ofa_net is always called with pretrained=True, which means it will works without training. But how to train the super network ?References:
# file:
# ofa/model_zoo.py
def ofa_net(net_id, pretrained=True):
if net_id == 'ofa_proxyless_d234_e346_k357_w1.3':
net = OFAProxylessNASNets(
dropout_rate=0, width_mult=1.3, ks_list=[3, 5, 7], expand_ratio_list=[3, 4, 6], depth_list=[2, 3, 4],
)
elif net_id == 'ofa_mbv3_d234_e346_k357_w1.0':
net = OFAMobileNetV3(
dropout_rate=0, width_mult=1.0, ks_list=[3, 5, 7], expand_ratio_list=[3, 4, 6], depth_list=[2, 3, 4],
)
elif net_id == 'ofa_mbv3_d234_e346_k357_w1.2':
net = OFAMobileNetV3(
dropout_rate=0, width_mult=1.2, ks_list=[3, 5, 7], expand_ratio_list=[3, 4, 6], depth_list=[2, 3, 4],
)
elif net_id == 'ofa_resnet50':
net = OFAResNets(
dropout_rate=0, depth_list=[0, 1, 2], expand_ratio_list=[0.2, 0.25, 0.35], width_mult_list=[0.65, 0.8, 1.0]
)
net_id = 'ofa_resnet50_d=0+1+2_e=0.2+0.25+0.35_w=0.65+0.8+1.0'
else:
raise ValueError('Not supported: %s' % net_id)
if pretrained:
url_base = 'https://hanlab.mit.edu/files/OnceForAll/ofa_nets/'
init = torch.load(
download_url(url_base + net_id, model_dir='.torch/ofa_nets'),
map_location='cpu')['state_dict']
net.load_state_dict(init)
return netCall site:
# In tutorial
ofa_network = ofa_net('ofa_mbv3_d234_e346_k357_w1.2', pretrained=True)
print('The OFA Network is ready.')
# What will happen if we give `pretrained=False`???
If you could revise
the fundmental principles of
computer system design
to improve security...
... what would you change?