From 5b99f0c38243dde7863dac95ef353f2e4ed167ab Mon Sep 17 00:00:00 2001
From: Joseph Omar <j.omar@soton.ac.uk>
Date: Wed, 4 Dec 2024 20:01:27 +0000
Subject: [PATCH] Using a dataset of extracted features rather than a
 backbone-based system

---
 entcl/cl.py                                   |  22 +-
 entcl/config.py                               |   3 +-
 entcl/data/__init__.py                        |   0
 entcl/data/cifar100/__init__.py               |   2 +
 entcl/data/cifar100/cifar100feats.py          |  55 ++++
 .../cifar100partition.py}                     |  91 ++----
 entcl/data/cifar100/prep_cifar100_feats.py    | 101 ++++++
 entcl/models/model.py                         |   7 +
 entcl/pretrain.py                             |   5 +-
 entcl/run.py                                  |   4 +-
 entcl/utils/ncd.py                            |  45 ++-
 entcl/utils/ood.py                            |  30 +-
 experiments/experiments3.ipynb                | 306 ++++++++++--------
 13 files changed, 415 insertions(+), 256 deletions(-)
 delete mode 100644 entcl/data/__init__.py
 create mode 100644 entcl/data/cifar100/__init__.py
 create mode 100644 entcl/data/cifar100/cifar100feats.py
 rename entcl/data/{cifar100.py => cifar100/cifar100partition.py} (85%)
 create mode 100644 entcl/data/cifar100/prep_cifar100_feats.py

diff --git a/entcl/cl.py b/entcl/cl.py
index 2a3d403..4efdcb3 100644
--- a/entcl/cl.py
+++ b/entcl/cl.py
@@ -1,36 +1,32 @@
 from typing import Dict, Tuple
-from entcl.data.util import TransformedTensorDataset
 import pandas as pd
 import torch
 from loguru import logger
 from tqdm import tqdm
 
 
-def cl_session(args, session_dataset: TransformedTensorDataset, model: torch.nn.Module, mapping: Dict[int, int]) -> torch.nn.Module:
+def cl_session(args, session_dataset: torch.utils.data.Dataset, model: torch.nn.Module, mapping: Dict[int, int]) -> torch.nn.Module:
     """
     Run a continual learning session on the given session dataset using the given model
     :param args: Arguments object with the attributes `device`, `cl_epochs`, `seed`.
-    :param session_dataset: TransformedTensorDataset with the session data. Should have OOD predtypes and pseudo labels.
+    :param session_dataset: torch.utils.data.Dataset with the session data. Should have OOD predtypes and pseudo labels.
     :param model: torch.nn.Module with the model to use for continual learning. `forward` should return `logits, features`.
     :return: torch.nn.Module with the updated model.
     """
     logger.debug(f"Begin Continual Learning Session {args.current_session}")
     # make sure the dataset has the correct shape
-    assert len(session_dataset.tensor_dataset.tensors) == 5, "Session Dataset should have 5 tensors, (data, true labels, true types, pred types, pseudo labels). Got: " + str(len(session_dataset.tensor_dataset.tensors))
-    for i, t in enumerate(session_dataset.tensor_dataset.tensors):
+    assert len(session_dataset.tensors) == 5, "Session Dataset should have 5 tensors, (data, true labels, true types, pred types, pseudo labels). Got: " + str(len(session_dataset.tensors))
+    for i, t in enumerate(session_dataset.tensors):
         if t.device != torch.device("cpu"):
             logger.warning(f"Tensor {i} is not on CPU")
     # create the required training stuff and things and junk
     
     # we are only training with nove data at the moment, so we need to whittle down the dataset to only the predicted novel samples
-    novel_samples_mask = session_dataset.tensor_dataset.tensors[3] == 1 # novel samples are labelled with 1. predtypes are the 4th tensor in the dataset
+    novel_samples_mask = session_dataset.tensors[3] == 1 # novel samples are labelled with 1. predtypes are the 4th tensor in the dataset
     novel_samples_mask = novel_samples_mask.cpu()
-    novel_tensors = [tensor[novel_samples_mask] for tensor in session_dataset.tensor_dataset.tensors]
+    novel_tensors = [tensor[novel_samples_mask] for tensor in session_dataset.tensors]
     
-    session_dataset = TransformedTensorDataset(
-        tensor_dataset=torch.utils.data.TensorDataset(*novel_tensors),
-        transform=session_dataset.transform,
-    )
+    session_dataset = torch.utils.data.TensorDataset(*novel_tensors)
     
     train_loader = torch.utils.data.DataLoader(
         dataset=session_dataset,
@@ -154,7 +150,7 @@ def _train(args, model: torch.nn.Module, train_loader: torch.utils.data.DataLoad
         logger.debug(f"x shape: {x.shape}, y shape: {y.shape}")
         logger.debug(f"Psuedo Labels (Unique): {torch.unique(y, sorted=True)}")
         optimiser.zero_grad()
-        logits, _ = model(x)
+        logits = model.forward_head(x)
         logger.debug(f"Logits Shape: {logits.shape}")
             
         loss = criterion(logits, y)
@@ -192,7 +188,7 @@ def _validate(args, model: torch.nn.Module, val_loader: torch.utils.data.DataLoa
             for true_label, pseudo_label in mapping.items():
                 y[y == true_label] = pseudo_label # for each y in y, if y == true_label, replace it with pseudo_label
             
