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ama3-planner.py
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ama3-planner.py
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#!/usr/bin/env python3
options = {
"lmcut" : "--search astar(lmcut())",
"blind" : "--search astar(blind())",
"hmax" : "--search astar(hmax())",
"ff" : "--search eager(single(ff()))",
"lff" : "--search lazy_greedy(ff())",
"lffpo" : "--evaluator h=ff() --search lazy_greedy(h, preferred=h)",
"gc" : "--search eager(single(goalcount()))",
"lgc" : "--search lazy_greedy(goalcount())",
"lgcpo" : "--evaluator h=goalcount() --search lazy_greedy(h, preferred=h)",
"cg" : "--search eager(single(cg()))",
"lcg" : "--search lazy_greedy(cg())",
"lcgpo" : "--evaluator h=cg() --search lazy_greedy(h, preferred=h)",
"lama" : "--alias lama-first",
"oldmands" : "--search astar(merge_and_shrink(shrink_strategy=shrink_bisimulation(max_states=50000,greedy=false),merge_strategy=merge_dfp(),label_reduction=exact(before_shrinking=true,before_merging=false)))",
"mands" : "--search astar(merge_and_shrink(shrink_strategy=shrink_bisimulation(greedy=false),merge_strategy=merge_sccs(order_of_sccs=topological,merge_selector=score_based_filtering(scoring_functions=[goal_relevance,dfp,total_order])),label_reduction=exact(before_shrinking=true,before_merging=false),max_states=50k,threshold_before_merge=1))",
"pdb" : "--search astar(pdb())",
"cpdb" : "--search astar(cpdbs())",
"ipdb" : "--search astar(ipdb())",
"zopdb" : "--search astar(zopdbs())",
}
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("domainfile", help="pathname to a PDDL domain file")
parser.add_argument("problem_dir", help="pathname to a directory containing init.png and goal.png")
parser.add_argument("heuristics", choices=options.keys(),
help="heuristics configuration passed to fast downward. The details are:\n"+
"\n".join([ " "*4+key+"\n"+" "*8+value for key,value in options.items()]))
parser.add_argument("cycle", type=int, default=1, nargs="?",
help="number of autoencoding cycles to perform on the initial/goal images")
parser.add_argument("sigma", type=float, default=None, nargs="?",
help="sigma of the Gaussian noise added to the normalized initial/goal images.")
args = parser.parse_args()
import subprocess
import os
import sys
import latplan
import latplan.model
from latplan.util import *
from latplan.util.planner import *
from latplan.util.plot import *
import latplan.util.stacktrace
import os.path
import keras.backend as K
import tensorflow as tf
import math
import time
import json
import numpy as np
float_formatter = lambda x: "%.3f" % x
np.set_printoptions(threshold=sys.maxsize,formatter={'float_kind':float_formatter})
def main(domainfile, problem_dir, heuristics, cycle, sigma):
network_dir = os.path.dirname(domainfile)
domainfile_rel = os.path.relpath(domainfile, network_dir)
def domain(path):
dom_prefix = domainfile_rel.replace("/","_")
root, ext = os.path.splitext(path)
return "{}_{}{}".format(os.path.splitext(dom_prefix)[0], root, ext)
def heur(path):
root, ext = os.path.splitext(path)
return "{}_{}{}".format(heuristics, root, ext)
log("loaded puzzle")
sae = latplan.model.load(network_dir,allow_failure=True)
log("loaded sae")
setup_planner_utils(sae, problem_dir, network_dir, "ama3")
p = puzzle_module(sae)
log("loaded puzzle")
log(f"loading init/goal")
init, goal = init_goal_misc(p,cycle,noise=sigma)
log(f"loaded init/goal")
log(f"start planning")
bits = np.concatenate((init,goal))
###### files ################################################################
ig = problem(ama(network(domain(heur(f"problem.ig")))))
problemfile = problem(ama(network(domain(heur(f"problem.pddl")))))
planfile = problem(ama(network(domain(heur(f"problem.plan")))))
tracefile = problem(ama(network(domain(heur(f"problem.trace")))))
csvfile = problem(ama(network(domain(heur(f"problem.csv")))))
pngfile = problem(ama(network(domain(heur(f"problem.png")))))
jsonfile = problem(ama(network(domain(heur(f"problem.json")))))
logfile = problem(ama(network(domain(heur(f"problem.log")))))
npzfile = problem(ama(network(domain(heur(f"problem.npz")))))
negfile = problem(ama(network(domain(heur(f"problem.negative")))))
valid = False
found = False
try:
###### preprocessing ################################################################
log(f"start generating problem")
os.path.exists(ig) or np.savetxt(ig,[bits],"%d")
echodo(["helper/ama3-problem.sh",ig,problemfile])
log(f"finished generating problem")
###### do planning #############################################
log(f"start planning")
echodo(["helper/fd-latest.sh", options[heuristics], problemfile, domainfile])
log(f"finished planning")
if not os.path.exists(planfile):
return valid
found = True
log(f"start running a validator")
echodo(["arrival", domainfile, problemfile, planfile, tracefile])
log(f"finished running a validator")
log(f"start parsing the plan")
with open(csvfile,"w") as f:
echodo(["lisp/ama3-read-latent-state-traces.bin", tracefile, str(len(init))],
stdout=f)
plan = np.loadtxt(csvfile, dtype=int)
log(f"finished parsing the plan")
if plan.ndim != 2:
assert plan.ndim == 1
print("Found a plan with length 0; single state in the plan.")
return valid
log(f"start plotting the plan")
sae.plot_plan(plan, pngfile, verbose=True)
log(f"finished plotting the plan")
log(f"start archiving the plan")
plan_images = sae.decode(plan)
np.savez_compressed(npzfile,img_states=plan_images)
log(f"finished archiving the plan")
log(f"start visually validating the plan image : transitions")
# note: only puzzle, hanoi, lightsout have the custom validator, which are all monochrome.
plan_images = sae.render(plan_images) # unnormalize the image
validation = p.validate_transitions([plan_images[0:-1], plan_images[1:]])
print(validation)
valid = bool(np.all(validation))
log(f"finished visually validating the plan image : transitions")
log(f"start visually validating the plan image : states")
print(p.validate_states(plan_images))
log(f"finished visually validating the plan image : states")
return valid
finally:
with open(jsonfile,"w") as f:
parameters = sae.parameters.copy()
del parameters["mean"]
del parameters["std"]
json.dump({
"network":network_dir,
"problem":os.path.normpath(problem_dir).split("/")[-1],
"domain" :os.path.normpath(problem_dir).split("/")[-2],
"noise":sigma,
"times":times,
"heuristics":heuristics,
"domainfile":domainfile,
"problemfile":problemfile,
"planfile":planfile,
"tracefile":tracefile,
"csvfile":csvfile,
"pngfile":pngfile,
"jsonfile":jsonfile,
"statistics":json.loads(echo_out(["helper/fd-parser.awk", logfile])),
"parameters":parameters,
"valid":valid,
"found":found,
"exhausted": os.path.exists(negfile),
"cycle":cycle,
}, f, indent=2)
if __name__ == '__main__':
try:
main(**vars(args))
except:
import latplan.util.stacktrace
latplan.util.stacktrace.format()