import torch
from math import sqrt
+from torch import multiprocessing
from torch import Tensor
from torch.autograd import Variable
######################################################################
+def generate_one_batch(s):
+ problem_number, batch_size, 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))
+ return [ input, target ]
+
class VignetteSet:
- def __init__(self, problem_number, nb_batches, batch_size):
+
+ def __init__(self, problem_number, nb_samples, batch_size, cuda = False):
+
+ if nb_samples%batch_size > 0:
+ print('nb_samples must be a mutiple of batch_size')
+ raise
+
+ self.cuda = cuda
self.batch_size = batch_size
self.problem_number = problem_number
- self.nb_batches = nb_batches
+ self.nb_batches = nb_samples // batch_size
self.nb_samples = self.nb_batches * self.batch_size
- self.targets = []
- self.inputs = []
+
+ seeds = torch.LongTensor(self.nb_batches).random_()
+ mp_args = []
+ for b in range(0, self.nb_batches):
+ mp_args.append( [ problem_number, batch_size, seeds[b] ])
+
+ self.data = []
+ for b in range(0, self.nb_batches):
+ self.data.append(generate_one_batch(mp_args[b]))
+
+ # Weird thing going on with the multi-processing, waiting for more info
+
+ # pool = multiprocessing.Pool(multiprocessing.cpu_count())
+ # self.data = pool.map(generate_one_batch, mp_args)
acc = 0.0
acc_sq = 0.0
-
for b in range(0, self.nb_batches):
- target = torch.LongTensor(self.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 torch.cuda.is_available():
- input = input.cuda()
- target = target.cuda()
+ input = self.data[b][0]
acc += input.sum() / input.numel()
acc_sq += input.pow(2).sum() / input.numel()
- self.targets.append(target)
- self.inputs.append(input)
mean = acc / self.nb_batches
std = sqrt(acc_sq / self.nb_batches - mean * mean)
for b in range(0, self.nb_batches):
- self.inputs[b].sub_(mean).div_(std)
+ self.data[b][0].sub_(mean).div_(std)
+ if cuda:
+ self.data[b][0] = self.data[b][0].cuda()
+ self.data[b][1] = self.data[b][1].cuda()
def get_batch(self, b):
- return self.inputs[b], self.targets[b]
+ return self.data[b]
######################################################################
class CompressedVignetteSet:
- def __init__(self, problem_number, nb_batches, batch_size):
+ def __init__(self, problem_number, nb_samples, batch_size, cuda = False):
+
+ if nb_samples%batch_size > 0:
+ print('nb_samples must be a mutiple of batch_size')
+ raise
+
+ self.cuda = cuda
self.batch_size = batch_size
self.problem_number = problem_number
- self.nb_batches = nb_batches
+ self.nb_batches = nb_samples // batch_size
self.nb_samples = self.nb_batches * self.batch_size
self.targets = []
self.input_storages = []
input = input.view(self.batch_size, 1, 128, 128).sub_(self.mean).div_(self.std)
target = self.targets[b]
- if torch.cuda.is_available():
+ if self.cuda:
input = input.cuda()
target = target.cuda()