def eval_damping():
""" Plot joint trajectories for different joint damping parameters """
# Load experiment and remove possible randomization wrappers
ex_dir = ask_for_experiment()
env, policy, _ = load_experiment(ex_dir)
env = inner_env(env)
env.domain_param = WAMBallInCupSim.get_nominal_domain_param()
data = []
t = []
dampings = [0., 1e-2, 1e-1, 1e0]
print_cbt(f'Run policy for damping coefficients: {dampings}')
for d in dampings:
env.reset(domain_param=dict(joint_damping=d))
ro = rollout(env,
policy,
render_mode=RenderMode(video=False),
eval=True)
t.append(ro.env_infos['t'])
data.append(ro.env_infos['qpos'])
fig, ax = plt.subplots(3, sharex='all')
ls = ['k-', 'b--', 'g-.', 'r:'] # line style setting for better visibility
for i, idx in enumerate([1, 3, 5]):
for j in range(len(dampings)):
ax[i].plot(t[j],
data[j][:, idx],
ls[j],
label=f'damping: {dampings[j]}')
if i == 0:
ax[i].legend()
ax[i].set_ylabel(f'joint {idx} pos [rad]')
ax[2].set_xlabel('time [s]')
plt.suptitle('Evaluation of joint damping coefficient')
plt.show()
def load_teachers(self):
"""Recursively load all teachers that can be found in the current experiment's directory."""
# Get the experiment's directory to load from
ex_dir = ask_for_experiment(max_display=10, env_name=self.env_real.name, perma=False)
self.load_teacher_experiment(ex_dir)
if len(self.teacher_policies) < self.num_teachers:
print(
f"You have loaded {len(self.teacher_policies)} teachers - load at least {self.num_teachers - len(self.teacher_policies)} more!"
)
self.load_teachers()
import torch as to
from matplotlib import pyplot as plt
import pyrado
from pyrado.logger.experiment import ask_for_experiment
from pyrado.plotting.gaussian_process import render_singletask_gp
from pyrado.utils.argparser import get_argparser
if __name__ == '__main__':
# Parse command line arguments
args = get_argparser().parse_args()
plt.rc('text', usetex=args.use_tex)
# Get the experiment's directory to load from
ex_dir = ask_for_experiment() if args.ex_dir is None else args.ex_dir
cands = to.load(osp.join(ex_dir, 'candidates.pt'))
cands_values = to.load(osp.join(ex_dir, 'candidates_values.pt')).unsqueeze(1)
dim_cand = cands.shape[1] # number of domain distribution parameters
if dim_cand%2 != 0:
raise pyrado.ShapeErr(msg='The dimension of domain distribution parameters must be a multiple of 2!')
# Select dimensions to plot (ignored for 1D mode)
if len(args.idcs) != 2:
raise pyrado.ShapeErr(msg='Select exactly 2 indices!')
fig_size = (12, 10)
# Plot
import pyrado
from pyrado.environment_wrappers.domain_randomization import remove_all_dr_wrappers
from pyrado.logger.experiment import ask_for_experiment
from pyrado.sampling.rollout import rollout
from pyrado.utils.argparser import get_argparser
from pyrado.utils.data_types import RenderMode
from pyrado.utils.experiments import load_experiment
from pyrado.utils.input_output import print_cbt
if __name__ == "__main__":
# Parse command line arguments
args = get_argparser().parse_args()
# Get the experiment's directory to load from if not given as command line argument
ex_dir = ask_for_experiment(
hparam_list=args.show_hparams) if args.dir is None else args.dir
# Load real trajectories
mode = ""
real_data_exists = True
try:
while mode not in ["ep", "sb"]:
mode = input(
"Pass ep for episodic and sb for step-based control mode: "
).lower()
qpos_real = np.load(osp.join(ex_dir, f"qpos_real_{mode}.npy"))
qvel_real = np.load(osp.join(ex_dir, f"qvel_real_{mode}.npy"))
except FileNotFoundError:
real_data_exists = False
print_cbt(
f"Did not find a recorded real trajectory (qpos_real_{mode} and qvel_real_{mode}) for this policy. "
import pyrado
from pyrado.environment_wrappers.domain_randomization import MetaDomainRandWrapper
from pyrado.domain_randomization.utils import print_domain_params
from pyrado.logger.experiment import ask_for_experiment, load_dict_from_yaml
from pyrado.sampling.rollout import rollout, after_rollout_query
