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utils.py
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utils.py
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from itertools import combinations
import numpy as np
import torch
def pdist(vectors):
distance_matrix = -2 * vectors.mm(torch.t(vectors)) + vectors.pow(2).sum(dim=1).view(1, -1) + vectors.pow(2).sum(
dim=1).view(-1, 1)
return distance_matrix
class PairSelector:
"""
Implementation should return indices of positive pairs and negative pairs that will be passed to compute
Contrastive Loss
return positive_pairs, negative_pairs
"""
def __init__(self):
pass
def get_pairs(self, embeddings, labels):
raise NotImplementedError
class AllPositivePairSelector(PairSelector):
"""
Discards embeddings and generates all possible pairs given labels.
If balance is True, negative pairs are a random sample to match the number of positive samples
"""
def __init__(self, balance=True):
super(AllPositivePairSelector, self).__init__()
self.balance = balance
def get_pairs(self, embeddings, labels):
labels = labels.cpu().data.numpy()
all_pairs = np.array(list(combinations(range(len(labels)), 2)))
all_pairs = torch.LongTensor(all_pairs)
positive_pairs = all_pairs[(labels[all_pairs[:, 0]] == labels[all_pairs[:, 1]]).nonzero()]
negative_pairs = all_pairs[(labels[all_pairs[:, 0]] != labels[all_pairs[:, 1]]).nonzero()]
if self.balance:
negative_pairs = negative_pairs[torch.randperm(len(negative_pairs))[:len(positive_pairs)]]
return positive_pairs, negative_pairs
class HardNegativePairSelector(PairSelector):
"""
Creates all possible positive pairs. For negative pairs, pairs with smallest distance are taken into consideration,
matching the number of positive pairs.
"""
def __init__(self, cpu=True):
super(HardNegativePairSelector, self).__init__()
self.cpu = cpu
def get_pairs(self, embeddings, labels):
if self.cpu:
embeddings = embeddings.cpu()
distance_matrix = pdist(embeddings)
labels = labels.cpu().data.numpy()
all_pairs = np.array(list(combinations(range(len(labels)), 2)))
all_pairs = torch.LongTensor(all_pairs)
positive_pairs = all_pairs[(labels[all_pairs[:, 0]] == labels[all_pairs[:, 1]]).nonzero()]
negative_pairs = all_pairs[(labels[all_pairs[:, 0]] != labels[all_pairs[:, 1]]).nonzero()]
negative_distances = distance_matrix[negative_pairs[:, 0], negative_pairs[:, 1]]
negative_distances = negative_distances.cpu().data.numpy()
top_negatives = np.argpartition(negative_distances, len(positive_pairs))[:len(positive_pairs)]
top_negative_pairs = negative_pairs[torch.LongTensor(top_negatives)]
return positive_pairs, top_negative_pairs
class TripletSelector:
"""
Implementation should return indices of anchors, positive and negative samples
return np array of shape [N_triplets x 3]
"""
def __init__(self):
pass
def get_triplets(self, embeddings, labels):
raise NotImplementedError
class AllTripletSelector(TripletSelector):
"""
Returns all possible triplets
May be impractical in most cases
"""
def __init__(self):
super(AllTripletSelector, self).__init__()
def get_triplets(self, embeddings, labels):
labels = labels.cpu().data.numpy()
triplets = []
for label in set(labels):
label_mask = (labels == label)
label_indices = np.where(label_mask)[0]
if len(label_indices) < 2:
continue
negative_indices = np.where(np.logical_not(label_mask))[0]
anchor_positives = list(combinations(label_indices, 2)) # All anchor-positive pairs
# Add all negatives for all positive pairs
temp_triplets = [[anchor_positive[0], anchor_positive[1], neg_ind] for anchor_positive in anchor_positives
for neg_ind in negative_indices]
triplets += temp_triplets
return torch.LongTensor(np.array(triplets))
def hardest_negative(loss_values):
hard_negative = np.argmax(loss_values)
return hard_negative if loss_values[hard_negative] > 0 else None
def random_hard_negative(loss_values):
hard_negatives = np.where(loss_values > 0)[0]
return np.random.choice(hard_negatives) if len(hard_negatives) > 0 else None
def semihard_negative(loss_values, margin):
semihard_negatives = np.where(np.logical_and(loss_values < margin, loss_values > 0))[0]
return np.random.choice(semihard_negatives) if len(semihard_negatives) > 0 else None
class FunctionNegativeTripletSelector(TripletSelector):
"""
For each positive pair, takes the hardest negative sample (with the greatest triplet loss value) to create a triplet
Margin should match the margin used in triplet loss.
negative_selection_fn should take array of loss_values for a given anchor-positive pair and all negative samples
and return a negative index for that pair
"""
def __init__(self, margin, negative_selection_fn, cpu=True):
super(FunctionNegativeTripletSelector, self).__init__()
self.cpu = cpu
self.margin = margin
self.negative_selection_fn = negative_selection_fn
def get_triplets(self, embeddings, labels):
if self.cpu:
embeddings = embeddings.cpu()
distance_matrix = pdist(embeddings)
distance_matrix = distance_matrix.cpu()
labels = labels.cpu().data.numpy()
triplets = []
for label in set(labels):
label_mask = (labels == label)
label_indices = np.where(label_mask)[0]
if len(label_indices) < 2:
continue
negative_indices = np.where(np.logical_not(label_mask))[0]
anchor_positives = list(combinations(label_indices, 2)) # All anchor-positive pairs
anchor_positives = np.array(anchor_positives)
ap_distances = distance_matrix[anchor_positives[:, 0], anchor_positives[:, 1]]
for anchor_positive, ap_distance in zip(anchor_positives, ap_distances):
loss_values = ap_distance - distance_matrix[torch.LongTensor(np.array([anchor_positive[0]])), torch.LongTensor(negative_indices)] + self.margin
loss_values = loss_values.data.cpu().numpy()
hard_negative = self.negative_selection_fn(loss_values)
if hard_negative is not None:
hard_negative = negative_indices[hard_negative]
triplets.append([anchor_positive[0], anchor_positive[1], hard_negative])
if len(triplets) == 0:
triplets.append([anchor_positive[0], anchor_positive[1], negative_indices[0]])
triplets = np.array(triplets)
return torch.LongTensor(triplets)
def HardestNegativeTripletSelector(margin, cpu=False): return FunctionNegativeTripletSelector(margin=margin,
negative_selection_fn=hardest_negative,
cpu=cpu)
def RandomNegativeTripletSelector(margin, cpu=False): return FunctionNegativeTripletSelector(margin=margin,
negative_selection_fn=random_hard_negative,
cpu=cpu)
def SemihardNegativeTripletSelector(margin, cpu=False): return FunctionNegativeTripletSelector(margin=margin,
negative_selection_fn=lambda x: semihard_negative(x, margin),
cpu=cpu)