-            logits, _ = model(x)
+            logits = model.forward_head(x)
             loss = criterion(logits, y)
             loss = loss.item()
             loss_total += loss
diff --git a/entcl/config.py b/entcl/config.py
index 68bca1b..1e4e3c3 100644
--- a/entcl/config.py
+++ b/entcl/config.py
@@ -1 +1,2 @@
-CIFAR100_DIR = '/cl/datasets/CIFAR/'
\ No newline at end of file
+CIFAR100_DIR = '/cl/datasets/CIFAR/'
+CIFAR100FEATSDIR = '/cl/datasets/CIFAR100Features/'
\ No newline at end of file
diff --git a/entcl/data/__init__.py b/entcl/data/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/entcl/data/cifar100/__init__.py b/entcl/data/cifar100/__init__.py
new file mode 100644
index 0000000..c584990
--- /dev/null
+++ b/entcl/data/cifar100/__init__.py
@@ -0,0 +1,2 @@
+from .cifar100feats import CIFAR100Features
+from .cifar100partition import PartitionedCIFAR100FeaturesDataset
\ No newline at end of file
diff --git a/entcl/data/cifar100/cifar100feats.py b/entcl/data/cifar100/cifar100feats.py
new file mode 100644
index 0000000..8700507
--- /dev/null
+++ b/entcl/data/cifar100/cifar100feats.py
@@ -0,0 +1,55 @@
+import torch
+import os
+from typing import Optional, Callable
+
+class CIFAR100Features(torch.utils.data.Dataset):
+    """
+    Dataset class for CIFAR100 features and labels
+    :param root: str with the root directory for the CIFAR100 features and labels
+    :param train: bool indicating whether to load the training or testing set
+    :param transform: Optional[Callable] with a function to transform the features
+    """
+    
+    def __init__(self, root:str, train:bool = True, transform:Optional[Callable] = None, target_transform: Optional[Callable] = None):
+        self.root = root
+        self.train = train
+        self.transform = transform
+        self.target_transform = target_transform
+        
+        self.file_path = os.path.join(self.root, "cifar100feats_train" if self.train else "cifar100feats_test")
+        self.feats, self.labels = torch.load(self.file_path)
+        
+    def __len__(self):
+        return len(self.labels)
+    
+    def __getitem__(self, idx):
+        """
+        Get an item from the dataset
+        :param idx: int with the index of the item to retrieve
+        :return: torch.Tensor with the features and int with the label
+        """
+        feats = self.feats[idx]
+        label = self.labels[idx]
+        
+        if self.transform is not None:
+            feats = self.transform(feats)
+        
+        if self.target_transform is not None:
+            label = self.target_transform(label)
+        
+        return feats, label
+    
+if __name__ == "__main__":
+    train_dataset = CIFAR100Features(root="/cl/datasets/CIFAR100Features/", train=True)
+    test_dataset = CIFAR100Features(root="/cl/datasets/CIFAR100Features/", train=False)
+    
+    print(f"Train Dataset Length: {len(train_dataset)}")
+    print(f"Test Dataset Length: {len(test_dataset)}")
+    
+    train_feats, train_labels = train_dataset[0]
+    test_feats, test_labels = test_dataset[0]
+    
+    print(f"Train Features Shape: {train_feats.shape}")
+    print(f"Train Label: {train_labels}")
+    print(f"Test Features Shape: {test_feats.shape}")
+    print(f"Test Label: {test_labels}")
\ No newline at end of file
diff --git a/entcl/data/cifar100.py b/entcl/data/cifar100/cifar100partition.py
similarity index 85%
rename from entcl/data/cifar100.py
rename to entcl/data/cifar100/cifar100partition.py
index 4e9d751..418384a 100644
--- a/entcl/data/cifar100.py
+++ b/entcl/data/cifar100/cifar100partition.py
@@ -1,28 +1,15 @@
 import os
-from typing import Dict, List, Union
-from entcl.data.util import TransformedTensorDataset
+from typing import Dict, List
+from entcl.data.cifar100.cifar100feats import CIFAR100Features
 import torch
 from torchvision import disable_beta_transforms_warning
 disable_beta_transforms_warning()
-from torchvision.datasets import CIFAR100 as _CIFAR100
+from tqdm import tqdm
 import torchvision.transforms.v2 as transforms
-from entcl.config import CIFAR100_DIR
+from entcl.config import CIFAR100FEATSDIR
 from loguru import logger
 