from pyrado.utils.argparser import get_argparser
from pyrado.utils.input_output import print_cbt
from pyrado.utils.data_types import RenderMode
if __name__ == '__main__':
# Parse command line arguments
args = get_argparser().parse_args()
# Get the experiment's directory to load from
ex_dir = ask_for_experiment()
if not osp.isdir(ex_dir):
raise pyrado.PathErr(given=ex_dir)
# Load the environment randomizer
env_sim = joblib.load(osp.join(ex_dir, 'env_sim.pkl'))
hparam = load_dict_from_yaml(osp.join(ex_dir, 'hyperparams.yaml'))
# Override the time step size if specified
if args.dt is not None:
env_sim.dt = args.dt
# Crawl through the given directory and check how many init policies and candidates there are
for root, dirs, files in os.walk(ex_dir):
if args.load_all:
found_policies = [p for p in files if p.endswith('_policy.pt')]
{"num_rpp": args.num_rollouts_per_config, "seed": args.seed},
{"metrics": dict_arraylike_to_float(metrics)},
save_dir=save_dir,
file_name="summary",
)
pyrado.save(df, f"df_sp_grid_{len(param_spec) if param_spec_dim is None else param_spec_dim}d.pkl", save_dir)
if __name__ == "__main__":
# Parse command line arguments
g_args = get_argparser().parse_args()
if g_args.load_all:
if not g_args.dir:
raise pyrado.PathErr(msg="load_all was set but no dir was given")
if not os.path.isdir(g_args.dir):
raise pyrado.PathErr(given=g_args.dir)
g_ex_dirs = [tmp[0] for tmp in os.walk(g_args.dir, followlinks=True) if "policy.pt" in tmp[2]]
elif g_args.dir is None:
g_ex_dirs = [ask_for_experiment(hparam_list=g_args.show_hyperparameters, max_display=50)]
else:
g_ex_dirs = [g_args.dir]
print(f"Evaluating all of {g_ex_dirs}.")
for g_ex_dir in g_ex_dirs:
print(f"Evaluating {g_ex_dir}.")
evaluate_policy(g_args, g_ex_dir)
def _main():
# Parse command line arguments
argparser = get_argparser()
argparser.add_argument(
"--average",
action="store_true",
help=
"average over all loaded policies (default: False); create only a single heatmap",
)
argparser.add_argument(
"--save_dir",
help="if --average is set, the directory to save the plot to")
args = argparser.parse_args()
# Get the experiment's directory to load from
if args.dir is None:
ex_dirs = []
while True:
ex_dirs.append(
ask_for_experiment(
show_hyper_parameters=args.show_hyperparameters,
max_display=50))
if input("Ask for more (Y/n)? ") == "n":
break
else:
ex_dirs = [d.strip() for d in args.dir.split(",")]
eval_parent_dirs = []
for ex_dir in ex_dirs:
eval_parent_dir = osp.join(ex_dir, "eval_domain_grid")
if not osp.isdir(eval_parent_dir):
raise pyrado.PathErr(given=eval_parent_dir)
eval_parent_dirs.append(eval_parent_dir)
if args.load_all:
list_eval_dirs = []
for eval_parent_dir in eval_parent_dirs:
list_eval_dirs += [tmp[0] for tmp in os.walk(eval_parent_dir)][1:]
else:
list_eval_dirs = [
osp.join(eval_parent_dir, "ENV_NAME", "ALGO_NAME")
for eval_parent_dir in eval_parent_dirs
]
dataframes, eval_dirs = [], []
for eval_dir in list_eval_dirs:
assert osp.isdir(eval_dir)
# Load the data
pickle_file = osp.join(eval_dir, "df_sp_grid_2d.pkl")
if not osp.isfile(pickle_file):
print(f"{pickle_file} is not a file! Skipping...")
continue
df = pd.read_pickle(pickle_file)
dataframes.append(df)
eval_dirs.append(eval_dir)
if args.average:
_plot([sum(dataframes) / len(dataframes)], [args.save_dir], True)
else:
_plot(dataframes, eval_dirs, args.save)
save_figure=args.save,
save_dir=eval_dir,
)
plt.show()
if __name__ == "__main__":
# Parse command line arguments
g_args = get_argparser().parse_args()
if g_args.load_all and g_args.dir:
if not os.path.isdir(g_args.dir):
raise pyrado.PathErr(given=g_args.dir)
g_ex_dirs = [
tmp[0] for tmp in os.walk(g_args.dir) if "policy.pt" in tmp[2]
]
elif g_args.dir is None:
g_ex_dirs = [
ask_for_experiment(
show_hyper_parameters=g_args.show_hyperparameters,
max_display=50)
]
else:
g_ex_dirs = [g_args.dir]
print(f"Plotting all of {g_ex_dirs}.")
for g_ex_dir in g_ex_dirs:
print(f"Plotting {g_ex_dir}.")
plot_policy(g_args, g_ex_dir)
