durations = dict(
long=dict(
max_hosts=1, ppn=16, cpp=1, gpu_set="0,1,2,3", wall_time="36hours",
project="rpp-bengioy", cleanup_time="10mins",
slack_time="10mins", n_repeats=6, step_time_limit="36hours"),
build=dict(
max_hosts=1, ppn=1, cpp=2, gpu_set="0", wall_time="20mins",
project="rpp-bengioy", cleanup_time="2mins",
slack_time="2mins", n_repeats=1, step_time_limit="2hours",
config=dict(do_train=False), n_param_settings=1,),
short=dict(
max_hosts=1, ppn=3, cpp=2, gpu_set="0", wall_time="30mins",
project="rpp-bengioy", cleanup_time="1mins",
slack_time="1mins", n_repeats=1, config=dict(max_steps=100)),
)
config = dict(
curriculum=[dict()],
n_train=64000, run_all_time_steps=True,
render_hook=air.AIR_ComparisonRenderHook(),
)
envs.run_experiment(
"{}_run".format(alg), config, readme, distributions=distributions,
alg=alg, task="arithmetic", durations=durations,
)
from dps import cfg
from dps.utils import Config
from auto_yolo.envs import run_experiment
if __name__ == "__main__":
_config = Config()
with _config:
cfg.update_from_command_line()
run_experiment("local_run", _config, "")
from auto_yolo import envs
from auto_yolo.models import yolo_air
import argparse
readme = "Plotting yolo_air on atari."
distributions = None
durations = dict()
parser = argparse.ArgumentParser()
parser.add_argument("--game")
args, _ = parser.parse_known_args()
config = dict(
render_hook=yolo_air.YoloAir_AtariRenderHook(N=32, pred_colour="xkcd:azure"),
render_step=1,
do_train=False,
n_train=16,
load_path="/media/data/dps_data/logs/atari_env=task=atari/exp_alg=yolo-air_seed=1742161367_2018_09_04_16_03_06/weights/best_of_stage_0",
kernel_size=2,
game=args.game,
postprocessing="",
)
envs.run_experiment(
"plot_yolo_air_atari_game={}".format(args.game), config, readme, alg="yolo_air",
task="atari", durations=durations, distributions=distributions,
)
build=dict(max_hosts=1,
ppn=1,
cpp=2,
gpu_set="0",
wall_time="2hours",
project="rpp-bengioy",
cleanup_time="2mins",
slack_time="2mins",
n_repeats=1,
step_time_limit="2hours",
config=dict(do_train=False)),
short=dict(max_hosts=1,
ppn=2,
cpp=2,
gpu_set="0",
wall_time="20mins",
project="rpp-bengioy",
cleanup_time="1mins",
slack_time="1mins",
n_repeats=1,
n_param_settings=4),
)
envs.run_experiment("test_math",
dict(n_train=16000),
readme,
alg="ground_truth_math",
task="arithmetic",
durations=durations,
distributions=distributions)
host_pool=[":"],
kind="parallel"),
)
n_digits = args.n_digits
config = dict(
curriculum=[dict()],
n_train=64000,
min_digits=n_digits,
max_digits=n_digits,
max_time_steps=n_digits,
run_all_time_steps=True,
patience=10000,
train_example_range=(0.0, 0.7),
val_example_range=(0.7, 0.8),
test_example_range=(0.8, 0.9),
)
alg = "dair" if args.dair else "air"
envs.run_experiment(
"{}_search_n_digits={}".format(alg, n_digits),
config,
readme,
distributions=distributions,
alg=alg,
task="arithmetic",
durations=durations,
)
dict(n_digits=8, cc_threshold=0.584),
dict(n_digits=9, cc_threshold=0.705),
dict(n_digits=10, cc_threshold=0.735),
dict(n_digits=11, cc_threshold=0.735),
dict(n_digits=12, cc_threshold=0.825),
dict(n_digits=13, cc_threshold=0.856),
dict(n_digits=14, cc_threshold=0.886),
dict(n_digits=15, cc_threshold=0.886),
dict(n_digits=16, cc_threshold=0.916),
dict(n_digits=17, cc_threshold=0.976),
dict(n_digits=18, cc_threshold=1.006),
dict(n_digits=19, cc_threshold=0.976),
dict(n_digits=20, cc_threshold=1.006),
]
else:
raise Exception()
for d in distributions:
n_digits = d['n_digits']
d.update(min_chars=n_digits, max_chars=n_digits)
envs.run_experiment(
"transfer_baseline_sc={}".format(args.sc),
config,
readme,
distributions=distributions,
alg="baseline",
task="scatter",
durations=durations,
)
from auto_yolo import envs
readme = "Testing ground_truth."
