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"""
Copyright (c) 2018 Intel Corporation
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 torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import torch as th
from .abstract_loss_func import AbstractLossClass
from metrics.registry import LOSSFUNC
#------------ AMSoftmax Loss ----------------------
def focal_loss(input_values, gamma):
"""Computes the focal loss"""
p = torch.exp(-input_values)
loss = (1 - p) ** gamma * input_values
return loss.mean()
@LOSSFUNC.register_module(module_name="am_softmax")
class AMSoftmaxLoss(AbstractLossClass):
"""Computes the AM-Softmax loss with cos or arc margin"""
margin_types = ['cos', 'arc']
def __init__(self, margin_type='cos', gamma=0., m=0.5, s=30, t=1.):
super().__init__()
assert margin_type in AMSoftmaxLoss.margin_types
self.margin_type = margin_type
assert gamma >= 0
self.gamma = gamma
assert m > 0
self.m = m
assert s > 0
self.s = s
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
self.th = math.cos(math.pi - m)
assert t >= 1
self.t = t
def forward(self, cos_theta, target):
if self.margin_type == 'cos':
phi_theta = cos_theta - self.m
else:
sine = torch.sqrt(1.0 - torch.pow(cos_theta, 2))
phi_theta = cos_theta * self.cos_m - sine * self.sin_m #cos(theta+m)
phi_theta = torch.where(cos_theta > self.th, phi_theta, cos_theta - self.sin_m * self.m)
index = torch.zeros_like(cos_theta, dtype=torch.uint8)
index.scatter_(1, target.data.view(-1, 1), 1)
output = torch.where(index, phi_theta, cos_theta)
if self.gamma == 0 and self.t == 1.:
return F.cross_entropy(self.s*output, target)
if self.t > 1:
h_theta = self.t - 1 + self.t*cos_theta
support_vecs_mask = (1 - index) * \
torch.lt(torch.masked_select(phi_theta, index).view(-1, 1).repeat(1, h_theta.shape[1]) - cos_theta, 0)
output = torch.where(support_vecs_mask, h_theta, output)
return F.cross_entropy(self.s*output, target)
return focal_loss(F.cross_entropy(self.s*output, target, reduction='none'), self.gamma)
@LOSSFUNC.register_module(module_name="am_softmax_ohem")
class AMSoftmax_OHEM(AbstractLossClass):
"""Computes the AM-Softmax loss with cos or arc margin"""
margin_types = ['cos', 'arc']
def __init__(self, margin_type='cos', gamma=0., m=0.5, s=30, t=1., ratio=1.):
super(self).__init__()
assert margin_type in AMSoftmaxLoss.margin_types
self.margin_type = margin_type
assert gamma >= 0
self.gamma = gamma
assert m > 0
self.m = m
assert s > 0
self.s = s
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
self.th = math.cos(math.pi - m)
assert t >= 1
self.t = t
self.ratio = ratio
# ------- online hard example mining --------------------
def get_subidx(self,x,y,ratio):
num_inst = x.size(0)
num_hns = int(ratio * num_inst)
x_ = x.clone()
inst_losses = th.autograd.Variable(th.zeros(num_inst)).cuda()
for idx, label in enumerate(y.data):
inst_losses[idx] = -x_.data[idx, label]
_, idxs = inst_losses.topk(num_hns)
return idxs
def forward(self, cos_theta, target):
if self.margin_type == 'cos':
phi_theta = cos_theta - self.m
else:
sine = torch.sqrt(1.0 - torch.pow(cos_theta, 2))
phi_theta = cos_theta * self.cos_m - sine * self.sin_m #cos(theta+m)
phi_theta = torch.where(cos_theta > self.th, phi_theta, cos_theta - self.sin_m * self.m)
index = torch.zeros_like(cos_theta, dtype=torch.uint8)
index.scatter_(1, target.data.view(-1, 1), 1)
output = torch.where(index, phi_theta, cos_theta)
out = F.log_softmax(output,dim=1)
idxs = self.get_subidx(out,target,self.ratio) # select hard examples
output2 = output.index_select(0, idxs)
target2 = target.index_select(0, idxs)
if self.gamma == 0 and self.t == 1.:
return F.cross_entropy(self.s*output2, target2)
if self.t > 1:
h_theta = self.t - 1 + self.t*cos_theta
support_vecs_mask = (1 - index) * \
torch.lt(torch.masked_select(phi_theta, index).view(-1, 1).repeat(1, h_theta.shape[1]) - cos_theta, 0)
output2 = torch.where(support_vecs_mask, h_theta, output2)
return F.cross_entropy(self.s*output2, target2)
return focal_loss(F.cross_entropy(self.s*output2, target2, reduction='none'), self.gamma) |