def tv_resnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-50 model with original Torchvision weights.
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfgs['tv_resnet50']
if pretrained:
load_pretrained(model, model.default_cfg, num_classes, in_chans)
return model
def resnet152(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['resnet152']
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def resnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-34 model.
"""
default_cfg = default_cfgs['resnet34']
model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def tv_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNeXt50-32x4d model with original Torchvision weights.
"""
default_cfg = default_cfgs['tv_resnext50_32x4d']
model = ResNet(
Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNeXt101-64x4d model.
"""
default_cfg = default_cfgs['resnext101_32x4d']
model = ResNet(
Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def resnet50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-50-D model.
"""
default_cfg = default_cfgs['resnet50d']
model = ResNet(
Bottleneck, [3, 4, 6, 3], stem_width=32, deep_stem=True, avg_down=True,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def scse_resnext50d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample
"""
default_cfg = default_cfgs['resnext50d_32x4d']
model = ResNet(
Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
stem_width=32, deep_stem=True, avg_down=True,
num_classes=num_classes, in_chans=in_chans, use_scse=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans, strict=False)
return model
def resnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-26 v1d model.
This is technically a 28 layer ResNet, sticking with 'd' modifier from Gluon for now.
"""
default_cfg = default_cfgs['resnet26d']
model = ResNet(
Bottleneck, [2, 2, 2, 2], stem_width=32, deep_stem=True, avg_down=True,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
def wide_resnet101_2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a Wide ResNet-101-2 model.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same.
"""
model = ResNet(
Bottleneck, [3, 4, 23, 3], base_width=128,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfgs['wide_resnet101_2']
if pretrained:
load_pretrained(model, model.default_cfg, num_classes, in_chans)
return model
def ig_resnext101_32x48d(pretrained=True, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data
and finetuned on ImageNet from Figure 5 in
`"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
Args:
pretrained (bool): load pretrained weights
num_classes (int): number of classes for classifier (default: 1000 for pretrained)
in_chans (int): number of input planes (default: 3 for pretrained / color)
"""
default_cfg = default_cfgs['ig_resnext101_32x48d']
model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=48,
num_classes=1000, in_chans=3, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
