Create pairwisecosface.py

ppcls/loss/pairwisecosface.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class PairwiseCosface(nn.Layer):
+    def __init__(self, margin, gamma):
+        super(PairwiseCosface, self).__init__()
+        self.margin = margin
+        self.gamma = gamma
+
+    def forward(self, embedding, targets):
+        if isinstance(embedding, dict):
+            embedding = embedding['features']
+        # Normalize embedding features
+        embedding = F.normalize(embedding, axis=1)
+        dist_mat = paddle.matmul(embedding, embedding, transpose_y=True)
+
+        N = dist_mat.shape[0]
+        is_pos = targets.reshape([N,1]).expand([N,N]).equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
+        is_neg = targets.reshape([N,1]).expand([N,N]).not_equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
+
+        # Mask scores related to itself
+        is_pos = is_pos - paddle.eye(N, N)
+
+        s_p = dist_mat * is_pos
+        s_n = dist_mat * is_neg
+
+        logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)
+        logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg)
+
+        loss = F.softplus(paddle.logsumexp(logit_p, axis=1) + paddle.logsumexp(logit_n, axis=1)).mean()
+  
+        return {"PairwiseCosface": loss}
+
+