distributions = None
durations = dict(
long=dict(
max_hosts=1, ppn=6, cpp=2, gpu_set="0,1", wall_time="24hours",
project="rpp-bengioy", cleanup_time="20mins",
slack_time="5mins", n_repeats=6, step_time_limit="24hours"),
build=dict(
max_hosts=1, ppn=1, cpp=2, gpu_set="0", wall_time="2hours",
project="rpp-bengioy", cleanup_time="2mins",
slack_time="2mins", n_repeats=1, step_time_limit="2hours",
config=dict(do_train=False)),
short=dict(
max_hosts=1, ppn=2, cpp=2, gpu_set="0", wall_time="20mins",
project="rpp-bengioy", cleanup_time="1mins",
slack_time="1mins", n_repeats=1, n_param_settings=4),
)
envs.run_experiment(
"test", dict(n_train=16000, do_train=True, render_step=5000),
readme, alg="ground_truth", task="arithmetic", durations=durations,
distributions=distributions
)
envs.run_experiment("simple_xo_continue",
config,
readme,
alg="simple_xo_continue",
task="xo",
name_variables="decoder_kind",
distributions=distributions,
durations=dict(long=dict(max_hosts=1,
ppn=6,
cpp=2,
gpu_set="0,1",
wall_time="12hours",
project="rpp-bengioy",
cleanup_time="10mins",
slack_time="5mins",
n_repeats=6,
step_time_limit="12hours"),
med=dict(max_hosts=1,
ppn=3,
cpp=2,
gpu_set="0",
wall_time="30mins",
project="rpp-bengioy",
cleanup_time="2mins",
slack_time="2mins",
n_repeats=3),
short=dict(max_hosts=1,
ppn=3,
cpp=2,
gpu_set="0",
wall_time="10mins",
project="rpp-bengioy",
cleanup_time="2mins",
slack_time="2mins",
n_repeats=3)))
from auto_yolo import envs
readme = "Testing simple variational autoencoder."
distributions = None
durations = dict(
long=dict(
max_hosts=1, ppn=6, cpp=2, gpu_set="0,1", wall_time="24hours",
project="rpp-bengioy", cleanup_time="20mins",
slack_time="5mins", n_repeats=6, step_time_limit="24hours"),
build=dict(
max_hosts=1, ppn=1, cpp=2, gpu_set="0", wall_time="2hours",
project="rpp-bengioy", cleanup_time="2mins",
slack_time="2mins", n_repeats=1, step_time_limit="2hours",
config=dict(do_train=False)),
short=dict(
max_hosts=1, ppn=2, cpp=2, gpu_set="0", wall_time="20mins",
project="rpp-bengioy", cleanup_time="1mins",
slack_time="1mins", n_repeats=1, n_param_settings=4),
)
envs.run_experiment(
"test", dict(n_train=16000), readme, alg="simple",
task="arithmetic", durations=durations, distributions=distributions
)
dict(n_digits=5, cc_threshold=0.02),
dict(n_digits=7, cc_threshold=0.6),
dict(n_digits=9, cc_threshold=0.52),
]
for d in distributions:
n_digits = d['n_digits']
d.update(
min_digits=n_digits,
max_digits=n_digits
)
durations = dict(
oak=dict(
max_hosts=1, ppn=1, cpp=2, gpu_set="0", wall_time="30mins",
cleanup_time="1mins", slack_time="1mins", n_repeats=1, kind="parallel", host_pool=":"),
)
config = dict(
curriculum=[dict()],
n_train=32, n_val=1000, stopping_criteria="AP,max", threshold=0.99,
min_digits=1, max_digits=1, do_train=False,
render_hook=yolo_air.YoloAir_ComparisonRenderHook(show_zero_boxes=False),
)
envs.run_experiment(
"comparison_baseline", config, readme, distributions=distributions,
alg="baseline", task="arithmetic", durations=durations,
)
build_object_decoder=build_net)
if args.sc == "AP":
config.update(stopping_criteria="AP,max", threshold=1.0)
elif args.sc == "count_error":
config.update(stopping_criteria="count_error,min", threshold=0.0)
elif args.sc == "count_1norm":
config.update(stopping_criteria="count_1norm,min", threshold=0.0)
else:
raise Exception()
if args.transfer:
config["min_chars"] = args.n_digits
config["max_chars"] = args.n_digits
config["n_train"] = 25000
task = "scatter"
else:
config["min_digits"] = args.n_digits
config["max_digits"] = args.n_digits
config["n_train"] = 64000
task = "arithmetic"
envs.run_experiment("baseline_search_sc={}_n_digits={}".format(
args.sc, args.n_digits),
config,
readme,
distributions=distributions,
alg="baseline",
durations=durations,
task=task)
