# 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 class ConvBNLayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size, stride=1, groups=1, is_vd_mode=False, act=None, name=None): super(ConvBNLayer, self).__init__() self.is_vd_mode = is_vd_mode self._pool2d_avg = nn.AvgPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True) self._conv = nn.Conv2D( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=(kernel_size - 1) // 2, groups=groups, weight_attr=ParamAttr(name=name + "_weights"), bias_attr=False) if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] self._batch_norm = nn.BatchNorm( out_channels, act=act, param_attr=ParamAttr(name=bn_name + '_scale'), bias_attr=ParamAttr(bn_name + '_offset'), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance', use_global_stats=False) def forward(self, inputs): # if self.is_vd_mode: # inputs = self._pool2d_avg(inputs) y = self._conv(inputs) y = self._batch_norm(y) return y class DeConvBNLayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size=4, stride=2, padding=1, groups=1, if_act=True, act=None, name=None): super(DeConvBNLayer, self).__init__() self.if_act = if_act self.act = act self.deconv = nn.Conv2DTranspose( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, weight_attr=ParamAttr(name=name + '_weights'), bias_attr=False) self.bn = nn.BatchNorm( num_channels=out_channels, act=act, param_attr=ParamAttr(name="bn_" + name + "_scale"), bias_attr=ParamAttr(name="bn_" + name + "_offset"), moving_mean_name="bn_" + name + "_mean", moving_variance_name="bn_" + name + "_variance", use_global_stats=False) def forward(self, x): x = self.deconv(x) x = self.bn(x) return x class PGFPN(nn.Layer): def __init__(self, in_channels, **kwargs): super(PGFPN, self).__init__() num_inputs = [2048, 2048, 1024, 512, 256] num_outputs = [256, 256, 192, 192, 128] self.out_channels = 128 # print(in_channels) self.conv_bn_layer_1 = ConvBNLayer( in_channels=3, out_channels=32, kernel_size=3, stride=1, act=None, name='FPN_d1') self.conv_bn_layer_2 = ConvBNLayer( in_channels=64, out_channels=64, kernel_size=3, stride=1, act=None, name='FPN_d2') self.conv_bn_layer_3 = ConvBNLayer( in_channels=256, out_channels=128, kernel_size=3, stride=1, act=None, name='FPN_d3') self.conv_bn_layer_4 = ConvBNLayer( in_channels=32, out_channels=64, kernel_size=3, stride=2, act=None, name='FPN_d4') self.conv_bn_layer_5 = ConvBNLayer( in_channels=64, out_channels=64, kernel_size=3, stride=1, act='relu', name='FPN_d5') self.conv_bn_layer_6 = ConvBNLayer( in_channels=64, out_channels=128, kernel_size=3, stride=2, act=None, name='FPN_d6') self.conv_bn_layer_7 = ConvBNLayer( in_channels=128, out_channels=128, kernel_size=3, stride=1, act='relu', name='FPN_d7') self.conv_bn_layer_8 = ConvBNLayer( in_channels=128, out_channels=128, kernel_size=1, stride=1, act=None, name='FPN_d8') self.conv_h0 = ConvBNLayer( in_channels=num_inputs[0], out_channels=num_outputs[0], kernel_size=1, stride=1, act=None, name="conv_h{}".format(0)) self.conv_h1 = ConvBNLayer( in_channels=num_inputs[1], out_channels=num_outputs[1], kernel_size=1, stride=1, act=None, name="conv_h{}".format(1)) self.conv_h2 = ConvBNLayer( in_channels=num_inputs[2], out_channels=num_outputs[2], kernel_size=1, stride=1, act=None, name="conv_h{}".format(2)) self.conv_h3 = ConvBNLayer( in_channels=num_inputs[3], out_channels=num_outputs[3], kernel_size=1, stride=1, act=None, name="conv_h{}".format(3)) self.conv_h4 = ConvBNLayer( in_channels=num_inputs[4], out_channels=num_outputs[4], kernel_size=1, stride=1, act=None, name="conv_h{}".format(4)) self.dconv0 = DeConvBNLayer( in_channels=num_outputs[0], out_channels=num_outputs[0 + 1], name="dconv_{}".format(0)) self.dconv1 = DeConvBNLayer( in_channels=num_outputs[1], out_channels=num_outputs[1 + 1], act=None, name="dconv_{}".format(1)) self.dconv2 = DeConvBNLayer( in_channels=num_outputs[2], out_channels=num_outputs[2 + 1], act=None, name="dconv_{}".format(2)) self.dconv3 = DeConvBNLayer( in_channels=num_outputs[3], out_channels=num_outputs[3 + 1], act=None, name="dconv_{}".format(3)) self.conv_g1 = ConvBNLayer( in_channels=num_outputs[1], out_channels=num_outputs[1], kernel_size=3, stride=1, act='relu', name="conv_g{}".format(1)) self.conv_g2 = ConvBNLayer( in_channels=num_outputs[2], out_channels=num_outputs[2], kernel_size=3, stride=1, act='relu', name="conv_g{}".format(2)) self.conv_g3 = ConvBNLayer( in_channels=num_outputs[3], out_channels=num_outputs[3], kernel_size=3, stride=1, act='relu', name="conv_g{}".format(3)) self.conv_g4 = ConvBNLayer( in_channels=num_outputs[4], out_channels=num_outputs[4], kernel_size=3, stride=1, act='relu', name="conv_g{}".format(4)) self.convf = ConvBNLayer( in_channels=num_outputs[4], out_channels=num_outputs[4], kernel_size=1, stride=1, act=None, name="conv_f{}".format(4)) def forward(self, x): c0, c1, c2, c3, c4, c5, c6 = x # FPN_Down_Fusion f = [c0, c1, c2] g = [None, None, None] h = [None, None, None] h[0] = self.conv_bn_layer_1(f[0]) h[1] = self.conv_bn_layer_2(f[1]) h[2] = self.conv_bn_layer_3(f[2]) g[0] = self.conv_bn_layer_4(h[0]) g[1] = paddle.add(g[0], h[1]) g[1] = F.relu(g[1]) g[1] = self.conv_bn_layer_5(g[1]) g[1] = self.conv_bn_layer_6(g[1]) g[2] = paddle.add(g[1], h[2]) g[2] = F.relu(g[2]) g[2] = self.conv_bn_layer_7(g[2]) f_down = self.conv_bn_layer_8(g[2]) # FPN UP Fusion f1 = [c6, c5, c4, c3, c2] g = [None, None, None, None, None] h = [None, None, None, None, None] h[0] = self.conv_h0(f1[0]) h[1] = self.conv_h1(f1[1]) h[2] = self.conv_h2(f1[2]) h[3] = self.conv_h3(f1[3]) h[4] = self.conv_h4(f1[4]) g[0] = self.dconv0(h[0]) g[1] = paddle.add(g[0], h[1]) g[1] = F.relu(g[1]) g[1] = self.conv_g1(g[1]) g[1] = self.dconv1(g[1]) g[2] = paddle.add(g[1], h[2]) g[2] = F.relu(g[2]) g[2] = self.conv_g2(g[2]) g[2] = self.dconv2(g[2]) g[3] = paddle.add(g[2], h[3]) g[3] = F.relu(g[3]) g[3] = self.conv_g3(g[3]) g[3] = self.dconv3(g[3]) g[4] = paddle.add(x=g[3], y=h[4]) g[4] = F.relu(g[4]) g[4] = self.conv_g4(g[4]) f_up = self.convf(g[4]) f_common = paddle.add(f_down, f_up) f_common = F.relu(f_common) return f_common