This commit is contained in:
318
ppocr/modeling/backbones/rec_resnet_31.py
Normal file
318
ppocr/modeling/backbones/rec_resnet_31.py
Normal file
@@ -0,0 +1,318 @@
|
||||
# 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 division
|
||||
from __future__ import print_function
|
||||
|
||||
import paddle
|
||||
from paddle import ParamAttr
|
||||
import paddle.nn as nn
|
||||
import paddle.nn.functional as F
|
||||
import numpy as np
|
||||
|
||||
__all__ = ["ResNet31"]
|
||||
|
||||
|
||||
def conv3x3(in_channel, out_channel, stride=1, conv_weight_attr=None):
|
||||
return nn.Conv2D(
|
||||
in_channel,
|
||||
out_channel,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
bias_attr=False,
|
||||
)
|
||||
|
||||
|
||||
class BasicBlock(nn.Layer):
|
||||
expansion = 1
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
channels,
|
||||
stride=1,
|
||||
downsample=False,
|
||||
conv_weight_attr=None,
|
||||
bn_weight_attr=None,
|
||||
):
|
||||
super().__init__()
|
||||
self.conv1 = conv3x3(
|
||||
in_channels, channels, stride, conv_weight_attr=conv_weight_attr
|
||||
)
|
||||
self.bn1 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
|
||||
self.relu = nn.ReLU()
|
||||
self.conv2 = conv3x3(channels, channels, conv_weight_attr=conv_weight_attr)
|
||||
self.bn2 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
|
||||
self.downsample = downsample
|
||||
if downsample:
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2D(
|
||||
in_channels,
|
||||
channels * self.expansion,
|
||||
1,
|
||||
stride,
|
||||
weight_attr=conv_weight_attr,
|
||||
bias_attr=False,
|
||||
),
|
||||
nn.BatchNorm2D(channels * self.expansion, weight_attr=bn_weight_attr),
|
||||
)
|
||||
else:
|
||||
self.downsample = nn.Sequential()
|
||||
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:
|
||||
residual = self.downsample(x)
|
||||
|
||||
out += residual
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet31(nn.Layer):
|
||||
"""
|
||||
Args:
|
||||
in_channels (int): Number of channels of input image tensor.
|
||||
layers (list[int]): List of BasicBlock number for each stage.
|
||||
channels (list[int]): List of out_channels of Conv2d layer.
|
||||
out_indices (None | Sequence[int]): Indices of output stages.
|
||||
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
|
||||
init_type (None | str): the config to control the initialization.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=3,
|
||||
layers=[1, 2, 5, 3],
|
||||
channels=[64, 128, 256, 256, 512, 512, 512],
|
||||
out_indices=None,
|
||||
last_stage_pool=False,
|
||||
init_type=None,
|
||||
):
|
||||
super(ResNet31, self).__init__()
|
||||
assert isinstance(in_channels, int)
|
||||
assert isinstance(last_stage_pool, bool)
|
||||
|
||||
self.out_indices = out_indices
|
||||
self.last_stage_pool = last_stage_pool
|
||||
|
||||
conv_weight_attr = None
|
||||
bn_weight_attr = None
|
||||
|
||||
if init_type is not None:
|
||||
support_dict = ["KaimingNormal"]
|
||||
assert init_type in support_dict, Exception(
|
||||
"resnet31 only support {}".format(support_dict)
|
||||
)
|
||||
conv_weight_attr = nn.initializer.KaimingNormal()
|
||||
bn_weight_attr = ParamAttr(
|
||||
initializer=nn.initializer.Uniform(), learning_rate=1
|
||||
)
|
||||
|
||||
# conv 1 (Conv Conv)
|
||||
self.conv1_1 = nn.Conv2D(
|
||||
in_channels,
|
||||
channels[0],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn1_1 = nn.BatchNorm2D(channels[0], weight_attr=bn_weight_attr)
|
||||
self.relu1_1 = nn.ReLU()
|
||||
|
||||
self.conv1_2 = nn.Conv2D(
|
||||
channels[0],
|
||||
channels[1],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn1_2 = nn.BatchNorm2D(channels[1], weight_attr=bn_weight_attr)
