forked from deepinsight/insightface
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
180 lines (155 loc) · 7.24 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import argparse
import logging
import os
import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
import torch.utils.data.distributed
from torch.nn.utils import clip_grad_norm_
import losses
from backbones import get_model
from dataset import MXFaceDataset, DataLoaderX
from torch.utils.data import DataLoader, Dataset
from vpl import VPL
from utils.utils_amp import MaxClipGradScaler
from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
from utils.utils_logging import AverageMeter, init_logging
from utils.utils_dist import concat_all_gather, batch_shuffle_ddp, batch_unshuffle_ddp
from utils.utils_config import get_config
def main(args):
cfg = get_config(args.config)
if not cfg.tf32:
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
try:
world_size = int(os.environ['WORLD_SIZE'])
rank = int(os.environ['RANK'])
dist_url = "tcp://{}:{}".format(os.environ["MASTER_ADDR"], os.environ["MASTER_PORT"])
except KeyError:
world_size = 1
rank = 0
dist_url = "tcp://127.0.0.1:12584"
dist.init_process_group(backend='nccl', init_method=dist_url, rank=rank, world_size=world_size)
local_rank = args.local_rank
torch.cuda.set_device(local_rank)
if not os.path.exists(cfg.output) and rank==0:
os.makedirs(cfg.output)
else:
time.sleep(2)
log_root = logging.getLogger()
init_logging(log_root, rank, cfg.output)
if rank==0:
logging.info(args)
logging.info(cfg)
train_set = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, shuffle=True)
train_loader = DataLoaderX(
local_rank=local_rank, dataset=train_set, batch_size=cfg.batch_size,
sampler=train_sampler, num_workers=2, pin_memory=True, drop_last=True)
dropout = 0.4 if cfg.dataset == "webface" else 0
backbone = get_model(cfg.network, dropout=dropout, fp16=cfg.fp16).to(local_rank)
backbone_onnx = get_model(cfg.network, dropout=dropout, fp16=False)
if args.resume:
try:
backbone_pth = os.path.join(cfg.output, "backbone.pth")
backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank)))
if rank==0:
logging.info("backbone resume successfully!")
except (FileNotFoundError, KeyError, IndexError, RuntimeError):
logging.info("resume fail, backbone init successfully!")
for ps in backbone.parameters():
dist.broadcast(ps, 0)
backbone = torch.nn.parallel.DistributedDataParallel(
module=backbone, broadcast_buffers=False, device_ids=[local_rank])
backbone.train()
cfg_vpl = cfg.vpl
vpl_momentum = cfg_vpl['momentum']
if vpl_momentum:
backbone_w = get_model(cfg.network, dropout=dropout, fp16=cfg.fp16).to(local_rank)
backbone_w.train()
for param_b, param_w in zip(backbone.module.parameters(), backbone_w.parameters()):
param_w.data.copy_(param_b.data)
param_w.requires_grad = False
margin_softmax = losses.get_loss(cfg.loss)
module_fc = VPL(
rank=rank, local_rank=local_rank, world_size=world_size, resume=args.resume,
batch_size=cfg.batch_size, margin_softmax=margin_softmax, num_classes=cfg.num_classes,
sample_rate=cfg.sample_rate, embedding_size=cfg.embedding_size, prefix=cfg.output,
cfg = cfg_vpl)
#print('AAA')
opt_backbone = torch.optim.SGD(
params=[{'params': backbone.parameters()}],
lr=cfg.lr / 512 * cfg.batch_size * world_size,
momentum=0.9, weight_decay=cfg.weight_decay)
opt_pfc = torch.optim.SGD(
params=[{'params': module_fc.parameters()}],
lr=cfg.lr / 512 * cfg.batch_size * world_size,
momentum=0.9, weight_decay=cfg.weight_decay)
#print('AAA')
scheduler_backbone = torch.optim.lr_scheduler.LambdaLR(
optimizer=opt_backbone, lr_lambda=cfg.lr_func)
scheduler_pfc = torch.optim.lr_scheduler.LambdaLR(
optimizer=opt_pfc, lr_lambda=cfg.lr_func)
start_epoch = 0
total_step = int(len(train_set) / cfg.batch_size / world_size * cfg.num_epoch)
if rank==0: logging.info("Total Step is: %d" % total_step)
#for epoch in range(start_epoch, cfg.num_epoch):
# _lr = cfg.lr_func(epoch)
# logging.info('%d:%f'%(epoch, _lr))
callback_verification = CallBackVerification(10000, rank, cfg.val_targets, cfg.rec)
callback_logging = CallBackLogging(50, rank, total_step, cfg.batch_size, world_size, None)
callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output)
loss = AverageMeter()
global_step = 0
grad_amp = MaxClipGradScaler(cfg.batch_size, 128 * cfg.batch_size, growth_interval=100) if cfg.fp16 else None
use_batch_shuffle = True
alpha = 0.999
for epoch in range(start_epoch, cfg.num_epoch):
train_sampler.set_epoch(epoch)
for step, (img, label) in enumerate(train_loader):
global_step += 1
#img = img.to(memory_format=torch.channels_last)
features = F.normalize(backbone(img))
feature_w = None
if vpl_momentum:
with torch.no_grad():
for param_b, param_w in zip(backbone.module.parameters(), backbone_w.parameters()):
param_w.data = param_w.data * alpha + param_b.data * (1. - alpha)
if use_batch_shuffle:
img_w, idx_unshuffle = batch_shuffle_ddp(img, rank, world_size)
feature_w = F.normalize(backbone_w(img_w))
if use_batch_shuffle:
feature_w = batch_unshuffle_ddp(feature_w, idx_unshuffle, rank, world_size)
feature_w = feature_w.detach()
x_grad, loss_v = module_fc.forward_backward(label, features, opt_pfc, feature_w)
if cfg.fp16:
features.backward(grad_amp.scale(x_grad))
grad_amp.unscale_(opt_backbone)
clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
grad_amp.step(opt_backbone)
grad_amp.update()
else:
features.backward(x_grad)
clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
opt_backbone.step()
opt_pfc.step()
module_fc.update()
opt_backbone.zero_grad()
opt_pfc.zero_grad()
loss.update(loss_v, 1)
callback_logging(global_step, loss, epoch, cfg.fp16, grad_amp)
callback_verification(global_step, backbone)
callback_checkpoint(global_step, backbone, module_fc, backbone_onnx)
scheduler_backbone.step()
scheduler_pfc.step()
dist.destroy_process_group()
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(description='PyTorch ArcFace-VPL Training')
parser.add_argument('config', type=str, help='py config file')
parser.add_argument('--local_rank', type=int, default=0, help='local_rank')
parser.add_argument('--resume', type=int, default=0, help='model resuming')
args_ = parser.parse_args()
main(args_)