def resnet101_v1b_gn(pretrained=False,
root=os.path.expanduser('~/.torch/models'),
**kwargs):
"""Constructs a ResNetV1b-101 GroupNorm model.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.
dilated: bool, default False
Whether to apply dilation strategy to ResNetV1b, yielding a stride 8 model.
norm_layer : object
Normalization layer used (default: :class:`nn.BatchNorm`)
Can be :class:`nn.BatchNorm` or :class:`other normalization`.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_global_stats : bool, default False
Whether forcing BatchNorm to use global statistics instead of minibatch statistics;
optionally set to True if finetuning using ImageNet classification pretrained models.
"""
from model.module.basic import GroupNorm
from model.module.convert import convert_norm_layer
model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], **kwargs)
norm_kwargs = {'num_groups': 32}
model = convert_norm_layer(model,
norm_layer=GroupNorm,
norm_kwargs=norm_kwargs)
if pretrained:
import torch
from model.model_store import get_model_file
model.load_state_dict(
torch.load(get_model_file('resnet%d_v%db_gn' % (101, 1),
root=root)))
from data.imagenet import ImageNetAttr
attrib = ImageNetAttr()
model.synset = attrib.synset
model.classes = attrib.classes
model.classes_long = attrib.classes_long
return model
def resnet101_v1b_gn(pretrained=None, **kwargs):
"""Constructs a ResNetV1b-101 GroupNorm model.
Parameters
----------
pretrained : str
the default pretrained weights for model.
dilated: bool, default False
Whether to apply dilation strategy to ResNetV1b, yielding a stride 8 model.
"""
from model.module.basic import GroupNorm
from model.module.convert import convert_norm_layer
model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], **kwargs)
norm_kwargs = {'num_groups': 32}
model = convert_norm_layer(model, norm_layer=GroupNorm, norm_kwargs=norm_kwargs)
if pretrained:
model.load_state_dict(torch.load(pretrained))
from data.imagenet import ImageNetAttr
attrib = ImageNetAttr()
model.synset = attrib.synset
model.classes = attrib.classes
model.classes_long = attrib.classes_long
return model