Create __init__.py

ppcls/losses/__init__.py

@@ -0,0 +1,55 @@
+# 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.
+
+import math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+ 
+class CircleMargin(nn.Layer):
+    def __init__(self, embedding_size, 
+                       class_num, 
+                       margin, 
+                       scale):
+        super(CircleSoftmax, self).__init__()
+        self.scale  = scale
+        self.margin = margin
+        self.embedding_size = embedding_size
+        self.class_num = class_num
+
+        weight_attr = paddle.ParamAttr(initializer = paddle.nn.initializer.XavierNormal())
+        self.fc0 = paddle.nn.Linear(self.embedding_size, self.class_num, weight_attr=weight_attr)
+ 
+    def forward(self, input, label):
+        feat_norm = paddle.sqrt(paddle.sum(paddle.square(input), axis=1, keepdim=True))
+        input = paddle.divide(input, feat_norm)
+
+        weight = self.fc0.weight
+        weight_norm = paddle.sqrt(paddle.sum(paddle.square(weight), axis=0, keepdim=True))
+        weight = paddle.divide(weight, weight_norm)
+ 
+        logits   = paddle.matmul(input, weight)
+
+        alpha_p = paddle.clip(-logits.detach() + 1 + self.margin, min=0.)
+        alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
+        delta_p = 1 - self.margin
+        delta_n = self.margin
+        index = paddle.fluid.layers.where(label != -1).reshape([-1])
+        m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
+        logits_p = alpha_p * (logits - delta_p)
+        logits_n = alpha_n * (logits - delta_n)
+        pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)
+        pre_logits = self.scale * pre_logits
+ 
+        return pre_logits