File size: 17,620 Bytes
62a2f1c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 |
from .utils import IntermediateLayerGetter
from ._deeplab import DeepLabHead, DeepLabHeadV3Plus, DeepLabV3
from .enhanced_deeplab import EnhancedDeepLabHead, EnhancedDeepLabHeadV3Plus, EnhancedDeepLabV3
from .backbone import (
resnet,
mobilenetv2,
hrnetv2,
xception
)
def _segm_hrnet(name, backbone_name, num_classes, pretrained_backbone,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
backbone = hrnetv2.__dict__[backbone_name](pretrained_backbone)
# HRNetV2 config:
# the final output channels is dependent on highest resolution channel config (c).
# output of backbone will be the inplanes to assp:
hrnet_channels = int(backbone_name.split('_')[-1])
inplanes = sum([hrnet_channels * 2 ** i for i in range(4)])
low_level_planes = 256 # all hrnet version channel output from bottleneck is the same
aspp_dilate = [12, 24, 36] # If follow paper trend, can put [24, 48, 72].
if name=='deeplabv3plus':
return_layers = {'stage4': 'out', 'layer1': 'low_level'}
classifier = EnhancedDeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
elif name=='deeplabv3':
return_layers = {'stage4': 'out'}
classifier = EnhancedDeepLabHead(inplanes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers, hrnet_flag=True)
model = EnhancedDeepLabV3(backbone, classifier)
return model
def _segm_resnet(name, backbone_name, num_classes, output_stride, pretrained_backbone,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
if output_stride==8:
replace_stride_with_dilation=[False, True, True]
aspp_dilate = [12, 24, 36]
else:
replace_stride_with_dilation=[False, False, True]
aspp_dilate = [6, 12, 18]
backbone = resnet.__dict__[backbone_name](
pretrained=pretrained_backbone,
replace_stride_with_dilation=replace_stride_with_dilation)
inplanes = 2048
low_level_planes = 256
if name=='deeplabv3plus':
return_layers = {'layer4': 'out', 'layer1': 'low_level'}
classifier = EnhancedDeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
elif name=='deeplabv3':
return_layers = {'layer4': 'out'}
classifier = EnhancedDeepLabHead(inplanes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
model = EnhancedDeepLabV3(backbone, classifier)
return model
def _segm_xception(name, backbone_name, num_classes, output_stride, pretrained_backbone,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
if output_stride==8:
replace_stride_with_dilation=[False, False, True, True]
aspp_dilate = [12, 24, 36]
else:
replace_stride_with_dilation=[False, False, False, True]
aspp_dilate = [6, 12, 18]
backbone = xception.xception(pretrained= 'imagenet' if pretrained_backbone else False, replace_stride_with_dilation=replace_stride_with_dilation)
inplanes = 2048
low_level_planes = 128
if name=='deeplabv3plus':
return_layers = {'conv4': 'out', 'block1': 'low_level'}
classifier = EnhancedDeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
elif name=='deeplabv3':
return_layers = {'conv4': 'out'}
classifier = EnhancedDeepLabHead(inplanes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
model = EnhancedDeepLabV3(backbone, classifier)
return model
def _segm_mobilenet(name, backbone_name, num_classes, output_stride, pretrained_backbone,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
if output_stride==8:
aspp_dilate = [12, 24, 36]
else:
aspp_dilate = [6, 12, 18]
backbone = mobilenetv2.mobilenet_v2(pretrained=pretrained_backbone, output_stride=output_stride)
# rename layers
backbone.low_level_features = backbone.features[0:4]
backbone.high_level_features = backbone.features[4:-1]
backbone.features = None
backbone.classifier = None
inplanes = 320
low_level_planes = 24
if name=='deeplabv3plus':
return_layers = {'high_level_features': 'out', 'low_level_features': 'low_level'}
classifier = EnhancedDeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
elif name=='deeplabv3':
return_layers = {'high_level_features': 'out'}
classifier = EnhancedDeepLabHead(inplanes, num_classes, aspp_dilate,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
model = EnhancedDeepLabV3(backbone, classifier)
return model
def _load_model(arch_type, backbone, num_classes, output_stride, pretrained_backbone, **kwargs):
use_eoaNet = kwargs.get('use_eoaNet', True)
msa_scales = kwargs.get('msa_scales', [1, 2, 4])
eog_beta = kwargs.get('eog_beta', 0.5)
if backbone=='mobilenetv2':
model = _segm_mobilenet(arch_type, backbone, num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
elif backbone.startswith('resnet'):
model = _segm_resnet(arch_type, backbone, num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
elif backbone.startswith('hrnetv2'):
model = _segm_hrnet(arch_type, backbone, num_classes, pretrained_backbone=pretrained_backbone,
use_eoaNet=use_eoaNet, msa_scales=msa_scales, eog_beta=eog_beta)
elif backbone=='xception':
model = _segm_xception(arch_type, backbone, num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
else:
raise NotImplementedError
return model
# Deeplab v3
def deeplabv3_hrnetv2_48(num_classes=21, output_stride=4, pretrained_backbone=False, # no pretrained backbone yet
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a HRNetV2-48 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'hrnetv2_48', num_classes, output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3_hrnetv2_32(num_classes=21, output_stride=4, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a HRNetV2-32 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'hrnetv2_32', num_classes, output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3_resnet50(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a ResNet-50 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'resnet50', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3_resnet101(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a ResNet-101 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'resnet101', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3_mobilenet(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a MobileNetv2 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'mobilenetv2', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3_xception(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a Xception backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3', 'xception', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
# Deeplab v3+
def deeplabv3plus_hrnetv2_48(num_classes=21, output_stride=4, pretrained_backbone=False, # no pretrained backbone yet
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3+ model with a HRNetV2-48 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'hrnetv2_48', num_classes, output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3plus_hrnetv2_32(num_classes=21, output_stride=4, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3+ model with a HRNetV2-32 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'hrnetv2_32', num_classes, output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3plus_resnet50(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3 model with a ResNet-50 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'resnet50', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3plus_resnet101(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3+ model with a ResNet-101 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'resnet101', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3plus_mobilenet(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3+ model with a MobileNetv2 backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'mobilenetv2', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta)
def deeplabv3plus_xception(num_classes=21, output_stride=8, pretrained_backbone=True,
use_eoaNet=True, msa_scales=[1, 2, 4], eog_beta=0.5):
"""Constructs a DeepLabV3+ model with a Xception backbone.
Args:
num_classes (int): number of classes.
output_stride (int): output stride for deeplab.
pretrained_backbone (bool): If True, use the pretrained backbone.
use_eoaNet (bool): If True, use Entropy-Optimized Attention Network.
msa_scales (list): Scales for Multi-Scale Attention.
eog_beta (float): Entropy threshold for Entropy-Optimized Gating.
"""
return _load_model('deeplabv3plus', 'xception', num_classes, output_stride=output_stride,
pretrained_backbone=pretrained_backbone, use_eoaNet=use_eoaNet,
msa_scales=msa_scales, eog_beta=eog_beta) |