julius/prototorch
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add one-hot support in functions/initializers.py

Browse Source
This commit is contained in:
blackfly 2020-04-27 12:47:44 +02:00
parent c11a3860df
commit 532f63b1de
1 changed files with 32 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

51
prototorch/functions/initializers.py
View File

@ -13,71 +13,84 @@ def register_initializer(function):
return function return function
def labels_from(distribution): def labels_from(distribution, one_hot=True):
"""Takes a distribution tensor and returns a labels tensor.""" """Takes a distribution tensor and returns a labels tensor."""
nclasses = distribution.shape[0] nclasses = distribution.shape[0]
llist = [[i] * n for i, n in zip(range(nclasses), distribution)] llist = [[i] * n for i, n in zip(range(nclasses), distribution)]
# labels = [l for cl in llist for l in cl] # flatten the list of lists # labels = [l for cl in llist for l in cl] # flatten the list of lists
labels = list(chain(*llist)) # flatten using itertools.chain flat_llist = list(chain(*llist)) # flatten label list with itertools.chain
return torch.tensor(labels, requires_grad=False) plabels = torch.tensor(flat_llist, requires_grad=False)
if one_hot:
return torch.eye(nclasses)[plabels]
return plabels
@register_initializer @register_initializer
def ones(x_train, y_train, prototype_distribution): def ones(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
protos = torch.ones(nprotos, *x_train.shape[1:]) protos = torch.ones(nprotos, *x_train.shape[1:])
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
return protos, plabels return protos, plabels
@register_initializer @register_initializer
def zeros(x_train, y_train, prototype_distribution): def zeros(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
protos = torch.zeros(nprotos, *x_train.shape[1:]) protos = torch.zeros(nprotos, *x_train.shape[1:])
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
return protos, plabels return protos, plabels
@register_initializer @register_initializer
def rand(x_train, y_train, prototype_distribution): def rand(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
protos = torch.rand(nprotos, *x_train.shape[1:]) protos = torch.rand(nprotos, *x_train.shape[1:])
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
return protos, plabels return protos, plabels
@register_initializer @register_initializer
def randn(x_train, y_train, prototype_distribution): def randn(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
protos = torch.randn(nprotos, *x_train.shape[1:]) protos = torch.randn(nprotos, *x_train.shape[1:])
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
return protos, plabels return protos, plabels
@register_initializer @register_initializer
def stratified_mean(x_train, y_train, prototype_distribution): def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
pdim = x_train.shape[1] pdim = x_train.shape[1]
protos = torch.empty(nprotos, pdim) protos = torch.empty(nprotos, pdim)
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
for i, l in enumerate(plabels): for i, label in enumerate(plabels):
xl = x_train[y_train == l] matcher = torch.eq(label.unsqueeze(dim=0), y_train)
if one_hot:
nclasses = y_train.size()[1]
matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
xl = x_train[matcher]
mean_xl = torch.mean(xl, dim=0) mean_xl = torch.mean(xl, dim=0)
protos[i] = mean_xl protos[i] = mean_xl
plabels = labels_from(prototype_distribution, one_hot=one_hot)
return protos, plabels return protos, plabels
@register_initializer @register_initializer
def stratified_random(x_train, y_train, prototype_distribution): def stratified_random(x_train, y_train, prototype_distribution, one_hot=True):
nprotos = torch.sum(prototype_distribution) nprotos = torch.sum(prototype_distribution)
pdim = x_train.shape[1] pdim = x_train.shape[1]
protos = torch.empty(nprotos, pdim) protos = torch.empty(nprotos, pdim)
plabels = labels_from(prototype_distribution) plabels = labels_from(prototype_distribution, one_hot)
for i, l in enumerate(plabels): for i, label in enumerate(plabels):
xl = x_train[y_train == l] matcher = torch.eq(label.unsqueeze(dim=0), y_train)
if one_hot:
nclasses = y_train.size()[1]
matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
xl = x_train[matcher]
rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1) rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
random_xl = xl[rand_index] random_xl = xl[rand_index]
protos[i] = random_xl protos[i] = random_xl
plabels = labels_from(prototype_distribution, one_hot=one_hot)
return protos, plabels return protos, plabels