnet31_ocr.py
"""
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
@ -18,12 +37,12 @@ def conv3x3(in_channel, out_channel, stride=1):
kernel_size=3, kernel_size=3,
stride=stride, stride=stride,
padding=1, padding=1,
bias_attr=False bias_attr=False)
)
class BasicBlock(nn.Layer): class BasicBlock(nn.Layer):
expansion = 1 expansion = 1
def __init__(self, in_channels, channels, stride=1, downsample=False): def __init__(self, in_channels, channels, stride=1, downsample=False):
super().__init__() super().__init__()
self.conv1 = conv3x3(in_channels, channels, stride) self.conv1 = conv3x3(in_channels, channels, stride)
@ -34,9 +53,13 @@ class BasicBlock(nn.Layer):
self.downsample = downsample self.downsample = downsample
if downsample: if downsample:
self.downsample = nn.Sequential( self.downsample = nn.Sequential(
nn.Conv2D(in_channels, channels * self.expansion, 1, stride, bias_attr=False), nn.Conv2D(
nn.BatchNorm2D(channels * self.expansion), in_channels,
) channels * self.expansion,
1,
stride,
bias_attr=False),
nn.BatchNorm2D(channels * self.expansion), )
else: else:
self.downsample = nn.Sequential() self.downsample = nn.Sequential()
self.stride = stride self.stride = stride
@ -57,7 +80,7 @@ class BasicBlock(nn.Layer):
out += residual out += residual
out = self.relu(out) out = self.relu(out)
return out return out
class ResNet31(nn.Layer): class ResNet31(nn.Layer):
@ -69,12 +92,13 @@ class ResNet31(nn.Layer):
out_indices (None | Sequence[int]): Indices of output stages. out_indices (None | Sequence[int]): Indices of output stages.
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
''' '''
def __init__(self,
in_channels=3, def __init__(self,
layers=[1, 2, 5, 3], in_channels=3,
channels=[64, 128, 256, 256, 512, 512, 512], layers=[1, 2, 5, 3],
out_indices=None, channels=[64, 128, 256, 256, 512, 512, 512],
last_stage_pool=False): out_indices=None,
last_stage_pool=False):
super(ResNet31, self).__init__() super(ResNet31, self).__init__()
assert isinstance(in_channels, int) assert isinstance(in_channels, int)
assert isinstance(last_stage_pool, bool) assert isinstance(last_stage_pool, bool)
@ -83,46 +107,56 @@ class ResNet31(nn.Layer):
self.last_stage_pool = last_stage_pool self.last_stage_pool = last_stage_pool
# conv 1 (Conv Conv) # conv 1 (Conv Conv)
self.conv1_1 = nn.Conv2D(in_channels, channels[0], kernel_size=3, stride=1, padding=1) self.conv1_1 = nn.Conv2D(
in_channels, channels[0], kernel_size=3, stride=1, padding=1)
self.bn1_1 = nn.BatchNorm2D(channels[0]) self.bn1_1 = nn.BatchNorm2D(channels[0])
self.relu1_1 = nn.ReLU() self.relu1_1 = nn.ReLU()
self.conv1_2 = nn.Conv2D(channels[0], channels[1], kernel_size=3, stride=1, padding=1) self.conv1_2 = nn.Conv2D(
channels[0], channels[1], kernel_size=3, stride=1, padding=1)
self.bn1_2 = nn.BatchNorm2D(channels[1]) self.bn1_2 = nn.BatchNorm2D(channels[1])
self.relu1_2 = nn.ReLU() self.relu1_2 = nn.ReLU()
# conv 2 (Max-pooling, Residual block, Conv) # conv 2 (Max-pooling, Residual block, Conv)
self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) self.pool2 = nn.MaxPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block2 = self._make_layer(channels[1], channels[2], layers[0]) self.block2 = self._make_layer(channels[1], channels[2], layers[0])
self.conv2 = nn.Conv2D(channels[2], channels[2], kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2D(
channels[2], channels[2], kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2D(channels[2]) self.bn2 = nn.BatchNorm2D(channels[2])
self.relu2 = nn.ReLU() self.relu2 = nn.ReLU()
# conv 3 (Max-pooling, Residual block, Conv) # conv 3 (Max-pooling, Residual block, Conv)
self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) self.pool3 = nn.MaxPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block3 = self._make_layer(channels[2], channels[3], layers[1]) self.block3 = self._make_layer(channels[2], channels[3], layers[1])
