def main(local_rank):
dist.init_process_group(backend='nccl', init_method='env://')
cfg.local_rank = local_rank
torch.cuda.set_device(local_rank)
cfg.rank = dist.get_rank()
cfg.world_size = dist.get_world_size()
trainset = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
train_sampler = torch.utils.data.distributed.DistributedSampler(
trainset, shuffle=True)
train_loader = DataLoaderX(local_rank=local_rank,
dataset=trainset,
batch_size=cfg.batch_size,
sampler=train_sampler,
num_workers=0,
pin_memory=True,
drop_last=False)
backbone = backbones.iresnet100(False).to(local_rank)
backbone.train()
# Broadcast init parameters
for ps in backbone.parameters():
dist.broadcast(ps, 0)
# DDP
backbone = torch.nn.parallel.DistributedDataParallel(
module=backbone, broadcast_buffers=False, device_ids=[cfg.local_rank])
backbone.train()
# Memory classifer
dist_sample_classifer = DistSampleClassifier(rank=dist.get_rank(),
local_rank=local_rank,
world_size=cfg.world_size)
# Margin softmax
margin_softmax = MarginSoftmax(s=64.0, m=0.4)
# Optimizer for backbone and classifer
optimizer = SGD([{
'params': backbone.parameters()
}, {
'params': dist_sample_classifer.parameters()
}],
lr=cfg.lr,
momentum=0.9,
weight_decay=cfg.weight_decay,
rescale=cfg.world_size)
# Lr scheduler
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer,
lr_lambda=cfg.lr_func)
n_epochs = cfg.num_epoch
start_epoch = 0
if local_rank == 0:
writer = SummaryWriter(log_dir='logs/shows')
#
total_step = int(
len(trainset) / cfg.batch_size / dist.get_world_size() * cfg.num_epoch)
if dist.get_rank() == 0:
print("Total Step is: %d" % total_step)
losses = AverageMeter()
global_step = 0
train_start = time.time()
for epoch in range(start_epoch, n_epochs):
train_sampler.set_epoch(epoch)
for step, (img, label) in enumerate(train_loader):
total_label, norm_weight = dist_sample_classifer.prepare(
label, optimizer)
features = F.normalize(backbone(img))
# Features all-gather
total_features = torch.zeros(features.size()[0] * cfg.world_size,
cfg.embedding_size,
device=local_rank)
dist.all_gather(list(total_features.chunk(cfg.world_size, dim=0)),
features.data)
total_features.requires_grad = True
# Calculate logits
logits = dist_sample_classifer(total_features, norm_weight)
logits = margin_softmax(logits, total_label)
with torch.no_grad():
max_fc = torch.max(logits, dim=1, keepdim=True)[0]
dist.all_reduce(max_fc, dist.ReduceOp.MAX)
# Calculate exp(logits) and all-reduce
logits_exp = torch.exp(logits - max_fc)
logits_sum_exp = logits_exp.sum(dim=1, keepdims=True)
dist.all_reduce(logits_sum_exp, dist.ReduceOp.SUM)
# Calculate prob
logits_exp.div_(logits_sum_exp)
# Get one-hot
grad = logits_exp
index = torch.where(total_label != -1)[0]
one_hot = torch.zeros(index.size()[0],
grad.size()[1],
device=grad.device)
one_hot.scatter_(1, total_label[index, None], 1)
# Calculate loss
loss = torch.zeros(grad.size()[0], 1, device=grad.device)
loss[index] = grad[index].gather(1, total_label[index, None])
dist.all_reduce(loss, dist.ReduceOp.SUM)
loss_v = loss.clamp_min_(1e-30).log_().mean() * (-1)
# Calculate grad
grad[index] -= one_hot
grad.div_(features.size()[0])
logits.backward(grad)
if total_features.grad is not None:
total_features.grad.detach_()
x_grad = torch.zeros_like(features)
# Feature gradient all-reduce
dist.reduce_scatter(
x_grad, list(total_features.grad.chunk(cfg.world_size, dim=0)))
x_grad.mul_(cfg.world_size)
# Backward backbone
features.backward(x_grad)
optimizer.step()
# Update classifer
dist_sample_classifer.update()
optimizer.zero_grad()
losses.update(loss_v, 1)
if cfg.local_rank == 0 and step % 50 == 0:
time_now = (time.time() - train_start) / 3600
time_total = time_now / ((global_step + 1) / total_step)
time_for_end = time_total - time_now
writer.add_scalar('time_for_end', time_for_end, global_step)
writer.add_scalar('loss', loss_v, global_step)
print(
"Speed %d samples/sec Loss %.4f Epoch: %d Global Step: %d Required: %1.f hours"
% ((cfg.batch_size * global_step /
(time.time() - train_start) * cfg.world_size),
losses.avg, epoch, global_step, time_for_end))
losses.reset()
global_step += 1
scheduler.step()
if dist.get_rank() == 0:
import os
if not os.path.exists(cfg.output):
os.makedirs(cfg.output)
torch.save(backbone.module.state_dict(),
os.path.join(cfg.output,
str(epoch) + 'backbone.pth'))
dist.destroy_process_group()
image_size=(112, 112),
batch_size=64,
epoch=1):
ver_list = []
ver_name_list = []
for name in val_targets:
path = os.path.join(data_dir, name + ".bin")
if os.path.exists(path):
data_set = load_bin(path, image_size)
ver_list.append(data_set)
ver_name_list.append(name)
print('ver', name)
for i in range(len(ver_list)):
acc1, std1, acc2, std2, xnorm, embeddings_list = test(
ver_list[i], model, batch_size, 10, None, None)
print('[%s][%d]XNorm: %f' % (ver_name_list[i], epoch, xnorm))
print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' %
(ver_name_list[i], epoch, acc2, std2))
if __name__ == "__main__":
data_dir = "/data/zhaoxin_data/face_data/Glint360k/glint360k/glint360k"
model_path = "/data/zhaoxin_data/face_classifier_logs/partial_fc/r100_glint350k/r100_glint360k_epoch8.pth"
ckpt = torch.load(model_path)
model = backbones.iresnet100(False).cuda()
model.load_state_dict(ckpt, strict=True)
eval(model,
data_dir, ['lfw', 'cfp_fp', 'agedb_30'],
batch_size=32,
epoch=1)