i found the cadene_models.py

to be a great resource to understand how to make the pretrained models work with fastai. you can adapt many models even if they aren’t in that cadene module yet (ie if they have the same base arch, the cuts will likely be the same).

#These models are dowloaded via the repo https://github.com/Cadene/pretrained-models.pytorch
#See licence here: https://github.com/Cadene/pretrained-models.pytorch/blob/master/LICENSE.txt
from torch import nn
from ..learner import model_meta
from ...core import *
pretrainedmodels = try_import('pretrainedmodels')
if not pretrainedmodels:
raise Exception('Error: `pretrainedmodels` is needed. `pip install pretrainedmodels`')
__all__ = ['inceptionv4', 'inceptionresnetv2', 'nasnetamobile', 'dpn92', 'xception_cadene', 'se_resnet50',
'se_resnet101', 'se_resnext50_32x4d', 'senet154', 'pnasnet5large']
def get_model(model_name:str, pretrained:bool, seq:bool=False, pname:str='imagenet', **kwargs):
pretrained = pname if pretrained else None
model = getattr(pretrainedmodels, model_name)(pretrained=pretrained, **kwargs)
return nn.Sequential(*model.children()) if seq else model
def inceptionv4(pretrained:bool=False):
model = get_model('inceptionv4', pretrained)
all_layers = list(model.children())
return nn.Sequential(*all_layers[0], *all_layers[1:])
model_meta[inceptionv4] = {'cut': -2, 'split': lambda m: (m[0][11], m[1])}
def nasnetamobile(pretrained:bool=False):
model = get_model('nasnetamobile', pretrained, num_classes=1000)
model.logits = noop
return nn.Sequential(model)
model_meta[nasnetamobile] = {'cut': noop, 'split': lambda m: (list(m[0][0].children())[8], m[1])}
def pnasnet5large(pretrained:bool=False):
model = get_model('pnasnet5large', pretrained, num_classes=1000)
model.logits = noop
return nn.Sequential(model)
model_meta[pnasnet5large] = {'cut': noop, 'split': lambda m: (list(m[0][0].children())[8], m[1])}
def inceptionresnetv2(pretrained:bool=False): return get_model('inceptionresnetv2', pretrained, seq=True)
def dpn92(pretrained:bool=False): return get_model('dpn92', pretrained, pname='imagenet+5k', seq=True)
def xception_cadene(pretrained=False): return get_model('xception', pretrained, seq=True)
def se_resnet50(pretrained:bool=False): return get_model('se_resnet50', pretrained)
def se_resnet101(pretrained:bool=False): return get_model('se_resnet101', pretrained)
def se_resnext50_32x4d(pretrained:bool=False): return get_model('se_resnext50_32x4d', pretrained)
def senet154(pretrained:bool=False): return get_model('senet154', pretrained)
model_meta[inceptionresnetv2] = {'cut': -2, 'split': lambda m: (m[0][9], m[1])}
model_meta[dpn92] = {'cut': -1, 'split': lambda m: (m[0][0][16], m[1])}
model_meta[xception_cadene] = {'cut': -1, 'split': lambda m: (m[0][11], m[1])}
model_meta[senet154] = {'cut': -3, 'split': lambda m: (m[0][3], m[1])}
_se_resnet_meta = {'cut': -2, 'split': lambda m: (m[0][3], m[1])}
model_meta[se_resnet50] = _se_resnet_meta
model_meta[se_resnet101] = _se_resnet_meta
model_meta[se_resnext50_32x4d] = _se_resnet_meta
# TODO: add "resnext101_32x4d" "resnext101_64x4d" after serialization issue is fixed:
# https://github.com/Cadene/pretrained-models.pytorch/pull/128