Delete utils directory

utils/comm.py

@@ -1,27 +0,0 @@
-#Adopted from ACSNet
-import models
-import torch
-import os
-
-
-def generate_model(opt):
-    model = getattr(models, opt.model)(opt.nclasses)
-    if opt.use_gpu:
-        model.cuda()
-        torch.backends.cudnn.benchmark = True
-
-    if opt.load_ckpt is not None:
-        model_dict = model.state_dict()
-        #load_ckpt_path = os.path.join('./checkpoints/exp'+str(opt.expID)+'/', opt.load_ckpt + '.pth')
-        load_ckpt_path = os.path.join('./checkpoints/exp-colondb/', 'ck_'+ str(opt.load_ckpt) + '.pth')
-        print(load_ckpt_path)
-        assert os.path.isfile(load_ckpt_path), 'No checkpoint found.'
-        print('Loading checkpoint......')
-        checkpoint = torch.load(load_ckpt_path)
-        new_dict = {k : v for k, v in checkpoint.items() if k in model_dict.keys()}
-        model_dict.update(new_dict)
-        model.load_state_dict(model_dict)
-
-        print('Done')
-
-    return model

utils/data_loader.py

@@ -1,42 +0,0 @@
-# Adopted from PRAnet
-import os
-from PIL import Image
-import torch.utils.data as data
-import torchvision.transforms as transforms
-
-class test_dataset:
-    def __init__(self, image_root, gt_root, testsize):
-        self.testsize = testsize
-        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
-        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.tif') or f.endswith('.png')]
-        self.images = sorted(self.images)
-        self.gts = sorted(self.gts)
-        self.transform = transforms.Compose([
-            transforms.Resize((self.testsize, self.testsize)),
-            transforms.ToTensor()
-            #transforms.Normalize([0.485, 0.456, 0.406],
-                                 #[0.229, 0.224, 0.225])
-            ])
-        self.gt_transform = transforms.ToTensor()
-        self.size = len(self.images)
-        self.index = 0
-
-    def load_data(self):
-        image = self.rgb_loader(self.images[self.index])
-        image = self.transform(image).unsqueeze(0)
-        gt = self.binary_loader(self.gts[self.index])
-        name = self.images[self.index].split('/')[-1]
-        if name.endswith('.jpg'):
-            name = name.split('.jpg')[0] + '.png'
-        self.index += 1
-        return image, gt, name
-
-    def rgb_loader(self, path):
-        with open(path, 'rb') as f:
-            img = Image.open(f)
-            return img.convert('RGB')
-
-    def binary_loader(self, path):
-        with open(path, 'rb') as f:
-            img = Image.open(f)
-            return img.convert('L')

utils/metrics.py

@@ -1,80 +0,0 @@
-import torch
-
-"""
-The evaluation implementation refers to the following paper:
-"Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation"
-https://github.com/Yuqi-cuhk/Polyp-Seg
-"""
-def evaluate(pred, gt, th):
-    if isinstance(pred, (list, tuple)):
-        pred = pred[0]
-
-    pred_binary = (pred >= th).float()
-    pred_binary_inverse = (pred_binary == 0).float()
-
-    gt_binary = (gt >= th).float()
-    gt_binary_inverse = (gt_binary == 0).float()
-
-    TP = pred_binary.mul(gt_binary).sum()
-    FP = pred_binary.mul(gt_binary_inverse).sum()
-    TN = pred_binary_inverse.mul(gt_binary_inverse).sum()
-    FN = pred_binary_inverse.mul(gt_binary).sum()
-
-    if TP.item() == 0:
-        # print('TP=0 now!')
-        # print('Epoch: {}'.format(epoch))
-        # print('i_batch: {}'.format(i_batch))
-        TP = torch.Tensor([1]).cuda()
-
-    # recall
-    Recall = TP / (TP + FN)
-
-    # Specificity or true negative rate
-    Specificity = TN / (TN + FP)
-
-    # Precision or positive predictive value
-    Precision = TP / (TP + FP)
-
-    # F1 score = Dice
-    F1 = 2 * Precision * Recall / (Precision + Recall)
-
-    # F2 score
-    F2 = 5 * Precision * Recall / (4 * Precision + Recall)
-
-    # Overall accuracy
-    ACC_overall = (TP + TN) / (TP + FP + FN + TN)
-
-    # IoU for poly
-    IoU_poly = TP / (TP + FP + FN)
-
-    # IoU for background
-    IoU_bg = TN / (TN + FP + FN)
-
-    # mean IoU
-    IoU_mean = (IoU_poly + IoU_bg) / 2.0
-
-    #Dice
-    Dice = (2 * TP)/(2*TP + FN + FP)
-    
-    return Recall, Specificity, Precision, F1, F2, ACC_overall, IoU_poly, IoU_bg, IoU_mean, Dice
-
-
-class Metrics(object):
-    def __init__(self, metrics_list):
-        self.metrics = {}
-        for metric in metrics_list:
-            self.metrics[metric] = 0
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            assert (k in self.metrics.keys()), "The k {} is not in metrics".format(k)
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-
-            self.metrics[k] += v
-
-    def mean(self, total):
-        mean_metrics = {}
-        for k, v in self.metrics.items():
-            mean_metrics[k] = v / total
-        return mean_metrics