self.conv3 = nn.Conv2D(channels[3], channels[3], kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2D(
channels[3], channels[3], kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2D(channels[3]) self.bn3 = nn.BatchNorm2D(channels[3])
self.relu3 = nn.ReLU() self.relu3 = nn.ReLU()
# conv 4 (Max-pooling, Residual block, Conv) # conv 4 (Max-pooling, Residual block, Conv)
self.pool4 = nn.MaxPool2D(kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True) self.pool4 = nn.MaxPool2D(
kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True)
self.block4 = self._make_layer(channels[3], channels[4], layers[2]) self.block4 = self._make_layer(channels[3], channels[4], layers[2])
self.conv4 = nn.Conv2D(channels[4], channels[4], kernel_size=3, stride=1, padding=1) self.conv4 = nn.Conv2D(
channels[4], channels[4], kernel_size=3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2D(channels[4]) self.bn4 = nn.BatchNorm2D(channels[4])
self.relu4 = nn.ReLU() self.relu4 = nn.ReLU()
# conv 5 ((Max-pooling), Residual block, Conv) # conv 5 ((Max-pooling), Residual block, Conv)
self.pool5 = None self.pool5 = None
if self.last_stage_pool: if self.last_stage_pool:
self.pool5 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) self.pool5 = nn.MaxPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block5 = self._make_layer(channels[4], channels[5], layers[3]) self.block5 = self._make_layer(channels[4], channels[5], layers[3])
self.conv5 = nn.Conv2D(channels[5], channels[5], kernel_size=3, stride=1, padding=1) self.conv5 = nn.Conv2D(
channels[5], channels[5], kernel_size=3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2D(channels[5]) self.bn5 = nn.BatchNorm2D(channels[5])
self.relu5 = nn.ReLU() self.relu5 = nn.ReLU()
self.out_channels = channels[-1] self.out_channels = channels[-1]
def _make_layer(self, input_channels, output_channels, blocks): def _make_layer(self, input_channels, output_channels, blocks):
layers = [] layers = []
for _ in range(blocks): for _ in range(blocks):
@ -130,19 +164,19 @@ class ResNet31(nn.Layer):
if input_channels != output_channels: if input_channels != output_channels:
downsample = nn.Sequential( downsample = nn.Sequential(
nn.Conv2D( nn.Conv2D(
input_channels, input_channels,
output_channels, output_channels,
kernel_size=1, kernel_size=1,
stride=1, stride=1,
bias_attr=False), bias_attr=False),
nn.BatchNorm2D(output_channels), nn.BatchNorm2D(output_channels), )
)
layers.append(
layers.append(BasicBlock(input_channels, output_channels, downsample=downsample)) BasicBlock(
input_channels, output_channels, downsample=downsample))
input_channels = output_channels input_channels = output_channels
return nn.Sequential(*layers) return nn.Sequential(*layers)
def forward(self, x): def forward(self, x):
x = self.conv1_1(x) x = self.conv1_1(x)
x = self.bn1_1(x) x = self.bn1_1(x)
@ -166,11 +200,11 @@ class ResNet31(nn.Layer):
x = block_layer(x) x = block_layer(x)
x = conv_layer(x) x = conv_layer(x)
x = bn_layer(x) x = bn_layer(x)
x= relu_layer(x) x = relu_layer(x)
outs.append(x) outs.append(x)
if self.out_indices is not None: if self.out_indices is not None:
return tuple([outs[i] for i in self.out_indices]) return tuple([outs[i] for i in self.out_indices])
return x return x

19
ppocr/modeling/heads/rec_sar_head.py
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@ -1,3 +1,22 @@
# copyright (c) 2021 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/main/mmocr/models/textrecog/encoders/sar_encoder.py
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/decoders/sar_decoder.py
"""
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function