######################################################################
def generate_one_batch(s):
- svrt.seed(s)
- target = torch.LongTensor(self.batch_size).bernoulli_(0.5)
+ problem_number, batch_size, cuda, random_seed = s
+ svrt.seed(random_seed)
+ target = torch.LongTensor(batch_size).bernoulli_(0.5)
input = svrt.generate_vignettes(problem_number, target)
input = input.float().view(input.size(0), 1, input.size(1), input.size(2))
- if self.cuda:
+ if cuda:
input = input.cuda()
target = target.cuda()
return [ input, target ]
self.nb_batches = nb_batches
self.nb_samples = self.nb_batches * self.batch_size
- seed_list = torch.LongTensor(self.nb_batches).random_().tolist()
+ seeds = torch.LongTensor(self.nb_batches).random_()
+ mp_args = []
+ for b in range(0, self.nb_batches):
+ mp_args.append( [ problem_number, batch_size, cuda, seeds[b] ])
# self.data = []
# for b in range(0, self.nb_batches):
- # self.data.append(generate_one_batch(seed_list[b]))
+ # self.data.append(generate_one_batch(mp_args[b]))
- self.data = Pool(cpu_count()).map(generate_one_batch, seed_list)
+ self.data = Pool(cpu_count()).map(generate_one_batch, mp_args)
acc = 0.0
acc_sq = 0.0