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from __future__ import absolute_import
import collections
import torch
class Fold(object):
class Node(object):
def __init__(self, op, step, index, *args):
self.op = op
self.step = step
self.index = index
self.args = args
self.split_idx = -1
self.batch = True
def split(self, num): ##lo uso cuando la red da mas de un tensor como output
u"""Split resulting node, if function returns multiple values."""
#print("op", self.op)
#print("step", self.step)
#print("arg", self.args)
nodes = []
for idx in range(num):
nodes.append(Fold.Node(
self.op, self.step, self.index, *self.args))
nodes[-1].split_idx = idx
#print("idx", idx)
#print("nodes", nodes)
#print("nodes", nodes[-1].split_idx)
return tuple(nodes)
def nobatch(self):
self.batch = False
return self
def get(self, values):
if self.split_idx >= 0:
#print("split index", self.split_idx)
#print("v0",values[self.step][self.op])
#print("v1",values[self.step][self.op][self.split_idx])
#print("v2",values[self.step][self.op][self.split_idx][self.index])
return values[self.step][self.op][self.split_idx][self.index]
else:
return values[self.step][self.op][self.index]
def __repr__(self):
return u"[%d:%d]%s" % (
self.step, self.index, self.op)
def __init__(self, volatile=False, cuda=False, variable=True):
self.steps = collections.defaultdict(
lambda: collections.defaultdict(list))
self.cached_nodes = collections.defaultdict(dict)
self.total_nodes = 0
self.volatile = volatile
self._cuda = cuda
self._variable = variable
def __repr__(self):
return str(self.steps.keys())
def cuda(self):
self._cuda = True
return self
def add(self, op, *args):
u"""Add op to the fold."""
self.total_nodes += 1
# si el nodo no fue visitado antes
if args not in self.cached_nodes[op]:
#arg a veces son solo los features del nodo, a veces tiene info de los hijos tambien
step = max([0] + [arg.step + 1 for arg in args if isinstance(arg, Fold.Node)]) #step es nivel
node = Fold.Node(op, step, len(self.steps[step][op]), *args)#voy creando nodos fold y agregndolos a cached nodes
#len(self.steps[step][op] es index, cuenta los nodos por nivel
#en steps guardo los nodos, por "step"=nivel, y operacion
self.steps[step][op].append(args)
self.cached_nodes[op][args] = node
return self.cached_nodes[op][args]
def _batch_args(self, arg_lists, values, op):
res = []
for arg in arg_lists:
#print("arg apply", arg)
r = []
#si es un nodo de fold
#si viene un "nodo" fold, obtengo todos los argumentos que tiene ese nodo y los concateno en un solo vector
#print("op", op)
if isinstance(arg[0], Fold.Node):
#print("arg", arg)
if arg[0].batch:
for x in arg:
#print("x", x)
r.append(x.get(values))
#print("r sin stack", r)
#print("r con stack", torch.stack(r))
res.append(torch.stack(r))
#if op == 'sampleEncoder':
# print("arg", arg)
# print("r", r)
#nunca uso este caso
'''
else:
for i in range(2, len(arg)):
if arg[i] != arg[0]:
raise ValueError(u"Can not use more then one of nobatch argument, got: %s." % str(arg))
x = arg[0]
res.append(x.get(values))
'''
else:
#print("else")
#si es un tensor de atributos
if isinstance(arg[0], torch.Tensor):
var = torch.stack(arg)
res.append(var)
#si es un nodo de arbol
else:
if op != "classifyLossEstimator" and op != "calcularLossAtributo" and op != "vectorMult" and op != "sampleEncoder": #en caso de que op sea alguna red
var = arg[0].radius
elif op == 'sampleEncoder':
print("arg", arg)
var = arg
elif op == "calcularLossAtributo": #en caso de estar calculano mse
#var = [(a.radius, a.childs()) for a in arg]
var = [a.radius for a in arg]
#print("var", var)
elif op == "classifyLossEstimator":
var = [a.childs() for a in arg] #en caso de estar calculando cross entropy
elif op == "vectorMult":
#print("arg",arg)
if isinstance(arg, torch.Tensor):
var = arg
else:
var = list(arg)
#print("var",var)
res.append(var)
#if op == 'sampleEncoder':
# print("res", res)
return res
def apply(self, nn, nodes):
u"""Apply current fold to given neural module."""
values = {}
for step in sorted(self.steps.keys()):
values[step] = {}
for op in self.steps[step]:
func = getattr(nn, op)
#if op == 'sampleEncoder':
# print("nodes", nodes)
##junto los atributos de los nodos que estan en el mismo step y op
try:
batched_args = self._batch_args(
zip(*self.steps[step][op]), values, op)
except Exception:
print("Error while executing node %s[%d] with args: %s" % (op, step, self.steps[step][op]))
raise
res = func(*batched_args)
#if op == 'bifurcationDecoder':
# print("res", res)
if isinstance(res, (tuple, list)):
values[step][op] = []
for x in res:
#values[step][op].append(torch.chunk(x, arg_size))
values[step][op].append(x)
else:
if len(res.shape) == 1 and op != 'vectorAdder' and op != 'vectorMult':
values[step][op] = res.reshape(-1, 4)
else: #los vectores de output del clasificador tienen tres elementos, no hago el reshape
values[step][op] = res
if op == 'vectorMult':
print("res", res)
try:
return self._batch_args(nodes, values, op)
except Exception:
print("cannot batch")
raise |