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train.py
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train.py
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import time
from datasets.change_detection import ChangeDetection
from models.model_zoo import get_model
from utils.options import Options
from utils.palette import color_map
from utils.metric import IOUandSek
import numpy as np
import os
import csv
from PIL import Image
import shutil
import torch
from torch.nn import CrossEntropyLoss, BCELoss, DataParallel, BCEWithLogitsLoss
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader
from tqdm import tqdm
class Trainer:
def __init__(self, args):
self.args = args
trainset = ChangeDetection(root=args.data_root, mode="train", use_pseudo_label=args.use_pseudo_label)
valset = ChangeDetection(root=args.data_root, mode="val")
self.trainloader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True,
pin_memory=False, num_workers=12, drop_last=True)
self.valloader = DataLoader(valset, batch_size=args.val_batch_size, shuffle=False,
pin_memory=True, num_workers=12, drop_last=False)
self.model = get_model(args.model, args.backbone, args.pretrained,
len(trainset.CLASSES)-1, args.lightweight)
if args.pretrain_from:
self.model.load_state_dict(torch.load(args.pretrain_from), strict=False)
if args.load_from:
self.model.load_state_dict(torch.load(args.load_from), strict=True)
if args.use_pseudo_label:
weight = torch.FloatTensor([1, 1, 1, 1, 1, 1]).cuda()
else:
weight = torch.FloatTensor([2, 1, 2, 2, 1, 1]).cuda()
self.criterion = CrossEntropyLoss(ignore_index=-1, weight=weight)
self.criterion_bin = BCELoss(reduction='none')
self.criterion_bin2 = BCEWithLogitsLoss(reduction='mean')
self.optimizer = Adam([{"params": [param for name, param in self.model.named_parameters()
if "backbone" in name], "lr": args.lr},
{"params": [param for name, param in self.model.named_parameters()
if "backbone" not in name], "lr": args.lr * 10.0}],
lr=args.lr, weight_decay=args.weight_decay)
self.model = DataParallel(self.model).cuda()
self.iters = 0
self.total_iters = len(self.trainloader) * args.epochs
self.previous_best = 0
def training(self):
tbar = tqdm(self.trainloader)
self.model.train()
total_loss = 0.0
total_loss_sem = 0.0
total_loss_bin = 0.0
for i, (img1, img2, mask1, mask2, mask_bin) in enumerate(tbar):
img1, img2 = img1.cuda(), img2.cuda()
mask1, mask2 = mask1.cuda(), mask2.cuda()
mask_bin = mask_bin.cuda()
# mask_bin = 0. 未变化区域
out1, out2, out_bin = self.model(img1, img2)
out_bin2 = torch.abs(out1 - out2)
out_bin2 = torch.sum(out_bin2, dim=1) / 2
loss1 = self.criterion(out1, mask1 - 1)
loss2 = self.criterion(out2, mask2 - 1)
loss_bin = self.criterion_bin(out_bin, mask_bin) + 0.5*self.criterion_bin2(out_bin2,mask_bin)
# loss_bin = self.criterion_bin(out_bin, mask_bin)
loss_bin[mask_bin == 1] *= 2
loss_bin = loss_bin.mean()
loss = loss_bin * 2 + loss1 + loss2
total_loss_sem += loss1.item() + loss2.item()
total_loss_bin += loss_bin.item()
total_loss += loss.item()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.iters += 1
lr = self.args.lr * (1 - self.iters / self.total_iters) ** 0.9
self.optimizer.param_groups[0]["lr"] = lr
self.optimizer.param_groups[1]["lr"] = lr * 10.0
tbar.set_description("Loss: %.3f, Semantic Loss: %.3f, Binary Loss: %.3f" %
(total_loss / (i + 1), total_loss_sem / (i + 1), total_loss_bin / (i + 1)))
def validation(self):
tbar = tqdm(self.valloader)
self.model.eval()
metric = IOUandSek(num_classes=len(ChangeDetection.CLASSES))
if self.args.save_mask:
cmap = color_map()
with torch.no_grad():
for img1, img2, mask1, mask2, id in tbar:
img1, img2 = img1.cuda(), img2.cuda()
out1, out2, out_bin = self.model(img1, img2, self.args.tta)
out1 = torch.argmax(out1, dim=1).cpu().numpy() + 1
out2 = torch.argmax(out2, dim=1).cpu().numpy() + 1
out_bin = (out_bin > 0.5).cpu().numpy().astype(np.uint8)
out1[out_bin == 0] = 0
out2[out_bin == 0] = 0
if self.args.save_mask:
for i in range(out1.shape[0]):
mask = Image.fromarray(out1[i].astype(np.uint8), mode="P")
mask.putpalette(cmap)
mask.save("outdir/masks/val/im1/" + id[i])
mask = Image.fromarray(out2[i].astype(np.uint8), mode="P")
mask.putpalette(cmap)
mask.save("outdir/masks/val/im2/" + id[i])
metric.add_batch(out1, mask1.numpy())
metric.add_batch(out2, mask2.numpy())
score, miou_cd, miou_seg = metric.evaluate()
tbar.set_description("Score: %.2f, IOU_CD: %.2f, IOU_Seg: %.2f" % (score * 100.0, miou_cd * 100.0, miou_seg * 100))
if self.args.load_from:
exit(0)
score *= 100.0
# 把每一轮的结果写入csv
datas=[]
valdic={'Score': score, 'IOU_CD':miou_cd * 100.0, 'IOU_Seg':miou_seg * 100.0}
header=list(valdic.keys())
datas.append(valdic)
with open('data_%s_%s.csv'%(args.backbone,args.model), 'a', newline='', encoding='utf-8') as f:
writer = csv.DictWriter(f, fieldnames=header) # 提前预览列名,当下面代码写入数据时,会将其一一对应。
writer.writeheader() # 写入列名
writer.writerows(datas) # 写入数据
if score >= self.previous_best:
if self.previous_best != 0:
model_path = "outdir/models/%s_%s_%.2f.pth" % \
(self.args.model, self.args.backbone, self.previous_best)
if os.path.exists(model_path):
os.remove(model_path)
torch.save(self.model.module.state_dict(), "outdir/models/%s_%s_%.2f.pth" %
(self.args.model, self.args.backbone, score))
self.previous_best = score
if __name__ == "__main__":
args = Options().parse()
trainer = Trainer(args)
print(args.backbone)
if args.load_from:
trainer.validation()
time_start=time.time()
for epoch in range(args.epochs):
print("\n==> Epoches %i, learning rate = %.5f\t\t\t\t previous best = %.2f" %
(epoch, trainer.optimizer.param_groups[0]["lr"], trainer.previous_best))
trainer.training()
trainer.validation()
time_end=time.time()
print('timecost',time_end-time_start,'s')
datas = []
valdic = {'backbone':args.backbone,'head':args.model,'time': time_end-time_start}
header = list(valdic.keys())
datas.append(valdic)
with open('data_%s_%s.csv'%(args.backbone,args.model), 'a', newline='', encoding='utf-8') as f:
writer = csv.DictWriter(f, fieldnames=header) # 提前预览列名,当下面代码写入数据时,会将其一一对应。
writer.writeheader() # 写入列名
writer.writerows(datas) # 写入数据