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Made VignetteSet.__init__ multi-proc.
author
Francois Fleuret
<francois@fleuret.org>
Fri, 16 Jun 2017 07:43:52 +0000
(09:43 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Fri, 16 Jun 2017 07:43:52 +0000
(09:43 +0200)
vignette_set.py
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diff --git
a/vignette_set.py
b/vignette_set.py
index
695fed3
..
72880ba
100755
(executable)
--- a/
vignette_set.py
+++ b/
vignette_set.py
@@
-22,6
+22,7
@@
import torch
from math import sqrt
import torch
from math import sqrt
+from multiprocessing import Pool, cpu_count
from torch import Tensor
from torch.autograd import Variable
from torch import Tensor
from torch.autograd import Variable
@@
-30,38
+31,47
@@
import svrt
######################################################################
######################################################################
+def generate_one_batch(s):
+ svrt.seed(s)
+ 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 self.cuda:
+ input = input.cuda()
+ target = target.cuda()
+ return [ input, target ]
+
class VignetteSet:
class VignetteSet:
+
def __init__(self, problem_number, nb_batches, batch_size, cuda = False):
self.cuda = cuda
self.batch_size = batch_size
self.problem_number = problem_number
self.nb_batches = nb_batches
self.nb_samples = self.nb_batches * self.batch_size
def __init__(self, problem_number, nb_batches, batch_size, cuda = False):
self.cuda = cuda
self.batch_size = batch_size
self.problem_number = problem_number
self.nb_batches = nb_batches
self.nb_samples = self.nb_batches * self.batch_size
- self.targets = []
- self.inputs = []
+
+ seed_list = torch.LongTensor(self.nb_batches).random_().tolist()
+
+ # self.data = []
+ # for b in range(0, self.nb_batches):
+ # self.data.append(generate_one_batch(seed_list[b]))
+
+ self.data = Pool(cpu_count()).map(generate_one_batch, seed_list)
acc = 0.0
acc_sq = 0.0
acc = 0.0
acc_sq = 0.0
-
for b in range(0, self.nb_batches):
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 self.cuda:
- input = input.cuda()
- target = target.cuda()
+ input = self.data[b][0]
acc += input.sum() / input.numel()
acc_sq += input.pow(2).sum() / input.numel()
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):
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)
def get_batch(self, b):
def get_batch(self, b):
- return self.
inputs[b], self.targets
[b]
+ return self.
data
[b]
######################################################################
######################################################################