Spaces:
Configuration error
Configuration error
File size: 11,685 Bytes
1ab1a09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
# 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.nn as nn
import paddle.nn.functional as F
import paddle
from paddleseg.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils
__all__ = ['FastSCNN']
@manager.MODELS.add_component
class FastSCNN(nn.Layer):
"""
The FastSCNN implementation based on PaddlePaddle.
As mentioned in the original paper, FastSCNN is a real-time segmentation algorithm (123.5fps)
even for high resolution images (1024x2048).
The original article refers to
Poudel, Rudra PK, et al. "Fast-scnn: Fast semantic segmentation network"
(https://arxiv.org/pdf/1902.04502.pdf).
Args:
num_classes (int): The unique number of target classes.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss.
If true, auxiliary loss will be added after LearningToDownsample module. Default: False.
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,
enable_auxiliary_loss=True,
align_corners=False,
pretrained=None):
super().__init__()
self.learning_to_downsample = LearningToDownsample(32, 48, 64)
self.global_feature_extractor = GlobalFeatureExtractor(
in_channels=64,
block_channels=[64, 96, 128],
out_channels=128,
expansion=6,
num_blocks=[3, 3, 3],
align_corners=True)
self.feature_fusion = FeatureFusionModule(64, 128, 128, align_corners)
self.classifier = Classifier(128, num_classes)
if enable_auxiliary_loss:
self.auxlayer = layers.AuxLayer(64, 32, num_classes)
self.enable_auxiliary_loss = enable_auxiliary_loss
self.align_corners = align_corners
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
logit_list = []
input_size = paddle.shape(x)[2:]
higher_res_features = self.learning_to_downsample(x)
x = self.global_feature_extractor(higher_res_features)
x = self.feature_fusion(higher_res_features, x)
logit = self.classifier(x)
logit = F.interpolate(
logit,
input_size,
mode='bilinear',
align_corners=self.align_corners)
logit_list.append(logit)
if self.enable_auxiliary_loss:
auxiliary_logit = self.auxlayer(higher_res_features)
auxiliary_logit = F.interpolate(
auxiliary_logit,
input_size,
mode='bilinear',
align_corners=self.align_corners)
logit_list.append(auxiliary_logit)
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class LearningToDownsample(nn.Layer):
"""
Learning to downsample module.
This module consists of three downsampling blocks (one conv and two separable conv)
Args:
dw_channels1 (int, optional): The input channels of the first sep conv. Default: 32.
dw_channels2 (int, optional): The input channels of the second sep conv. Default: 48.
out_channels (int, optional): The output channels of LearningToDownsample module. Default: 64.
"""
def __init__(self, dw_channels1=32, dw_channels2=48, out_channels=64):
super(LearningToDownsample, self).__init__()
self.conv_bn_relu = layers.ConvBNReLU(
in_channels=3, out_channels=dw_channels1, kernel_size=3, stride=2)
self.dsconv_bn_relu1 = layers.SeparableConvBNReLU(
in_channels=dw_channels1,
out_channels=dw_channels2,
kernel_size=3,
stride=2,
padding=1)
self.dsconv_bn_relu2 = layers.SeparableConvBNReLU(
in_channels=dw_channels2,
out_channels=out_channels,
kernel_size=3,
stride=2,
padding=1)
def forward(self, x):
x = self.conv_bn_relu(x)
x = self.dsconv_bn_relu1(x)
x = self.dsconv_bn_relu2(x)
return x
class GlobalFeatureExtractor(nn.Layer):
"""
Global feature extractor module.
This module consists of three InvertedBottleneck blocks (like inverted residual introduced by MobileNetV2) and
a PPModule (introduced by PSPNet).
Args:
in_channels (int): The number of input channels to the module.
block_channels (tuple): A tuple represents output channels of each bottleneck block.
out_channels (int): The number of output channels of the module. Default:
expansion (int): The expansion factor in bottleneck.
num_blocks (tuple): It indicates the repeat time of each bottleneck.
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.
