def __call__(self):
arch = self.configer.get('network', 'backbone')
from lib.models.backbones.hrnet.hrnet_config import MODEL_CONFIGS
if arch == 'hrnet18':
arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet18'],
bn_type='inplace_abn',
bn_momentum=0.1)
arch_net = ModuleHelper.load_model(arch_net,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network='hrnet')
elif arch == 'hrnet32':
arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet32'],
bn_type='inplace_abn',
bn_momentum=0.1)
arch_net = ModuleHelper.load_model(arch_net,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network='hrnet')
elif arch == 'hrnet48':
arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet48'],
bn_type='inplace_abn',
bn_momentum=0.1)
arch_net = ModuleHelper.load_model(arch_net,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network='hrnet')
elif arch == 'hrnet64':
arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet64'],
bn_type='inplace_abn',
bn_momentum=0.1)
arch_net = ModuleHelper.load_model(arch_net,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network='hrnet')
elif arch == 'hrnet2x20':
arch_net = HighResolutionNext(MODEL_CONFIGS['hrnet2x20'],
bn_type=self.configer.get(
'network', 'bn_type'))
arch_net = ModuleHelper.load_model(arch_net,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network='hrnet')
else:
raise Exception('Architecture undefined!')
return arch_net
def _resnet(arch, block, layers, pretrained, progress, **kwargs):
model = ResNet(block, layers, **kwargs)
if pretrained:
import os
from lib.models.tools.module_helper import ModuleHelper
if os.path.exists(pretrained):
ModuleHelper.load_model(model, pretrained, all_match=False)
else:
state_dict = load_state_dict_from_url(pretrained,
progress=progress)
model.load_state_dict(state_dict, strict=False)
return model
def mobilenetv2(self):
model = MobileNetV2()
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False)
return model
def squeezenet(self):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = SqueezeNet()
model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
return model
def deepbase_resnet101(self, pretrained=None, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 23, 3],
deep_base=True,
norm_type=self.configer.get('network', 'norm_type'),
**kwargs)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def resnet34(self, pretrained=None, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(BasicBlock, [3, 4, 6, 3],
deep_base=False,
norm_type=self.configer.get('network', 'norm_type'),
**kwargs)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def wide_resnet38(self, **kwargs):
"""Constructs a WideResNet-38 model.
"""
model = WiderResNetA2([3, 3, 6, 3, 1, 1],
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network="wide_resnet")
return model
def deepbase_resnet50(self, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 6, 3],
deep_base=True,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'))
return model
def resnet18(self, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(BasicBlock, [2, 2, 2, 2],
deep_base=False,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'))
return model
def deepbase_dcn_resnet101(self, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = DCNResNet(Bottleneck, [3, 4, 23, 3],
deep_base=True,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
all_match=False,
pretrained=self.configer.get(
'network', 'pretrained'),
network="dcnet")
return model
def resnet152(self, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 8, 36, 3],
deep_base=False,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
all_match=False,
pretrained=self.configer.get(
'network', 'pretrained'),
network="resnet152")
return model
def deepbase_resnest269(self, **kwargs):
model = ResNeSt(Bottleneck, [3, 30, 48, 8],
radix=2,
groups=1,
bottleneck_width=64,
dilated=True,
dilation=4,
deep_stem=True,
stem_width=64,
avg_down=True,
avd=True,
avd_first=False,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network="resnest")
return model
def resnext101_32x48d(self, **kwargs):
"""Constructs a ResNeXt-101 32x48d model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
pretrained = False
progress = False
kwargs['groups'] = 32
kwargs['width_per_group'] = 48
model = ResNext('resnext101_32x48d',
Bottleneck, [3, 4, 23, 3],
pretrained,
progress,
bn_type=self.configer.get('network', 'bn_type'),
**kwargs)
model = ModuleHelper.load_model(model,
pretrained=self.configer.get(
'network', 'pretrained'),
all_match=False,
network="resnext")
return model
def squeezenet_dilated8(self):
model = DilatedSqueezeNet()
model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
return model