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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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