mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-10-30 17:29:13 +00:00
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
# 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/whai362/PSENet/blob/python3/models/neck/fpn.py
|
|
"""
|
|
|
|
import paddle.nn as nn
|
|
import paddle
|
|
import math
|
|
import paddle.nn.functional as F
|
|
|
|
|
|
class Conv_BN_ReLU(nn.Layer):
|
|
def __init__(self,
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0):
|
|
super(Conv_BN_ReLU, self).__init__()
|
|
self.conv = nn.Conv2D(
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
bias_attr=False)
|
|
self.bn = nn.BatchNorm2D(out_planes, momentum=0.1)
|
|
self.relu = nn.ReLU()
|
|
|
|
for m in self.sublayers():
|
|
if isinstance(m, nn.Conv2D):
|
|
n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
|
|
m.weight = paddle.create_parameter(
|
|
shape=m.weight.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Normal(
|
|
0, math.sqrt(2. / n)))
|
|
elif isinstance(m, nn.BatchNorm2D):
|
|
m.weight = paddle.create_parameter(
|
|
shape=m.weight.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(1.0))
|
|
m.bias = paddle.create_parameter(
|
|
shape=m.bias.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(0.0))
|
|
|
|
def forward(self, x):
|
|
return self.relu(self.bn(self.conv(x)))
|
|
|
|
|
|
class FPN(nn.Layer):
|
|
def __init__(self, in_channels, out_channels):
|
|
super(FPN, self).__init__()
|
|
|
|
# Top layer
|
|
self.toplayer_ = Conv_BN_ReLU(
|
|
in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
|
|
# Lateral layers
|
|
self.latlayer1_ = Conv_BN_ReLU(
|
|
in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
|
|
|
|
self.latlayer2_ = Conv_BN_ReLU(
|
|
in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
|
|
|
|
self.latlayer3_ = Conv_BN_ReLU(
|
|
in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
|
|
|
|
# Smooth layers
|
|
self.smooth1_ = Conv_BN_ReLU(
|
|
out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
|
|
|
self.smooth2_ = Conv_BN_ReLU(
|
|
out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
|
|
|
self.smooth3_ = Conv_BN_ReLU(
|
|
out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
|
|
|
self.out_channels = out_channels * 4
|
|
for m in self.sublayers():
|
|
if isinstance(m, nn.Conv2D):
|
|
n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
|
|
m.weight = paddle.create_parameter(
|
|
shape=m.weight.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Normal(
|
|
0, math.sqrt(2. / n)))
|
|
elif isinstance(m, nn.BatchNorm2D):
|
|
m.weight = paddle.create_parameter(
|
|
shape=m.weight.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(1.0))
|
|
m.bias = paddle.create_parameter(
|
|
shape=m.bias.shape,
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(0.0))
|
|
|
|
def _upsample(self, x, scale=1):
|
|
return F.upsample(x, scale_factor=scale, mode='bilinear')
|
|
|
|
def _upsample_add(self, x, y, scale=1):
|
|
return F.upsample(x, scale_factor=scale, mode='bilinear') + y
|
|
|
|
def forward(self, x):
|
|
f2, f3, f4, f5 = x
|
|
p5 = self.toplayer_(f5)
|
|
|
|
f4 = self.latlayer1_(f4)
|
|
p4 = self._upsample_add(p5, f4, 2)
|
|
p4 = self.smooth1_(p4)
|
|
|
|
f3 = self.latlayer2_(f3)
|
|
p3 = self._upsample_add(p4, f3, 2)
|
|
p3 = self.smooth2_(p3)
|
|
|
|
f2 = self.latlayer3_(f2)
|
|
p2 = self._upsample_add(p3, f2, 2)
|
|
p2 = self.smooth3_(p2)
|
|
|
|
p3 = self._upsample(p3, 2)
|
|
p4 = self._upsample(p4, 4)
|
|
p5 = self._upsample(p5, 8)
|
|
|
|
fuse = paddle.concat([p2, p3, p4, p5], axis=1)
|
|
return fuse
|