315 lines
9.6 KiB
Python
315 lines
9.6 KiB
Python
# copyright (c) 2021 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):
|
|
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
|
|
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
|