-CIFAR100_TRANSFORM = transforms.Compose(
-    [
-        transforms.Resize(int(224/0.875), interpolation=3),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225]
-        )
-        
-    ]
-)
-
-class CIFAR100Dataset:
+class PartitionedCIFAR100FeaturesDataset:
     def __init__(
         self,
         known: int = 50,
@@ -32,29 +19,22 @@ class CIFAR100Dataset:
         cl_n_prevnovel: int = 20,
         sessions: int = 5,
         mutex: bool = True,
-        force_download = False,
     ):
-        """
-        CIFAR100 dataset with incremental learning settings.
-        :param known: Number of known classes. Default: 50
-        :param pretrain_n_known: Number of samples per known class for pretraining. Default: 400
-        :param cl_n_known: Number of samples per known class for each CL session. Default: 20
-        :param cl_n_novel: Number of samples per novel class for each CL session. Default: 400
-        :param cl_n_prevnovel: Number of samples per previously novel class for each CL session. Default: 20
-        """
         self.num_classes = 100
+        
         if known >= self.num_classes:
             raise ValueError("Number of known classes cannot be greater than 100")
-        self.transform = CIFAR100_TRANSFORM
+        
         self.known = known
         self.sessions = sessions
         self.novel = self.known - self.known
         self.pretrain_n_known = pretrain_n_known
-
+    
         self.cl_n_known = cl_n_known
         self.cl_n_novel = cl_n_novel
         self.cl_n_prevnovel = cl_n_prevnovel
         self.novel_inc = (self.num_classes - self.known) // self.sessions
+        
         # Verify the CL settings
         logger.debug(
             "Verifying incremental learning settings\n"
@@ -65,27 +45,23 @@ class CIFAR100Dataset:
             + f"Samples per previously novel class per CL session: {self.cl_n_prevnovel}\n"
             + f"CL sessions: {self.sessions}"
         )
-        self._verify_splits()
-        
         
-        download = (not os.path.exists(os.path.join(CIFAR100_DIR, "cifar-100-python"))) or force_download
-        logger.debug(f"Download: {download}")
+        self._verify_splits()
         
+
+        # Train Set --------------------------------------------------------------
         # load and sort the data into master lists
         logger.debug("Loading and Sorting CIFAR100 Train split")
-        master_train_data: Dict[int, torch.Tensor] = self._split_data_by_class(_CIFAR100(
-            CIFAR100_DIR, train=True, transform=transforms.ToTensor(), download=download
-        ))
+        master_train_data: Dict[int, torch.Tensor] = self._split_data_by_class(CIFAR100Features(root=CIFAR100FEATSDIR, train=True))
         # split the data into datasets for each session
         logger.debug("Splitting Train Data for Sessions")
         self.train_datasets = self._split_train_data_for_sessions(master_train_data, mutex=mutex)
-        
         del master_train_data
         
+        # Test Set --------------------------------------------------------------
         logger.debug("Loading and Sorting CIFAR100 Test split")
-        master_test_data: Dict[int, torch.Tensor] = self._split_data_by_class(_CIFAR100(
-            CIFAR100_DIR, train=False,transform=transforms.ToTensor(), download=download
-        ))
+        master_test_data: Dict[int, torch.Tensor] = self._split_data_by_class(CIFAR100Features(root=CIFAR100FEATSDIR, train=False))
+        
         logger.debug("Splitting Test Data for Sessions")
         self.test_datasets = self._split_test_data_for_sessions(master_test_data)
         
@@ -99,8 +75,6 @@ class CIFAR100Dataset:
         :param train: Whether to get the training set. Default: True
         :return: Dataset for the given session
         """
-        if session == "pretrain":
-            session = 0
         if session not in self.train_datasets:
             raise ValueError(f"Session {session} does not exist, only sessions {list(self.train_datasets.keys())} exist")
         
@@ -168,7 +142,7 @@ class CIFAR100Dataset:
         types = torch.full((labels.size(0),), 0, dtype=torch.long) # they are all known classes, so type is 0
         logger.debug(f"Creating dataset for session 0 (pretraining). There are {len(samples)} samples, and {len(labels)} labels. There are {labels.unique().size(0)} different classes")
         logger.debug(f"Classes in Pretraining Dataset: {labels.unique(sorted=True)}")
-        datasets[0] = TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(samples, labels, types), transform=self.transform)
+        datasets[0] = torch.utils.data.TensorDataset(samples, labels, types)
         
         # CL sessions' datasets
         logger.debug("Splitting data for CL sessions")    
@@ -208,7 +182,7 @@ class CIFAR100Dataset:
             
             logger.debug(f"Creating dataset for session {session}. There are {len(samples)} samples, and {len(labels)} labels. There are {labels.unique().size(0)} different classes")
             logger.debug(f"Classes in this Session {session}'s Train Dataset: {labels.unique(sorted=True)}")
-            datasets[session] = TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(samples, labels, ood_labels), transform=self.transform)
+            datasets[session] = torch.utils.data.TensorDataset(samples, labels, ood_labels)
 
         return datasets
     
@@ -235,7 +209,7 @@ class CIFAR100Dataset:
         
         logger.debug(f"Creating dataset for session 0 (pretraining). There are {len(samples)} samples, and {len(labels)} labels. There are {labels.unique().size(0)} different classes")
         logger.debug(f"Classes in Session 0's Test Dataset: {labels.unique(sorted=True)}")
-        datasets[0] = TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(samples, labels, types), transform=self.transform)
+        datasets[0] = torch.utils.data.TensorDataset(samples, labels, types)
         
         del samples, labels, types # free up memory
         
@@ -263,7 +237,7 @@ class CIFAR100Dataset:
             logger.debug(f"Creating OLD dataset for session {session}. There are {len(old_samples)} samples, and {len(old_labels)} labels. There are {old_labels.unique().size(0)} different classes")
             logger.debug(f"Classes in Session {session}'s OLD Dataset: {old_labels.unique(sorted=True)}")
             
-            datasets[session] = {"old": TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(old_samples, old_labels, old_types), transform=self.transform)}
+            datasets[session] = {"old": torch.utils.data.TensorDataset(old_samples, old_labels, old_types)}
             
