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from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.distributed as dist
import math
def l2_norm(input, axis=1):
norm = torch.norm(input, p=2, dim=axis, keepdim=True)
output = torch.div(input, norm)
return output
def calc_logits(embeddings, kernel):
""" calculate original logits
"""
embeddings = l2_norm(embeddings, axis=1)
kernel_norm = l2_norm(kernel, axis=0)
cos_theta = torch.mm(embeddings, kernel_norm)
cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
with torch.no_grad():
origin_cos = cos_theta.clone()
return cos_theta, origin_cos
@torch.no_grad()
def all_gather_tensor(input_tensor):
""" allgather tensor (difference size in 0-dim) from all workers
"""
world_size = dist.get_world_size()
tensor_size = torch.tensor([input_tensor.shape[0]], dtype=torch.int64).cuda()
tensor_size_list = [torch.ones_like(tensor_size) for _ in range(world_size)]
dist.all_gather(tensor_list=tensor_size_list, tensor=tensor_size, async_op=False)
max_size = torch.cat(tensor_size_list, dim=0).max()
padded = torch.empty(max_size.item(), *input_tensor.shape[1:], dtype=input_tensor.dtype).cuda()
padded[:input_tensor.shape[0]] = input_tensor
padded_list = [torch.ones_like(padded) for _ in range(world_size)]
dist.all_gather(tensor_list=padded_list, tensor=padded, async_op=False)
slices = []
for ts, t in zip(tensor_size_list, padded_list):
slices.append(t[:ts.item()])
return torch.cat(slices, dim=0)
def calc_top1_acc(original_logits, label,ddp=False):
"""
Compute the top1 accuracy during training
:param original_logits: logits w/o margin, [bs, C]
:param label: labels [bs]
:return: acc in all gpus
"""
assert (original_logits.size()[0] == label.size()[0])
with torch.no_grad():
_, max_index = torch.max(original_logits, dim=1, keepdim=False) # local max logit
count = (max_index == label).sum()
if ddp:
dist.all_reduce(count, dist.ReduceOp.SUM)
return count.item() / (original_logits.size()[0] * dist.get_world_size())
else:
return count.item() / (original_logits.size()[0])
def l2_norm(input, axis=1):
norm = torch.norm(input, p=2, dim=axis, keepdim=True)
output = torch.div(input, norm)
return output
class FC_ddp2(nn.Module):
"""
Implement of (CVPR2021 Consistent Instance False Positive Improves Fairness in Face Recognition)
No model parallel is used
"""
def __init__(self,
in_features,
out_features,
scale=64.0,
margin=0.4,
mode='cosface',
use_cifp=False,
reduction='mean',
ddp=False):
""" Args:
in_features: size of each input features
out_features: size of each output features
scale: norm of input feature
margin: margin
"""
super(FC_ddp2, self).__init__()
self.in_features = in_features
self.out_features = out_features # num of classes
self.scale = scale
self.margin = margin
self.mode = mode
self.use_cifp = use_cifp
self.kernel = Parameter(torch.Tensor(in_features, out_features))
self.ddp = ddp
nn.init.normal_(self.kernel, std=0.01)
self.criteria = torch.nn.CrossEntropyLoss(reduction=reduction)
def apply_margin(self, target_cos_theta):
assert self.mode in ['cosface', 'arcface'], 'Please check the mode'
if self.mode == 'arcface':
cos_m = math.cos(self.margin)
sin_m = math.sin(self.margin)
theta = math.cos(math.pi - self.margin)
sinmm = math.sin(math.pi - self.margin) * self.margin
sin_theta = torch.sqrt(1.0 - torch.pow(target_cos_theta, 2))
cos_theta_m = target_cos_theta * cos_m - sin_theta * sin_m
target_cos_theta_m = torch.where(
target_cos_theta > theta, cos_theta_m, target_cos_theta - sinmm)
elif self.mode == 'cosface':
target_cos_theta_m = target_cos_theta - self.margin
return target_cos_theta_m
def forward(self, embeddings, label, return_logits=False):
"""
:param embeddings: local gpu [bs, 512]
:param label: local labels [bs]
:param return_logits: bool
:return:
loss: computed local loss, w/wo CIFP
acc: local accuracy in one gpu
output: local logits with margins, with gradients, scaled, [bs, C].
