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train.py
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import torch
from torch.autograd import Variable
import argparse
from datetime import datetime
from lib.TransFuse import TransFuse_S as TransModel
from utils.data import get_loader, test_dataset
from utils.utils import adjust_lr, AvgMeter
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from test_isic import mean_dice_np, mean_iou_np
import logging
import os
def structure_loss(pred, mask):
weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
wbce = (weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))
pred = torch.sigmoid(pred)
inter = ((pred * mask)*weit).sum(dim=(2, 3))
union = ((pred + mask)*weit).sum(dim=(2, 3))
wiou = 1 - (inter + 1)/(union - inter+1)
return (wbce + wiou).mean()
def train(train_loader, model, optimizer, epoch, best_loss):
model.train()
loss_record1,loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter()
accum = 0
for i, pack in enumerate(train_loader, start=1):
# ---- data prepare ----
images, gts = pack
images = Variable(images).cuda()
gts = Variable(gts).cuda()
# ---- forward ----
P1, P2,P3= model(images)
# ---- loss function ----
loss_P1 = structure_loss(P1, gts)
loss_P2 = structure_loss(P2, gts)
loss_P3 = structure_loss(P3, gts)
# loss_P4 = structure_loss(P4, gts)
loss = 0.25*loss_P1 + 0.25*loss_P2 + 0.5*loss_P3 #+ 0.4*loss_P4
# ---- backward ----
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm)
optimizer.step()
optimizer.zero_grad()
# ---- recording loss ----
loss_record1.update(loss_P1.data, opt.batchsize)
loss_record2.update(loss_P2.data, opt.batchsize)
loss_record3.update(loss_P3.data, opt.batchsize)
# loss_record4.update(loss_P4.data, opt.batchsize)
# ---- train visualization ----
if i % 350 == 0 or i == total_step:
print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], '
'[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'.
format(datetime.now(), epoch, opt.epoch, i, total_step,
loss_record1.show(), loss_record2.show(), loss_record3.show()))
save_path = 'snapshots/{}/'.format(opt.train_save)
os.makedirs(save_path, exist_ok=True)
if (epoch+1) % 1 == 0:
meanloss = test(model, opt.test_path)
if meanloss < best_loss:
print('new best loss: ', meanloss)
best_loss = meanloss
torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch)
print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch)
return best_loss
def test(model, path, dataset):
model.eval()
data_path = os.path.join(path, dataset)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
test_loader = test_dataset(image_root, gt_root, opt.trainsize)
num1 = len(os.listdir(gt_root))
DSC = 0.0
for i in range(test_loader.size):
image, gt,_ = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
with torch.no_grad():
res = model(image)
res = F.upsample( res, size=gt.shape, mode='bilinear', align_corners=False)
res = res.sigmoid().data.cpu().numpy().squeeze()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
input = res
target = np.array(gt)
N = gt.shape
smooth = 1
input_flat = np.reshape(input, (-1))
target_flat = np.reshape(target, (-1))
intersection = (input_flat * target_flat)
dice = (2 * intersection.sum() + smooth) / (input.sum() + target.sum() + smooth)
dice = '{:.4f}'.format(dice)
dice = float(dice)
DSC = DSC + dice
# print("meandice:",DSC / num1)
return DSC / num1
if __name__ == '__main__':
dict_plot = {'CVC-300':[], 'CVC-ClinicDB':[], 'Kvasir':[], 'CVC-ColonDB':[], 'ETIS-LaribPolypDB':[], 'test':[]}
name = ['CVC-300', 'CVC-ClinicDB', 'Kvasir', 'CVC-ColonDB', 'ETIS-LaribPolypDB', 'test']
##################model_name#############################
#########################################################
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', type=int, default=30, help='epoch number')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
parser.add_argument('--batchsize', type=int, default=2, help='training batch size')
parser.add_argument('--grad_norm', type=float, default=2.0, help='gradient clipping norm')
parser.add_argument('--trainsize', type=int,default=256, help='training dataset size')
parser.add_argument('--train_path', type=str,default='./data/ClinicDB/train/', help='path to train dataset')
parser.add_argument('--test_path', type=str,default='./data/ClinicDB/', help='path to test dataset')
parser.add_argument('--train_save', type=str, default='refer')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 of adam optimizer')
parser.add_argument('--beta2', type=float, default=0.999, help='beta2 of adam optimizer')
parser.add_argument('--optimizer', type=str,default='AdamW', help='choosing optimizer AdamW or SGD')
opt = parser.parse_args()
logging.basicConfig(filename='train_log.log',
format='[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]',
level=logging.INFO, filemode='a', datefmt='%Y-%m-%d %I:%M:%S %p')
# ---- build models ----
model = TransModel(pretrained=True).cuda()
params = model.parameters()
if opt.optimizer == 'AdamW':
optimizer = torch.optim.AdamW(params, opt.lr, weight_decay=1e-4)
else:
optimizer = torch.optim.SGD(params, opt.lr, weight_decay=1e-4, momentum=0.9)
# optimizer = torch.optim.Adam(params, opt.lr, betas=(opt.beta1, opt.beta2))
image_root = '{}/images/'.format(opt.train_path)
gt_root = '{}/masks/'.format(opt.train_path)
train_loader = get_loader(image_root, gt_root, batchsize=opt.batchsize, trainsize=opt.trainsize, augmentation = False)
total_step = len(train_loader)
print("#"*20, "Start Training", "#"*20)
best_loss = 1e5
for epoch in range(1, opt.epoch + 1):
adjust_lr(optimizer, opt.lr, epoch, 0.1, 200)
best_loss = train(train_loader, model, optimizer, epoch, opt.test_path)