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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: [email protected]
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNeSt models"""
import torch
from .resnet import ResNet, Bottleneck
__all__ = ['resnest50', 'resnest101', 'resnest200', 'resnest269']
_url_format = 'https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/{}-{}.pth'
_model_sha256 = {name: checksum for checksum, name in [
('528c19ca', 'resnest50'),
('22405ba7', 'resnest101'),
('75117900', 'resnest200'),
('0cc87c48', 'resnest269'),
]}
def short_hash(name):
if name not in _model_sha256:
raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
return _model_sha256[name][:8]
resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
name in _model_sha256.keys()
}
def resnest50(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=2, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=False, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50'], progress=True, check_hash=True))
return model
def resnest101(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 23, 3],
radix=2, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=64, avg_down=True,
avd=True, avd_first=False, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest101'], progress=True, check_hash=True))
return model
def resnest200(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 24, 36, 3],
radix=2, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=64, avg_down=True,
avd=True, avd_first=False, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest200'], progress=True, check_hash=True))
return model
def resnest269(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 30, 48, 8],
radix=2, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=64, avg_down=True,
avd=True, avd_first=False, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest269'], progress=True, check_hash=True))
return model
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