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# Copyright 2020 The Chromium Authors
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import collections
import itertools
import logging
import sys
from typing import Dict
import gold_inexact_matching.iterative_parameter_optimizer\
as iterative_optimizer
from gold_inexact_matching import common_typing as ct
from gold_inexact_matching import parameter_set
Sparse2DIntArray = Dict[int, Dict[int, int]]
class LocalMinimaParameterOptimizer(
iterative_optimizer.IterativeParameterOptimizer):
"""A ParameterOptimizer to find local minima.
Works on any number of variable parameters and is faster than brute
forcing, but not guaranteed to find all interesting parameter combinations.
"""
MIN_EDGE_THRESHOLD_WEIGHT = 0
MIN_MAX_DIFF_WEIGHT = MIN_DELTA_THRESHOLD_WEIGHT = 0
def __init__(self, args: ct.ParsedCmdArgs, test_name: str):
super().__init__(args, test_name)
# These are (or will be) maps of ints to maps of ints to ints, i.e. a 2D
# array containing ints, just using maps instead of lists. They hold the
# most permissive value visited so far that resulted in a comparison failure
# for a particular parameter given the other two parameters. These are used
# to prune combinations we don't care about, similar to skipping
# combinations that produce a higher weight than our smallest.
# Delta -> Edge -> Max Diff
self._permissive_max_diff_map: Sparse2DIntArray = {}
# Max Diff -> Edge -> Delta
self._permissive_delta_map: Sparse2DIntArray = {}
# Max Diff -> Delta -> Edge
self._permissive_edge_map: Sparse2DIntArray = {}
@classmethod
def AddArguments(cls, parser: ct.CmdArgParser) -> ct.ArgumentGroupTuple:
common_group, sobel_group, fuzzy_group = super(
LocalMinimaParameterOptimizer, cls).AddArguments(parser)
common_group.add_argument(
'--use-bfs',
action='store_true',
default=False,
help='Use a breadth-first search instead of a depth-first search. This '
'will likely be significantly slower, but is more likely to find '
'multiple local minima with the same weight.')
sobel_group.add_argument(
'--edge-threshold-weight',
default=1,
type=int,
help='The weight associated with the edge threshold. Higher values '
'will penalize a more permissive parameter value more harshly.')
fuzzy_group.add_argument(
'--max-diff-weight',
default=3,
type=int,
help='The weight associated with the maximum number of different '
'pixels. Higher values will penalize a more permissive parameter value '
'more harshly.')
fuzzy_group.add_argument(
'--delta-threshold-weight',
default=10,
type=int,
help='The weight associated with the per-channel delta sum. Higher '
'values will penalize a more permissive parameter value more harshly.')
return common_group, sobel_group, fuzzy_group
def _VerifyArgs(self) -> None:
super()._VerifyArgs()
assert self._args.edge_threshold_weight >= self.MIN_EDGE_THRESHOLD_WEIGHT
assert self._args.max_diff_weight >= self.MIN_MAX_DIFF_WEIGHT
assert self._args.delta_threshold_weight >= self.MIN_DELTA_THRESHOLD_WEIGHT
def _RunOptimizationImpl(self) -> None:
visited_parameters = set()
to_visit = collections.deque()
smallest_weight = sys.maxsize
smallest_parameters = []
to_visit.append(self._GetMostPermissiveParameters())
# Do a search, only considering adjacent parameters if:
# 1. Their weight is less than or equal to the smallest found weight.
# 2. They haven't been visited already.
# 3. They are not guaranteed to fail based on previously tested parameters.
# 4. The current parameters result in a successful comparison.
while to_visit:
current_parameters = None
if self._args.use_bfs:
current_parameters = to_visit.popleft()
else:
current_parameters = to_visit.pop()
weight = self._GetWeight(current_parameters)
if weight > smallest_weight:
continue
if current_parameters in visited_parameters:
continue
if self._ParametersAreGuaranteedToFail(current_parameters):
visited_parameters.add(current_parameters)
continue
visited_parameters.add(current_parameters)
success, _, _ = self._RunComparisonForParameters(current_parameters)
if success:
for adjacent in self._AdjacentParameters(current_parameters):
to_visit.append(adjacent)
if smallest_weight == weight:
logging.info('Found additional smallest parameter %s',
current_parameters)
smallest_parameters.append(current_parameters)
else:
logging.info('Found new smallest parameter with weight %d: %s',
weight, current_parameters)
smallest_weight = weight
smallest_parameters = [current_parameters]
else:
self._UpdateMostPermissiveFailedParameters(current_parameters)
print(f'Found {len(smallest_parameters)} parameter(s) with the smallest '
f'weight:')
for p in smallest_parameters:
print(p)
def _ParametersAreGuaranteedToFail(self,
parameters: parameter_set.ParameterSet
) -> bool:
"""Checks whether the given ParameterSet is guaranteed to fail.
