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, run_template
from relay_util import cnn_setup, cnn_trial, cnn_teardown
if __name__ == '__main__':
run_template(validate_config=validate,
gen_trial_params=common_trial_params(
'relay', 'opt_comparison', cnn_trial, cnn_setup,
cnn_teardown,
['network', 'device', 'batch_size', 'opt_level'],
['networks', 'devices', 'batch_sizes', 'opt_levels']))
from validate_config import validate
from exp_templates import (common_trial_params, common_early_exit,
run_template)
from language_data import N_LETTERS
from pt_rnn import RNN, samples
def rnn_setup(device, hidden_size, lang, letters):
rnn = RNN(N_LETTERS, hidden_size, N_LETTERS)
return [lambda: samples(rnn, lang, letters)]
def rnn_trial(thunk):
return thunk()
def rnn_teardown(thunk):
pass
if __name__ == '__main__':
run_template(validate_config=validate,
check_early_exit=common_early_exit({'frameworks': 'pt'}),
gen_trial_params=common_trial_params(
'pt', 'char_rnn',
rnn_trial, rnn_setup, rnn_teardown,
['device', 'hidden_size', 'language', 'input'],
['devices', 'hidden_sizes', 'languages', 'inputs']))
net, num_states, shapes = import_gluon_rnn(network)
net.initialize(ctx=context)
net.hybridize()
shape_list = [shapes['data']
] + [shapes['state%s' % i] for i in range(num_states)]
mx_inputs = [
mx.nd.array(np.random.rand(*shape).astype('float32'), ctx=context)
for shape in shape_list
]
return [lambda: net(*mx_inputs)[0].asnumpy()]
def rnn_trial(thunk):
thunk()
def rnn_teardown(thunk):
pass
if __name__ == '__main__':
run_template(validate_config=validate,
check_early_exit=common_early_exit({'frameworks': 'mxnet'}),
gen_trial_params=common_trial_params('mxnet', 'gluon_rnns',
rnn_trial, rnn_setup,
rnn_teardown,
['device', 'network'],
['devices', 'networks']))
return cnn_setup(network,
dev,
batch_size,
opt_level,
use_passes=True,
passes=pass_list)
def gen_trial_params(config):
# We must preprocess the passes to work with passes_setup.
# I.e., we must serialize it so it can be written to CSV,
# so we separate the pass list by |'s and the opt_level with
# a semicolon
passes = [
';'.join([str(pass_spec[0]), '|'.join(pass_spec[1])])
for pass_spec in config['passes']
]
return [
'relay', 'pass_comparison', config['dry_run'],
config['n_times_per_input'], config['n_inputs'], cnn_trial,
passes_setup, cnn_teardown,
['network', 'device', 'batch_size', 'pass_spec'],
[config['networks'], config['devices'], config['batch_sizes'], passes]
]
if __name__ == '__main__':
run_template(validate_config=validate, gen_trial_params=gen_trial_params)