Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,207 Bytes
9669aec |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
#!/usr/bin/python
# -*- encoding: utf-8 -*-
from logger import setup_logger
from model import BiSeNet
from face_dataset import FaceMask
from loss import OhemCELoss
from evaluate import evaluate
from optimizer import Optimizer
import cv2
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.distributed as dist
import os
import os.path as osp
import logging
import time
import datetime
import argparse
respth = './res'
if not osp.exists(respth):
os.makedirs(respth)
logger = logging.getLogger()
def parse_args():
parse = argparse.ArgumentParser()
parse.add_argument(
'--local_rank',
dest = 'local_rank',
type = int,
default = -1,
)
return parse.parse_args()
def train():
args = parse_args()
torch.cuda.set_device(args.local_rank)
dist.init_process_group(
backend = 'nccl',
init_method = 'tcp://127.0.0.1:33241',
world_size = torch.cuda.device_count(),
rank=args.local_rank
)
setup_logger(respth)
# dataset
n_classes = 19
n_img_per_gpu = 16
n_workers = 8
cropsize = [448, 448]
data_root = '/home/zll/data/CelebAMask-HQ/'
ds = FaceMask(data_root, cropsize=cropsize, mode='train')
sampler = torch.utils.data.distributed.DistributedSampler(ds)
dl = DataLoader(ds,
batch_size = n_img_per_gpu,
shuffle = False,
sampler = sampler,
num_workers = n_workers,
pin_memory = True,
drop_last = True)
# model
ignore_idx = -100
net = BiSeNet(n_classes=n_classes)
net.cuda()
net.train()
net = nn.parallel.DistributedDataParallel(net,
device_ids = [args.local_rank, ],
output_device = args.local_rank
)
score_thres = 0.7
n_min = n_img_per_gpu * cropsize[0] * cropsize[1]//16
LossP = OhemCELoss(thresh=score_thres, n_min=n_min, ignore_lb=ignore_idx)
Loss2 = OhemCELoss(thresh=score_thres, n_min=n_min, ignore_lb=ignore_idx)
Loss3 = OhemCELoss(thresh=score_thres, n_min=n_min, ignore_lb=ignore_idx)
## optimizer
momentum = 0.9
weight_decay = 5e-4
lr_start = 1e-2
max_iter = 80000
power = 0.9
warmup_steps = 1000
warmup_start_lr = 1e-5
optim = Optimizer(
model = net.module,
lr0 = lr_start,
momentum = momentum,
wd = weight_decay,
warmup_steps = warmup_steps,
warmup_start_lr = warmup_start_lr,
max_iter = max_iter,
power = power)
## train loop
msg_iter = 50
loss_avg = []
st = glob_st = time.time()
diter = iter(dl)
epoch = 0
for it in range(max_iter):
try:
im, lb = next(diter)
if not im.size()[0] == n_img_per_gpu:
raise StopIteration
except StopIteration:
epoch += 1
sampler.set_epoch(epoch)
diter = iter(dl)
im, lb = next(diter)
im = im.cuda()
lb = lb.cuda()
H, W = im.size()[2:]
lb = torch.squeeze(lb, 1)
optim.zero_grad()
out, out16, out32 = net(im)
lossp = LossP(out, lb)
loss2 = Loss2(out16, lb)
loss3 = Loss3(out32, lb)
loss = lossp + loss2 + loss3
loss.backward()
optim.step()
loss_avg.append(loss.item())
# print training log message
if (it+1) % msg_iter == 0:
loss_avg = sum(loss_avg) / len(loss_avg)
lr = optim.lr
ed = time.time()
t_intv, glob_t_intv = ed - st, ed - glob_st
eta = int((max_iter - it) * (glob_t_intv / it))
eta = str(datetime.timedelta(seconds=eta))
msg = ', '.join([
'it: {it}/{max_it}',
'lr: {lr:4f}',
'loss: {loss:.4f}',
'eta: {eta}',
'time: {time:.4f}',
]).format(
it = it+1,
max_it = max_iter,
lr = lr,
loss = loss_avg,
time = t_intv,
eta = eta
)
logger.info(msg)
loss_avg = []
st = ed
if dist.get_rank() == 0:
if (it+1) % 5000 == 0:
state = net.module.state_dict() if hasattr(net, 'module') else net.state_dict()
if dist.get_rank() == 0:
torch.save(state, './res/cp/{}_iter.pth'.format(it))
evaluate(dspth='/home/zll/data/CelebAMask-HQ/test-img', cp='{}_iter.pth'.format(it))
# dump the final model
save_pth = osp.join(respth, 'model_final_diss.pth')
# net.cpu()
state = net.module.state_dict() if hasattr(net, 'module') else net.state_dict()
if dist.get_rank() == 0:
torch.save(state, save_pth)
logger.info('training done, model saved to: {}'.format(save_pth))
if __name__ == "__main__":
train()
|