1 files changed, 0 insertions, 92 deletions
diff --git a/utils/triplet_loss.py b/utils/triplet_loss.py
deleted file mode 100644
index 5e3a97a..0000000
--- a/utils/triplet_loss.py
+++ /dev/null
@@ -1,92 +0,0 @@
-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