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utils.py
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from .models.resnet import *
from .models.preresnet import *
from .models.resnext import *
from .models.senet import *
from .models.densenet import *
from .models.condensenet import *
from .models.dpn import *
from .models.darknet import *
from .models.squeezenet import *
from .models.squeezenext import *
from .models.shufflenet import *
from .models.shufflenetv2 import *
from .models.menet import *
from .models.mobilenet import *
from .models.mobilenetv2 import *
from .models.nasnet import *
from .models.others.shufflenet_v2 import *
__all__ = ['get_model']
_models = {
'resnet10': resnet10,
'resnet12': resnet12,
'resnet14': resnet14,
'resnet16': resnet16,
'resnet18_wd4': resnet18_wd4,
'resnet18_wd2': resnet18_wd2,
'resnet18_w3d4': resnet18_w3d4,
'slk_resnet18': resnet18,
'slk_resnet34': resnet34,
'slk_resnet50': resnet50,
'resnet50b': resnet50b,
'slk_resnet101': resnet101,
'resnet101b': resnet101b,
'slk_resnet152': resnet152,
'resnet152b': resnet152b,
'resnet200': resnet200,
'resnet200b': resnet200b,
'seresnet18': seresnet18,
'seresnet34': seresnet34,
'seresnet50': seresnet50,
'seresnet50b': seresnet50b,
'seresnet101': seresnet101,
'seresnet101b': seresnet101b,
'seresnet152': seresnet152,
'seresnet152b': seresnet152b,
'seresnet200': seresnet200,
'seresnet200b': seresnet200b,
'preresnet10': preresnet10,
'preresnet12': preresnet12,
'preresnet14': preresnet14,
'preresnet16': preresnet16,
'preresnet18_wd4': preresnet18_wd4,
'preresnet18_wd2': preresnet18_wd2,
'preresnet18_w3d4': preresnet18_w3d4,
'preresnet18': preresnet18,
'preresnet34': preresnet34,
'preresnet50': preresnet50,
'preresnet50b': preresnet50b,
'preresnet101': preresnet101,
'preresnet101b': preresnet101b,
'preresnet152': preresnet152,
'preresnet152b': preresnet152b,
'preresnet200': preresnet200,
'preresnet200b': preresnet200b,
'sepreresnet18': sepreresnet18,
'sepreresnet34': sepreresnet34,
'sepreresnet50': sepreresnet50,
'sepreresnet50b': sepreresnet50b,
'sepreresnet101': sepreresnet101,
'sepreresnet101b': sepreresnet101b,
'sepreresnet152': sepreresnet152,
'sepreresnet152b': sepreresnet152b,
'sepreresnet200': sepreresnet200,
'sepreresnet200b': sepreresnet200b,
'resnext50_32x4d': resnext50_32x4d,
'resnext101_32x4d': resnext101_32x4d,
'resnext101_64x4d': resnext101_64x4d,
'seresnext50_32x4d': seresnext50_32x4d,
'seresnext101_32x4d': seresnext101_32x4d,
'seresnext101_64x4d': seresnext101_64x4d,
'senet52': senet52,
'senet103': senet103,
'senet154': senet154,
'slk_densenet121': densenet121,
'slk_densenet161': densenet161,
'slk_densenet169': densenet169,
'slk_densenet201': densenet201,
'condensenet74_c4_g4': condensenet74_c4_g4,
'condensenet74_c8_g8': condensenet74_c8_g8,
'dpn68': dpn68,
'dpn68b': dpn68b,
'dpn98': dpn98,
'dpn107': dpn107,
'dpn131': dpn131,
'darknet_ref': darknet_ref,
'darknet_tiny': darknet_tiny,
'darknet19': darknet19,
'squeezenet_v1_0': squeezenet_v1_0,
'squeezenet_v1_1': squeezenet_v1_1,
'squeezeresnet_v1_0': squeezeresnet_v1_0,
'squeezeresnet_v1_1': squeezeresnet_v1_1,
'sqnxt23_w1': sqnxt23_w1,
'sqnxt23_w3d2': sqnxt23_w3d2,
'sqnxt23_w2': sqnxt23_w2,
'sqnxt23v5_w1': sqnxt23v5_w1,
'sqnxt23v5_w3d2': sqnxt23v5_w3d2,
'sqnxt23v5_w2': sqnxt23v5_w2,
'shufflenet_g1_w1': shufflenet_g1_w1,
'shufflenet_g2_w1': shufflenet_g2_w1,
'shufflenet_g3_w1': shufflenet_g3_w1,
'shufflenet_g4_w1': shufflenet_g4_w1,
'shufflenet_g8_w1': shufflenet_g8_w1,
'shufflenet_g1_w3d4': shufflenet_g1_w3d4,
'shufflenet_g3_w3d4': shufflenet_g3_w3d4,
'shufflenet_g1_wd2': shufflenet_g1_wd2,
'shufflenet_g3_wd2': shufflenet_g3_wd2,
'shufflenet_g1_wd4': shufflenet_g1_wd4,
'shufflenet_g3_wd4': shufflenet_g3_wd4,
'shufflenetv2_wd2': shufflenetv2_wd2,
'shufflenetv2_w1': shufflenetv2_w1,
'shufflenetv2_w2d3': shufflenetv2_w2d3,
'shufflenetv2_w2': shufflenetv2_w2,
'menet108_8x1_g3': menet108_8x1_g3,
'menet128_8x1_g4': menet128_8x1_g4,
'menet160_8x1_g8': menet160_8x1_g8,
'menet228_12x1_g3': menet228_12x1_g3,
'menet256_12x1_g4': menet256_12x1_g4,
'menet348_12x1_g3': menet348_12x1_g3,
'menet352_12x1_g8': menet352_12x1_g8,
'menet456_24x1_g3': menet456_24x1_g3,
'mobilenet_w1': mobilenet_w1,
'mobilenet_w3d4': mobilenet_w3d4,
'mobilenet_wd2': mobilenet_wd2,
'mobilenet_wd4': mobilenet_wd4,
'fdmobilenet_w1': fdmobilenet_w1,
'fdmobilenet_w3d4': fdmobilenet_w3d4,
'fdmobilenet_wd2': fdmobilenet_wd2,
'fdmobilenet_wd4': fdmobilenet_wd4,
'mobilenetv2_w1': mobilenetv2_w1,
'mobilenetv2_w3d4': mobilenetv2_w3d4,
'mobilenetv2_wd2': mobilenetv2_wd2,
'mobilenetv2_wd4': mobilenetv2_wd4,
'nasnet_a_mobile': nasnet_a_mobile,
'oth_shufflenetv2_wd2': oth_shufflenetv2_wd2,
}
def get_model(name, **kwargs):
# try:
# import torchvision.models as ptv_models
# net = ptv_models.__dict__[name](**kwargs)
# return net
# except KeyError as e:
# upstream_supported = str(e)
name = name.lower()
if name not in _models:
# raise ValueError('{}\n\t{}'.format(upstream_supported, '\n\t'.join(sorted(_models.keys()))))
raise ValueError('Unsupported model: {}'.format(name))
net = _models[name](**kwargs)
return net