A ParameterSet is guaranteed to fail if we have already tried and failed
with a similar ParameterSet that was more permissive. Specifically, if we
have tried and failed with a ParameterSet with all but one parameters
matching, and the non-matching parameter was more permissive than the
current one.
Args:
parameters: The ParameterSet instance to check.
Returns:
True if |parameters| is guaranteed to fail based on previously tried
parameters, otherwise False.
"""
permissive_max_diff = self._permissive_max_diff_map.get(
parameters.delta_threshold, {}).get(parameters.edge_threshold, -1)
if parameters.max_diff < permissive_max_diff:
return True
permissive_delta = self._permissive_delta_map.get(
parameters.max_diff, {}).get(parameters.edge_threshold, -1)
if parameters.delta_threshold < permissive_delta:
return True
permissive_edge = self._permissive_edge_map.get(
parameters.max_diff, {}).get(parameters.delta_threshold, sys.maxsize)
if parameters.edge_threshold > permissive_edge:
return True
return False
def _UpdateMostPermissiveFailedParameters(
self, parameters: parameter_set.ParameterSet) -> None:
"""Updates the array of most permissive failed parameters.
This is used in conjunction with _ParametersAreGuaranteedToFail to prune
ParameterSets without having to actually test them. Values are updated if
|parameters| shares two parameters with a a previously failed ParameterSet,
but |parameters|' third parameter is more permissive.
Args:
parameters: A ParameterSet to pull updated values from.
"""
permissive_max_diff = self._permissive_max_diff_map.setdefault(
parameters.delta_threshold, {}).get(parameters.edge_threshold, -1)
permissive_max_diff = max(permissive_max_diff, parameters.max_diff)
self._permissive_max_diff_map[parameters.delta_threshold][
parameters.edge_threshold] = permissive_max_diff
permissive_delta = self._permissive_delta_map.setdefault(
parameters.max_diff, {}).get(parameters.edge_threshold, -1)
permissive_delta = max(permissive_delta, parameters.delta_threshold)
self._permissive_delta_map[parameters.max_diff][
parameters.edge_threshold] = permissive_delta
permissive_edge = self._permissive_edge_map.setdefault(
parameters.max_diff, {}).get(parameters.delta_threshold, sys.maxsize)
permissive_edge = min(permissive_edge, parameters.edge_threshold)
self._permissive_edge_map[parameters.max_diff][
parameters.delta_threshold] = permissive_edge
def _AdjacentParameters(self, starting_parameters):
max_diff = starting_parameters.max_diff
delta_threshold = starting_parameters.delta_threshold
edge_threshold = starting_parameters.edge_threshold
max_diff_step = self._args.max_diff_step
delta_threshold_step = self._args.delta_threshold_step
edge_threshold_step = self._args.edge_threshold_step
max_diffs = [
max(self._args.min_max_diff, max_diff - max_diff_step), max_diff,
min(self._args.max_max_diff, max_diff + max_diff_step)
]
delta_thresholds = [
max(self._args.min_delta_threshold,
delta_threshold - delta_threshold_step), delta_threshold,
min(self._args.max_delta_threshold,
delta_threshold + delta_threshold_step)
]
edge_thresholds = [
max(self._args.min_edge_threshold,
edge_threshold - edge_threshold_step), edge_threshold,
min(self._args.max_edge_threshold, edge_threshold + edge_threshold_step)
]
for combo in itertools.product(max_diffs, delta_thresholds,
edge_thresholds):
adjacent = parameter_set.ParameterSet(combo[0], combo[1], combo[2])
if adjacent != starting_parameters:
yield adjacent
def _GetWeight(self, parameters: parameter_set.ParameterSet) -> int:
return (parameters.max_diff * self._args.max_diff_weight +
parameters.delta_threshold * self._args.delta_threshold_weight +
(self.MAX_EDGE_THRESHOLD - parameters.edge_threshold) *
self._args.edge_threshold_weight)
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