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- # 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,
- groups=1,
- if_act=True,
- act=None,
- name=None):
- super(ConvBNLayer, self).__init__()
- self.if_act = if_act
- self.act = act
- 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)
-
- 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")
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- return x
- class DeConvBNLayer(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride,
- 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=(kernel_size - 1) // 2,
- 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")
- def forward(self, x):
- x = self.deconv(x)
- x = self.bn(x)
- return x
- class FPN_Up_Fusion(nn.Layer):
- def __init__(self, in_channels):
- super(FPN_Up_Fusion, self).__init__()
- in_channels = in_channels[::-1]
- out_channels = [256, 256, 192, 192, 128]
-
- self.h0_conv = ConvBNLayer(in_channels[0], out_channels[0], 1, 1, act=None, name='fpn_up_h0')
- self.h1_conv = ConvBNLayer(in_channels[1], out_channels[1], 1, 1, act=None, name='fpn_up_h1')
- self.h2_conv = ConvBNLayer(in_channels[2], out_channels[2], 1, 1, act=None, name='fpn_up_h2')
- self.h3_conv = ConvBNLayer(in_channels[3], out_channels[3], 1, 1, act=None, name='fpn_up_h3')
- self.h4_conv = ConvBNLayer(in_channels[4], out_channels[4], 1, 1, act=None, name='fpn_up_h4')
- self.g0_conv = DeConvBNLayer(out_channels[0], out_channels[1], 4, 2, act=None, name='fpn_up_g0')
- self.g1_conv = nn.Sequential(
- ConvBNLayer(out_channels[1], out_channels[1], 3, 1, act='relu', name='fpn_up_g1_1'),
- DeConvBNLayer(out_channels[1], out_channels[2], 4, 2, act=None, name='fpn_up_g1_2')
- )
- self.g2_conv = nn.Sequential(
- ConvBNLayer(out_channels[2], out_channels[2], 3, 1, act='relu', name='fpn_up_g2_1'),
- DeConvBNLayer(out_channels[2], out_channels[3], 4, 2, act=None, name='fpn_up_g2_2')
- )
- self.g3_conv = nn.Sequential(
- ConvBNLayer(out_channels[3], out_channels[3], 3, 1, act='relu', name='fpn_up_g3_1'),
- DeConvBNLayer(out_channels[3], out_channels[4], 4, 2, act=None, name='fpn_up_g3_2')
- )
- self.g4_conv = nn.Sequential(
- ConvBNLayer(out_channels[4], out_channels[4], 3, 1, act='relu', name='fpn_up_fusion_1'),
- ConvBNLayer(out_channels[4], out_channels[4], 1, 1, act=None, name='fpn_up_fusion_2')
- )
- def _add_relu(self, x1, x2):
- x = paddle.add(x=x1, y=x2)
- x = F.relu(x)
- return x
- def forward(self, x):
- f = x[2:][::-1]
- h0 = self.h0_conv(f[0])
- h1 = self.h1_conv(f[1])
- h2 = self.h2_conv(f[2])
- h3 = self.h3_conv(f[3])
- h4 = self.h4_conv(f[4])
- g0 = self.g0_conv(h0)
- g1 = self._add_relu(g0, h1)
- g1 = self.g1_conv(g1)
- g2 = self.g2_conv(self._add_relu(g1, h2))
- g3 = self.g3_conv(self._add_relu(g2, h3))
- g4 = self.g4_conv(self._add_relu(g3, h4))
- return g4
- class FPN_Down_Fusion(nn.Layer):
- def __init__(self, in_channels):
- super(FPN_Down_Fusion, self).__init__()
- out_channels = [32, 64, 128]
- self.h0_conv = ConvBNLayer(in_channels[0], out_channels[0], 3, 1, act=None, name='fpn_down_h0')
- self.h1_conv = ConvBNLayer(in_channels[1], out_channels[1], 3, 1, act=None, name='fpn_down_h1')
- self.h2_conv = ConvBNLayer(in_channels[2], out_channels[2], 3, 1, act=None, name='fpn_down_h2')
- self.g0_conv = ConvBNLayer(out_channels[0], out_channels[1], 3, 2, act=None, name='fpn_down_g0')
- self.g1_conv = nn.Sequential(
- ConvBNLayer(out_channels[1], out_channels[1], 3, 1, act='relu', name='fpn_down_g1_1'),
- ConvBNLayer(out_channels[1], out_channels[2], 3, 2, act=None, name='fpn_down_g1_2')
- )
- self.g2_conv = nn.Sequential(
- ConvBNLayer(out_channels[2], out_channels[2], 3, 1, act='relu', name='fpn_down_fusion_1'),