             
             # new dataset -------------------------------------
@@ -279,7 +253,7 @@ class CIFAR100Dataset:
             logger.debug(f"Creating NEW dataset for session {session}. There are {len(new_samples)} samples, and {len(new_labels)} labels. There are {new_labels.unique().size(0)} different classes")
             logger.debug(f"Classes in Session {session}'s NEW Dataset: {new_labels.unique(sorted=True)}")
             
-            datasets[session]["new"] = TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(new_samples, new_labels, new_types), transform=self.transform)
+            datasets[session]["new"] = torch.utils.data.TensorDataset(new_samples, new_labels, new_types)
             
             
             # all dataset -------------------------------------
@@ -290,12 +264,12 @@ class CIFAR100Dataset:
             logger.debug(f"Creating ALL dataset for session {session}. There are {len(all_samples)} samples, and {len(all_labels)} labels. There are {all_labels.unique().size(0)} different classes")
             logger.debug(f"Classes in Session {session}'s ALL Dataset: {all_labels.unique(sorted=True)}")
             
-            datasets[session]["all"] = TransformedTensorDataset(tensor_dataset=torch.utils.data.TensorDataset(all_samples, all_labels, all_types), transform=self.transform)
+            datasets[session]["all"] = torch.utils.data.TensorDataset(all_samples, all_labels, all_types)
             
         return datasets
                 
     
-    def _split_data_by_class(self, dataset: _CIFAR100, batch_size=64, num_workers=0):
+    def _split_data_by_class(self, dataset: CIFAR100Features, batch_size=128, num_workers=4):
         # Create a DataLoader to load the dataset in batches
         dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
 
@@ -315,7 +289,6 @@ class CIFAR100Dataset:
             all_data[class_id] = torch.stack(all_data[class_id])
 
         return all_data
-    
         
     def _verify_splits(self):
         # verify sessions
@@ -352,21 +325,23 @@ class CIFAR100Dataset:
             raise ValueError(
                 f'Number of samples per previously novel class for each CL session should be between 0 and (500 - cl_n_novel) / sessions. Given "cl_n_prevnovel": {self.cl_n_prevnovel}, "cl_n_novel": {self.cl_n_novel}, "sessions": {self.sessions}'
             )
-
+            
+            
 if __name__ == "__main__":
     from time import sleep
     logger.debug("Entry Point: cifar100.py")
-    if CIFAR100_DIR is None:
+    if CIFAR100Features is None:
         raise ValueError("CIFAR100_DIR is not set. Please set it in entcl/config.py")
-    cifar100 = CIFAR100Dataset()
+    
+    cifar100 = PartitionedCIFAR100FeaturesDataset()
     for session in range(cifar100.sessions + 1):
         logger.debug(f"Session {session}")
         train = cifar100.get_dataset(session, train=True)
         test = cifar100.get_dataset(session, train=False)
         logger.debug(f"Train Len: {len(train)}, Image Shape {train[0][0].shape}, Label Shape {train[0][1].shape}")
-        logger.debug(f"Test Len: {len(test)}, Image Shape {test[0][0].shape}, Label Shape {test[0][1].shape}")
-        
-
-
-    sleep(5)
+        if session == 0:
+            logger.debug(f"Test Len: {len(test)}, Image Shape {test[0][0].shape}, Label Shape {test[0][1].shape}")
+        else:
+            for key in test.keys():
+                logger.debug(f"Test {key} Len: {len(test[key])}, Image Shape {test[key][0][0].shape}, Label Shape {test[key][0][1].shape}")
     
