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InternAgent / examples /AutoSeg_VOC12 /EntropyOptimizedAttentionNet /network /modeling.py
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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)