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train.py
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import numpy as np
import torch
import os
from torch import optim
from torch.utils.data import DataLoader
from torchvision import transforms
import voc12.data
import voc12.data_cpn
from tool import pyutils, imutils
import argparse
import importlib
from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
# from IPython import embed
import time
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", default=8, type=int)
parser.add_argument("--max_epoches", default=10, type=int)
parser.add_argument("--network", default="network.conformer_CAM", type=str)
parser.add_argument("--lr", default=5e-5, type=float)
parser.add_argument("--num_workers", default=0, type=int)
parser.add_argument("--wt_dec", default=5e-4, type=float)
parser.add_argument("--train_list", default="voc12/train_aug.txt", type=str)
parser.add_argument("--arch", default='sm21', type=str)
parser.add_argument("--val_list", default="voc12/val.txt", type=str)
parser.add_argument("--session_name", default="MECPformer_0525", type=str)
parser.add_argument("--crop_size", default=512, type=int) # 512
parser.add_argument("--weights", required=True, type=str)
parser.add_argument("--voc12_root", default='../VOCdevkit/VOC2012', type=str)
parser.add_argument("--tblog_dir", default='./tblog', type=str)
parser.add_argument("--save_dir", default='./model_MECPformer/', type=str)
parser.add_argument("--nb_classes", default=21, type=int)
args = parser.parse_args()
pyutils.Logger(args.session_name + '.log')
print(vars(args))
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = getattr(importlib.import_module(args.network), 'Net_' + args.arch)()
tblogger = SummaryWriter(args.tblog_dir)
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wt_dec, eps=1e-8)
checkpoint = torch.load(args.weights, map_location='cpu')
if 'model' in checkpoint.keys():
checkpoint = checkpoint['model']
else:
checkpoint = checkpoint
model_dict = model.state_dict()
for k in ['trans_cls_head.weight', 'trans_cls_head.bias']:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint[k]
for k in ['conv_cls_head.weight', 'conv_cls_head.bias']:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint[k]
cls_token_checkpoint = checkpoint['cls_token']
new_cls_token = cls_token_checkpoint.repeat(1, args.nb_classes, 1)
checkpoint['cls_token'] = new_cls_token
pretrained_dict = {k: v for k, v in checkpoint.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = torch.nn.DataParallel(model).cuda()
model.train()
avg_meter = pyutils.AverageMeter('loss')
timer = pyutils.Timer('train_MECPformer_beginning:')
start_time = time.time()
for ep in range(args.max_epoches):
print('train_MECPformer_ep:', ep)
train_dataset = voc12.data_cpn.VOC12ClsDataset_ME(args.train_list, voc12_root=args.voc12_root,
epoch=ep,
transform=transforms.Compose([
imutils.RandomResizeLong(320, 640),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.3, contrast=0.3,
saturation=0.3,
hue=0.1),
np.asarray,
imutils.Normalize(),
imutils.RandomCrop(args.crop_size),
imutils.HWC_to_CHW,
torch.from_numpy
]))
train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=0, pin_memory=True, drop_last=True)
for iter, pack in enumerate(train_data_loader):
# if iter % 5000 == 0:
# print('===iter:', iter)
img = pack[1]
N, C, H, W = img.size()
label = pack[2]
bg_score = torch.ones((N, 1))
label = torch.cat((bg_score, label), dim=1)
label = label.cuda().unsqueeze(2).unsqueeze(3)
img1 = pack[3]
img2 = pack[4]
logits_conv1, logits_trans1, trans_patch_logits1, cams1 = model('transcam', img1)
loss1 = F.multilabel_soft_margin_loss((logits_conv1 + logits_trans1).unsqueeze(2).unsqueeze(3)[:, 1:, :, :],
label[:, 1:, :, :])
logits_conv2, logits_trans2, trans_patch_logits2, cams2 = model('transcam', img2)
loss2 = F.multilabel_soft_margin_loss((logits_conv2 + logits_trans2).unsqueeze(2).unsqueeze(3)[:, 1:, :, :],
label[:, 1:, :, :])
loss = (loss1 + loss2) / 2
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_meter.add({'loss': loss.item()})
else:
print('epoch: %5d' % ep,
'loss: %.4f' % avg_meter.get('loss'), flush=True)
avg_meter.pop()
if ep == 0:
torch.save(model.module.state_dict(),
os.path.join(args.save_dir, args.session_name + '_' + str(ep) + '.pth'))
if ep >= 4:
torch.save(model.module.state_dict(),
os.path.join(args.save_dir, args.session_name + '_' + str(ep) + '.pth'))
if ep % 2 == 0:
torch.save(model.module.state_dict(),
os.path.join(args.save_dir, args.session_name + '_' + str(ep) + '.pth'))
timer = pyutils.Timer('train_MECPformer_over!!!')
print('run_time:{} h'.format(round((time.time() - start_time) / 60 / 60, 4)))