\ No newline at end of file
diff --git a/entcl/data/cifar100/prep_cifar100_feats.py b/entcl/data/cifar100/prep_cifar100_feats.py
new file mode 100644
index 0000000..258128f
--- /dev/null
+++ b/entcl/data/cifar100/prep_cifar100_feats.py
@@ -0,0 +1,101 @@
+import argparse
+import os
+import torch
+from entcl.config import CIFAR100_DIR, CIFAR100FEATSDIR
+from torchvision.datasets import CIFAR100
+from tqdm import tqdm
+
+from torchvision import disable_beta_transforms_warning
+disable_beta_transforms_warning()
+import torchvision.transforms.v2 as transforms
+
+CIFAR100_TRANSFORM = transforms.Compose(
+    [
+        transforms.Resize(int(224/0.875), interpolation=3, antialias=True),
+        transforms.CenterCrop(224),
+        transforms.ToImageTensor(),
+        transforms.ConvertImageDtype(torch.float32),
+        transforms.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )
+        
+    ]
+)
+
+def prep_cifar100_feats(
+    device: torch.device,
+    num_workers: int,
+    batch_size: int,
+    backbone_path: str,
+    backbone_name: str
+):
+    model = torch.hub.load(backbone_path, backbone_name, pretrained=True)
+    model.to(device)
+    
+    train_dataset = CIFAR100(root=CIFAR100_DIR, train=True, transform=CIFAR100_TRANSFORM, download=True)
+    test_dataset = CIFAR100(root=CIFAR100_DIR, train=False, transform=CIFAR100_TRANSFORM, download=True)
+    
+    train_loader = torch.utils.data.DataLoader(
+        dataset=train_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=False,
+    )
+    
+    test_loader = torch.utils.data.DataLoader(
+        dataset=test_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=False,
+    )
+    
+    train_feats, train_labels = _extract_feats(model, train_loader, device)
+    test_feats, test_labels = _extract_feats(model, test_loader, device)
+    
+    os.makedirs(CIFAR100FEATSDIR, exist_ok=True)
+    
+    train_path = os.path.join(CIFAR100FEATSDIR, "cifar100feats_train")
+    test_path = os.path.join(CIFAR100FEATSDIR, "cifar100feats_test")
+    
+    torch.save((train_feats, train_labels), train_path)
+    torch.save((test_feats, test_labels), test_path)
+    
+    print(f"Saved CIFAR100 Features (train) to {train_path}\nSize on Disk is {os.path.getsize(train_path)}")
+    print("")
+    print(f"Saved CIFAR100 Features (test) to {test_path}\nSize on Disk is {os.path.getsize(test_path)}")
+    
+def _extract_feats(model, loader, device):
+    feats = []
+    labels = []
+    
+    model.eval()
+    
+    with torch.no_grad():
+        for data, target in tqdm(loader, desc="Extracting Features"):
+            data = data.to(device)
+            feats.append(model(data).cpu())
+            labels.append(target)
+    
+    feats = torch.cat(feats)
+    labels = torch.cat(labels)
+    
+    return feats, labels
+    
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch_size", type=int, default=128, help="Batch size for training")
+    parser.add_argument("--num_workers", type=int,default=4, help="Number of workers for the DataLoader")
+    parser.add_argument("--device", type=int, default=0, help="Device to Use")
+    parser.add_argument("--backbone_path", type=str, default="facebookresearch/dino:main", help="torch hub url for backbone")
+    parser.add_argument("--backbone_name", type=str, default="dino_vitb16", help="Name of the backbone model")
+    
+    args = parser.parse_args()
+    args.device = torch.device(f"cuda:{args.device}")
+    
+    prep_cifar100_feats(device=args.device, num_workers=args.num_workers, batch_size=args.batch_size, backbone_path=args.backbone_path, backbone_name=args.backbone_name)
\ No newline at end of file
diff --git a/entcl/models/model.py b/entcl/models/model.py
index bb4d0ac..3d5d9b4 100644
--- a/entcl/models/model.py
+++ b/entcl/models/model.py
@@ -24,6 +24,13 @@ class ENTCLModel(torch.nn.Module):
         feats = self.backbone(x)
         logits = self.head(feats)
         return logits, feats
+    
+    def forward_backbone(self, x):
+        return self.backbone(x)
+    
+    def forward_head(self, x):
+        return self.head(x)
+    
 
     def train(self, mode=True):
         super().train(mode)
diff --git a/entcl/pretrain.py b/entcl/pretrain.py
index 28a8701..3d44526 100644
--- a/entcl/pretrain.py
+++ b/entcl/pretrain.py
@@ -58,6 +58,7 @@ def pretrain(args, model):
     elif args.mode in ["pretrain", "both"]:
         logger.debug("No pretrained model to load, training from scratch")
         model = model.to(args.device)
+        
         for epoch in range(args.pretrain_epochs):
             
             logger.debug(f"Epoch {epoch} Started")
@@ -103,7 +104,7 @@ def _train(args, model, train_loader, optimiser, criterion):
         logger.debug(f"True Labels (Unique): {torch.unique(y, sorted=True)}")
         
         optimiser.zero_grad()
-        logits, _ = model(x)
+        logits = model.forward_head(x)
         logger.debug(f"Logits Shape: {logits.shape}")
             
         loss = criterion(logits, y)
@@ -126,7 +127,7 @@ def _validate(args, model, val_loader, criterion):
             logger.debug(f"x shape: {x.shape}, y shape: {y.shape}")
             logger.debug(f"True Labels (Unique): {torch.unique(y, sorted=True)}")
                 
-            logits, _ = model(x)
+            logits = model.forward_head(x)
             logger.debug(f"logits shape: {logits.shape}")
             
             loss = criterion(logits, y)
diff --git a/entcl/run.py b/entcl/run.py
index 269f8f8..5cc75c5 100644
--- a/entcl/run.py
+++ b/entcl/run.py
@@ -148,9 +148,9 @@ if __name__ == "__main__":
     
     # initialise dataset
     if args.dataset == 'cifar100':
-        from entcl.data.cifar100 import CIFAR100Dataset
+        from entcl.data.cifar100 import PartitionedCIFAR100FeaturesDataset
         args.novel_classes_per_session = (100 - args.known) // args.sessions
-        args.dataset = CIFAR100Dataset(known=args.known, pretrain_n_known=args.pretrain_n_known, cl_n_known=args.cl_n_known, cl_n_novel=args.cl_n_novel, cl_n_prevnovel=args.cl_n_prevnovel, sessions=5)
+        args.dataset = PartitionedCIFAR100FeaturesDataset(known=args.known, pretrain_n_known=args.pretrain_n_known, cl_n_known=args.cl_n_known, cl_n_novel=args.cl_n_novel, cl_n_prevnovel=args.cl_n_prevnovel, sessions=5)
         
         
     if args.head == 'linear':
diff --git a/entcl/utils/ncd.py b/entcl/utils/ncd.py
index 5bdb669..12ad6e6 100644
--- a/entcl/utils/ncd.py
+++ b/entcl/utils/ncd.py
@@ -1,6 +1,5 @@
 import os
-from typing import Dict, Union
-from entcl.data.util import TransformedTensorDataset
+from typing import Dict, Tuple, Union
 from loguru import logger
 import numpy as np
 import pandas as pd
@@ -14,13 +13,14 @@ from tqdm import tqdm
 
