From: Francois Fleuret Date: Thu, 15 Nov 2018 10:50:40 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=f70ffe019d9fea1ce719836734cfbfac12532fe4;p=pytorch.git Update. --- diff --git a/mine_mnist.py b/mine_mnist.py index 82f6530..6f65136 100755 --- a/mine_mnist.py +++ b/mine_mnist.py @@ -12,27 +12,30 @@ from torch.nn import functional as F ###################################################################### -# Returns a pair of tensors (a, b, c), where a and b are Nx1x28x28 -# tensors containing images, with a[i] and b[i] of same class for any -# i, and c is a 1d long tensor with the count of pairs per class used. +# Returns a pair of tensors (a, b, c), where a and b are tensors +# containing each half of the samples, with a[i] and b[i] of same +# class for any i, and c is a 1d long tensor with the count of pairs +# per class used. def create_pair_set(used_classes, input, target): - u = [] + ua, ub = [], [] for i in used_classes: used_indices = torch.arange(input.size(0), device = target.device)\ .masked_select(target == i.item()) x = input[used_indices] x = x[torch.randperm(x.size(0))] - # Careful with odd numbers of samples in a class - x = x[0:2 * (x.size(0) // 2)].reshape(-1, 2, 28, 28) - u.append(x) + ua.append(x.narrow(0, 0, x.size(0)//2)) + ub.append(x.narrow(0, x.size(0)//2, x.size(0)//2)) - x = torch.cat(u, 0) - x = x[torch.randperm(x.size(0))] - c = torch.tensor([x.size(0) for x in u]) + a = torch.cat(ua, 0) + b = torch.cat(ub, 0) + perm = torch.randperm(a.size(0)) + a = a[perm].contiguous() + b = b[perm].contiguous() + c = torch.tensor([x.size(0) for x in ua]) - return x.narrow(1, 0, 1).contiguous(), x.narrow(1, 1, 1).contiguous(), c + return a, b, c ######################################################################