Moved VignetteSet and CompressedVignetteSet in their own file.

[pysvrt.git] / cnn-svrt.py

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 8840c4b..084606a 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -36,7 +36,7 @@ from torch import nn
 from torch.nn import functional as fn
 from torchvision import datasets, transforms, utils
 
-import svrt
+from vignette_set import VignetteSet, CompressedVignetteSet
 
 ######################################################################
 
@@ -85,75 +85,6 @@ def log_string(s):
 
 ######################################################################
 
-class VignetteSet:
-    def __init__(self, problem_number, nb_batches):
-        self.batch_size = args.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 = []
-
-        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()
-            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 = math.sqrt(acc_sq / self.nb_batches - mean * mean)
-        for b in range(0, self.nb_batches):
-            self.inputs[b].sub_(mean).div_(std)
-
-    def get_batch(self, b):
-        return self.inputs[b], self.targets[b]
-
-######################################################################
-
-class CompressedVignetteSet:
-    def __init__(self, problem_number, nb_batches):
-        self.batch_size = args.batch_size
-        self.problem_number = problem_number
-        self.nb_batches = nb_batches
-        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 = math.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():
-            input = input.cuda()
-            target = target.cuda()
-
-        return input, target
-
-######################################################################
-
 # Afroze's ShallowNet
 
 #                       map size   nb. maps
@@ -231,11 +162,11 @@ for arg in vars(args):
 
 for problem_number in range(1, 24):
     if args.compress_vignettes:
-        train_set = CompressedVignetteSet(problem_number, args.nb_train_batches)
-        test_set = CompressedVignetteSet(problem_number, args.nb_test_batches)
+        train_set = CompressedVignetteSet(problem_number, args.nb_train_batches, args.batch_size)
+        test_set = CompressedVignetteSet(problem_number, args.nb_test_batches, args.batch_size)
     else:
-        train_set = VignetteSet(problem_number, args.nb_train_batches)
-        test_set = VignetteSet(problem_number, args.nb_test_batches)
+        train_set = VignetteSet(problem_number, args.nb_train_batches, args.batch_size)
+        test_set = VignetteSet(problem_number, args.nb_test_batches, args.batch_size)
 
     model = AfrozeShallowNet()