 
 def _extract_features(
-    args, dataset: TransformedTensorDataset, model: torch.nn.Module
+    args, dataset: torch.utils.data.Dataset, model: torch.nn.Module
 ) -> torch.Tensor:
     """
     Extracts features from the data in the dataset using the provided model's backbone.
     :param args: Arguments object with the attributes `device`, .
-    :param dataset: TransformedTensorDataset with the data to extract features from.
+    :param dataset: torch.utils.data.Dataset with the data to extract features from.
     """
+    raise DeprecationWarning("This function is not needed anymore, dataset should already be a feature dataset")
     logger.debug("Extracting Features")
     dataloader = torch.utils.data.DataLoader(
         dataset,
@@ -188,38 +188,37 @@ def _cluster_features(args, features: torch.Tensor) -> torch.Tensor:
 
 def find_novel_classes_for_session(
     args,
-    session_dataset: TransformedTensorDataset,
+    session_dataset: torch.utils.data.Dataset,
     model: torch.nn.Module,
-) -> Union[torch.Tensor, TransformedTensorDataset]:
+) -> Tuple[torch.utils.data.Dataset, Dict[int, int]]:
     """
     Finds the novel classes in the given session dataset using KMeans clustering and the given model
     :param args: Arguments object with the attributes `device`, `ncd_findk_method`, `novel_classes_per_session`, `seed`.
-    :param session_dataset: TransformedTensorDataset with the session data.
+    :param session_dataset: torch.utils.data.Dataset with the session data.
     :param model: torch.nn.Module with the model to use for clustering.
     :return: torch.Tensor with the pseudo-labels for the novel classes.
     """
 
-    # first we need to create a TransformedTensorDataset with only the predicted novel samples
+    # first we need to create a torch.utils.data.Dataset with only the predicted novel samples
     novel_samples_mask = (
-        session_dataset.tensor_dataset.tensors[3] == 1
+        session_dataset.tensors[3] == 1
     )  # OOD samples are marked with 2. the predicted types tensor (known/novel labels) is the third tensor in the dataset
     novel_samples_mask = novel_samples_mask.cpu()  # idk why this is not already on cpu
 
     novel_tensors = [
         tensor[novel_samples_mask].cpu()
-        for tensor in session_dataset.tensor_dataset.tensors
+        for tensor in session_dataset.tensors
     ]  # get only the predicted novel samples
 
     for i, t in enumerate(novel_tensors):
         logger.debug(f"Tensor[{i}] Shape: {t.shape}")
 
-    novel_dataset = TransformedTensorDataset(
-        tensor_dataset=torch.utils.data.TensorDataset(*novel_tensors),
-        transform=session_dataset.transform,
-    )
+    novel_dataset = torch.utils.data.TensorDataset(*novel_tensors)
 
     # extract features from the novel samples
-    novel_features = _extract_features(args, novel_dataset, model)
+    #novel_features = _extract_features(args, novel_dataset, model) # using a feature dataset so this is not needed
+    
+    novel_features = novel_dataset.tensors[0]  # the first tensor in the dataset is the data tensor
 
     # cluster the features
     pseudo_labels = _cluster_features(args, novel_features)
@@ -230,15 +229,15 @@ def find_novel_classes_for_session(
     
     # calculate the clustering accuracy (not used in the dataset, only for logging and testing)
     mapping = generate_mapping(
-        novel_dataset.tensor_dataset.tensors[1], pseudo_labels, args
+        novel_dataset.tensors[1], pseudo_labels, args
     )
 
     # next we need to align the pseudo labels tensor with the original dataset, giving known samples a pseudo label of -1
     pseudo_labels_aligned = torch.full(
-        session_dataset.tensor_dataset.tensors[2].shape,
+        session_dataset.tensors[2].shape,
         -1,
         dtype=torch.long,
-        device=session_dataset.tensor_dataset.tensors[2].device,
+        device=session_dataset.tensors[2].device,
     )
     pseudo_labels_aligned[novel_samples_mask] = (
         pseudo_labels  # whereever the sample is novel, assign the corresponding pseudo label.
@@ -247,11 +246,7 @@ def find_novel_classes_for_session(
     # create a new dataset with the pseudo labels alongside the original tensors and the same transform
     # NOTE: the tensors attribute of a TensorDataset is a tuple. we can't append to it so instead we create a new TensorDataset with the new pseudo labels tensor.
     # NOTE: The dataset at the end of NCD is in the for data, true labels, true type, predicted type, psuedo labels
-    new_dataset = TransformedTensorDataset(
-        tensor_dataset=torch.utils.data.TensorDataset(
-            *session_dataset.tensor_dataset.tensors, pseudo_labels_aligned.cpu()
-        ),
-        transform=session_dataset.transform,
-    )
-
+    new_dataset = torch.utils.data.TensorDataset(
+            *session_dataset.tensors, pseudo_labels_aligned.cpu()
+        )
     return new_dataset, mapping
diff --git a/entcl/utils/ood.py b/entcl/utils/ood.py
index aae758c..76c1e93 100644
--- a/entcl/utils/ood.py
+++ b/entcl/utils/ood.py
@@ -1,6 +1,5 @@
 import os
 from typing import Iterable, Tuple, Union
-from entcl.data.util import TransformedTensorDataset
 from entcl.utils.util import generate_unique_path
 from loguru import logger
 import numpy as np
@@ -10,7 +9,7 @@ import torch
 from tqdm import tqdm
 
