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
def test_optimizer_factory_init():
# set init using its name
model: Model = new_model()
optim = Optimizer('sgd', params=model.parameters())
assert optim.get_space() == SGD.get_space()
optim.init(**SGD.defaults())
def test_build_optimizer(optimizer):
model = new_model()
optimizer = Optimizer(
optimizer,
params=model.parameters()
)
optimizer.init(**optimizer.defaults)
optimizer_work(optimizer, model)
@staticmethod
def get_space():
return {'gamma': 'loguniform(0.97, 1)'}
if __name__ == '__main__':
model = Model(
'logreg',
input_size=(290,),
output_size=(10,)
)
optimizer = Optimizer('sgd', params=model.parameters())
# If you use an hyper parameter optimizer, it will generate this for you
optimizer.init(lr=1e-4, momentum=0.02, weight_decay=1e-3)
schedule = LRSchedule(schedule=MyExponentialLR)
schedule.init(optimizer=optimizer, gamma=0.97)
optimizer.zero_grad()
input = torch.randn((10, 290))
output = model(input)
loss = output.sum()
loss.backward()
optimizer.step()
print(optimizer.param_groups[0]['lr'])
schedule.epoch(1)
def setup():
model = Model('logreg', input_size=(28, ), output_size=(10, ))
optimizer = Optimizer('sgd', params=model.parameters())
optimizer.init(**optimizer.defaults)
return model, optimizer