init

metrics.py

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+from __future__ import print_function
+from __future__ import division
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import Parameter
+import math
+
+
+class ArcMarginProduct(nn.Module):
+    r"""Implement of large margin arc distance: :
+        Args:
+            in_features: size of each input sample
+            out_features: size of each output sample
+            s: norm of input feature
+            m: margin
+            cos(theta + m)
+        """
+    def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
+        super(ArcMarginProduct, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.s = s
+        self.m = m
+        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = easy_margin
+        self.cos_m = math.cos(m)
+        self.sin_m = math.sin(m)
+        self.th = math.cos(math.pi - m)
+        self.mm = math.sin(math.pi - m) * m
+
+    def forward(self, input, label):
+        # --------------------------- cos(theta) & phi(theta) ---------------------------
+        cosine = F.linear(F.normalize(input), F.normalize(self.weight))
+        sine = torch.sqrt((1.0 - torch.pow(cosine, 2)).clamp(0, 1))
+        phi = cosine * self.cos_m - sine * self.sin_m
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        # --------------------------- convert label to one-hot ---------------------------
+        # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
+        one_hot = torch.zeros(cosine.size(), device='cuda')
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        # -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)  # you can use torch.where if your torch.__version__ is 0.4
+        output *= self.s
+        # print(output)
+
+        return output
+
+
+class AddMarginProduct(nn.Module):
+    r"""Implement of large margin cosine distance: :
+    Args:
+        in_features: size of each input sample
+        out_features: size of each output sample
+        s: norm of input feature
+        m: margin
+        cos(theta) - m
+    """
+
+    def __init__(self, in_features, out_features, s=30.0, m=0.40):
+        super(AddMarginProduct, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.s = s
+        self.m = m
+        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
+        nn.init.xavier_uniform_(self.weight)
+
+    def forward(self, input, label):
+        # --------------------------- cos(theta) & phi(theta) ---------------------------
+        cosine = F.linear(F.normalize(input), F.normalize(self.weight))
+        phi = cosine - self.m
+        # --------------------------- convert label to one-hot ---------------------------
+        one_hot = torch.zeros(cosine.size(), device='cuda')
+        # one_hot = one_hot.cuda() if cosine.is_cuda else one_hot
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        # -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)  # you can use torch.where if your torch.__version__ is 0.4
+        output *= self.s
+        # print(output)
+
+        return output
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(' \
+               + 'in_features=' + str(self.in_features) \
+               + ', out_features=' + str(self.out_features) \
+               + ', s=' + str(self.s) \
+               + ', m=' + str(self.m) + ')'
+
+
+class SphereProduct(nn.Module):
+    r"""Implement of large margin cosine distance: :
+    Args:
+        in_features: size of each input sample
+        out_features: size of each output sample
+        m: margin
+        cos(m*theta)
+    """
+    def __init__(self, in_features, out_features, m=4):
+        super(SphereProduct, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.m = m
+        self.base = 1000.0
+        self.gamma = 0.12
+        self.power = 1
+        self.LambdaMin = 5.0
+        self.iter = 0
+        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
+        nn.init.xavier_uniform(self.weight)
+
+        # duplication formula
+        self.mlambda = [
+            lambda x: x ** 0,
+            lambda x: x ** 1,
+            lambda x: 2 * x ** 2 - 1,
+            lambda x: 4 * x ** 3 - 3 * x,
+            lambda x: 8 * x ** 4 - 8 * x ** 2 + 1,
+            lambda x: 16 * x ** 5 - 20 * x ** 3 + 5 * x
+        ]
+
+    def forward(self, input, label):
+        # lambda = max(lambda_min,base*(1+gamma*iteration)^(-power))
+        self.iter += 1
+        self.lamb = max(self.LambdaMin, self.base * (1 + self.gamma * self.iter) ** (-1 * self.power))
+
+        # --------------------------- cos(theta) & phi(theta) ---------------------------
+        cos_theta = F.linear(F.normalize(input), F.normalize(self.weight))
+        cos_theta = cos_theta.clamp(-1, 1)
+        cos_m_theta = self.mlambda[self.m](cos_theta)
+        theta = cos_theta.data.acos()
+        k = (self.m * theta / 3.14159265).floor()
+        phi_theta = ((-1.0) ** k) * cos_m_theta - 2 * k
+        NormOfFeature = torch.norm(input, 2, 1)
+
+        # --------------------------- convert label to one-hot ---------------------------
+        one_hot = torch.zeros(cos_theta.size())
+        one_hot = one_hot.cuda() if cos_theta.is_cuda else one_hot
+        one_hot.scatter_(1, label.view(-1, 1), 1)
+
+        # --------------------------- Calculate output ---------------------------
+        output = (one_hot * (phi_theta - cos_theta) / (1 + self.lamb)) + cos_theta
+        output *= NormOfFeature.view(-1, 1)
+
+        return output
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(' \
+               + 'in_features=' + str(self.in_features) \
+               + ', out_features=' + str(self.out_features) \
+               + ', m=' + str(self.m) + ')'