 def _get_scores(
-    session_dataset: TransformedTensorDataset, model: torch.nn.Module, args
+    session_dataset: torch.utils.data.Dataset, model: torch.nn.Module, args
 ) -> Union[
     Tuple[torch.Tensor, torch.Tensor],
     Tuple[None, None],
@@ -45,7 +44,7 @@ def _get_scores(
     for x, _, _ in tqdm(session_loader, desc="Calculating Scores", leave=True, unit="batch"):
         x = x.to(args.device)
         with torch.no_grad():
-            logits, _ = model(x)
+            logits = model.forward_head(x)
             logits = logits[:, : args.dataset.known]  # TODO this is just a stopgap. the head needs to be expanded after all            
             if compute_entropy:
                 softmax = torch.nn.functional.softmax(logits, dim=1)
@@ -150,16 +149,16 @@ def _resolve_conflicts(
 
 def label_ood_for_session(
     args,
-    session_dataset: TransformedTensorDataset,
+    session_dataset: torch.utils.data.Dataset,
     model: torch.nn.Module,
-) -> Union[TransformedTensorDataset, torch.Tensor]:
+) -> torch.utils.data.Dataset:
     """
     OOD Labelling for a session dataset. This function computes entropy and/or energy scores for the dataset based on `args.ood_score` and fits a Gaussian Mixture Model to each of the scores. The GMM has 2 components, one for in-distribution samples and one for OOD samples. The function then resolves conflicts between the entropy and energy predictions by selecting the type with the highest confidence. Finally, the function returns a new dataset for the session, including the predicted types.
     :param args: Objects with the attributes `ood_score`, `ood_eps`, `seed` and `device` (Program Arguments).
     :param session_dataset: Dataset for the session.
     :param model: The model to evaluate.
     :param return_new_dataset: Whether to return the new dataset ready for the session or just the predicted types.
-    :return: A TransformedTensorDataset with the predicted types or just a torch.Tensor of the predicted types.
+    :return: A torch.utils.data.Dataset with the predicted types
     """
     logger.debug("Starting OOD Labelling for Session")
 
@@ -201,14 +200,11 @@ def label_ood_for_session(
     
     logger.debug("Returning New Dataset")
     # return the new dataset with the predicted types
-    session_dataset = TransformedTensorDataset(
-        tensor_dataset=torch.utils.data.TensorDataset(
-            session_dataset.tensor_dataset.tensors[0],  # the data
-            session_dataset.tensor_dataset.tensors[1],  # the labels
-            session_dataset.tensor_dataset.tensors[2],  # the real types
+    session_dataset = torch.utils.data.TensorDataset(
+            session_dataset.tensors[0],  # the data
+            session_dataset.tensors[1],  # the labels
+            session_dataset.tensors[2],  # the real types
             final_predtypes.cpu(),  # the predicted types (duh)
-        ),
-        transform=session_dataset.transform,
     )
 
     # compute the OOD Accuracy
@@ -218,7 +214,7 @@ def label_ood_for_session(
 
 
 def _compute_ood_accuracy(
-    session_dataset: TransformedTensorDataset, entropies, energies, args
+    session_dataset: torch.utils.data.Dataset, entropies, energies, args
 ) -> None:
     """
     Computes the Accuracy of the OOD Labelling for a session dataset.
@@ -230,9 +226,9 @@ def _compute_ood_accuracy(
     # Create the DataFrame
     df = pd.DataFrame(
         {
-            "label": session_dataset.tensor_dataset.tensors[1].cpu().numpy(),
-            "type": session_dataset.tensor_dataset.tensors[2].cpu().numpy(),
-            "predtype": session_dataset.tensor_dataset.tensors[3].cpu().numpy(),
+            "label": session_dataset.tensors[1].cpu().numpy(),
+            "type": session_dataset.tensors[2].cpu().numpy(),
+            "predtype": session_dataset.tensors[3].cpu().numpy(),
         }
     )
     
