mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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359 lines
12 KiB
Python
359 lines
12 KiB
Python
# copyright (c) 2019 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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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 nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../../..')))
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from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
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class DSConv(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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padding,
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stride=1,
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groups=None,
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if_act=True,
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act="relu",
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**kwargs):
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super(DSConv, self).__init__()
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if groups == None:
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groups = in_channels
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self.if_act = if_act
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self.act = act
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self.conv1 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias_attr=False)
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self.bn1 = nn.BatchNorm(num_channels=in_channels, act=None)
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self.conv2 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=int(in_channels * 4),
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kernel_size=1,
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stride=1,
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bias_attr=False)
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self.bn2 = nn.BatchNorm(num_channels=int(in_channels * 4), act=None)
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self.conv3 = nn.Conv2D(
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in_channels=int(in_channels * 4),
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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bias_attr=False)
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self._c = [in_channels, out_channels]
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if in_channels != out_channels:
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self.conv_end = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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bias_attr=False)
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def forward(self, inputs):
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x = self.conv1(inputs)
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x = self.bn1(x)
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x = self.conv2(x)
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x = self.bn2(x)
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if self.if_act:
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if self.act == "relu":
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x = F.relu(x)
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elif self.act == "hardswish":
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x = F.hardswish(x)
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else:
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print("The activation function({}) is selected incorrectly.".
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format(self.act))
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exit()
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x = self.conv3(x)
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if self._c[0] != self._c[1]:
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x = x + self.conv_end(inputs)
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return x
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class DBFPN(nn.Layer):
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def __init__(self, in_channels, out_channels, **kwargs):
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super(DBFPN, self).__init__()
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self.out_channels = out_channels
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weight_attr = paddle.nn.initializer.KaimingUniform()
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self.in2_conv = nn.Conv2D(
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in_channels=in_channels[0],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in3_conv = nn.Conv2D(
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in_channels=in_channels[1],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in4_conv = nn.Conv2D(
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in_channels=in_channels[2],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.in5_conv = nn.Conv2D(
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in_channels=in_channels[3],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p5_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p4_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p3_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.p2_conv = nn.Conv2D(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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def forward(self, x):
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c2, c3, c4, c5 = x
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in5 = self.in5_conv(c5)
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in4 = self.in4_conv(c4)
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in3 = self.in3_conv(c3)
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in2 = self.in2_conv(c2)
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out4 = in4 + F.upsample(
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in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16
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out3 = in3 + F.upsample(
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out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8
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out2 = in2 + F.upsample(
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out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4
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p5 = self.p5_conv(in5)
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p4 = self.p4_conv(out4)
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p3 = self.p3_conv(out3)
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p2 = self.p2_conv(out2)
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p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
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p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
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p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
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fuse = paddle.concat([p5, p4, p3, p2], axis=1)
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return fuse
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class RSELayer(nn.Layer):
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def __init__(self, in_channels, out_channels, kernel_size, shortcut=True):
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super(RSELayer, self).__init__()
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weight_attr = paddle.nn.initializer.KaimingUniform()
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self.out_channels = out_channels
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self.in_conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=self.out_channels,
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kernel_size=kernel_size,
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padding=int(kernel_size // 2),
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False)
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self.se_block = SEModule(self.out_channels)
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self.shortcut = shortcut
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def forward(self, ins):
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x = self.in_conv(ins)
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if self.shortcut:
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out = x + self.se_block(x)
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else:
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out = self.se_block(x)
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return out
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class RSEFPN(nn.Layer):
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def __init__(self, in_channels, out_channels, shortcut=True, **kwargs):
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super(RSEFPN, self).__init__()
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self.out_channels = out_channels
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self.ins_conv = nn.LayerList()
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self.inp_conv = nn.LayerList()
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for i in range(len(in_channels)):
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self.ins_conv.append(
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RSELayer(
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in_channels[i],
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out_channels,
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kernel_size=1,
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shortcut=shortcut))
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self.inp_conv.append(
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RSELayer(
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out_channels,
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out_channels // 4,
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kernel_size=3,
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shortcut=shortcut))
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def forward(self, x):
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c2, c3, c4, c5 = x
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in5 = self.ins_conv[3](c5)
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in4 = self.ins_conv[2](c4)
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in3 = self.ins_conv[1](c3)
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in2 = self.ins_conv[0](c2)
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out4 = in4 + F.upsample(
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in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16
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out3 = in3 + F.upsample(
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out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8
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out2 = in2 + F.upsample(
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out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4
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p5 = self.inp_conv[3](in5)
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p4 = self.inp_conv[2](out4)
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p3 = self.inp_conv[1](out3)
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p2 = self.inp_conv[0](out2)
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p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
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p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
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p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
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fuse = paddle.concat([p5, p4, p3, p2], axis=1)
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return fuse
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class LKPAN(nn.Layer):
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def __init__(self, in_channels, out_channels, mode='large', **kwargs):
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super(LKPAN, self).__init__()
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self.out_channels = out_channels
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weight_attr = paddle.nn.initializer.KaimingUniform()
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self.ins_conv = nn.LayerList()
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self.inp_conv = nn.LayerList()
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# pan head
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self.pan_head_conv = nn.LayerList()
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self.pan_lat_conv = nn.LayerList()
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if mode.lower() == 'lite':
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p_layer = DSConv
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elif mode.lower() == 'large':
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p_layer = nn.Conv2D
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else:
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raise ValueError(
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"mode can only be one of ['lite', 'large'], but received {}".
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format(mode))
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for i in range(len(in_channels)):
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self.ins_conv.append(
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nn.Conv2D(
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in_channels=in_channels[i],
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out_channels=self.out_channels,
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kernel_size=1,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False))
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self.inp_conv.append(
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p_layer(
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in_channels=self.out_channels,
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out_channels=self.out_channels // 4,
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kernel_size=9,
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padding=4,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False))
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if i > 0:
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self.pan_head_conv.append(
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nn.Conv2D(
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in_channels=self.out_channels // 4,
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out_channels=self.out_channels // 4,
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kernel_size=3,
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padding=1,
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stride=2,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False))
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self.pan_lat_conv.append(
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p_layer(
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in_channels=self.out_channels // 4,
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out_channels=self.out_channels // 4,
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kernel_size=9,
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padding=4,
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weight_attr=ParamAttr(initializer=weight_attr),
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bias_attr=False))
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def forward(self, x):
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c2, c3, c4, c5 = x
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in5 = self.ins_conv[3](c5)
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in4 = self.ins_conv[2](c4)
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in3 = self.ins_conv[1](c3)
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in2 = self.ins_conv[0](c2)
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out4 = in4 + F.upsample(
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in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16
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out3 = in3 + F.upsample(
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out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8
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out2 = in2 + F.upsample(
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out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4
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f5 = self.inp_conv[3](in5)
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f4 = self.inp_conv[2](out4)
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f3 = self.inp_conv[1](out3)
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f2 = self.inp_conv[0](out2)
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pan3 = f3 + self.pan_head_conv[0](f2)
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pan4 = f4 + self.pan_head_conv[1](pan3)
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pan5 = f5 + self.pan_head_conv[2](pan4)
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p2 = self.pan_lat_conv[0](f2)
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p3 = self.pan_lat_conv[1](pan3)
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p4 = self.pan_lat_conv[2](pan4)
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p5 = self.pan_lat_conv[3](pan5)
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p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
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p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
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p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
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fuse = paddle.concat([p5, p4, p3, p2], axis=1)
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return fuse
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