--- a
+++ b/training/inductivenet_trainers.py
@@ -0,0 +1,360 @@
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import torch.optim.optimizer
+from torch.utils.data import DataLoader
+
+from data.hyperkvasir import KvasirSegmentationDataset, KvasirMNVset
+from evaluation.metrics import iou
+from losses.consistency_losses import *
+from perturbation.model import ModelOfNaturalVariation
+from training.vanilla_trainer import VanillaTrainer
+from utils import logging
+from training.consistency_trainers import ConsistencyTrainer
+from models.segmentation_models import InductiveNet
+from models.ensembles import TrainedEnsemble
+from data.hyperkvasir import KvasirSegmentationDataset, KvasirMNVset
+from evaluation.metrics import iou
+from losses.consistency_losses import *
+from perturbation.model import ModelOfNaturalVariation
+from training.vanilla_trainer import VanillaTrainer
+from utils import logging
+from data.etis import EtisDataset
+
+
+class InductiveNetConsistencyTrainer:
+    def __init__(self, id, config):
+        """
+
+        :param model: String describing the model type. Can be DeepLab, TriUnet, ... TODO
+        :param config: Contains hyperparameters : lr, epochs, batch_size, T_0, T_mult
+        """
+        self.config = config
+        self.device = config["device"]
+        self.lr = config["lr"]
+        self.batch_size = config["batch_size"]
+        self.epochs = config["epochs"]
+        self.id = id
+        self.model_str = "InductiveNet"
+        self.mnv = ModelOfNaturalVariation(T0=1).to(self.device)
+        self.nakedcloss = NakedConsistencyLoss()
+        self.closs = ConsistencyLoss()
+        self.model = InductiveNet().to(self.device)
+
+        self.optimizer = torch.optim.Adam(self.model.parameters(), self.lr)
+        self.jaccard = vanilla_losses.JaccardLoss()
+        self.mse = nn.MSELoss()
+        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, T_0=50, T_mult=2)
+        self.train_set = KvasirSegmentationDataset("Datasets/HyperKvasir", split="train", augment=False)
+        self.val_set = KvasirSegmentationDataset("Datasets/HyperKvasir", split="val")
+        self.test_set = KvasirSegmentationDataset("Datasets/HyperKvasir", split="test")
+        self.train_loader = DataLoader(self.train_set, batch_size=self.batch_size, shuffle=True)
+        self.val_loader = DataLoader(self.val_set)
+        self.test_loader = DataLoader(self.test_set)
+
+    def train_epoch(self):
+        self.model.train()
+        losses = []
+        for x, y, fname in self.train_loader:
+            image = x.to("cuda")
+            mask = y.to("cuda")
+            aug_img, aug_mask = self.mnv(image, mask)
+            self.optimizer.zero_grad()
+            aug_output, _ = self.model(aug_img)
+            output, reconstruction = self.model(image)
+            mean_iou = torch.mean(iou(output, mask))
+            loss = 0.5 * (self.closs(aug_mask, mask, aug_output, output, mean_iou) + self.mse(
+                image, reconstruction))
+            loss.backward()
+            self.optimizer.step()
+            losses.append(np.abs(loss.item()))
+        return np.mean(losses)
+
+    def train(self):
+        best_val_loss = 10
+        print("Starting Segmentation training")
+        best_consistency = 0
+        for i in range(self.epochs):
+            training_loss = np.abs(self.train_epoch())
+            val_loss, ious, closs = self.validate(epoch=i, plot=False)
+            gen_ious = self.validate_generalizability(epoch=i, plot=False)
+            mean_iou = float(torch.mean(ious))
+            gen_iou = float(torch.mean(gen_ious))
+            consistency = 1 - np.mean(closs)
+            test_iou = np.mean(self.test().numpy())
+
+            self.config["lr"] = [group['lr'] for group in self.optimizer.param_groups]
+            logging.log_full(epoch=i, id=self.id, config=self.config, result_dict=
+            {"train_loss": training_loss, "val_loss": val_loss,
+             "iid_val_iou": mean_iou, "iid_test_iou": test_iou, "ood_iou": gen_iou,
+             "consistency": consistency}, type="consistency")
+
+            self.scheduler.step(i)
+            print(
+                f"Epoch {i} of {self.epochs} \t"
+                f" lr={[group['lr'] for group in self.optimizer.param_groups]} \t"
+                f" loss={training_loss} \t"
+                f" val_loss={val_loss} \t"
+                f" ood_iou={gen_iou}\t"
+                f" val_iou={mean_iou} \t"
+                f" gen_prop={gen_iou / mean_iou}"
+            )
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                np.save(
+                    f"experiments/Data/Augmented-Pipelines/{self.model_str}/{self.id}",
+                    test_iou)
+                print(f"Saving new best model. IID test-set mean iou: {test_iou}")
+                torch.save(self.model.state_dict(),
+                           f"Predictors/Augmented/{self.model_str}/{self.id}")
+                print("saved in: ", f"Predictors/Augmented/{self.model_str}/{self.id}")
+
+            if consistency > best_consistency:
+                best_consistency = consistency
+                torch.save(self.model.state_dict(),
+                           f"Predictors/Augmented/{self.model_str}/maximum_consistency{self.id}")
+            torch.save(self.model.state_dict(),
+                       f"Predictors/Augmented/{self.model_str}/{self.id}_last_epoch")
+
+    def test(self):
+        self.model.eval()
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, fname in self.test_loader:
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                output, _ = self.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()))
+        return ious
+
+    def validate(self, epoch, plot=False):
+        self.model.eval()
+        losses = []
+        closses = []
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, fname in self.val_loader:
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                aug_img, aug_mask = self.mnv(image, mask)
+                output, reconstruction = self.model(image)
+                aug_output, _ = self.model(aug_img)  # todo consider train on augmented vs non-augmented?
