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from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class BatchTripletLoss(nn.Module):
def __init__(
self,
is_hard: bool = True,
is_mean: bool = True,
margin: Optional[float] = 0.2,
):
super().__init__()
self.is_hard = is_hard
self.is_mean = is_mean
self.margin = margin
def forward(self, x, y):
p, n, c = x.size()
dist = self._batch_distance(x)
flat_dist_mask = torch.tril_indices(n, n, offset=-1, device=dist.device)
flat_dist = dist[:, flat_dist_mask[0], flat_dist_mask[1]]
if self.is_hard:
positive_negative_dist = self._hard_distance(dist, y, p, n)
else: # is_all
positive_negative_dist = self._all_distance(dist, y, p, n)
non_zero_counts = None
if self.margin:
losses = F.relu(self.margin + positive_negative_dist).view(p, -1)
non_zero_counts = (losses != 0).sum(1).float()
if self.is_mean:
loss_metric = self._none_zero_mean(losses, non_zero_counts)
else: # is_sum
loss_metric = losses.sum(1)
else: # Soft margin
losses = F.softplus(positive_negative_dist).view(p, -1)
if self.is_mean:
loss_metric = losses.mean(1)
else: # is_sum
loss_metric = losses.sum(1)
return {
'loss': loss_metric,
'dist': flat_dist,
'counts': non_zero_counts
}
@staticmethod
def _batch_distance(x):
# Euclidean distance p x n x n
x_squared_sum = torch.sum(x ** 2, dim=2)
x1_squared_sum = x_squared_sum.unsqueeze(2)
x2_squared_sum = x_squared_sum.unsqueeze(1)
x1_times_x2_sum = x @ x.transpose(1, 2)
dist = torch.sqrt(
F.relu(x1_squared_sum - 2 * x1_times_x2_sum + x2_squared_sum)
)
return dist
@staticmethod
def _hard_distance(dist, y, p, n):
positive_mask = y.unsqueeze(1) == y.unsqueeze(2)
negative_mask = y.unsqueeze(1) != y.unsqueeze(2)
hard_positive = dist[positive_mask].view(p, n, -1).max(-1).values
hard_negative = dist[negative_mask].view(p, n, -1).min(-1).values
positive_negative_dist = hard_positive - hard_negative
return positive_negative_dist
@staticmethod
def _all_distance(dist, y, p, n):
# Unmask identical samples
positive_mask = torch.eye(
n, dtype=torch.bool, device=y.device
) ^ (y.unsqueeze(1) == y.unsqueeze(2))
negative_mask = y.unsqueeze(1) != y.unsqueeze(2)
all_positive = dist[positive_mask].view(p, n, -1, 1)
all_negative = dist[negative_mask].view(p, n, 1, -1)
positive_negative_dist = all_positive - all_negative
return positive_negative_dist
@staticmethod
def _none_zero_mean(losses, non_zero_counts):
# Non-zero parted mean
non_zero_mean = losses.sum(1) / non_zero_counts
non_zero_mean[non_zero_counts == 0] = 0
return non_zero_mean
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