# copyright (c) 2019 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle from paddle import nn import paddle.nn.functional as F from paddle import ParamAttr import os import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../../..'))) from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule, ConvBNLayer class DBFPN(nn.Layer): def __init__(self, in_channels, out_channels, **kwargs): super(DBFPN, self).__init__() self.out_channels = out_channels weight_attr = paddle.nn.initializer.KaimingUniform() self.in2_conv = nn.Conv2D( in_channels=in_channels[0], out_channels=self.out_channels, kernel_size=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.in3_conv = nn.Conv2D( in_channels=in_channels[1], out_channels=self.out_channels, kernel_size=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.in4_conv = nn.Conv2D( in_channels=in_channels[2], out_channels=self.out_channels, kernel_size=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.in5_conv = nn.Conv2D( in_channels=in_channels[3], out_channels=self.out_channels, kernel_size=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.p5_conv = nn.Conv2D( in_channels=self.out_channels, out_channels=self.out_channels // 4, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.p4_conv = nn.Conv2D( in_channels=self.out_channels, out_channels=self.out_channels // 4, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.p3_conv = nn.Conv2D( in_channels=self.out_channels, out_channels=self.out_channels // 4, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.p2_conv = nn.Conv2D( in_channels=self.out_channels, out_channels=self.out_channels // 4, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) def forward(self, x): c2, c3, c4, c5 = x in5 = self.in5_conv(c5) in4 = self.in4_conv(c4) in3 = self.in3_conv(c3) in2 = self.in2_conv(c2) out4 = in4 + F.upsample( in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16 out3 = in3 + F.upsample( out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8 out2 = in2 + F.upsample( out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4 p5 = self.p5_conv(in5) p4 = self.p4_conv(out4) p3 = self.p3_conv(out3) p2 = self.p2_conv(out2) p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1) p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1) p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1) fuse = paddle.concat([p5, p4, p3, p2], axis=1) return fuse class CALayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size, shortcut=True): super(CALayer, self).__init__() weight_attr = paddle.nn.initializer.KaimingUniform() self.out_channels = out_channels self.in_conv = nn.Conv2D( in_channels=in_channels, out_channels=self.out_channels, kernel_size=kernel_size, padding=int(kernel_size // 2), weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False) self.se_block = SEModule(self.out_channels) self.shortcut = shortcut def forward(self, ins): x = self.in_conv(ins) if self.shortcut: out = x + self.se_block(x) else: out = self.se_block(x) return out class CAFPN(nn.Layer): def __init__(self, in_channels, out_channels, shortcut=True, **kwargs): super(CAFPN, self).__init__() self.out_channels = out_channels self.ins_conv = [] self.inp_conv = [] for i in range(len(in_channels)): self.ins_conv.append( CALayer( in_channels[i], out_channels, kernel_size=1, shortcut=shortcut)) self.inp_conv.append( CALayer( out_channels, out_channels // 4, kernel_size=3, shortcut=shortcut)) def forward(self, x): c2, c3, c4, c5 = x in5 = self.ins_conv[3](c5) in4 = self.ins_conv[2](c4) in3 = self.ins_conv[1](c3) in2 = self.ins_conv[0](c2) out4 = in4 + F.upsample( in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16 out3 = in3 + F.upsample( out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8 out2 = in2 + F.upsample( out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4 p5 = self.inp_conv[3](in5) p4 = self.inp_conv[2](out4) p3 = self.inp_conv[1](out3) p2 = self.inp_conv[0](out2) p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1) p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1) p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1) fuse = paddle.concat([p5, p4, p3, p2], axis=1) return fuse class FEPAN(nn.Layer): def __init__(self, in_channels, out_channels, **kwargs): super(FEPAN, self).__init__() self.out_channels = out_channels weight_attr = paddle.nn.initializer.KaimingUniform() self.ins_conv = [] self.inp_conv = [] # pan head self.pan_head_conv = [] self.pan_lat_conv = [] for i in range(len(in_channels)): self.ins_conv.append( nn.Conv2D( in_channels=in_channels[0], out_channels=self.out_channels, kernel_size=1, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False)) self.inp_conv.append( ConvBNLayer( in_channels=self.out_channels, out_channels=self.out_channels // 4, kernel_size=9, padding=4)) if i > 0: self.pan_head_conv.append( nn.Conv2D( in_channels=self.out_channels // 4, out_channels=self.out_channels // 4, kernel_size=3, padding=1, stride=2, weight_attr=ParamAttr(initializer=weight_attr), bias_attr=False)) self.pan_lat_conv.append( ConvBNLayer( in_channels=self.out_channels // 4, out_channels=self.out_channels // 4, kernel_size=9, padding=4)) def forward(self, x): c2, c3, c4, c5 = x in5 = self.ins_conv[3](c5) in4 = self.ins_conv[2](c4) in3 = self.ins_conv[1](c3) in2 = self.ins_conv[0](c2) out4 = in4 + F.upsample( in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16 out3 = in3 + F.upsample( out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8 out2 = in2 + F.upsample( out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4 f5 = self.inp_conv[3](in5) f4 = self.inp_conv[2](out4) f3 = self.inp_conv[1](out3) f2 = self.inp_conv[0](out2) pan3 = f3 + self.pan_head[0](f2) pan4 = f4 + self.pan_head[1](pan3) pan5 = f5 + self.pan_head[2](pan4) p2 = self.pan_lat[0](f2) p3 = self.pan_lat[1](pan3) p4 = self.pan_lat[2](pan4) p5 = self.pan_lat[3](pan5) p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1) p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1) p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1) fuse = paddle.concat([p5, p4, p3, p2], axis=1) return fuse