X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=vignette_set.py;h=0b6de7e17d7db8fc4897f844c889acf549cda562;hb=cefdf80cffc5f897dc728d68bf927f522e3e1608;hp=c46beea3b2a809fcd7ff49db2be0e6d0d6bd992e;hpb=abbbb61852f54e90df6ac5b5f4dcb71d06f88f49;p=pysvrt.git

diff --git a/vignette_set.py b/vignette_set.py
index c46beea..0b6de7e 100755
--- a/vignette_set.py
+++ b/vignette_set.py
@@ -22,7 +22,7 @@
 
 import torch
 from math import sqrt
-from multiprocessing import Pool, cpu_count
+from torch import multiprocessing
 
 from torch import Tensor
 from torch.autograd import Variable
@@ -32,14 +32,11 @@ import svrt
 ######################################################################
 
 def generate_one_batch(s):
-    problem_number, batch_size, cuda, random_seed = 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))
-    if cuda:
-        input = input.cuda()
-        target = target.cuda()
     return [ input, target ]
 
 class VignetteSet:
@@ -54,13 +51,16 @@ class VignetteSet:
         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] ])
+            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]))
+        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
 
-        self.data = Pool(cpu_count()).map(generate_one_batch, mp_args)
+        # pool = multiprocessing.Pool(multiprocessing.cpu_count())
+        # self.data = pool.map(generate_one_batch, mp_args)
 
         acc = 0.0
         acc_sq = 0.0
@@ -73,6 +73,9 @@ class VignetteSet:
         std = sqrt(acc_sq / self.nb_batches - mean * mean)
         for b in range(0, self.nb_batches):
             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.data[b]