def resnet101(pretrained=False, root='~/.encoding/models', **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
kwargs['radix'] = 0
kwargs['rectified_conv'] = True
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
rectify_model_urls['resnet101'], progress=True, check_hash=True,
map_location=torch.device('cpu')))
return model
def __init__(self,
arch='resnest50_2s2x40d',
dropout_rate=0.,
num_classes=1000,
fix_bn=False,
partial_bn=False):
super(OfficialResNeSt, self).__init__()
self.num_classes = num_classes
self.fix_bn = fix_bn
self.partial_bn = partial_bn
if arch == 'resnest50':
self.model = resnest50(num_classes=num_classes,
final_drop=dropout_rate)
elif arch == 'resnest50_2s2x40d':
radix = 2
groups = 2
width_per_group = 40
avg_first = False
self.model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=radix,
groups=groups,
bottleneck_width=width_per_group,
deep_stem=True,
stem_width=32,
avg_down=True,
avd=True,
avd_first=avg_first,
final_drop=dropout_rate,
num_classes=num_classes)
elif arch == 'resnest50_2s2x40d_fast':
radix = 2
groups = 2
width_per_group = 40
avg_first = True
self.model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=radix,
groups=groups,
bottleneck_width=width_per_group,
deep_stem=True,
stem_width=32,
avg_down=True,
avd=True,
avd_first=avg_first,
final_drop=dropout_rate,
num_classes=num_classes)
else:
raise ValueError('no such value')
def resnext50_32x4d(pretrained=False, root='~/.encoding/models', **kwargs):
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
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
"""
kwargs['radix'] = 0
kwargs['groups'] = 32
kwargs['bottleneck_width'] = 4
kwargs['rectified_conv'] = True
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(torch.hub.load_state_dict_from_url(
rectify_model_urls['resnext50_32x4d'], progress=True, check_hash=True,
map_location=torch.device('cpu')))
return model
def resnest50(num_classes, pretrained=True, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3],
radix=2,
groups=1,
bottleneck_width=64,
num_classes=num_classes,
deep_stem=True,
stem_width=32,
avg_down=True,
avd=True,
avd_first=False,
dilation=2,
**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 get_model(pretrained=True, n_class=24):
# model = torchvision.models.resnext50_32x4d(pretrained=False)
# model = torchvision.models.resnext101_32x8d(pretrained=False)
model = ResNet(**MODEL_CONFIGS["resnest50_fast_1s1x64d"])
n_features = model.fc.in_features
model.fc = nn.Linear(n_features, 264)
# model.load_state_dict(torch.load('resnext50_32x4d_extra_2.pt'))
# model.load_state_dict(torch.load('resnext101_32x8d_wsl_extra_4.pt'))
fn = '../input/birds-cp-1/resnest50_fast_1s1x64d_conf_1.pt'
model.load_state_dict(torch.load(fn, map_location='cpu'))
model.fc = nn.Linear(n_features, n_class)
return model