+
+                batch_ious = torch.Tensor([iou(output_i, mask_j) for output_i, mask_j in zip(output, mask)])
+                loss = 0.5 * (self.closs(aug_mask, mask, aug_output, output, torch.mean(batch_ious)) + self.mse(
+                    image, reconstruction))
+                losses.append(np.abs(loss.item()))
+                closses.append(self.nakedcloss(aug_mask, mask, aug_output, output).item())
+                ious = torch.cat((ious, batch_ious.cpu().flatten()))
+
+                if plot:
+                    plt.imshow(output[0, 0].cpu().numpy(), alpha=0.5)
+                    plt.imshow(reconstruction[0].permute(1, 2, 0).cpu().numpy())
+                    # plt.imshow((output[0, 0].cpu().numpy() > 0.5).astype(int), alpha=0.5)
+                    # plt.imshow(y[0, 0].cpu().numpy().astype(int), alpha=0.5)
+                    # plt.title("IoU {} at epoch {}".format(iou(output[0, 0], mask[0, 0]), epoch))
+                    plt.show()
+                    plot = False  # plot one example per epoch
+        avg_val_loss = np.mean(losses)
+        avg_closs = np.mean(closses)
+        return avg_val_loss, ious, closses
+
+    def validate_generalizability(self, epoch, plot=False):
+        self.model.eval()
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, index in DataLoader(EtisDataset("Datasets/ETIS-LaribPolypDB")):
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                output, _ = self.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()))
+                if plot:
+                    plt.imshow(image[0].permute(1, 2, 0).cpu().numpy())
+                    plt.imshow((output[0, 0].cpu().numpy() > 0.5).astype(int), alpha=0.5)
+                    plt.title("IoU {} at epoch {}".format(iou(output[0, 0], mask[0, 0]), epoch))
+                    plt.show()
+                    plot = False  # plot one example per epoch (hacky, but works)
+            return ious
+
+
+class InductiveNetVanillaTrainer(InductiveNetConsistencyTrainer):
+
+    def __init__(self, id, config):
+        super(InductiveNetVanillaTrainer, self).__init__(id, config)
+
+    def train(self):
+        best_val_loss = 10
+        print("Starting Segmentation training")
+        best_consistency = 0
+        for i in range(self.epochs):
+            training_loss = np.abs(self.train_epoch())
+            val_loss, ious, closs = self.validate(epoch=i, plot=False)
+            gen_ious = self.validate_generalizability(epoch=i, plot=False)
+            mean_iou = float(torch.mean(ious))
+            gen_iou = float(torch.mean(gen_ious))
+            consistency = 1 - np.mean(closs)
+            test_iou = np.mean(self.test().numpy())
+
+            self.config["lr"] = [group['lr'] for group in self.optimizer.param_groups]
+            logging.log_full(epoch=i, id=self.id, config=self.config, result_dict=
+            {"train_loss": training_loss, "val_loss": val_loss,
+             "iid_val_iou": mean_iou, "iid_test_iou": test_iou, "ood_iou": gen_iou,
+             "consistency": consistency}, type="consistency")
+
+            self.scheduler.step(i)
+            print(
+                f"Epoch {i} of {self.epochs} \t"
+                f" lr={[group['lr'] for group in self.optimizer.param_groups]} \t"
+                f" loss={training_loss} \t"
+                f" val_loss={val_loss} \t"
+                f" ood_iou={gen_iou}\t"
+                f" val_iou={mean_iou} \t"
+                f" gen_prop={gen_iou / mean_iou}"
+            )
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                np.save(
+                    f"experiments/Data/Normal-Pipelines/{self.model_str}/{self.id}",
+                    test_iou)
+                print(f"Saving new best model. IID test-set mean iou: {test_iou}")
+                torch.save(self.model.state_dict(),
+                           f"Predictors/Vanilla/{self.model_str}/{self.id}")
+                print("saved in: ", f"Predictors/Vanilla/{self.model_str}/{self.id}")
+
+            if consistency > best_consistency:
+                best_consistency = consistency
+                torch.save(self.model.state_dict(),
+                           f"Predictors/Vanilla/{self.model_str}/maximum_consistency{self.id}")
+            torch.save(self.model.state_dict(),
+                       f"Predictors/Vanilla/{self.model_str}/{self.id}_last_epoch")
+
+    def train_epoch(self):
+        self.model.train()
+        losses = []
+        for x, y, fname in self.train_loader:
+            image = x.to("cuda")
+            mask = y.to("cuda")
+            self.optimizer.zero_grad()
+            output, reconstruction = self.model(image)
+            mean_iou = torch.mean(iou(output, mask))
+            loss = 0.5 * (self.jaccard(output, mask) + self.mse(
+                image, reconstruction))
+            loss.backward()
+            self.optimizer.step()
+            losses.append(np.abs(loss.item()))
+        return np.mean(losses)
+
+    def test(self):
+        self.model.eval()
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, fname in self.test_loader:
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                output = self.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()))
+        return ious
+
+    def validate(self, epoch, plot=False):
+        self.model.eval()
+        losses = []
+        closses = []
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, fname in self.val_loader:
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                aug_img, aug_mask = self.mnv(image, mask)
+                output, reconstruction = self.model(image)
+                aug_output, _ = self.model(aug_img)  # todo consider train on augmented vs non-augmented?
