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train.py
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train.py
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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
from PIL import Image
import shutil
import torch
import torchcontrib
from torch.nn import CrossEntropyLoss, BCELoss, DataParallel
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=16, drop_last=True)
self.valloader = DataLoader(valset, batch_size=args.val_batch_size, shuffle=False,
pin_memory=True, num_workers=16, 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.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 = 0.0
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()
out1, out2, out_bin = self.model(img1, img2)
loss1 = self.criterion(out1, mask1 - 1)
loss2 = self.criterion(out2, mask2 - 1)
loss_bin = self.criterion_bin(out_bin, mask_bin)
loss_bin[mask_bin == 0] *= 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 == 1] = 0
out2[out_bin == 1] = 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, sek = metric.evaluate()
tbar.set_description("Score: %.2f, IOU: %.2f, SeK: %.2f" % (score * 100.0, miou * 100.0, sek * 100.0))
if self.args.load_from:
exit(0)
score *= 100.0
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)
if args.load_from:
trainer.validation()
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()