def collect_iteration_data(roller, iteration, train_envs, test_envs,
latest_model_filename):
setup = P.get_current_parameters()["Setup"]
# Collect data with current policy
num_train_samples = PARAMS["train_envs_per_iteration"]
train_envs_i = sample_n_from_list(train_envs, num_train_samples)
if PARAMS["test_on_train"]:
test_envs_i = train_envs_i
else:
test_envs_i = sample_n_from_list(test_envs,
PARAMS["test_envs_per_iteration"])
train_data_i = rollout_on_env_set(roller,
train_envs_i,
iteration,
latest_model_filename,
test=False)
test_data_i = rollout_on_env_set(roller,
test_envs_i,
iteration,
latest_model_filename,
test=True)
if setup["eval_landmark_side"]:
evaler = DataEvalLandmarkSide(setup["run_name"], save_images=False)
evaler.evaluate_dataset(test_data_i)
results = evaler.get_results()
print("Results:", results)
evaler.write_summaries(setup["run_name"], "dagger_eval", iteration)
if setup["eval_nl"]:
evaler = DataEvalNL(setup["run_name"],
entire_trajectory=not PARAMS["segment_level"],
save_images=False)
evaler.evaluate_dataset(test_data_i)
results = evaler.get_results()
print("Results:", results)
evaler.write_summaries(setup["run_name"], "dagger_eval", iteration)
# Kill the simulators after each rollout to save CPU cycles and avoid the slowdown
os.system("killall -9 MyProject5-Linux-Shipping")
os.system("killall -9 MyProject5")
return train_data_i, test_data_i
def evaluate():
P.initialize_experiment()
setup = P.get_current_parameters()["Setup"]
# At this point test and dev have been swapped.
# Whatever we've been developing on called "test" is hereafter called dev
# Test is the data that hasn't been touched at all
eval_envs = get_correct_eval_env_id_list()
dataset = faux_dataset_random_pt(eval_envs)
#dataset = faux_dataset_random_landmark(eval_envs)
results = {}
if setup["eval_landmark_side"]:
evaler = DataEvalLandmarkSide(setup["run_name"], save_images=False)
evaler.evaluate_dataset(dataset)
results = evaler.get_results()
results["all_dist"] = []
print("Results:", results)
def evaluate():
P.initialize_experiment()
params = P.get_current_parameters()
setup = params["Setup"]
# import pdb;pdb.set_trace()
models = []
for i in range(setup["num_workers"]):
model, model_loaded = load_model()
if setup["restore_weights_name"]:
restore_pretrained_weights(model, setup["restore_weights_name"],
setup["fix_restored_weights"])
models.append(model)
eval_envs = get_correct_eval_env_id_list()
roll_out_params = RollOutParams() \
.setModelName(setup["model"]) \
.setModelFile(setup["model_file"]) \
.setRunName(setup["run_name"]) \
.setSetupName(P.get_setup_name()) \
.setEnvList(eval_envs) \
.setMaxDeviation(400) \
.setHorizon(100) \
.setStepsToForceStop(10) \
.setPlot(False) \
.setShowAction(False) \
.setIgnorePolicyStop(False) \
.setPlotDir("evaluate/" + setup["run_name"]) \
.setSavePlots(True) \
.setRealtimeFirstPerson(False) \
.setSaveSamples(False) \
.setBuildTrainData(False) \
.setSegmentReset("always") \
.setSegmentLevel(True) \
.setFirstSegmentOnly(False) \
.setDebug(setup["debug"]) \
.setCuda(setup["cuda"])
custom_eval = "Eval" in params and params["Eval"]["custom_eval"]
instructions = None
if custom_eval:
examples = params["Eval"]["examples"]
eval_envs, eval_sets, eval_segs, instructions = tuple(
map(lambda m: list(m), list(zip(*examples))))
print("!! Running custom evaluation with the following setup:")
print(examples)
roll_out_params.setEnvList(eval_envs)
roll_out_params.setSegList(eval_segs)
roll_out_params.setCustomInstructions(instructions)
if setup["num_workers"] > 1:
roller = ParallelPolicyRoller(num_workers=setup["num_workers"])
else:
roller = PolicyRoller()
dataset = roller.roll_out_policy(roll_out_params)
results = {}
if setup["eval_landmark_side"]:
evaler = DataEvalLandmarkSide(setup["run_name"])
evaler.evaluate_dataset(dataset)
results = evaler.get_results()
if setup["eval_nl"]:
evaler = DataEvalNL(setup["run_name"],
save_images=True,
entire_trajectory=False,
custom_instr=instructions)