+
+                batch_ious = torch.Tensor([iou(output_i, mask_j) for output_i, mask_j in zip(output, mask)])
+                loss = 0.5 * (self.jaccard(output, mask) + self.mse(
+                    image, reconstruction))
+                losses.append(np.abs(loss.item()))
+                closses.append(self.nakedcloss(aug_mask, mask, aug_output, output).item())
+                ious = torch.cat((ious, batch_ious.cpu().flatten()))
+
+                if plot:
+                    plt.imshow(output[0, 0].cpu().numpy(), alpha=0.5)
+                    plt.imshow(reconstruction[0].permute(1, 2, 0).cpu().numpy())
+                    # plt.imshow((output[0, 0].cpu().numpy() > 0.5).astype(int), alpha=0.5)
+                    # plt.imshow(y[0, 0].cpu().numpy().astype(int), alpha=0.5)
+                    # plt.title("IoU {} at epoch {}".format(iou(output[0, 0], mask[0, 0]), epoch))
+                    plt.show()
+                    plot = False  # plot one example per epoch
+        avg_val_loss = np.mean(losses)
+        avg_closs = np.mean(closses)
+        return avg_val_loss, ious, avg_closs
+
+    def validate_generalizability(self, epoch, plot=False):
+        self.model.eval()
+        ious = torch.empty((0,))
+        with torch.no_grad():
+            for x, y, index in DataLoader(EtisDataset("Datasets/ETIS-LaribPolypDB")):
+                image = x.to("cuda")
+                mask = y.to("cuda")
+                output, _ = self.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()))
+                if plot:
+                    plt.imshow(image[0].permute(1, 2, 0).cpu().numpy())
+                    plt.imshow((output[0, 0].cpu().numpy() > 0.5).astype(int), alpha=0.5)
+                    plt.title("IoU {} at epoch {}".format(iou(output[0, 0], mask[0, 0]), epoch))
+                    plt.show()
+                    plot = False  # plot one example per epoch (hacky, but works)
+            return ious
+
+
+class InductiveNetAugmentationTrainer(InductiveNetConsistencyTrainer):
+    """
+        Uses vanilla data augmentation with p=0.5 instead of a a custom loss
+    """
+
+    def __init__(self, id, config):
+        super(InductiveNetAugmentationTrainer, self).__init__(id, config)
+        self.jaccard = vanilla_losses.JaccardLoss()
+        self.mse = vanilla_losses.MSELoss()
+        self.prob = 0
+        self.dataset = KvasirMNVset("Datasets/HyperKvasir", "train", inpaint=config["use_inpainter"])
+        self.train_loader = DataLoader(self.dataset, batch_size=config["batch_size"], shuffle=True)
+
+    def get_iou_weights(self, image, mask):
+        self.model.eval()
+        with torch.no_grad():
+            output, _ = self.model(image)
+        return torch.mean(iou(output, mask))
+
+    def get_consistency(self, image, mask, augmented, augmask):
+        self.model.eval()
+        with torch.no_grad():
+            output, _ = self.model(image)
+        self.model.train()
+        return torch.mean(self.nakedcloss(output, mask, augmented, augmask))
+
+    def train_epoch(self):
+        self.model.train()
+        losses = []
+        for x, y, fname, flag in self.train_loader:
+            image = x.to("cuda")
+            mask = y.to("cuda")
+            self.optimizer.zero_grad()
+            output, reconstruction = self.model(image)
+            mean_iou = torch.mean(iou(output, mask))
+            loss = 0.5 * (self.jaccard(output, mask) + self.mse(
+                image, reconstruction))
+            loss.backward()
+            self.optimizer.step()
+            losses.append(np.abs(loss.item()))
+        return np.mean(losses)
+
+
+class InductiveNetEnsembleTrainer(InductiveNetConsistencyTrainer):
+    def __init__(self, id, config):
+        super(InductiveNetEnsembleTrainer, self).__init__(id, config)
+        self.model = TrainedEnsemble("Singular")
+        self.optimizer = torch.optim.Adam(self.model.parameters(), self.lr)
+        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, T_0=50, T_mult=2)