From: Francois Fleuret Date: Thu, 15 Jun 2017 21:37:20 +0000 (+0200) Subject: Cosmetics. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=24c605e252da6bd8a74fe363192bdbfc2f6b688d;p=pysvrt.git Cosmetics. --- diff --git a/cnn-svrt.py b/cnn-svrt.py index bbce4c9..8840c4b 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -67,7 +67,7 @@ parser.add_argument('--log_file', parser.add_argument('--compress_vignettes', action='store_true', default = False, - help = 'Should we use lossless compression of vignette to reduce the memory footprint') + help = 'Use lossless compression to reduce the memory footprint') args = parser.parse_args() @@ -97,26 +97,28 @@ class VignetteSet: acc = 0.0 acc_sq = 0.0 - for k in range(0, self.nb_batches): + 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.float().sum() / input.numel() - acc_sq += input.float().pow(2).sum() / input.numel() + 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 k in range(0, self.nb_batches): - self.inputs[k].sub_(mean).div_(std) + 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 @@ -128,7 +130,7 @@ class CompressedVignetteSet: acc = 0.0 acc_sq = 0.0 - for k in range(0, self.nb_batches): + 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() @@ -193,7 +195,7 @@ def train_model(model, train_set): optimizer = optim.SGD(model.parameters(), lr = 1e-2) - for k in range(0, args.nb_epochs): + for e in range(0, args.nb_epochs): acc_loss = 0.0 for b in range(0, train_set.nb_batches): input, target = train_set.get_batch(b) @@ -203,7 +205,7 @@ def train_model(model, train_set): model.zero_grad() loss.backward() optimizer.step() - log_string('train_loss {:d} {:f}'.format(k, acc_loss)) + log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss)) return model