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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNeSt ablation study models"""
import torch
from .resnet import ResNet, Bottleneck
__all__ = ['resnest50_fast_1s1x64d', 'resnest50_fast_2s1x64d', 'resnest50_fast_4s1x64d',
'resnest50_fast_1s2x40d', 'resnest50_fast_2s2x40d', 'resnest50_fast_4s2x40d',
'resnest50_fast_1s4x24d']
_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
_model_sha256 = {name: checksum for checksum, name in [
('d8fbf808', 'resnest50_fast_1s1x64d'),
('44938639', 'resnest50_fast_2s1x64d'),
('f74f3fc3', 'resnest50_fast_4s1x64d'),
('32830b84', 'resnest50_fast_1s2x40d'),
('9d126481', 'resnest50_fast_2s2x40d'),
('41d14ed0', 'resnest50_fast_4s2x40d'),
('d4a4f76f', 'resnest50_fast_1s4x24d'),
]}
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_fast_1s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=1, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_1s1x64d'], progress=True, check_hash=True))
return model
def resnest50_fast_2s1x64d(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=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_2s1x64d'], progress=True, check_hash=True))
return model
def resnest50_fast_4s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=4, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_4s1x64d'], progress=True, check_hash=True))
return model
def resnest50_fast_1s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=1, groups=2, bottleneck_width=40,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_1s2x40d'], progress=True, check_hash=True))
return model
def resnest50_fast_2s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=2, groups=2, bottleneck_width=40,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_2s2x40d'], progress=True, check_hash=True))
return model
def resnest50_fast_4s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=4, groups=2, bottleneck_width=40,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_4s2x40d'], progress=True, check_hash=True))
return model
def resnest50_fast_1s4x24d(pretrained=False, root='~/.encoding/models', **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=1, groups=4, bottleneck_width=24,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True, **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
resnest_model_urls['resnest50_fast_1s4x24d'], progress=True, check_hash=True))
return model
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