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train.py

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+#Adopted from the ACSNet
+
+import torch
+from torch.utils.data import DataLoader
+from torch.optim.lr_scheduler import LambdaLR
+from tqdm import tqdm
+import datasets
+from utils.metrics import evaluate
+from opt import opt
+from utils.comm import generate_model
+from utils.loss import DeepSupervisionLoss,  BceDiceLoss
+from utils.metrics import Metrics
+import torch.nn as nn
+
+
+def valid(model, valid_dataloader, total_batch):
+
+    model.eval()
+
+    # Metrics_logger initialization
+    metrics = Metrics(['recall', 'specificity', 'precision', 'F1', 'F2',
+                       'ACC_overall', 'IoU_poly', 'IoU_bg', 'IoU_mean'])
+
+    with torch.no_grad():
+        bar = tqdm(enumerate(valid_dataloader), total=total_batch)
+        for i, data in bar:
+            img, gt = data['image'], data['label']
+
+            if opt.use_gpu:
+                img = img.cuda()
+                gt = gt.cuda()
+
+            output = model(img)
+            _recall, _specificity, _precision, _F1, _F2, \
+            _ACC_overall, _IoU_poly, _IoU_bg, _IoU_mean = evaluate(output, gt, 0.5)
+
+            metrics.update(recall= _recall, specificity= _specificity, precision= _precision,
+                            F1= _F1, F2= _F2, ACC_overall= _ACC_overall, IoU_poly= _IoU_poly,
+                            IoU_bg= _IoU_bg, IoU_mean= _IoU_mean
+                        )
+
+    metrics_result = metrics.mean(total_batch)
+
+    return metrics_result
+
+
+def train():
+
+    model = generate_model(opt)
+    #model = nn.DataParallel(model)
+
+    # load data
+    train_data = getattr(datasets, opt.dataset)(opt.root, opt.train_data_dir, mode='train')
+    train_dataloader = DataLoader(train_data, opt.batch_size, shuffle=True, num_workers=opt.num_workers)
+    valid_data = getattr(datasets, opt.dataset)(opt.root, opt.valid_data_dir, mode='valid')
+    valid_dataloader = DataLoader(valid_data, batch_size=1, shuffle=False, num_workers=opt.num_workers)
+    val_total_batch = int(len(valid_data) / 1)
+
+
+    # load optimizer and scheduler
+    optimizer = torch.optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.mt, weight_decay=opt.weight_decay)
+
+    lr_lambda = lambda epoch: 1.0 - pow((epoch / opt.nEpoch), opt.power)
+    scheduler = LambdaLR(optimizer, lr_lambda)
+
+    # train
+    print('Start training')
+    print('---------------------------------\n')
+
+    for epoch in range(opt.nEpoch):
+        print('------ Epoch', epoch + 1)
+        model.train()
+        total_batch = int(len(train_data) / opt.batch_size)
+        bar = tqdm(enumerate(train_dataloader), total=total_batch)
+
+        for i, data in bar:
+            img = data['image']
+            gt = data['label']
+
+
+            if opt.use_gpu:
+                img = img.cuda()
+                gt = gt.cuda()
+
+            optimizer.zero_grad()
+            output = model(img)
+
+            #loss = BceDiceLoss()(output, gt)
+            loss = DeepSupervisionLoss(output, gt)
+            loss.backward()
+
+            optimizer.step()
+            bar.set_postfix_str('loss: %.5s' % loss.item())
+
+        scheduler.step()
+
+        metrics_result = valid(model, valid_dataloader, val_total_batch)
+
+        print("Valid Result:")
+        print('recall: %.4f, specificity: %.4f, precision: %.4f, F1: %.4f,'
+              ' F2: %.4f, ACC_overall: %.4f, IoU_poly: %.4f, IoU_bg: %.4f, IoU_mean: %.4f'
+              % (metrics_result['recall'], metrics_result['specificity'], metrics_result['precision'],
+                 metrics_result['F1'], metrics_result['F2'], metrics_result['ACC_overall'],
+                 metrics_result['IoU_poly'], metrics_result['IoU_bg'], metrics_result['IoU_mean']))
+
+        if ((epoch + 1) % opt.ckpt_period == 0):
+            torch.save(model.state_dict(), './checkpoints/exp' + str(opt.expID)+"/ck_{}.pth".format(epoch + 1))
+
+
+if __name__ == '__main__':
+
+    if opt.mode == 'train':
+        print('---PolpySeg Train---')
+        train()
+
+    print('Done')