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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 = dist.tril(-1)
flat_dist = flat_dist[flat_dist != 0].view(p, -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)
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)
return loss_metric, flat_dist, non_zero_counts
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_metric, flat_dist, None
@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
class JointBatchTripletLoss(BatchTripletLoss):
def __init__(
self,
hpm_num_parts: int,
is_hard: bool = True,
is_mean: bool = True,
margins: tuple[float, float] = (0.2, 0.2)
):
super().__init__(is_hard, is_mean)
self.hpm_num_parts = hpm_num_parts
self.margin_hpm, self.margin_pn = margins
def forward(self, x, y):
p, n, c = x.size()
dist = self._batch_distance(x)
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)
hpm_part_loss = F.relu(
self.margin_hpm + positive_negative_dist[:self.hpm_num_parts]
)
pn_part_loss = F.relu(
self.margin_pn + positive_negative_dist[self.hpm_num_parts:]
)
losses = torch.cat((hpm_part_loss, pn_part_loss)).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)
return loss_metric, dist, non_zero_counts
|
