108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
"""ProtoTorch initialization functions."""
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from itertools import chain
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import torch
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INITIALIZERS = dict()
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def register_initializer(function):
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"""Add the initializer to the registry."""
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INITIALIZERS[function.__name__] = function
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return function
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def labels_from(distribution, one_hot=True):
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"""Takes a distribution tensor and returns a labels tensor."""
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nclasses = distribution.shape[0]
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llist = [[i] * n for i, n in zip(range(nclasses), distribution)]
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# labels = [l for cl in llist for l in cl] # flatten the list of lists
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flat_llist = list(chain(*llist)) # flatten label list with itertools.chain
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plabels = torch.tensor(flat_llist, requires_grad=False)
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if one_hot:
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return torch.eye(nclasses)[plabels]
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return plabels
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@register_initializer
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def ones(x_train, y_train, prototype_distribution, one_hot=True):
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nprotos = torch.sum(prototype_distribution)
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protos = torch.ones(nprotos, *x_train.shape[1:])
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plabels = labels_from(prototype_distribution, one_hot)
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return protos, plabels
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@register_initializer
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def zeros(x_train, y_train, prototype_distribution, one_hot=True):
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nprotos = torch.sum(prototype_distribution)
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protos = torch.zeros(nprotos, *x_train.shape[1:])
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plabels = labels_from(prototype_distribution, one_hot)
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return protos, plabels
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@register_initializer
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def rand(x_train, y_train, prototype_distribution, one_hot=True):
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nprotos = torch.sum(prototype_distribution)
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protos = torch.rand(nprotos, *x_train.shape[1:])
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plabels = labels_from(prototype_distribution, one_hot)
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return protos, plabels
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@register_initializer
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def randn(x_train, y_train, prototype_distribution, one_hot=True):
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nprotos = torch.sum(prototype_distribution)
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protos = torch.randn(nprotos, *x_train.shape[1:])
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plabels = labels_from(prototype_distribution, one_hot)
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return protos, plabels
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@register_initializer
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def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
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nprotos = torch.sum(prototype_distribution)
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pdim = x_train.shape[1]
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protos = torch.empty(nprotos, pdim)
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plabels = labels_from(prototype_distribution, one_hot)
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for i, label in enumerate(plabels):
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matcher = torch.eq(label.unsqueeze(dim=0), y_train)
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if one_hot:
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nclasses = y_train.size()[1]
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matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
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xl = x_train[matcher]
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mean_xl = torch.mean(xl, dim=0)
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protos[i] = mean_xl
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plabels = labels_from(prototype_distribution, one_hot=one_hot)
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return protos, plabels
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@register_initializer
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def stratified_random(x_train,
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y_train,
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prototype_distribution,
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one_hot=True,
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epsilon=1e-7):
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nprotos = torch.sum(prototype_distribution)
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pdim = x_train.shape[1]
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protos = torch.empty(nprotos, pdim)
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plabels = labels_from(prototype_distribution, one_hot)
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for i, label in enumerate(plabels):
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matcher = torch.eq(label.unsqueeze(dim=0), y_train)
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if one_hot:
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nclasses = y_train.size()[1]
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matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
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xl = x_train[matcher]
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rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
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random_xl = xl[rand_index]
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protos[i] = random_xl + epsilon
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plabels = labels_from(prototype_distribution, one_hot=one_hot)
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return protos, plabels
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def get_initializer(funcname):
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"""Deserialize the initializer."""
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if callable(funcname):
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return funcname
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if funcname in INITIALIZERS:
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return INITIALIZERS.get(funcname)
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raise NameError(f'Initializer {funcname} was not found.')
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