"""
sample_num = embeddings.size(0)
if not self.use_cifp:
cos_theta, origin_cos = calc_logits(embeddings, self.kernel)
target_cos_theta = cos_theta[torch.arange(0, sample_num), label].view(-1, 1)
target_cos_theta_m = self.apply_margin(target_cos_theta)
cos_theta.scatter_(1, label.view(-1, 1).long(), target_cos_theta_m)
else:
cos_theta, origin_cos = calc_logits(embeddings, self.kernel)
cos_theta_, _ = calc_logits(embeddings, self.kernel.detach())
mask = torch.zeros_like(cos_theta) # [bs，C]
mask.scatter_(1, label.view(-1, 1).long(), 1.0) # one-hot label / gt mask
tmp_cos_theta = cos_theta - 2 * mask
tmp_cos_theta_ = cos_theta_ - 2 * mask
target_cos_theta = cos_theta[torch.arange(0, sample_num), label].view(-1, 1)
target_cos_theta_ = cos_theta_[torch.arange(0, sample_num), label].view(-1, 1)
target_cos_theta_m = self.apply_margin(target_cos_theta)
far = 1 / (self.out_features - 1) # ru+ value
# far = 1e-5
topk_mask = torch.greater(tmp_cos_theta, target_cos_theta)
topk_sum = torch.sum(topk_mask.to(torch.int32))
if self.ddp:
dist.all_reduce(topk_sum)
far_rank = math.ceil(far * (sample_num * (self.out_features - 1) * dist.get_world_size() - topk_sum))
cos_theta_neg_topk = torch.topk((tmp_cos_theta - 2 * topk_mask.to(torch.float32)).flatten(),
k=far_rank)[0] # [far_rank]
cos_theta_neg_topk = all_gather_tensor(cos_theta_neg_topk.contiguous()) # top k across all gpus
cos_theta_neg_th = torch.topk(cos_theta_neg_topk, k=far_rank)[0][-1]
cond = torch.mul(torch.bitwise_not(topk_mask), torch.greater(tmp_cos_theta, cos_theta_neg_th))
cos_theta_neg_topk = torch.mul(cond.to(torch.float32), tmp_cos_theta)
cos_theta_neg_topk_ = torch.mul(cond.to(torch.float32), tmp_cos_theta_)
cond = torch.greater(target_cos_theta_m, cos_theta_neg_topk)
cos_theta_neg_topk = torch.where(cond, cos_theta_neg_topk, cos_theta_neg_topk_)
cos_theta_neg_topk = torch.pow(cos_theta_neg_topk, 2) # F = z^p = cos^2
times = torch.sum(torch.greater(cos_theta_neg_topk, 0).to(torch.float32), dim=1, keepdim=True)
times = torch.where(torch.greater(times, 0), times, torch.ones_like(times))
cos_theta_neg_topk = torch.sum(cos_theta_neg_topk, dim=1, keepdim=True) / times # ri+/ru+
target_cos_theta_m = target_cos_theta_m - (1 + target_cos_theta_) * cos_theta_neg_topk
cos_theta.scatter_(1, label.view(-1, 1).long(), target_cos_theta_m)
output = cos_theta * self.scale
loss = self.criteria(output, label)
acc = calc_top1_acc(origin_cos * self.scale, label,self.ddp)
if return_logits:
return loss, acc, output
return loss, acc
class FC_ddp(nn.Module):
"""
Implement of (CVPR2021 Consistent Instance False Positive Improves Fairness in Face Recognition)
No model parallel is used
"""
def __init__(self,
in_features,
out_features,
scale=8.0,
margin=0.2,
mode='cosface',
use_cifp=False,
reduction='mean'):
""" Args:
in_features: size of each input features
out_features: size of each output features
scale: norm of input feature
margin: margin
"""
super(FC_ddp, self).__init__()
self.in_features = in_features
self.out_features = out_features # num of classes
self.scale = scale
self.margin = margin
self.mode = mode
self.use_cifp = use_cifp
# self.kernel = Parameter(torch.Tensor(in_features, out_features))
# nn.init.normal_(self.kernel, std=0.01)
self.criteria = torch.nn.CrossEntropyLoss(reduction=reduction)
self.sig = torch.nn.Sigmoid()
def apply_margin(self, target_cos_theta):
assert self.mode in ['cosface', 'arcface'], 'Please check the mode'
if self.mode == 'arcface':
cos_m = math.cos(self.margin)
sin_m = math.sin(self.margin)
theta = math.cos(math.pi - self.margin)
sinmm = math.sin(math.pi - self.margin) * self.margin
sin_theta = torch.sqrt(1.0 - torch.pow(target_cos_theta, 2))
cos_theta_m = target_cos_theta * cos_m - sin_theta * sin_m
target_cos_theta_m = torch.where(
target_cos_theta > theta, cos_theta_m, target_cos_theta - sinmm)
elif self.mode == 'cosface':
target_cos_theta_m = target_cos_theta - self.margin
return target_cos_theta_m
def forward(self, embeddings, label, return_logits=False):
"""
:param embeddings: local gpu [bs, 512]
:param label: local labels [bs]
:param return_logits: bool
:return:
loss: computed local loss, w/wo CIFP
acc: local accuracy in one gpu
output: local logits with margins, with gradients, scaled, [bs, C].
"""
sample_num = embeddings.size(0)
cos_theta = self.sig(embeddings)
target_cos_theta = cos_theta[torch.arange(0, sample_num), label].view(-1, 1)
# target_cos_theta_m = target_cos_theta - self.margin
target_cos_theta = target_cos_theta - self.margin
# cos_theta.scatter_(1, label.view(-1, 1).long(), target_cos_theta_m)
out = cos_theta.clone()
out.scatter_(1, label.view(-1, 1).long(), target_cos_theta)
out = out * self.scale
loss = self.criteria(out, label)
return loss
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