evaler.evaluate_dataset(dataset)
results = evaler.get_results()
print("Results:", results)
def evaluate():
P.initialize_experiment()
params = P.get_current_parameters()
setup = params["Setup"]
models = []
for i in range(setup["num_workers"]):
model, model_loaded = load_model()
models.append(model)
eval_envs = list(sorted(get_correct_eval_env_id_list()))
round_size = P.get_current_parameters()["Data"].get("collect_n_at_a_time")
# TODO: Scrap RollOutParams and use parameter server JSON params instead
roll_out_params = RollOutParams() \
.setModelName(setup["model"]) \
.setModelFile(setup["model_file"]) \
.setRunName(setup["run_name"]) \
.setSetupName(P.get_setup_name()) \
.setEnvList(eval_envs) \
.setMaxDeviation(800) \
.setHorizon(setup["trajectory_length"]) \
.setStepsToForceStop(20) \
.setPlot(False) \
.setShowAction(False) \
.setIgnorePolicyStop(False) \
.setPlotDir("evaluate/" + setup["run_name"]) \
.setSavePlots(False) \
.setRealtimeFirstPerson(False) \
.setSaveSamples(False) \
.setBuildTrainData(False) \
.setSegmentReset("always") \
.setSegmentLevel(False) \
.setFirstSegmentOnly(False) \
.setDebug(setup["debug"]) \
.setCuda(setup["cuda"]) \
.setRealDrone(setup["real_drone"])
custom_eval = "Eval" in params and params["Eval"]["custom_eval"]
instructions = None
if custom_eval:
examples = params["Eval"]["examples"]
eval_envs, eval_sets, eval_segs, instructions = tuple(
map(lambda m: list(m), list(zip(*examples))))
print("!! Running custom evaluation with the following setup:")
print(examples)
roll_out_params.setEnvList(eval_envs)
roll_out_params.setSegList(eval_segs)
roll_out_params.setCustomInstructions(instructions)
if setup["num_workers"] > 1:
roller = ParallelPolicyRoller(num_workers=setup["num_workers"])
else:
roller = PolicyRoller()
if round_size:
eval_dataset_name = data_io.paths.get_eval_tmp_dataset_name(
setup["model"], setup["run_name"])
eval_dataset_path = data_io.paths.get_dataset_dir(eval_dataset_name)
cumulative_dataset = []
if os.path.exists(eval_dataset_path):
result = query_user_load_discard(eval_dataset_path)
if result == "load":
print("Loading dataset and continuing evaluation")
cumulative_dataset = load_multiple_env_data_from_dir(
eval_dataset_path)
elif result == "discard":
print("Discarding existing evaluation data")
shutil.rmtree(eval_dataset_path)
elif result == "cancel":
print("Cancelling evaluation")
return
os.makedirs(eval_dataset_path, exist_ok=True)
collected_envs = set([
rollout[0]["env_id"] for rollout in cumulative_dataset
if len(rollout) > 0
])
eval_envs = [e for e in eval_envs if e not in collected_envs]
if setup.get("compute_results_no_rollout", False):
eval_envs = []
for i in range(0, len(eval_envs), round_size):
j = min(len(eval_envs), i + round_size)
round_envs = eval_envs[i:j]
roll_out_params.setEnvList(round_envs)
dataset = roller.roll_out_policy(roll_out_params)
# Save this data
for rollout in dataset:
if len(rollout) == 0:
print(
"WARNING! DROPPING EMPTY ROLLOUTS! SHOULDN'T DO THIS")
continue
## rollout is a list of samples:
env_id = rollout[0]["env_id"] if "metadata" in rollout[
0] else rollout[0]["env_id"]
if True:
if len(rollout) > 0:
save_dataset_to_path(
os.path.join(eval_dataset_path, str(env_id)),
rollout)
## rollout is a list of segments, each is a list of samples
else:
if len(rollout) > 0:
save_dataset_to_path(
os.path.join(eval_dataset_path, str(env_id)),
rollout)
cumulative_dataset += dataset
print(f"Saved cumulative dataset to: {eval_dataset_path}")
dataset = cumulative_dataset
else:
dataset = roller.roll_out_policy(roll_out_params)
results = {}
if setup["eval_landmark_side"]:
evaler = DataEvalLandmarkSide(setup["run_name"],
save_images=True,
world_size=setup["world_size_m"])
evaler.evaluate_dataset(dataset)
results = evaler.get_results()
if setup["eval_nl"]:
evaler = DataEvalNL(setup["run_name"],
save_images=True,
entire_trajectory=False,
custom_instr=instructions)
evaler.evaluate_dataset(dataset)
results = evaler.get_results()
print("Results:", results)