def alexnet(pretrained=True, **kwargs):
cust_nc = None
if pretrained and 'nc' in kwargs: cust_nc = kwargs['nc']; kwargs['nc'] = 1000
net = Alexnet(**kwargs)
if pretrained:
return load_pretrained(net, urls.alexnet_url, 'alexnet', nc=cust_nc,
attr='net[21]', inn=4096)
return net
def alexnet_mnist(pretrained=True, **kwargs):
cust_nc = kwargs['nc'] if 'nc' in kwargs else None
kwargs['ic'] = 1; kwargs['nc'] = 10
net = Alexnet(**kwargs)
if pretrained:
return load_pretrained(net, urls.alexnet_mnist_url, 'alexnet_mnist',
nc=cust_nc, attr='net[21]', inn=4096)
return net
def resnet34(pretrained=True, **kwargs):
kwargs['layers'] = [3, 4, 6, 3]
cust_nc = None
if pretrained and 'nc' in kwargs: cust_nc = kwargs['nc']; kwargs['nc'] = 1000
net = Resnet(**kwargs)
if pretrained:
return load_pretrained(net, urls.resnet34_url, 'resnet34', nc=cust_nc)
return net
def resnet18(pretrained=True, **kwargs):
kwargs['layers'] = [2, 2, 2, 2]
cust_nc = None
if pretrained and 'nc' in kwargs: cust_nc = kwargs['nc']; kwargs['nc'] = 1000
net = Resnet(**kwargs)
if pretrained:
# TODO check inspect module and change the fname
return load_pretrained(net, urls.resnet18_url, 'resnet18', nc=cust_nc)
return net
def resnet152(pretrained=True, **kwargs):
kwargs['layers'] = [3, 8, 36, 3]
kwargs['btype'] = 'bottleneck'
kwargs['ex'] = 4; kwargs['fdown'] = True
cust_nc = None
if pretrained and 'nc' in kwargs: cust_nc = kwargs['nc']; kwargs['nc'] = 1000
net = Resnet(**kwargs)
if pretrained:
return load_pretrained(net, urls.resnet152_url, 'resnet152', nc=cust_nc,
inn=512*kwargs['ex'])
return net
def alexnet(pretrained=True, **kwargs):
cust_nc = None
if pretrained and 'nc' in kwargs:
cust_nc = kwargs['nc']
kwargs['nc'] = 1000
net = Alexnet(**kwargs)
if pretrained:
return load_pretrained(net,
urls.alexnet_url,
'alexnet',
nc=cust_nc,
attr='classifier',
inn=9216)
return net
def get_net(net,
pretrained=True,
pretrain_url=None,
fname=None,
kwargs_net=None,
**kwargs_load_pretrained):
"""
Function to load a model and do some required cutting and splitting on it.
Arguments
---------
net : scratchai.nets.*
The class which to initialize,
*not an initialized net.*
pretrained : bool
Whether to load a pretrained net or not.
pretrain_url : str
The url from which to load a pretrained model.
It should be just the file id, if the file is
hosted on Google Drive.
fname : str
The file name with which to store the pretrained
file.
kwargs_net : dict
The extra parameters which are passed while
initializing the net
kwargs_load_pretrained : dict
The extra parameters passed to load_pretrained.
"""
cust_nc = kwargs_net['nc'] if 'nc' in kwargs_net else None
if pretrained and cust_nc is not None: kwargs_net.pop('nc')
net = net(**kwargs_net)
if pretrained:
return load_pretrained(net,
pretrain_url,
fname,
nc=cust_nc,
**kwargs_load_pretrained)
return net