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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.
from functools import partial
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg import utils
from paddleseg.models import layers
from paddleseg.cvlibs import manager
@manager.MODELS.add_component
class LRASPP(nn.Layer):
"""
Semantic segmentation model with a light R-ASPP head.
The original article refers to
Howard, Andrew, et al. "Searching for mobilenetv3."
(https://arxiv.org/pdf/1909.11065.pdf)
Args:
num_classes (int): The number of target classes.
backbone(nn.Layer): Backbone network, such as stdc1net and resnet18. The backbone must
has feat_channels, of which the length is 5.
backbone_indices (List(int), optional): The values indicate the indices of backbone output
used as the input of the LR-ASPP head.
Default: [0, 1, 3].
lraspp_head_inter_chs (List(int), optional): The intermediate channels of LR-ASPP head.
Default: [32, 64].
lraspp_head_out_ch (int, optional): The output channels of each ASPP branch in the LR-ASPP head.
Default: 128
resize_mode (str, optional): The resize mode for the upsampling operation in the LR-ASPP head.
Default: bilinear.
use_gap (bool, optional): If true, use global average pooling in the LR-ASPP head; otherwise, use
a 49x49 kernel for average pooling.
Default: True.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices=[0, 1, 3],
lraspp_head_inter_chs=[32, 64],
lraspp_head_out_ch=128,
resize_mode='bilinear',
use_gap=True,
pretrained=None):
super().__init__()
# backbone
assert hasattr(backbone, 'feat_channels'), \
"The backbone should has feat_channels."
assert len(backbone.feat_channels) >= len(backbone_indices), \
f"The length of input backbone_indices ({len(backbone_indices)}) should not be" \
f"greater than the length of feat_channels ({len(backbone.feat_channels)})."
assert len(backbone.feat_channels) > max(backbone_indices), \
f"The max value ({max(backbone_indices)}) of backbone_indices should be " \
f"less than the length of feat_channels ({len(backbone.feat_channels)})."
self.backbone = backbone
assert len(backbone_indices) >= 1, "The lenght of backbone_indices " \
"should not be lesser than 1"
# head
assert len(backbone_indices) == len(
lraspp_head_inter_chs
) + 1, "The length of backbone_indices should be 1 greater than lraspp_head_inter_chs."
self.backbone_indices = backbone_indices
self.lraspp_head = LRASPPHead(backbone_indices, backbone.feat_channels,
lraspp_head_inter_chs, lraspp_head_out_ch,
num_classes, resize_mode, use_gap)
# pretrained
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
x_hw = paddle.shape(x)[2:]
feats_backbone = self.backbone(x)
assert len(feats_backbone) >= len(self.backbone_indices), \
f"The nums of backbone feats ({len(feats_backbone)}) should be greater or " \
f"equal than the nums of backbone_indices ({len(self.backbone_indices)})"
y = self.lraspp_head(feats_backbone)
y = F.interpolate(y, x_hw, mode='bilinear', align_corners=False)
logit_list = [y]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class LRASPPHead(nn.Layer):
def __init__(self,
indices,
in_chs,
mid_chs,
out_ch,
n_classes,
resize_mode,
use_gap,
align_corners=False):
super().__init__()
self.indices = indices[-2::-1]
self.in_chs = [in_chs[i] for i in indices[::-1]]
self.mid_chs = mid_chs[::-1]
self.convs = nn.LayerList()
self.conv_ups = nn.LayerList()
for in_ch, mid_ch in zip(self.in_chs[1:], self.mid_chs):
self.convs.append(
nn.Conv2D(
in_ch, mid_ch, kernel_size=1, bias_attr=False))
self.conv_ups.append(layers.ConvBNReLU(out_ch + mid_ch, out_ch, 1))
self.conv_w = nn.Sequential(
nn.AvgPool2D(
kernel_size=(49, 49), stride=(16, 20))
if not use_gap else nn.AdaptiveAvgPool2D(1),
nn.Conv2D(
self.in_chs[0], out_ch, 1, bias_attr=False),
nn.Sigmoid())
self.conv_v = layers.ConvBNReLU(self.in_chs[0], out_ch, 1)
self.conv_t = nn.Conv2D(out_ch, out_ch, kernel_size=1, bias_attr=False)
self.conv_out = nn.Conv2D(
out_ch, n_classes, kernel_size=1, bias_attr=False)
self.interp = partial(
F.interpolate, mode=resize_mode, align_corners=align_corners)
def forward(self, in_feat_list):
x = in_feat_list[-1]
x = self.conv_v(x) * self.interp(self.conv_w(x), paddle.shape(x)[2:])
y = self.conv_t(x)
for idx, conv, conv_up in zip(self.indices, self.convs, self.conv_ups):
feat = in_feat_list[idx]
y = self.interp(y, paddle.shape(feat)[2:])
y = paddle.concat([y, conv(feat)], axis=1)
y = conv_up(y)
y = self.conv_out(y)
return y
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