),
medium=dict(
max_hosts=1, ppn=6, cpp=1, gpu_set=gpu_set, pmem=pmem, project=project,
wall_time="6hours", cleanup_time="5mins", slack_time="5mins", n_repeats=6,
config=dict(stage_steps=3000, max_steps=12000), copy_locally=True,
),
short=dict(
max_hosts=1, ppn=6, cpp=1, gpu_set=gpu_set, pmem=pmem, project=project,
wall_time="180mins", cleanup_time="2mins", slack_time="2mins", n_repeats=6,
config=dict(max_steps=3000, render_step=500, eval_step=500, display_step=100, stage_steps=600),
distributions=None, copy_locally=True,
),
build=dict(
max_hosts=1, ppn=1, cpp=1, gpu_set="0", pmem=pmem, project=project,
wall_time="60mins", cleanup_time="2mins", slack_time="2mins", n_repeats=1,
config=dict(do_train=False, render_first=False, render_final=False),
distributions=None,
),
)
config = basic_config.copy()
config.update(env_configs['hard_shapes'])
config.update(alg_configs['fixed_sqair'])
config.update(late_config)
run_experiment(
"hard_shapes_sqair",
config, "sqair on hard_shapes.",
distributions=distributions, durations=durations
)
distributions=None,
config=dict(max_steps=500, curriculum=[dict(), dict()]),
),
build=dict(
ppn=1,
gpu_set="0",
wall_time="60mins",
n_repeats=1,
distributions=None,
config=dict(do_train=False, render_first=False, render_final=False),
),
)
config = basic_config.copy()
config.update(env_configs['moving_mnist'])
alg_name = 'conv_sqair' if args.conv else 'sqair'
config.update(
alg_configs[alg_name],
max_digits=args.max_digits,
n_objects=args.max_digits,
render_step=10000000,
curriculum=[dict(), dict(max_steps=100)],
)
run_experiment("moving_mnist_sqair",
config,
"sqair on moving_mnist.",
name_variables="max_digits",
distributions=distributions,
durations=durations)
(False, 6): dict(disc_step_bias=5., step_success_prob=0.516),
}[(args.conv, args.max_digits)]
config.update(**search_params)
if args.max_digits == 6:
config.update(
curriculum=([
dict(min_digits=i, max_digits=i, n_objects=6, fixed_presence=False)
for i in range(1, 2)
] + [
dict(min_digits=i, max_digits=i, n_objects=i, fixed_presence=True)
for i in range(2, 13)
]),
# curriculum=(
# [dict(min_digits=i, max_digits=i, n_objects=6, fixed_presence=False) for i in range(1, 7)]
# + [dict(min_digits=i, max_digits=i, n_objects=i, fixed_presence=True) for i in range(7, 13)]
# ),
)
else:
config.update(
curriculum=[dict(min_digits=i, max_digits=i) for i in range(1, 13)],
n_objects=12,
)
run_experiment("eval_moving_mnist_sqair_fixed",
config,
"sqair on moving_mnist.",
name_variables="max_digits",
durations=durations)
)),
)
config = basic_config.copy()
if args.small:
config.update(env_configs['big_shapes_small'])
else:
config.update(env_configs['big_shapes'])
config.update(alg_configs['shapes_silot'])
if args.small:
config.batch_size = 16
config.n_prop_objects = 25
else:
config.batch_size = 8
config.n_prop_objects = 36
config.update(
min_shapes=args.max_shapes - 9,
max_shapes=args.max_shapes,
stage_steps=40000,
render_step=1000000,
)
run_experiment("shapes_silot",
config,
"silot on shapes.",
name_variables="max_shapes",
durations=durations)
eval_step=100,
display_step=100,
stage_steps=600,
curriculum=[dict()]),
),
build=dict(ppn=1,
cpp=1,
gpu_set="0",
wall_time="180mins",
n_repeats=1,
distributions=None,
config=dict(do_train=False,
get_updater=DummyUpdater,
render_hook=None,
curriculum=[dict()] + [
dict(max_digits=i, n_train=100, n_val=1000)
for i in range(1, 13)
])),
)
config = basic_config.copy()
config.update(env_configs['moving_mnist'])
config.update(alg_configs['silot'], max_digits=args.max_digits)
config.update(final_count_prior_log_odds=0.0125, stage_steps=40000)
run_experiment("moving_mnist_silot",
config,
"silot on moving_mnist.",
name_variables="max_digits",
durations=durations)
from dps.utils import Config
from dps.mnist_example import mnist_config, mlp_config
from dps.hyper import run_experiment
env_configs = {'mnist': mnist_config}
alg_configs = {'mlp': mlp_config}
if __name__ == "__main__":
config = Config()
run_experiment("mnist_mlp_experiment",
config,
"",
cl_mode='strict',
env_configs=env_configs,
alg_configs=alg_configs)
tasks_per_gpu=4,
n_repeats=4,
distributions=None,
config=dict(max_steps=3000,
render_step=500,
eval_step=100,
display_step=100,
stage_steps=600,
curriculum=[dict()]),
),
build=dict(wall_time="180mins",
tasks_per_gpu=1,
n_repeats=1,
distributions=None,
config=dict(
do_train=False,
get_updater=DummyUpdater,
render_hook=None,
)),
)
config = basic_config.copy()
config.update(env_configs[args.game])
config.update(alg_configs['atari_train_silot'])
run_experiment("atari_silot",
config,
"silot on atari.",
name_variables="game",
durations=durations)
config=dict(max_steps=100,
render_step=25,
eval_step=25,
display_step=25),
),
build=dict(
max_hosts=1,
ppn=1,
cpp=1,
gpu_set=gpu_set,
pmem=pmem,
project=project,
wall_time="120mins",
cleanup_time="2mins",
slack_time="2mins",
n_repeats=1,
config=dict(do_train=False, render_first=False, render_final=False),
distributions=None,
),
)
config = basic_config.copy()
config.update(env_configs['moving_mnist'])
config.update(alg_configs['exp_silot'])
run_experiment("test_silot",
config,
"First test of silot.",
distributions=distributions,
durations=durations)
curriculum=[
copy_update(v, min_shapes=i, max_shapes=i)
for i, v in zip(shapes_baseline_n_shapes, shapes_baseline_values['AP'])]
)
alg_configs['shapes_baseline_count_1norm'] = alg_configs['shapes_baseline_test'].copy(
curriculum=[
copy_update(v, min_shapes=i, max_shapes=i)
for i, v in zip(shapes_baseline_n_shapes, shapes_baseline_values['count_1norm'])]
)
alg_configs['shapes_baseline_mota'] = alg_configs['shapes_baseline_test'].copy(
curriculum=[
copy_update(v, min_shapes=i, max_shapes=i)
for i, v in zip(shapes_baseline_n_shapes, shapes_baseline_values['MOT:mota'])]
)
for k, v in env_configs.items():
v['env_name'] = k
for k, v in alg_configs.items():
v['alg_name'] = k
if __name__ == "__main__":
config = basic_config.copy()
run_experiment(
"test_silot", config, "First test of silot.",
alg_configs=alg_configs, env_configs=env_configs,
cl_mode='strict')
