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train_dianwang.py
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train_dianwang.py
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'''
@Author: xxxmy
@Github: github.com/VectXmy
@Date: 2019-09-26
@Email: [email protected]
'''
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
# os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
from model.fcos import FCOSDetector
import torch
import torchvision.transforms as transforms
# from dataloader.VOC_dataset import VOCDataset
from dataloader.dataset import Dataset
import math, time
# from torch.utils.tensorboard import SummaryWriter
from tensorboardX import SummaryWriter
model = FCOSDetector(mode="training")
# model = torch.nn.DataParallel(model.cuda(), device_ids=range(torch.cuda.device_count()))
model = model.cuda()
# model=FCOSDetector(mode="training")
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-4)
BATCH_SIZE = 16
EPOCHS = 30
WARMPUP_STEPS_RATIO = 0.12
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.432, 0.474, 0.379), (0.249, 0.254, 0.246))
])
# 电网5部件：(0.423, 0.444, 0.394), (0.220, 0.213, 0.245)
# 电网绝缘子：(0.432, 0.474, 0.379), (0.249, 0.254, 0.246)
# VOC: (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
# 2012_train 2007_val
# cfg = {'images_root': '/home', 'train_path': '/home/benkebishe01/fcos_sample/dataloader/2007_trainval.txt',
# 'test_path': '/home/benkebishe01/fcos_sample/dataloader/2007_test.txt',
# 'img_size': 512}
cfg = {'images_root': '/home', 'train_path': '/mnt/hdd1/benkebishe01/VOCdevkit_insulator/2007_train.txt',
'test_path': '/mnt/hdd1/benkebishe01/VOCdevkit_insulator/2007_val.txt',
'img_size': 512}
train_dataset = Dataset(cfg['images_root'], cfg['train_path'], img_size=cfg['img_size'], transform=transform,
train=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(
Dataset(cfg['images_root'], cfg['test_path'], img_size=cfg['img_size'],
transform=transform, train=False),
batch_size=4,
shuffle=False, )
steps_per_epoch = len(train_dataset) // BATCH_SIZE
TOTAL_STEPS = steps_per_epoch * EPOCHS
WARMPUP_STEPS = TOTAL_STEPS * WARMPUP_STEPS_RATIO
# global GLOBAL_STEPS
GLOBAL_STEPS = 1
LR_INIT = 5e-5
LR_END = 1e-6
draw = False
if draw:
writer = SummaryWriter(comment='9')
def lr_func():
if GLOBAL_STEPS < WARMPUP_STEPS:
lr = GLOBAL_STEPS / WARMPUP_STEPS * LR_INIT
else:
lr = LR_END + 0.5 * (LR_INIT - LR_END) * (
(1 + math.cos((GLOBAL_STEPS - WARMPUP_STEPS) / (TOTAL_STEPS - WARMPUP_STEPS) * math.pi))
)
return float(lr)
model.train()
for epoch in range(EPOCHS):
loss_ = []
for epoch_step, (batch_imgs, batch_boxes, batch_classes, loc_target, cls_target) in enumerate(train_loader):
# batch_imgs, batch_boxes, batch_classes, loc_target, cls_target = data
batch_imgs = batch_imgs.cuda()
batch_boxes = batch_boxes.cuda()
batch_classes = batch_classes.cuda()
loc_target = loc_target.cuda()
cls_target = cls_target.cuda()
lr = lr_func()
for param in optimizer.param_groups:
param['lr'] = lr
# start_time = time.time()
optimizer.zero_grad()
loss_anchor, loss_fcos, loss = model([batch_imgs, batch_boxes, batch_classes, loc_target, cls_target])
# loss = loss.mean()
loss.backward()
optimizer.step()
# end_time = time.time()
# cost_time = int((end_time-start_time)*1000)
print(
"global_steps:%d epoch:%d steps:%d/%d cls_loss:%.4f reg_loss:%.4f loc_anchor:%.4f cls_anchor:%.4f lr=%.4e" % \
(GLOBAL_STEPS, epoch + 1, epoch_step + 1, steps_per_epoch, loss_fcos[0], loss_fcos[1], loss_anchor[0],
loss_anchor[1], lr))
'''
print(
"global_steps:%d epoch:%d steps:%d/%d loss_fcos:%.4f loss_anchor:%.4f cnt_loss:%.4f"% \
(GLOBAL_STEPS, epoch + 1, epoch_step + 1, steps_per_epoch, loss_fcos[0]+loss_fcos[2], loss_anchor[0]+loss_anchor[1], loss_fcos[1]))
'''
if draw:
writer.add_scalar("loss/loss", loss, global_step=GLOBAL_STEPS)
writer.add_scalar("loss/cls_loss", loss_fcos[0], global_step=GLOBAL_STEPS)
# writer.add_scalar("loss/cnt_loss",loss_fcos[1], global_step=GLOBAL_STEPS)
writer.add_scalar("loss/reg_loss", loss_fcos[1], global_step=GLOBAL_STEPS)
writer.add_scalar("loss/loc_anchor", loss_anchor[0], global_step=GLOBAL_STEPS)
writer.add_scalar("loss/cls_anchor", loss_anchor[1], global_step=GLOBAL_STEPS)
writer.add_scalar("lr", lr, global_step=GLOBAL_STEPS)
GLOBAL_STEPS += 1
loss_.append(loss)
loss_avg = torch.mean(torch.stack(loss_))
torch.save(model.state_dict(), "/mnt/hdd1/benkebishe01/dianwang/insulator/voc_epoch%d_loss%.4f.pth" % (
epoch + 1, loss_avg.item()))