), 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')