- ConvBNLayer(out_channels[2], out_channels[2], 1, 1, act=None, name='fpn_down_fusion_2')
- )
- def forward(self, x):
- f = x[:3]
- h0 = self.h0_conv(f[0])
- h1 = self.h1_conv(f[1])
- h2 = self.h2_conv(f[2])
- g0 = self.g0_conv(h0)
- g1 = paddle.add(x=g0, y=h1)
- g1 = F.relu(g1)
- g1 = self.g1_conv(g1)
- g2 = paddle.add(x=g1, y=h2)
- g2 = F.relu(g2)
- g2 = self.g2_conv(g2)
- return g2
- class Cross_Attention(nn.Layer):
- def __init__(self, in_channels):
- super(Cross_Attention, self).__init__()
- self.theta_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_theta')
- self.phi_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_phi')
- self.g_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_g')
- self.fh_weight_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fh_weight')
- self.fh_sc_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fh_sc')
- self.fv_weight_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fv_weight')
- self.fv_sc_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fv_sc')
- self.f_attn_conv = ConvBNLayer(in_channels * 2, in_channels, 1, 1, act='relu', name='f_attn')
- def _cal_fweight(self, f, shape):
- f_theta, f_phi, f_g = f
- #flatten
- f_theta = paddle.transpose(f_theta, [0, 2, 3, 1])
- f_theta = paddle.reshape(f_theta, [shape[0] * shape[1], shape[2], 128])
- f_phi = paddle.transpose(f_phi, [0, 2, 3, 1])
- f_phi = paddle.reshape(f_phi, [shape[0] * shape[1], shape[2], 128])
- f_g = paddle.transpose(f_g, [0, 2, 3, 1])
- f_g = paddle.reshape(f_g, [shape[0] * shape[1], shape[2], 128])
- #correlation
- f_attn = paddle.matmul(f_theta, paddle.transpose(f_phi, [0, 2, 1]))
- #scale
- f_attn = f_attn / (128**0.5)
- f_attn = F.softmax(f_attn)
- #weighted sum
- f_weight = paddle.matmul(f_attn, f_g)
- f_weight = paddle.reshape(
- f_weight, [shape[0], shape[1], shape[2], 128])
- return f_weight
- def forward(self, f_common):
- f_shape = paddle.shape(f_common)
- # print('f_shape: ', f_shape)
- f_theta = self.theta_conv(f_common)
- f_phi = self.phi_conv(f_common)
- f_g = self.g_conv(f_common)
- ######## horizon ########
- fh_weight = self._cal_fweight([f_theta, f_phi, f_g],
- [f_shape[0], f_shape[2], f_shape[3]])
- fh_weight = paddle.transpose(fh_weight, [0, 3, 1, 2])
- fh_weight = self.fh_weight_conv(fh_weight)
- #short cut
- fh_sc = self.fh_sc_conv(f_common)
- f_h = F.relu(fh_weight + fh_sc)
- ######## vertical ########
- fv_theta = paddle.transpose(f_theta, [0, 1, 3, 2])
- fv_phi = paddle.transpose(f_phi, [0, 1, 3, 2])
- fv_g = paddle.transpose(f_g, [0, 1, 3, 2])
- fv_weight = self._cal_fweight([fv_theta, fv_phi, fv_g],
- [f_shape[0], f_shape[3], f_shape[2]])
- fv_weight = paddle.transpose(fv_weight, [0, 3, 2, 1])
- fv_weight = self.fv_weight_conv(fv_weight)
- #short cut
- fv_sc = self.fv_sc_conv(f_common)
- f_v = F.relu(fv_weight + fv_sc)
- ######## merge ########
- f_attn = paddle.concat([f_h, f_v], axis=1)
- f_attn = self.f_attn_conv(f_attn)
- return f_attn
- class SASTFPN(nn.Layer):
- def __init__(self, in_channels, with_cab=False, **kwargs):
- super(SASTFPN, self).__init__()
- self.in_channels = in_channels
- self.with_cab = with_cab
- self.FPN_Down_Fusion = FPN_Down_Fusion(self.in_channels)
- self.FPN_Up_Fusion = FPN_Up_Fusion(self.in_channels)
- self.out_channels = 128
- self.cross_attention = Cross_Attention(self.out_channels)
- def forward(self, x):
- #down fpn
- f_down = self.FPN_Down_Fusion(x)
- #up fpn
- f_up = self.FPN_Up_Fusion(x)
- #fusion
- f_common = paddle.add(x=f_down, y=f_up)
- f_common = F.relu(f_common)
- if self.with_cab:
- # print('enhence f_common with CAB.')
- f_common = self.cross_attention(f_common)
- return f_common
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