Renamed vignette_set.py to svrtset.py

[pysvrt.git] / vignette_set.py

diff --git a/vignette_set.py b/vignette_set.py
deleted file mode 100755 (executable)
index aef23d8..0000000
--- a/vignette_set.py
+++ /dev/null
@@ -1,129 +0,0 @@
-
-#  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 mutiple 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 mutiple 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
-
-######################################################################