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# Copyright (c) 2020 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
from paddleseg.cvlibs import manager
from paddleseg.utils import utils
@manager.MODELS.add_component
class ISANet(nn.Layer):
"""Interlaced Sparse Self-Attention for Semantic Segmentation.
The original article refers to Lang Huang, et al. "Interlaced Sparse Self-Attention for Semantic Segmentation"
(https://arxiv.org/abs/1907.12273).
Args:
num_classes (int): The unique number of target classes.
backbone (Paddle.nn.Layer): A backbone network.
backbone_indices (tuple): The values in the tuple indicate the indices of output of backbone.
isa_channels (int): The channels of ISA Module.
down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
is even, e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices=(2, 3),
isa_channels=256,
down_factor=(8, 8),
enable_auxiliary_loss=True,
align_corners=False,
pretrained=None):
super().__init__()
self.backbone = backbone
self.backbone_indices = backbone_indices
in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
self.head = ISAHead(num_classes, in_channels, isa_channels, down_factor,
enable_auxiliary_loss)
self.align_corners = align_corners
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
feats = self.backbone(x)
feats = [feats[i] for i in self.backbone_indices]
logit_list = self.head(feats)
logit_list = [
F.interpolate(
logit,
paddle.shape(x)[2:],
mode='bilinear',
align_corners=self.align_corners,
align_mode=1) for logit in logit_list
]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class ISAHead(nn.Layer):
"""
The ISAHead.
Args:
num_classes (int): The unique number of target classes.
in_channels (tuple): The number of input channels.
isa_channels (int): The channels of ISA Module.
down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
"""
def __init__(self, num_classes, in_channels, isa_channels, down_factor,
enable_auxiliary_loss):
super(ISAHead, self).__init__()
self.in_channels = in_channels[-1]
inter_channels = self.in_channels // 4
self.inter_channels = inter_channels
self.down_factor = down_factor
self.enable_auxiliary_loss = enable_auxiliary_loss
self.in_conv = layers.ConvBNReLU(
self.in_channels, inter_channels, 3, bias_attr=False)
self.global_relation = SelfAttentionBlock(inter_channels, isa_channels)
self.local_relation = SelfAttentionBlock(inter_channels, isa_channels)
self.out_conv = layers.ConvBNReLU(
inter_channels * 2, inter_channels, 1, bias_attr=False)
self.cls = nn.Sequential(
nn.Dropout2D(p=0.1), nn.Conv2D(inter_channels, num_classes, 1))
self.aux = nn.Sequential(
layers.ConvBNReLU(
in_channels=1024,
out_channels=256,
kernel_size=3,
bias_attr=False),
nn.Dropout2D(p=0.1),
nn.Conv2D(256, num_classes, 1))
def forward(self, feat_list):
C3, C4 = feat_list
x = self.in_conv(C4)
x_shape = paddle.shape(x)
P_h, P_w = self.down_factor
Q_h, Q_w = paddle.ceil(x_shape[2] / P_h).astype('int32'), paddle.ceil(
x_shape[3] / P_w).astype('int32')
pad_h, pad_w = (Q_h * P_h - x_shape[2]).astype('int32'), (
Q_w * P_w - x_shape[3]).astype('int32')
if pad_h > 0 or pad_w > 0:
padding = paddle.concat(
[
pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
pad_h - pad_h // 2
],
axis=0)
feat = F.pad(x, padding)
else:
feat = x
feat = feat.reshape([0, x_shape[1], Q_h, P_h, Q_w, P_w])
feat = feat.transpose([0, 3, 5, 1, 2,
4]).reshape([-1, self.inter_channels, Q_h, Q_w])
feat = self.global_relation(feat)
feat = feat.reshape([x_shape[0], P_h, P_w, x_shape[1], Q_h, Q_w])
feat = feat.transpose([0, 4, 5, 3, 1,
2]).reshape([-1, self.inter_channels, P_h, P_w])
feat = self.local_relation(feat)
feat = feat.reshape([x_shape[0], Q_h, Q_w, x_shape[1], P_h, P_w])
feat = feat.transpose([0, 3, 1, 4, 2, 5]).reshape(
[0, self.inter_channels, P_h * Q_h, P_w * Q_w])
if pad_h > 0 or pad_w > 0:
feat = paddle.slice(
feat,
axes=[2, 3],
starts=[pad_h // 2, pad_w // 2],
ends=[pad_h // 2 + x_shape[2], pad_w // 2 + x_shape[3]])
feat = self.out_conv(paddle.concat([feat, x], axis=1))
output = self.cls(feat)
if self.enable_auxiliary_loss:
auxout = self.aux(C3)
return [output, auxout]
else:
return [output]
class SelfAttentionBlock(layers.AttentionBlock):
"""General self-attention block/non-local block.
Args:
in_channels (int): Input channels of key/query feature.
channels (int): Output channels of key/query transform.
"""
def __init__(self, in_channels, channels):
super(SelfAttentionBlock, self).__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
out_channels=in_channels,
share_key_query=False,
query_downsample=None,
key_downsample=None,
key_query_num_convs=2,
key_query_norm=True,
value_out_num_convs=1,
value_out_norm=False,
matmul_norm=True,
with_out=False)
self.output_project = self.build_project(
in_channels, in_channels, num_convs=1, use_conv_module=True)
def forward(self, x):
context = super(SelfAttentionBlock, self).forward(x, x)
return self.output_project(context)
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