def assume(self,
condition,
manager: PyManager = None,
bwd: bool = False) -> 'Box2State':
if self.is_bottom():
return self
if isinstance(condition, tuple):
condition = list(condition)
if isinstance(condition, list):
for feature, (lower, upper) in condition:
self.bounds[feature.name].meet(IntervalLattice(lower, upper))
return self
elif isinstance(condition, BinaryBooleanOperation):
if condition.operator == BinaryBooleanOperation.Operator.Or:
right = deepcopy(self).assume(condition.right, bwd=bwd)
return self.assume(condition.left, bwd=bwd).join(right)
elif condition.operator == BinaryBooleanOperation.Operator.And:
assert isinstance(condition.left, BinaryComparisonOperation)
assert isinstance(condition.right, BinaryComparisonOperation)
assert isinstance(condition.left.left, Literal)
assert condition.left.operator == BinaryComparisonOperation.Operator.LtE
assert isinstance(condition.left.right, VariableIdentifier)
assert isinstance(condition.right.left, VariableIdentifier)
assert condition.right.operator == BinaryComparisonOperation.Operator.LtE
assert isinstance(condition.right.right, Literal)
lower = eval(condition.left.left.val)
upper = eval(condition.right.right.val)
assert condition.left.right.name == condition.right.left.name
self.bounds[condition.left.right.name].meet(
IntervalLattice(lower, upper))
return self
elif isinstance(condition, BinaryComparisonOperation):
if condition.operator == BinaryComparisonOperation.Operator.Gt:
assert isinstance(condition.left, VariableIdentifier)
assert isinstance(condition.right, Literal)
lower = eval(condition.right.val)
upper = 1
self.bounds[condition.left.name].meet(
IntervalLattice(lower, upper))
return self
elif condition.operator == BinaryComparisonOperation.Operator.LtE:
assert isinstance(condition.left, VariableIdentifier)
assert isinstance(condition.right, Literal)
lower = 0
upper = eval(condition.right.val)
self.bounds[condition.left.name].meet(
IntervalLattice(lower, upper))
return self
# elif isinstance(condition, PyTcons1):
# abstract1 = self.domain(manager, self.environment, array=PyTcons1Array([condition]))
# self.state = self.state.meet(abstract1)
# return self
elif isinstance(condition, set):
assert len(condition) == 1
self.assume(condition.pop(), bwd=bwd)
return self
raise NotImplementedError(
f"Assumption of {condition.__class__.__name__} is unsupported!")
def __init__(self, inputs: Set[VariableIdentifier], precursory: State = None):
super().__init__(precursory=precursory)
self.inputs = {input.name for input in inputs}
# PRE: all the inputs in [0, 1]
self.bounds = {input: (IntervalLattice(0, 0), IntervalLattice(1, 1))
for input in self.inputs}
# the free coeff is indicated by '_' in the dictionary self.poly
self.poly = {input: ({'_': 0.0}, {'_': 1.0}) for input in self.inputs}
self.flag = None
def affine(self, left: List[PyVar], right: List[PyTexpr1]) -> 'NeurifyState':
if self.is_bottom():
return self
for lhs, expr in zip(left, right):
name = str(lhs)
rhs = texpr_to_dict(expr)
_inf, inf = deepcopy(rhs), deepcopy(rhs)
_sup, sup = deepcopy(rhs), deepcopy(rhs)
self.poly[name] = (_inf, _sup)
# while there is a non-input-variable in inf
while any(variable in inf and variable not in self.inputs for variable in self.poly):
for variable in self.poly:
if variable in inf and variable not in self.inputs: # should be replaced
coeff = inf[variable]
if coeff > 0:
replacement = self.poly[variable][LOW]
elif coeff < 0:
replacement = self.poly[variable][UP]
else: # coeff == 0
replacement = dict()
replacement['_'] = 0.0
del inf[variable]
for var, val in replacement.items():
if var in inf:
inf[var] += coeff * val
else:
inf[var] = coeff * val
