--- a
+++ b/evaluation/stress_test.py
@@ -0,0 +1,87 @@
+import random
+from os import listdir
+from os.path import join
+
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from data.etis import EtisDataset
+from data.hyperkvasir import KvasirSegmentationDataset
+from models.segmentation_models import DeepLab
+from evaluation.metrics import iou
+from utils.logging import log_iou
+
+
+class AdditiveNoise(nn.Module):
+    def __init__(self, noise_factor):
+        super().__init__()
+        self.noise_factor = noise_factor
+
+    def forward(self, img):
+        return img + torch.randn(img.shape).to("cuda") * random.randint(0, 5) * self.noise_factor
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(noise_factor={})'.format(self.noise_factor)
+
+
+class segmentation_stressors(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, img, mask):
+        return img, mask
+
+
+def apply_stressors(image, mask):
+    img_augs = transforms.Compose([AdditiveNoise(0.05),
+                                   transforms.RandomErasing(0.5, scale=(0.02, 0.08)),
+                                   transforms.RandomInvert(p=0.1)])
+    img_and_mask_augs = segmentation_stressors()
+    image = img_augs(image)
+    image, mask = img_and_mask_augs(image, mask)
+    return image, mask
+
+
+def stresstesttest():
+    dataset = KvasirSegmentationDataset("Datasets/HyperKvasir")
+    for x, y, fname in DataLoader(dataset):
+        image, mask = apply_stressors(x.to("cuda"), y.to("cuda"))
+        plt.imshow(image[0].permute(1, 2, 0).cpu().numpy())
+        # plt.imshow(mask.cpu().numpy().squeeze().T, alpha=0.5)
+        plt.show()
+        input()
+
+
+def perform_stresstest(modelpath, stressors=True):
+    dataset = EtisDataset("Datasets/ETIS-LaribPolypDB")
+    # dataset = KvasirSegmentationDataset("Datasets/HyperKvasir")
+    for predictor_name in listdir(modelpath):
+        if len(predictor_name.split("-")) == 3:
+            model = DeepLab(1).to("cuda")
+            model = torch.nn.Sequential(model, torch.nn.Sigmoid())
+
+            model.eval()
+            test = torch.load(join(modelpath, predictor_name))
+            # print(test)
+            model.load_state_dict(test)
+            ious = torch.empty((0,))
+
+            for x, y, fname in tqdm(DataLoader(dataset)):
+                if stressors:
+                    image, mask = apply_stressors(x.to("cuda"), y.to("cuda"))
+                else:
+                    image, mask = x.to("cuda"), y.to("cuda")
+                with torch.no_grad():
+                    output = model(image)
+                    batch_ious = torch.Tensor([iou(output_i, mask_j) for output_i, mask_j in zip(output, mask)])
+                    ious = torch.cat((ious, batch_ious.flatten()))
+            log_iou("logs/kvasir-no-pretrain-baseline.log", -1, predictor_name.split("-")[-1], ious)
+
+
+if __name__ == '__main__':
+    perform_stresstest("Predictors/BaselineDeepLab", stressors=False)
+    # EtisDataset("")