rename lib to opengait

opengait/modeling/losses/triplet.py

@@ -0,0 +1,71 @@
+import torch
+import torch.nn.functional as F
+
+from .base import BaseLoss, gather_and_scale_wrapper
+
+
+class TripletLoss(BaseLoss):
+    def __init__(self, margin, loss_term_weight=1.0):
+        super(TripletLoss, self).__init__(loss_term_weight)
+        self.margin = margin
+
+    @gather_and_scale_wrapper
+    def forward(self, embeddings, labels):
+        # embeddings: [n, p, c], label: [n]
+        embeddings = embeddings.permute(
+            1, 0, 2).contiguous()  # [n, p, c] -> [p, n, c]
+        embeddings = embeddings.float()
+
+        ref_embed, ref_label = embeddings, labels
+        dist = self.ComputeDistance(embeddings, ref_embed)  # [p, n1, n2]
+        mean_dist = dist.mean(1).mean(1)
+        ap_dist, an_dist = self.Convert2Triplets(labels, ref_label, dist)
+        dist_diff = ap_dist - an_dist
+        loss = F.relu(dist_diff + self.margin)
+
+        hard_loss = torch.max(loss, -1)[0]
+        loss_avg, loss_num = self.AvgNonZeroReducer(loss)
+
+        self.info.update({
+            'loss': loss_avg.detach().clone(),
+            'hard_loss': hard_loss.detach().clone(),
+            'loss_num': loss_num.detach().clone(),
+            'mean_dist': mean_dist.detach().clone()})
+
+        return loss_avg, self.info
+
+    def AvgNonZeroReducer(self, loss):
+        eps = 1.0e-9
+        loss_sum = loss.sum(-1)
+        loss_num = (loss != 0).sum(-1).float()
+
+        loss_avg = loss_sum / (loss_num + eps)
+        loss_avg[loss_num == 0] = 0
+        return loss_avg, loss_num
+
+    def ComputeDistance(self, x, y):
+        """
+            x: [p, n_x, c]
+            y: [p, n_y, c]
+        """
+        x2 = torch.sum(x ** 2, -1).unsqueeze(2)  # [p, n_x, 1]
+        y2 = torch.sum(y ** 2, -1).unsqueeze(1)  # [p, 1, n_y]
+        inner = x.matmul(y.transpose(-1, -2))  # [p, n_x, n_y]
+        dist = x2 + y2 - 2 * inner
+        dist = torch.sqrt(F.relu(dist))  # [p, n_x, n_y]
+        return dist
+
+    def Convert2Triplets(self, row_labels, clo_label, dist):
+        """
+            row_labels: tensor with size [n_r]
+            clo_label : tensor with size [n_c]
+        """
+        matches = (row_labels.unsqueeze(1) ==
+                   clo_label.unsqueeze(0)).byte()  # [n_r, n_c]
+        diffenc = matches ^ 1  # [n_r, n_c]
+        mask = matches.unsqueeze(2) * diffenc.unsqueeze(1)
+        a_idx, p_idx, n_idx = torch.where(mask)
+
+        ap_dist = dist[:, a_idx, p_idx]
+        an_dist = dist[:, a_idx, n_idx]
+        return ap_dist, an_dist