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wfc_run.py
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wfc_run.py
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# -*- coding: utf-8 -*-
"""Base code to load commands from xml and run them."""
from __future__ import annotations
import argparse
import datetime
import logging
from typing import List, Literal, TypedDict, Union
import wfc.wfc_control as wfc_control
import xml.etree.ElementTree as ET
import os
class RunInstructions(TypedDict):
loc: Literal["lexical", "hilbert", "spiral", "entropy", "anti-entropy", "simple", "random"]
choice: Literal["lexical", "rarest", "weighted", "random"]
backtracking: bool
global_constraint: Literal[False, "allpatterns"]
def string2bool(strn: Union[bool, str]) -> bool:
if isinstance(strn, bool):
return strn
return strn.lower() in ["true"]
def run_default(run_experiment: str = "simple", samples: str = "samples_reference.xml") -> None:
log_filename = f"log_{datetime.datetime.now().isoformat()}".replace(":", ".")
xdoc = ET.ElementTree(file=samples)
default_allowed_attempts = 10
default_backtracking = str(False)
log_stats_to_output = wfc_control.make_log_stats()
for xnode in xdoc.getroot():
name = xnode.get("name", "NAME")
if "overlapping" == xnode.tag:
# seed = 3262
tile_size = int(xnode.get("tile_size", 1))
# seed for random generation, can be any number
tile_size = int(xnode.get("tile_size", 1)) # size of tile, in pixels
pattern_width = int(xnode.get("N", 2)) # Size of the patterns we want.
# 2x2 is the minimum, larger scales get slower fast.
symmetry = int(xnode.get("symmetry", 8))
ground = int(xnode.get("ground", 0))
periodic_input = string2bool(
xnode.get("periodic", "False")
) # Does the input wrap?
periodic_output = string2bool(
xnode.get("periodic", "False")
) # Do we want the output to wrap?
generated_size = (int(xnode.get("width", 48)), int(xnode.get("height", 48)))
screenshots = int(
xnode.get("screenshots", 1)
) # Number of times to run the algorithm, will produce this many distinct outputs
iteration_limit = int(
xnode.get("iteration_limit", 0)
) # After this many iterations, time out. 0 = never time out.
allowed_attempts = int(
xnode.get("allowed_attempts", default_allowed_attempts)
) # Give up after this many contradictions
backtracking = string2bool(xnode.get("backtracking", default_backtracking))
visualize_experiment = False
run_instructions: List[RunInstructions] = [ # simple
{
"loc": "entropy",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
}
]
# run_instructions = [{"loc": "entropy", "choice": "weighted", "backtracking": True, "global_constraint": "allpatterns"}]
if run_experiment == "choice":
run_instructions = [
{
"loc": "lexical",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "random",
"choice": "weighted",
"backtracking": False,
"global_constraint": False,
},
{
"loc": "lexical",
"choice": "random",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "random",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "random",
"choice": "random",
"backtracking": False,
"global_constraint": False,
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": True,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": True,
"global_constraint": False,
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": True,
"global_constraint": "allpatterns",
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": True,
"global_constraint": "allpatterns",
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": False,
"global_constraint": "allpatterns",
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": False,
"global_constraint": "allpatterns",
},
]
if run_experiment == "heuristic":
run_instructions = [
{
"loc": "hilbert",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "spiral",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "anti-entropy",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "simple",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
{
"loc": "random",
"choice": "weighted",
"backtracking": backtracking,
"global_constraint": False,
},
]
if run_experiment == "backtracking":
run_instructions = [
{
"loc": "entropy",
"choice": "weighted",
"backtracking": True,
"global_constraint": "allpatterns",
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": False,
"global_constraint": "allpatterns",
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": True,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": False,
"global_constraint": False,
},
]
if run_experiment == "backtracking_heuristic":
run_instructions = [
{
"loc": "lexical",
"choice": "weighted",
"backtracking": True,
"global_constraint": "allpatterns",
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": False,
"global_constraint": "allpatterns",
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": True,
"global_constraint": False,
},
{
"loc": "lexical",
"choice": "weighted",
"backtracking": False,
"global_constraint": False,
},
{
"loc": "random",
"choice": "weighted",
"backtracking": True,
"global_constraint": "allpatterns",
},
{
"loc": "random",
"choice": "weighted",
"backtracking": False,
"global_constraint": "allpatterns",
},
{
"loc": "random",
"choice": "weighted",
"backtracking": True,
"global_constraint": False,
},
{
"loc": "random",
"choice": "weighted",
"backtracking": False,
"global_constraint": False,
},
]
if run_experiment == "choices":
run_instructions = [
{
"loc": "entropy",
"choice": "rarest",
"backtracking": False,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "weighted",
"backtracking": False,
"global_constraint": False,
},
{
"loc": "entropy",
"choice": "random",
"backtracking": False,
"global_constraint": False,
},
]
for experiment in run_instructions:
for x in range(screenshots):
print(f"-: {name} > {x}")
try:
solution = wfc_control.execute_wfc(
name,
tile_size=tile_size,
pattern_width=pattern_width,
rotations=symmetry,
output_size=generated_size,
ground=ground,
attempt_limit=allowed_attempts,
output_periodic=periodic_output,
input_periodic=periodic_input,
loc_heuristic=experiment["loc"],
choice_heuristic=experiment["choice"],
backtracking=experiment["backtracking"],
global_constraint=experiment["global_constraint"],
log_filename=log_filename,
log_stats_to_output=log_stats_to_output,
visualize=visualize_experiment,
logging=True,
)
print(solution)
except Exception as exc:
print(f"Skipped because: {exc}")
if False: # These are included for my colab experiments, remove them if you're not me
os.system(
'cp -rf "/content/wfc/output/*.tsv" "/content/drive/My Drive/wfc_exper/2"'
)
os.system(
'cp -r "/content/wfc/output" "/content/drive/My Drive/wfc_exper/2"'
)
def main() -> None:
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser(
description="Geneates examples from bundled samples which will be saved to the output/ directory.",
)
parser.add_argument(
"-e", "--experiment",
type=str,
default="simple",
choices=["simple", "choice", "choices", "heuristic", "backtracking", "backtracking_heuristic"],
help="Which experiment to run, defaults to simple.",
)
parser.add_argument(
"-s", "--samples",
type=str,
required=True,
metavar="XML_FILE",
default="samples_reference.xml",
help="An XML file with input data. If unsure then use '-s samples_reference.xml'",
)
args = parser.parse_args()
run_default(run_experiment=args.experiment, samples=args.samples)
if __name__ == "__main__":
main()