# while there is a non-input-variable in sup
while any(variable in sup and variable not in self.inputs for variable in self.poly):
for variable in self.poly:
if variable in sup and variable not in self.inputs: # should be replaced
coeff = sup[variable]
if coeff > 0:
replacement = self.poly[variable][UP]
elif coeff < 0:
replacement = self.poly[variable][LOW]
else: # coeff == 0
replacement = dict()
replacement['_'] = 0.0
del sup[variable]
for var, val in replacement.items():
if var in sup:
sup[var] += coeff * val
else:
sup[var] = coeff * val
ext_bounds = {k: IntervalLattice(l.lower, u.upper) for k, (l, u) in self.bounds.items()}
lower = evaluate(inf, ext_bounds)
upper = evaluate(sup, ext_bounds)
self.bounds[name] = (IntervalLattice(lower.lower, lower.upper),
IntervalLattice(upper.lower, upper.upper))
return self
def resize_bounds(self, var_name, new_bounds):
new_low_lower = new_bounds.lower
new_low_upper = self.bounds[var_name][LOW].upper
new_up_lower = self.bounds[var_name][UP].lower
new_up_upper = new_bounds.upper
if new_low_lower > new_low_upper:
new_low_upper = new_low_lower
if new_up_upper < new_up_lower:
new_up_lower = new_up_upper
self.bounds[var_name] = (
IntervalLattice(new_low_lower, new_low_upper),
IntervalLattice(new_up_lower, new_up_upper)
)
def relu(self,
stmt: PyVar,
active: bool = False,
inactive: bool = False) -> 'Symbolic3State':
if self.is_bottom():
return self
self.flag = None
name = str(stmt)
lattice: IntervalLattice = self.bounds[name]
lower, upper = lattice.lower, lattice.upper
if upper <= 0 or inactive:
zero = dict()
zero['_'] = 0.0
self.symbols[name] = (name, zero)
self.bounds[name] = IntervalLattice(0, 0)
self.flag = -1
elif 0 <= lower or active:
if active and lower < 0:
bounds = self.bounds[name]
self.bounds[name] = bounds.meet(IntervalLattice(0, upper))
del self.symbols[name]
self.flag = 1
else:
_active, _inactive = deepcopy(self.bounds), deepcopy(self.bounds)
_active[name] = _active[name].meet(IntervalLattice(0, upper))
_inactive[name] = _inactive[name].meet(IntervalLattice(0, 0))
if any(element.is_bottom() for element in _active.values()):
zero = dict()
zero['_'] = 0.0
self.symbols[name] = (name, zero)
self.flag = -1
elif any(element.is_bottom() for element in _inactive.values()):
self.flag = 1
else:
del self.symbols[name]
self.flag = None
join = dict()
for variable, itv in _active.items():
join[variable] = itv.join(_inactive[variable])
self.bounds[name] = join[name].meet(IntervalLattice(0, upper))
self.flag = None
return self
def __init__(self,
inputs: Set[VariableIdentifier],
precursory: State = None):
super().__init__(precursory=precursory)
self.inputs = {input.name for input in inputs}
self.bounds = dict()
for input in self.inputs:
self.bounds[input] = IntervalLattice(0, 1)
self.flag = None
def evaluate(dictionary, bounds):
result = IntervalLattice(0, 0)
for var, val in dictionary.items():
coeff = IntervalLattice(val, val)
if var != '_':
result = result._add(coeff._mult(bounds[var]))
else:
result = result._add(coeff)
return result
def affine(self, left: List[PyVar], right: List[PyTexpr1]) -> 'Box2State':
if self.is_bottom():
return self
for lhs, expr in zip(left, right):
name = str(lhs)
rhs = texpr_to_dict(expr)
inf = deepcopy(rhs)
sup = deepcopy(rhs)
lower = evaluate(inf, self.bounds)
upper = evaluate(sup, self.bounds)
self.bounds[name] = IntervalLattice(lower.lower, upper.upper)
return self
def _meet(self, other: 'NeurifyState') -> 'NeurifyState':
for var in self.bounds:
other_bound = IntervalLattice(other.bounds[var][LOW].lower, other.bounds[var][UP].upper)
self_bound = IntervalLattice(self.bounds[var][LOW].lower, self.bounds[var][UP].upper)
self_bound.meet(other_bound)
self.bounds[var] = (
IntervalLattice(self_bound.lower, self_bound.lower),
