Delete train.py

train.py

@@ -1,116 +0,0 @@
-#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')