Renamed vignette_set.py to svrtset.py

[pysvrt.git] / svrtset.py

diff --git a/svrtset.py b/svrtset.py
new file mode 100755 (executable)
index 0000000..cbc71a3
--- /dev/null
+++ b/svrtset.py
@@ -0,0 +1,129 @@
+
+#  svrt is the ``Synthetic Visual Reasoning Test'', an image
+#  generator for evaluating classification performance of machine
+#  learning systems, humans and primates.
+#
+#  Copyright (c) 2017 Idiap Research Institute, http://www.idiap.ch/
+#  Written by Francois Fleuret <francois.fleuret@idiap.ch>
+#
+#  This file is part of svrt.
+#
+#  svrt is free software: you can redistribute it and/or modify it
+#  under the terms of the GNU General Public License version 3 as
+#  published by the Free Software Foundation.
+#
+#  svrt is distributed in the hope that it will be useful, but
+#  WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+#  General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with svrt.  If not, see <http://www.gnu.org/licenses/>.
+
+import torch
+from math import sqrt
+from torch import multiprocessing
+
+from torch import Tensor
+from torch.autograd import Variable
+
+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_samples, batch_size, cuda = False):
+
+        if nb_samples%batch_size > 0:
+            print('nb_samples must be a multiple of batch_size')
+            raise
+
+        self.cuda = cuda
+        self.batch_size = batch_size
+        self.problem_number = problem_number
+        self.nb_batches = nb_samples // batch_size
+        self.nb_samples = self.nb_batches * self.batch_size
+
+        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):
+            input = self.data[b][0]
+            acc += input.sum() / input.numel()
+            acc_sq += input.pow(2).sum() /  input.numel()
+
+        mean = acc / self.nb_batches
+        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]
+
+######################################################################
+
+class CompressedVignetteSet:
+    def __init__(self, problem_number, nb_samples, batch_size, cuda = False):
+
+        if nb_samples%batch_size > 0:
+            print('nb_samples must be a multiple of batch_size')
+            raise
+
+        self.cuda = cuda
+        self.batch_size = batch_size
+        self.problem_number = problem_number
+        self.nb_batches = nb_samples // batch_size
+        self.nb_samples = self.nb_batches * self.batch_size
+        self.targets = []
+        self.input_storages = []
+
+        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)
+            acc += input.float().sum() / input.numel()
+            acc_sq += input.float().pow(2).sum() /  input.numel()
+            self.targets.append(target)
+            self.input_storages.append(svrt.compress(input.storage()))
+
+        self.mean = acc / self.nb_batches
+        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 self.cuda:
+            input = input.cuda()
+            target = target.cuda()
+
+        return input, target
+
+######################################################################