IntervalLattice(self_bound.upper, self_bound.upper))
self.poly[var] = (
{'_': self.bounds[var][LOW].lower},
{'_': self.bounds[var][UP].upper})
return self
def relu(self,
stmt: PyVar,
active: bool = False,
inactive: bool = False) -> 'Box2State':
if self.is_bottom():
return self
name = str(stmt)
lattice: IntervalLattice = self.bounds[name]
lower, upper = lattice.lower, lattice.upper
if upper <= 0 or inactive:
# l_j = u_j = 0
self.bounds[name] = IntervalLattice(0, 0)
# 0 <= x_j <= 0
self.flag = -1
elif 0 <= lower or active:
if active and lower < 0:
self.bounds[name] = IntervalLattice(0, upper)
self.flag = 1
else: # case (c) in Fig. 4, equation (4)
_active, _inactive = deepcopy(self.bounds), deepcopy(self.bounds)
_active[name] = _active[name].meet(IntervalLattice(0, upper))
_inactive[name] = _inactive[name].meet(IntervalLattice(0, 0))
if any(element.is_bottom() for element in _active.values()):
self.flag = -1
elif any(element.is_bottom() for element in _inactive.values()):
self.flag = 1
else:
self.flag = None
join = dict()
for variable, itv in _active.items():
join[variable] = itv.join(_inactive[variable])
self.bounds[name] = join[name].meet(IntervalLattice(0, upper))
self.flag = None
return self
def affine(self, left: List[PyVar],
right: List[PyTexpr1]) -> 'Symbolic3State':
if self.is_bottom():
return self
assignments = dict()
for lhs, expr in zip(left, right):
name = str(lhs)
rhs = texpr_to_dict(expr)
for sym, val in self.symbols.values():
rhs = substitute_in_dict(rhs, sym, val)
assignments[name] = (name, rhs)
bound = evaluate(rhs, self.bounds)
self.bounds[name] = IntervalLattice(bound.lower, bound.upper)
self.symbols = assignments
return self
def relu(self, stmt: PyVar, active: bool = False, inactive: bool = False) -> 'NeurifyState':
if self.is_bottom():
return self
name = str(stmt)
low_lattice, up_lattice = self.bounds[name]
low_lower, low_upper = low_lattice.lower, low_lattice.upper
up_lower, up_upper = up_lattice.lower, up_lattice.upper
self.set_flag(low_lower, up_upper, active, inactive)
if low_upper <= 0 or inactive:
self.bounds[name] = (IntervalLattice(0, 0), self.bounds[name][UP])
self.poly[name] = ({'_': 0.0}, self.poly[name][UP])
elif 0 <= low_lower or active:
if active and low_lower < 0:
self.bounds[name] = (IntervalLattice(0, low_upper), self.bounds[name][UP])
self.poly[name] = ({'_': 0.0}, self.poly[name][UP])
else:
m = low_upper / (low_upper - low_lower)
sup = self.poly[name][LOW] # sup does side effects over self.poly[name][UP]
for var, val in sup.items():
sup[var] = m * val
self.bounds[name] = (IntervalLattice(low_lower*m, low_upper*m), self.bounds[name][UP])
if up_upper <= 0 or inactive:
self.bounds[name] = (self.bounds[name][LOW], IntervalLattice(0, 0))
self.poly[name] = (self.poly[name][LOW], {'_': 0.0})
elif 0 <= up_lower or active:
if active and up_lower < 0:
self.bounds[name] = (self.bounds[name][LOW], IntervalLattice(0, up_upper))
else:
m = up_upper / (up_upper - up_lower)
q = up_upper * (-up_lower) / (up_upper - up_lower)
sup = self.poly[name][UP] # sup does side effects over self.poly[name][UP]
for var, val in sup.items():
sup[var] = m * val
sup['_'] += q
self.bounds[name] = (self.bounds[name][LOW], IntervalLattice(up_lower*m+q, up_upper*m+q))
return self
def resize_bounds(self, var_name, new_bounds):
self.bounds[var_name] = IntervalLattice(new_bounds.lower,
new_bounds.upper)
def get_bounds(self, var_name):
low, up = self.bounds[var_name]
return IntervalLattice(low.lower, up.upper)
def __assume_helper(self, feature, lower, upper):
self.bounds[feature.name] = (IntervalLattice(lower, lower), IntervalLattice(upper, upper))
self.poly[feature.name] = (
{'_': self.bounds[feature.name][LOW].lower},
{'_': self.bounds[feature.name][UP].upper}
)
