def iresgroupfix152(pretrained=False, **kwargs):
"""Constructs a iResGroupFix-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = iResGroupFix(ResGroupBlock, [3, 8, 36, 3], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(torch.load(download_from_url(model_urls['iresgroupfix152'],
root=default_cache_path)))
return model
def pyconvresnet152(pretrained=False, **kwargs):
"""Constructs a PyConvResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = PyConvResNet(PyConvBlock, [3, 8, 36, 3], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(torch.load(download_from_url(model_urls['pyconvresnet152'],
root=default_cache_path)))
return model
def seiresnet1001(pretrained=False, **kwargs):
"""Constructs a iResNet-1001 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = seiResNet(Bottleneck, [4, 155, 170, 4], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(torch.load(download_from_url(model_urls['seiresnet1001'],
root=default_cache_path)))
return model
def seiresnet34(pretrained=False, **kwargs):
"""Constructs a iResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = seiResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(torch.load(download_from_url(model_urls['seiresnet34'],
root=default_cache_path)))
return model
def resstage200(pretrained=False, **kwargs):
"""Constructs a ResStage-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResStage(Bottleneck, [3, 24, 36, 3], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(
torch.load(
download_from_url(model_urls['resstage200'],
root=default_cache_path)))
return model
def resgroup101(pretrained=False, **kwargs):
"""Constructs a ResGroup-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResGroup(ResGroupBlock, [3, 4, 23, 3], **kwargs)
if pretrained:
os.makedirs(default_cache_path, exist_ok=True)
model.load_state_dict(
torch.load(
download_from_url(model_urls['resgroup101'],
root=default_cache_path)))
return model