"""
def __init__(self, in_channels, block_channels, out_channels, expansion,
num_blocks, align_corners):
super(GlobalFeatureExtractor, self).__init__()
self.bottleneck1 = self._make_layer(InvertedBottleneck, in_channels,
block_channels[0], num_blocks[0],
expansion, 2)
self.bottleneck2 = self._make_layer(
InvertedBottleneck, block_channels[0], block_channels[1],
num_blocks[1], expansion, 2)
self.bottleneck3 = self._make_layer(
InvertedBottleneck, block_channels[1], block_channels[2],
num_blocks[2], expansion, 1)
self.ppm = layers.PPModule(
block_channels[2],
out_channels,
bin_sizes=(1, 2, 3, 6),
dim_reduction=True,
align_corners=align_corners)
def _make_layer(self,
block,
in_channels,
out_channels,
blocks,
expansion=6,
stride=1):
layers = []
layers.append(block(in_channels, out_channels, expansion, stride))
for _ in range(1, blocks):
layers.append(block(out_channels, out_channels, expansion, 1))
return nn.Sequential(*layers)
def forward(self, x):
x = self.bottleneck1(x)
x = self.bottleneck2(x)
x = self.bottleneck3(x)
x = self.ppm(x)
return x
class InvertedBottleneck(nn.Layer):
"""
Single Inverted bottleneck implementation.
Args:
in_channels (int): The number of input channels to bottleneck block.
out_channels (int): The number of output channels of bottleneck block.
expansion (int, optional). The expansion factor in bottleneck. Default: 6.
stride (int, optional). The stride used in depth-wise conv. Defalt: 2.
"""
def __init__(self, in_channels, out_channels, expansion=6, stride=2):
super().__init__()
self.use_shortcut = stride == 1 and in_channels == out_channels
expand_channels = in_channels * expansion
self.block = nn.Sequential(
# pw
layers.ConvBNReLU(
in_channels=in_channels,
out_channels=expand_channels,
kernel_size=1,
bias_attr=False),
# dw
layers.ConvBNReLU(
in_channels=expand_channels,
out_channels=expand_channels,
kernel_size=3,
stride=stride,
padding=1,
groups=expand_channels,
bias_attr=False),
# pw-linear
layers.ConvBN(
in_channels=expand_channels,
out_channels=out_channels,
kernel_size=1,
bias_attr=False))
def forward(self, x):
out = self.block(x)
if self.use_shortcut:
out = x + out
return out
class FeatureFusionModule(nn.Layer):
"""
Feature Fusion Module Implementation.
This module fuses high-resolution feature and low-resolution feature.
Args:
high_in_channels (int): The channels of high-resolution feature (output of LearningToDownsample).
low_in_channels (int): The channels of low-resolution feature (output of GlobalFeatureExtractor).
out_channels (int): The output channels of this module.
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.
"""
def __init__(self, high_in_channels, low_in_channels, out_channels,
align_corners):
super().__init__()
# Only depth-wise conv
self.dwconv = layers.ConvBNReLU(
in_channels=low_in_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
groups=128,
bias_attr=False)
self.conv_low_res = layers.ConvBN(out_channels, out_channels, 1)
self.conv_high_res = layers.ConvBN(high_in_channels, out_channels, 1)
self.align_corners = align_corners
def forward(self, high_res_input, low_res_input):
low_res_input = F.interpolate(
low_res_input,
paddle.shape(high_res_input)[2:],
mode='bilinear',
align_corners=self.align_corners)
low_res_input = self.dwconv(low_res_input)
low_res_input = self.conv_low_res(low_res_input)
high_res_input = self.conv_high_res(high_res_input)
x = high_res_input + low_res_input
return F.relu(x)
class Classifier(nn.Layer):
"""
The Classifier module implementation.
This module consists of two depth-wise conv and one conv.
Args:
input_channels (int): The input channels to this module.
num_classes (int): The unique number of target classes.
"""
def __init__(self, input_channels, num_classes):
super().__init__()
self.dsconv1 = layers.SeparableConvBNReLU(
in_channels=input_channels,
out_channels=input_channels,
kernel_size=3,
padding=1)
self.dsconv2 = layers.SeparableConvBNReLU(
in_channels=input_channels,
out_channels=input_channels,
kernel_size=3,
padding=1)
self.conv = nn.Conv2D(
in_channels=input_channels, out_channels=num_classes, kernel_size=1)
self.dropout = nn.Dropout(p=0.1) # dropout_prob
def forward(self, x):
x = self.dsconv1(x)
x = self.dsconv2(x)
x = self.dropout(x)
x = self.conv(x)
return x
|