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import torch
import torch.nn as nn
import torch.nn.functional as F
class BatchAllTripletLoss(nn.Module):
def __init__(self, margin: float = 0.2):
super().__init__()
self.margin = margin
def forward(self, x, y):
# Duplicate labels for each part
p, n, c = x.size()
y = y.repeat(p, 1)
# 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)
)
hard_positive_mask = y.unsqueeze(1) == y.unsqueeze(2)
hard_negative_mask = y.unsqueeze(1) != y.unsqueeze(2)
all_hard_positive = dist[hard_positive_mask].view(p, n, -1, 1)
all_hard_negative = dist[hard_negative_mask].view(p, n, 1, -1)
positive_negative_dist = all_hard_positive - all_hard_negative
all_loss = F.relu(self.margin + positive_negative_dist).view(p, -1)
# Non-zero parted mean
non_zero_counts = (all_loss != 0).sum(1)
parted_loss_mean = all_loss.sum(1) / non_zero_counts
parted_loss_mean[non_zero_counts == 0] = 0
loss = parted_loss_mean.mean()
return loss
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