|
||||
self.relu1_2 = nn.ReLU()
|
||||
|
||||
# conv 2 (Max-pooling, Residual block, Conv)
|
||||
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],
|
||||
conv_weight_attr=conv_weight_attr,
|
||||
bn_weight_attr=bn_weight_attr,
|
||||
)
|
||||
self.conv2 = nn.Conv2D(
|
||||
channels[2],
|
||||
channels[2],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn2 = nn.BatchNorm2D(channels[2], weight_attr=bn_weight_attr)
|
||||
self.relu2 = nn.ReLU()
|
||||
|
||||
# conv 3 (Max-pooling, Residual block, Conv)
|
||||
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],
|
||||
conv_weight_attr=conv_weight_attr,
|
||||
bn_weight_attr=bn_weight_attr,
|
||||
)
|
||||
self.conv3 = nn.Conv2D(
|
||||
channels[3],
|
||||
channels[3],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn3 = nn.BatchNorm2D(channels[3], weight_attr=bn_weight_attr)
|
||||
self.relu3 = nn.ReLU()
|
||||
|
||||
# conv 4 (Max-pooling, Residual block, Conv)
|
||||
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],
|
||||
conv_weight_attr=conv_weight_attr,
|
||||
bn_weight_attr=bn_weight_attr,
|
||||
)
|
||||
self.conv4 = nn.Conv2D(
|
||||
channels[4],
|
||||
channels[4],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn4 = nn.BatchNorm2D(channels[4], weight_attr=bn_weight_attr)
|
||||
self.relu4 = nn.ReLU()
|
||||
|
||||
# conv 5 ((Max-pooling), Residual block, Conv)
|
||||
self.pool5 = None
|
||||
if self.last_stage_pool:
|
||||
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],
|
||||
conv_weight_attr=conv_weight_attr,
|
||||
bn_weight_attr=bn_weight_attr,
|
||||
)
|
||||
self.conv5 = nn.Conv2D(
|
||||
channels[5],
|
||||
channels[5],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
)
|
||||
self.bn5 = nn.BatchNorm2D(channels[5], weight_attr=bn_weight_attr)
|
||||
self.relu5 = nn.ReLU()
|
||||
|
||||
self.out_channels = channels[-1]
|
||||
|
||||
def _make_layer(
|
||||
self,
|
||||
input_channels,
|
||||
output_channels,
|
||||
blocks,
|
||||
conv_weight_attr=None,
|
||||
bn_weight_attr=None,
|
||||
):
|
||||
layers = []
|
||||
for _ in range(blocks):
|
||||
downsample = None
|
||||
if input_channels != output_channels:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2D(
|
||||
input_channels,
|
||||
output_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
weight_attr=conv_weight_attr,
|
||||
bias_attr=False,
|
||||
),
|
||||
nn.BatchNorm2D(output_channels, weight_attr=bn_weight_attr),
|
||||
)
|
||||
|
||||
layers.append(
|
||||
BasicBlock(
|
||||
input_channels,
|
||||
output_channels,
|
||||
downsample=downsample,
|
||||
conv_weight_attr=conv_weight_attr,
|
||||
bn_weight_attr=bn_weight_attr,
|
||||
)
|
||||
)
|
||||
input_channels = output_channels
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1_1(x)
|
||||
x = self.bn1_1(x)
|
||||
x = self.relu1_1(x)
|
||||
|
||||
x = self.conv1_2(x)
|
||||
x = self.bn1_2(x)
|
||||
x = self.relu1_2(x)
|
||||
|
||||
outs = []
|
||||
for i in range(4):
|
||||
layer_index = i + 2
|
||||
pool_layer = getattr(self, f"pool{layer_index}")
|
||||
block_layer = getattr(self, f"block{layer_index}")
|
||||
conv_layer = getattr(self, f"conv{layer_index}")
|
||||
bn_layer = getattr(self, f"bn{layer_index}")
|
||||
relu_layer = getattr(self, f"relu{layer_index}")
|
||||
|
||||
if pool_layer is not None:
|
||||
x = pool_layer(x)
|
||||
x = block_layer(x)
|
||||
x = conv_layer(x)
|
||||
x = bn_layer(x)
|
||||
x = relu_layer(x)
|
||||
|
||||
outs.append(x)
|
||||
|
||||
if self.out_indices is not None:
|
||||
return tuple([outs[i] for i in self.out_indices])
|
||||
|
||||
return x
|
||||
Reference in New Issue
Block a user