return (net, image_shape)
def cnn_setup(network, dev, batch_size):
net, image_shape = instantiate_network(network, batch_size, dev)
device = torch.device(
'cuda' if dev == 'gpu' and torch.cuda.is_available() else 'cpu')
target = net.to(device)
input_tensor = np.random.randn(*image_shape).astype(np.float32)
input = torch.autograd.Variable(torch.from_numpy(input_tensor))
input = input.to(device)
return [target, input]
def cnn_trial(target, input):
return target(input)
def cnn_teardown(target, input):
pass
if __name__ == '__main__':
run_template(validate_config=validate,
check_early_exit=common_early_exit({'frameworks': 'pt'}),
gen_trial_params=common_trial_params(
'pt', 'cnn_comp', cnn_trial, cnn_setup, cnn_teardown,
['network', 'device', 'batch_size'],
['networks', 'devices', 'batch_sizes']))
from validate_config import validate
from exp_templates import (common_trial_params, common_early_exit,
run_template)
from relay_util import cnn_setup, cnn_trial, cnn_teardown
if __name__ == '__main__':
run_template(validate_config=validate,
check_early_exit=common_early_exit({'frameworks': 'relay'}),
gen_trial_params=common_trial_params(
'relay', 'cnn_comp', cnn_trial, cnn_setup, cnn_teardown,
['network', 'device', 'batch_size', 'opt_level'],
['networks', 'devices', 'batch_sizes', 'relay_opt']))