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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.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils
@manager.MODELS.add_component
class DMNet(nn.Layer):
"""
The DMNet implementation based on PaddlePaddle.
The original article refers to
Junjun He, Zhongying Deng, Yu Qiao. "Dynamic Multi-scale Filters for Semantic Segmentation"
Args:
num_classes (int): The unique number of target classes.
backbone (paddle.nn.Layer): Backbone network, currently support Resnet50_vd/Resnet101_vd.
mid_channels (int): The middle channels of convolution layer. Default: 512.
filter_sizes (list, tuple): The filter size of generated convolution kernel used in Dynamic Convolutional Module. Default: [1, 3, 5, 7].
fusion (bool): Add one conv to fuse DCM output feature. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
mid_channels=512,
filter_sizes=[1, 3, 5, 7],
fusion=False,
pretrained=None):
super().__init__()
self.backbone = backbone
self.dcm_modules = nn.LayerList()
for filter_size in filter_sizes:
self.dcm_modules.append(
DCM(filter_size, fusion, self.backbone.feat_channels[-1],
mid_channels), )
self.bottleneck = layers.ConvBNReLU(
self.backbone.feat_channels[-1] + len(filter_sizes) * mid_channels,
mid_channels,
3,
padding=1, )
self.cls = nn.Conv2D(mid_channels, num_classes, 1)
self.fcn_head = nn.Sequential(
layers.ConvBNReLU(
self.backbone.feat_channels[2], mid_channels, 3, padding=1),
nn.Conv2D(mid_channels, num_classes, 1), )
self.pretrained = pretrained
self.init_weight()
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
def forward(self, x):
feats = self.backbone(x)
x = feats[-1]
dcm_outs = [x]
for dcm_module in self.dcm_modules:
dcm_outs.append(dcm_module(x))
dcm_outs = paddle.concat(dcm_outs, axis=1)
x = self.bottleneck(dcm_outs)
x = self.cls(x)
x = F.interpolate(
x, scale_factor=8, mode='bilinear', align_corners=True)
output = [x]
if self.training:
fcn_out = self.fcn_head(feats[2])
fcn_out = F.interpolate(
fcn_out, scale_factor=8, mode='bilinear', align_corners=True)
output.append(fcn_out)
return output
return output
class DCM(nn.Layer):
"""
Dynamic Convolutional Module used in DMNet.
Args:
filter_size (int): The filter size of generated convolution kernel used in Dynamic Convolutional Module.
fusion (bool): Add one conv to fuse DCM output feature.
in_channels (int): Input channels.
channels (int): Channels after modules, before conv_seg.
"""
def __init__(self, filter_size, fusion, in_channels, channels):
super().__init__()
self.filter_size = filter_size
self.fusion = fusion
self.channels = channels
pad = (self.filter_size - 1) // 2
if (self.filter_size - 1) % 2 == 0:
self.pad = (pad, pad, pad, pad)
else:
self.pad = (pad + 1, pad, pad + 1, pad)
self.avg_pool = nn.AdaptiveAvgPool2D(filter_size)
self.filter_gen_conv = nn.Conv2D(in_channels, channels, 1)
self.input_redu_conv = layers.ConvBNReLU(in_channels, channels, 1)
self.norm = layers.SyncBatchNorm(channels)
self.act = nn.ReLU()
if self.fusion:
self.fusion_conv = layers.ConvBNReLU(channels, channels, 1)
def forward(self, x):
generated_filter = self.filter_gen_conv(self.avg_pool(x))
x = self.input_redu_conv(x)
b, c, h, w = x.shape
x = x.reshape([1, b * c, h, w])
generated_filter = generated_filter.reshape(
[b * c, 1, self.filter_size, self.filter_size])
x = F.pad(x, self.pad, mode='constant', value=0)
output = F.conv2d(x, weight=generated_filter, groups=b * c)
output = output.reshape([b, self.channels, h, w])
output = self.norm(output)
output = self.act(output)
if self.fusion:
output = self.fusion_conv(output)
return output
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