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train_mtunet_Synapse.py
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train_mtunet_Synapse.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
import logging
import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn.modules.loss import CrossEntropyLoss
import torchvision
# import matplotlib.pyplot as plt
from utils.utils import DiceLoss
from torch.utils.data import DataLoader
from dataset.dataset_Synapse import Synapsedataset, RandomGenerator
import argparse
from tqdm import tqdm
import os
from torchvision import transforms
from utils.test_Synapse import inference
from model.MTUNet import MTUNet
import numpy as np
from medpy.metric import dc,hd95
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", default=12, help="batch size")
parser.add_argument("--lr", default=0.0001, help="learning rate")
parser.add_argument("--max_epochs", default=100)
parser.add_argument("--img_size", default=224)
parser.add_argument("--save_path", default="./checkpoint/Synapse/mtunet")
parser.add_argument("--n_gpu", default=1)
parser.add_argument("--checkpoint", default=None)
parser.add_argument("--list_dir", default="path/to/dataset/Synapse/lists_Synapse")
parser.add_argument("--root_dir", default="path/to/dataset/Synapse/")
parser.add_argument("--volume_path", default="path/to/dataset/Synapse/test")
parser.add_argument("--z_spacing", default=10)
parser.add_argument("--num_classes", default=9)
parser.add_argument('--test_save_dir', default='./predictions', help='saving prediction as nii!')
parser.add_argument("--patches_size", default=16)
parser.add_argument("--n-skip", default=1)
args = parser.parse_args()
model=MTUNet(args.num_classes) # 9
if args.checkpoint:
model.load_state_dict(torch.load(args.checkpoint))
train_dataset = Synapsedataset(args.root_dir, args.list_dir, split="train", transform=
transforms.Compose(
[RandomGenerator(output_size=[args.img_size, args.img_size])]))
Train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
db_test =Synapsedataset(base_dir=args.volume_path, list_dir=args.list_dir, split="test")
testloader = DataLoader(db_test, batch_size=1, shuffle=False)
if args.n_gpu > 1:
model = nn.DataParallel(model)
model = model.cuda()
model.train()
ce_loss = CrossEntropyLoss()
dice_loss = DiceLoss(args.num_classes)
save_interval = args.n_skip # int(max_epoch/6)
iterator = tqdm(range(0, args.max_epochs), ncols=70)
iter_num = 0
Loss = []
Test_Accuracy = []
Best_dcs = 0.7
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
max_iterations = args.max_epochs * len(Train_loader)
base_lr = args.lr
optimizer = optim.Adam(model.parameters(), lr=base_lr, weight_decay=0.0001)
# optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
for epoch in iterator:
model.train()
train_loss = 0
for i_batch, sampled_batch in enumerate(Train_loader):
image_batch, label_batch = sampled_batch["image"], sampled_batch["label"]
image_batch, label_batch = image_batch.type(torch.FloatTensor), label_batch.type(torch.FloatTensor)
image_batch, label_batch = image_batch.cuda(), label_batch.cuda()
outputs = model(image_batch)
loss_ce = ce_loss(outputs, label_batch[:].long())
loss_dice = dice_loss(outputs, label_batch[:], softmax=True)
loss = loss_dice * 0.5+ loss_ce * 0.5
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9
#lr_ = base_lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
iter_num = iter_num + 1
logging.info('iteration %d : loss : %f lr_: %f' % (iter_num, loss.item(), lr_))
train_loss += loss.item()
Loss.append(train_loss/len(train_dataset))
# loss visualization
# fig1, ax1 = plt.subplots(figsize=(11, 8))
# ax1.plot(range(epoch + 1), Loss)
# ax1.set_title("Average trainset loss vs epochs")
# ax1.set_xlabel("Epoch")
# ax1.set_ylabel("Current loss")
# plt.savefig('loss_vs_epochs_gauss.png')
# plt.clf()
# plt.close()
if (epoch + 1) % save_interval == 0:
avg_dcs, avg_hd = inference(args, model, testloader, args.test_save_dir)
# avg_dcs, avg_hd = test()
if avg_dcs > Best_dcs:
save_mode_path = os.path.join(args.save_path, 'epoch={}_lr={}_avg_dcs={}_avg_hd={}.pth'.format(epoch, lr_, avg_dcs, avg_hd))
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
#temp = 1
Best_dcs = avg_dcs
Test_Accuracy.append(avg_dcs)
# val visualization
# fig2, ax2 = plt.subplots(figsize=(11, 8))
# ax2.plot(range(int((epoch + 1) // save_interval)), Test_Accuracy)
# ax2.set_title("Average val dataset dice score vs epochs")
# ax2.set_xlabel("Epoch")
# ax2.set_ylabel("Current dice score")
# plt.savefig('val_dsc_vs_epochs_gauss.png')
# plt.clf()
# plt.close()
if epoch >= args.max_epochs - 1:
save_mode_path = os.path.join(args.save_path, 'epoch={}_lr={}_avg_dcs={}.pth'.format(epoch, lr_, avg_dcs))
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
iterator.close()
break