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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.models import layers
class NonLocal2D(nn.Layer):
"""Basic Non-local module.
This model is the implementation of "Non-local Neural Networks"
(https://arxiv.org/abs/1711.07971)
Args:
in_channels (int): Channels of the input feature map.
reduction (int): Channel reduction ratio. Default: 2.
use_scale (bool): Whether to scale pairwise_weight by `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`. Default: True.
sub_sample (bool): Whether to utilize max pooling after pairwise function. Default: False.
mode (str): Options are `gaussian`, `concatenation`, `embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
"""
def __init__(self,
in_channels,
reduction=2,
use_scale=True,
sub_sample=False,
mode='embedded_gaussian'):
super(NonLocal2D, self).__init__()
self.in_channels = in_channels
self.reduction = reduction
self.use_scale = use_scale
self.sub_sample = sub_sample
self.mode = mode
if mode not in [
'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation'
]:
raise ValueError(
"Mode should be in 'gaussian', 'concatenation','embedded_gaussian' or 'dot_product'."
)
self.inter_channels = max(in_channels // reduction, 1)
self.g = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1)
self.conv_out = layers.ConvBNReLU(
in_channels=self.inter_channels,
out_channels=self.in_channels,
kernel_size=1,
bias_attr=False)
if self.mode != "gaussian":
self.theta = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1)
self.phi = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1)
if self.mode == "concatenation":
self.concat_project = layers.ConvBNReLU(
in_channels=self.inter_channels * 2,
out_channels=1,
kernel_size=1,
bias_attr=False)
if self.sub_sample:
max_pool_layer = nn.MaxPool2D(kernel_size=(2, 2))
self.g = nn.Sequential(self.g, max_pool_layer)
if self.mode != 'gaussian':
self.phi = nn.Sequential(self.phi, max_pool_layer)
else:
self.phi = max_pool_layer
def gaussian(self, theta_x, phi_x):
pairwise_weight = paddle.matmul(theta_x, phi_x)
pairwise_weight = F.softmax(pairwise_weight, axis=-1)
return pairwise_weight
def embedded_gaussian(self, theta_x, phi_x):
pairwise_weight = paddle.matmul(theta_x, phi_x)
if self.use_scale:
pairwise_weight /= theta_x.shape[-1]**0.5
pairwise_weight = F.softmax(pairwise_weight, -1)
return pairwise_weight
def dot_product(self, theta_x, phi_x):
pairwise_weight = paddle.matmul(theta_x, phi_x)
pairwise_weight /= pairwise_weight.shape[-1]
return pairwise_weight
def concatenation(self, theta_x, phi_x):
h = theta_x.shape[2]
w = phi_x.shape[3]
theta_x = paddle.tile(theta_x, [1, 1, 1, w])
phi_x = paddle.tile(phi_x, [1, 1, h, 1])
concat_feature = paddle.concat([theta_x, phi_x], axis=1)
pairwise_weight = self.concat_project(concat_feature)
n, _, h, w = pairwise_weight.shape
pairwise_weight = paddle.reshape(pairwise_weight, [n, h, w])
pairwise_weight /= pairwise_weight.shape[-1]
return pairwise_weight
def forward(self, x):
n, c, h, w = x.shape
g_x = paddle.reshape(self.g(x), [n, self.inter_channels, -1])
g_x = paddle.transpose(g_x, [0, 2, 1])
if self.mode == 'gaussian':
theta_x = paddle.reshape(x, [n, self.inter_channels, -1])
theta_x = paddle.transpose(theta_x, [0, 2, 1])
if self.sub_sample:
phi_x = paddle.reshape(
self.phi(x), [n, self.inter_channels, -1])
else:
phi_x = paddle.reshape(x, [n, self.in_channels, -1])
elif self.mode == 'concatenation':
theta_x = paddle.reshape(
self.theta(x), [n, self.inter_channels, -1, 1])
phi_x = paddle.reshape(self.phi(x), [n, self.inter_channels, 1, -1])
else:
theta_x = paddle.reshape(
self.theta(x), [n, self.inter_channels, -1])
theta_x = paddle.transpose(theta_x, [0, 2, 1])
phi_x = paddle.reshape(self.phi(x), [n, self.inter_channels, -1])
pairwise_func = getattr(self, self.mode)
pairwise_weight = pairwise_func(theta_x, phi_x)
y = paddle.matmul(pairwise_weight, g_x)
y = paddle.transpose(y, [0, 2, 1])
y = paddle.reshape(y, [n, self.inter_channels, h, w])
output = x + self.conv_out(y)
return output
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