def system_check():
import traceback
try:
import torch
from olympus.models import Model
from olympus.optimizers import Optimizer
batch = torch.randn((32, 3, 64, 64)).cuda()
model = Model('resnet18', input_size=(3, 64, 64),
output_size=(10, )).cuda()
model.init()
optimizer = Optimizer('sgd', params=model.parameters())
optimizer.init(**optimizer.defaults)
optimizer.zero_grad()
loss = model(batch).sum()
optimizer.backward(loss)
optimizer.step()
return True
except:
error(traceback.format_exc())
return False
@staticmethod
def get_space():
return {
'l1': 'uniform(32, 64, discrete=True)',
'l2': 'uniform(32, 64, discrete=True)',
'l3': 'uniform(32, 64, discrete=True)',
'l4': 'uniform(32, 64, discrete=True)'
}
# Register my model
builders = {'my_model': MyCustomNASModel}
if __name__ == '__main__':
model = Model(
model=MyCustomNASModel,
input_size=(290,),
output_size=(10,),
# Fix this hyper-parameter right away
l1=21
)
# If you use an hyper parameter optimizer, it will generate this for you
model.init(l2=33, l3=33, l4=32)
input = torch.randn((10, 290))
out = model(input)
print(out)