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# copyright (c) 2022 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.
"""
Code is refer from:
https://github.com/RuijieJ/pren/blob/main/Nets/Aggregation.py
"""
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
class PoolAggregate(nn.Layer):
def __init__(self, n_r, d_in, d_middle=None, d_out=None):
super(PoolAggregate, self).__init__()
if not d_middle:
d_middle = d_in
if not d_out:
d_out = d_in
self.d_in = d_in
self.d_middle = d_middle
self.d_out = d_out
self.act = nn.Swish()
self.n_r = n_r
self.aggs = self._build_aggs()
def _build_aggs(self):
aggs = []
for i in range(self.n_r):
aggs.append(
self.add_sublayer(
'{}'.format(i),
nn.Sequential(
('conv1', nn.Conv2D(
self.d_in, self.d_middle, 3, 2, 1, bias_attr=False)
), ('bn1', nn.BatchNorm(self.d_middle)),
('act', self.act), ('conv2', nn.Conv2D(
self.d_middle, self.d_out, 3, 2, 1, bias_attr=False
)), ('bn2', nn.BatchNorm(self.d_out)))))
return aggs
def forward(self, x):
b = x.shape[0]
outs = []
for agg in self.aggs:
y = agg(x)
p = F.adaptive_avg_pool2d(y, 1)
outs.append(p.reshape((b, 1, self.d_out)))
out = paddle.concat(outs, 1)
return out
class WeightAggregate(nn.Layer):
def __init__(self, n_r, d_in, d_middle=None, d_out=None):
super(WeightAggregate, self).__init__()
if not d_middle:
d_middle = d_in
if not d_out:
d_out = d_in
self.n_r = n_r
self.d_out = d_out
self.act = nn.Swish()
self.conv_n = nn.Sequential(
('conv1', nn.Conv2D(
d_in, d_in, 3, 1, 1,
bias_attr=False)), ('bn1', nn.BatchNorm(d_in)),
('act1', self.act), ('conv2', nn.Conv2D(
d_in, n_r, 1, bias_attr=False)), ('bn2', nn.BatchNorm(n_r)),
('act2', nn.Sigmoid()))
self.conv_d = nn.Sequential(
('conv1', nn.Conv2D(
d_in, d_middle, 3, 1, 1,
bias_attr=False)), ('bn1', nn.BatchNorm(d_middle)),
('act1', self.act), ('conv2', nn.Conv2D(
d_middle, d_out, 1,
bias_attr=False)), ('bn2', nn.BatchNorm(d_out)))
def forward(self, x):
b, _, h, w = x.shape
hmaps = self.conv_n(x)
fmaps = self.conv_d(x)
r = paddle.bmm(
hmaps.reshape((b, self.n_r, h * w)),
fmaps.reshape((b, self.d_out, h * w)).transpose((0, 2, 1)))
return r
class GCN(nn.Layer):
def __init__(self, d_in, n_in, d_out=None, n_out=None, dropout=0.1):
super(GCN, self).__init__()
if not d_out:
d_out = d_in
if not n_out:
n_out = d_in
self.conv_n = nn.Conv1D(n_in, n_out, 1)
self.linear = nn.Linear(d_in, d_out)
self.dropout = nn.Dropout(dropout)
self.act = nn.Swish()
def forward(self, x):
x = self.conv_n(x)
x = self.dropout(self.linear(x))
return self.act(x)
class PRENFPN(nn.Layer):
def __init__(self, in_channels, n_r, d_model, max_len, dropout):
super(PRENFPN, self).__init__()
assert len(in_channels) == 3, "in_channels' length must be 3."
c1, c2, c3 = in_channels # the depths are from big to small
# build fpn
assert d_model % 3 == 0, "{} can't be divided by 3.".format(d_model)
self.agg_p1 = PoolAggregate(n_r, c1, d_out=d_model // 3)
self.agg_p2 = PoolAggregate(n_r, c2, d_out=d_model // 3)
self.agg_p3 = PoolAggregate(n_r, c3, d_out=d_model // 3)
self.agg_w1 = WeightAggregate(n_r, c1, 4 * c1, d_model // 3)
self.agg_w2 = WeightAggregate(n_r, c2, 4 * c2, d_model // 3)
self.agg_w3 = WeightAggregate(n_r, c3, 4 * c3, d_model // 3)
self.gcn_pool = GCN(d_model, n_r, d_model, max_len, dropout)
self.gcn_weight = GCN(d_model, n_r, d_model, max_len, dropout)
self.out_channels = d_model
def forward(self, inputs):
f3, f5, f7 = inputs
rp1 = self.agg_p1(f3)
rp2 = self.agg_p2(f5)
rp3 = self.agg_p3(f7)
rp = paddle.concat([rp1, rp2, rp3], 2) # [b,nr,d]
rw1 = self.agg_w1(f3)
rw2 = self.agg_w2(f5)
rw3 = self.agg_w3(f7)
rw = paddle.concat([rw1, rw2, rw3], 2) # [b,nr,d]
y1 = self.gcn_pool(rp)
y2 = self.gcn_weight(rw)
y = 0.5 * (y1 + y2)
return y # [b,max_len,d]
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