X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pysvrt.git;a=blobdiff_plain;f=vignette_set.py;h=5062f3e89d698029d7455fa6937e8ea01eb3129b;hp=ea5215944bddbf7dcf0d5b50efcd20293f76da6a;hb=15f2d2cf0a655234cfa435789e26238b95f5a371;hpb=c71899cfec905c50302be54725a97d7fbff08f54

diff --git a/vignette_set.py b/vignette_set.py
index ea52159..5062f3e 100755
--- a/vignette_set.py
+++ b/vignette_set.py
@@ -22,6 +22,7 @@
 
 import torch
 from math import sqrt
+from torch import multiprocessing
 
 from torch import Tensor
 from torch.autograd import Variable
@@ -30,45 +31,73 @@ import svrt
 
 ######################################################################
 
+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 = []
@@ -84,14 +113,14 @@ class CompressedVignetteSet:
             self.input_storages.append(svrt.compress(input.storage()))
 
         self.mean = acc / self.nb_batches
-        self.std = math.sqrt(acc_sq / self.nb_batches - self.mean * self.mean)
+        self.std = sqrt(acc_sq / self.nb_batches - self.mean * self.mean)
 
     def get_batch(self, b):
         input = torch.ByteTensor(svrt.uncompress(self.input_storages[b])).float()
         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()