import numpy as np
from zonoml import *
from elina_interval import *
from elina_abstract0 import *
from elina_manager import *
from elina_dimension import *
from functools import reduce
from ai_milp import *
from krelu import encode_krelu_cons
from config import *
if config.device == Device.CPU:
from fppoly import *
else:
from fppoly_gpu import *
def update_relu_expr_bounds(man, element, layerno, lower_bound_expr, upper_bound_expr, lbi, ubi):
for var in upper_bound_expr.keys():
uexpr = upper_bound_expr[var].expr
varsid = upper_bound_expr[var].varsid
bound = upper_bound_expr[var].bound
k = len(varsid)
varsid = np.ascontiguousarray(varsid, dtype=np.uintp)
for j in range(k):
nnz_u = 0
for l in range(k):
if uexpr[l+1] != 0:
nnz_u+=1
#if nnz_l > 1:
#lexpr = np.ascontiguousarray(lexpr, dtype=np.double)
#update_relu_lower_bound_for_neuron(man, element, layerno, varsid[j], lexpr, varsid, k)
if nnz_u > 1 and bound < 2*ubi[var]:
uexpr = np.ascontiguousarray(uexpr, dtype=np.double)
update_relu_upper_bound_for_neuron(man, element, layerno, varsid[j], uexpr, varsid, k)
def refine_relu_with_solver_bounds(nn, self, man, element, nlb, nub, relu_groups, timeout_lp, timeout_milp, use_default_heuristic, domain):
"""
refines the relu transformer
Arguments
---------
self : Object
will be a DeepzonoNode or DeeppolyNode
man : ElinaManagerPtr
manager which is responsible for element
element : ElinaAbstract0Ptr
the element in which the results after affine transformation are stored
nlb: list of list of doubles
contains the lower bounds for all neurons upto layer layerno
nub: list of list of doubles
contains the upper bounds for all neurons upto layer layerno
use_milp: bool array
whether to use milp or lp for refinement
Return
------
the updated abstract element
"""
layerno = nn.calc_layerno()
predecessor_index = nn.predecessors[layerno+1][0] - 1
if domain == 'deepzono':
offset, length = self.abstract_information
else:
offset = 0
length = get_num_neurons_in_layer(man, element, predecessor_index)
lbi = nlb[predecessor_index]
ubi = nub[predecessor_index]
first_FC = -1
timeout = timeout_milp
for i in range(nn.numlayer):
if nn.layertypes[i] == 'FC':
first_FC = i
break
if nn.activation_counter==0:
if domain=='deepzono':
encode_krelu_cons(nn, man, element, offset, predecessor_index, length, lbi, ubi, relu_groups, False, 'refinezono')
element = relu_zono_layerwise(man,True,element,offset, length, use_default_heuristic)
return element
else:
lower_bound_expr, upper_bound_expr = encode_krelu_cons(nn, man, element, offset, predecessor_index, length, lbi, ubi, relu_groups, False, 'refinepoly')
handle_relu_layer(*self.get_arguments(man, element), use_default_heuristic)
#if config.refine_neurons == True:
update_relu_expr_bounds(man, element, layerno, lower_bound_expr, upper_bound_expr, lbi, ubi)
else:
if predecessor_index==first_FC:
use_milp = 1
else:
use_milp = 0
timeout = timeout_lp
use_milp = use_milp and config.use_milp
candidate_vars = []
for i in range(length):
if((lbi[i]<0 and ubi[i]>0) or (lbi[i]>0)):
candidate_vars.append(i)
#TODO handle residual layers here
if config.refine_neurons==True:
resl, resu, indices = get_bounds_for_layer_with_milp(nn, nn.specLB, nn.specUB, predecessor_index, predecessor_index, length, nlb, nub, relu_groups, use_milp, candidate_vars, timeout)
nlb[predecessor_index] = resl
nub[predecessor_index] = resu
lbi = nlb[predecessor_index]
ubi = nub[predecessor_index]
if domain == 'deepzono':
encode_krelu_cons(nn, man, element, offset, predecessor_index, length, lbi, ubi, relu_groups, False, 'refinezono')
if config.refine_neurons==True:
j = 0
for i in range(length):
if((j < len(indices)) and (i==indices[j])):
element = relu_zono_refined(man,True, element,i+offset, resl[i],resu[i])
j=j+1
else:
element = relu_zono(man,True,element,i+offset)
return element
else:
element = relu_zono_layerwise(man,True,element,offset, length, use_default_heuristic)
return element
else:
if config.refine_neurons==True:
for j in indices:
update_bounds_for_neuron(man,element,predecessor_index,j,resl[j],resu[j])
lower_bound_expr, upper_bound_expr = encode_krelu_cons(nn, man, element, offset, predecessor_index, length, lbi, ubi, relu_groups, False, 'refinepoly')
handle_relu_layer(*self.get_arguments(man, element), use_default_heuristic)
update_relu_expr_bounds(man, element, layerno, lower_bound_expr, upper_bound_expr, lbi, ubi)