diff --git a/experiments/experiments3.ipynb b/experiments/experiments3.ipynb
index ac9d55c..287d60c 100644
--- a/experiments/experiments3.ipynb
+++ b/experiments/experiments3.ipynb
@@ -25,160 +25,158 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/v2/_deprecated.py:41: UserWarning: The transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `transforms.Compose([transforms.ToImageTensor(), transforms.ConvertImageDtype()])`.\n",
-      "  warnings.warn(\n",
-      "\u001b[32m2024-12-02 15:25:35.075\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m53\u001b[0m - \u001b[34m\u001b[1mVerifying incremental learning settings\n",
+      "\u001b[32m2024-12-03 12:06:35.001\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m59\u001b[0m - \u001b[34m\u001b[1mVerifying incremental learning settings\n",
       "Known classes: 50\n",
       "Pretraining samples per known class: 400\n",
       "Samples per known class per CL session: 20\n",
       "Samples per novel class per CL session: 400\n",
       "Samples per previously novel class per CL session: 20\n",
       "CL sessions: 5\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:35.076\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m66\u001b[0m - \u001b[34m\u001b[1mDownload: False\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:35.077\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m69\u001b[0m - \u001b[34m\u001b[1mLoading and Sorting CIFAR100 Train split\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:35.002\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m72\u001b[0m - \u001b[34m\u001b[1mDownload: False\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:35.003\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m75\u001b[0m - \u001b[34m\u001b[1mLoading and Sorting CIFAR100 Train split\u001b[0m\n",
       "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/v2/_deprecated.py:41: UserWarning: The transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `transforms.Compose([transforms.ToImageTensor(), transforms.ConvertImageDtype()])`.\n",
       "  warnings.warn(\n",
-      "\u001b[32m2024-12-02 15:25:42.365\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m74\u001b[0m - \u001b[34m\u001b[1mSplitting Train Data for Sessions\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.366\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m152\u001b[0m - \u001b[34m\u001b[1mSplitting data for 5 sessions\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.367\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m155\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 0 (pretraining)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.411\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m163\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 0 (pretraining). There are 20000 samples, and 20000 labels. There are 50 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.412\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m164\u001b[0m - \u001b[34m\u001b[1mClasses in Pretraining Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.400\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m80\u001b[0m - \u001b[34m\u001b[1mSplitting Train Data for Sessions\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.401\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m158\u001b[0m - \u001b[34m\u001b[1mSplitting data for 5 sessions\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.401\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m161\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 0 (pretraining)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.440\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m169\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 0 (pretraining). There are 20000 samples, and 20000 labels. There are 50 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.442\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mClasses in Pretraining Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.413\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m168\u001b[0m - \u001b[34m\u001b[1mSplitting data for CL sessions\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.414\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 1\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.414\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.421\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 50 (inc), ending at 60 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.436\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m203\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 1. There are 5000 samples, and 5000 labels. There are 60 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.438\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m204\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 1's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.443\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mSplitting data for CL sessions\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.443\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m176\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 1\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.443\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.450\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m186\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 50 (inc), ending at 60 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.460\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m209\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 1. There are 5000 samples, and 5000 labels. There are 60 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.462\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m210\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 1's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.439\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 2\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.440\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.446\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 60 (inc), ending at 70 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.451\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 60 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.463\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m203\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 2. There are 5200 samples, and 5200 labels. There are 70 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.465\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m204\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 2's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.463\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m176\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 2\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.463\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.469\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m186\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 60 (inc), ending at 70 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.473\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m195\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 60 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.480\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m209\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 2. There are 5200 samples, and 5200 labels. There are 70 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.482\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m210\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 2's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.465\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 3\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.466\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.471\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 70 (inc), ending at 80 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.476\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 70 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.490\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m203\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 3. There are 5400 samples, and 5400 labels. There are 80 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.492\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m204\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 3's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.483\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m176\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 3\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.484\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.490\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m186\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 70 (inc), ending at 80 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.495\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m195\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 70 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.504\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m209\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 3. There are 5400 samples, and 5400 labels. There are 80 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.507\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m210\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 3's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.493\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 4\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.494\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.498\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 80 (inc), ending at 90 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.503\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 80 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.518\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m203\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 4. There are 5600 samples, and 5600 labels. There are 90 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.520\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m204\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 4's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.508\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m176\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 4\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.508\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.513\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m186\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 80 (inc), ending at 90 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.517\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m195\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 80 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.526\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m209\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 4. There are 5600 samples, and 5600 labels. There are 90 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.528\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m210\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 4's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.521\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m170\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 5\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.521\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.525\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 90 (inc), ending at 100 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.530\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 90 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.548\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m203\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 5. There are 5800 samples, and 5800 labels. There are 100 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.549\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m204\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 5's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.528\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m176\u001b[0m - \u001b[34m\u001b[1mSplitting data for session 5\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.529\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m180\u001b[0m - \u001b[34m\u001b[1mThere are 50 known classes. Starting at 0 (inc), ending at 50 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.532\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m186\u001b[0m - \u001b[34m\u001b[1mThere are 10 novel classes. Starting at 90 (inc), ending at 100 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.537\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m195\u001b[0m - \u001b[34m\u001b[1mThere are 10 previously novel classes. Starting at 50 (inc), ending at 90 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.547\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m209\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 5. There are 5800 samples, and 5800 labels. There are 100 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:41.549\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_train_data_for_sessions\u001b[0m:\u001b[36m210\u001b[0m - \u001b[34m\u001b[1mClasses in this Session 5's Train Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "        90, 91, 92, 93, 94, 95, 96, 97, 98, 99])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:42.557\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m79\u001b[0m - \u001b[34m\u001b[1mLoading and Sorting CIFAR100 Test split\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.017\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m83\u001b[0m - \u001b[34m\u001b[1mSplitting Test Data for Sessions\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.018\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m219\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 0\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.024\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m230\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 0 (pretraining). There are 5000 samples, and 5000 labels. There are 50 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.025\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m231\u001b[0m - \u001b[34m\u001b[1mClasses in Session 0's Test Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:41.550\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mLoading and Sorting CIFAR100 Test split\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.969\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m89\u001b[0m - \u001b[34m\u001b[1mSplitting Test Data for Sessions\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.970\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m225\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 0\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.976\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m236\u001b[0m - \u001b[34m\u001b[1mCreating dataset for session 0 (pretraining). There are 5000 samples, and 5000 labels. There are 50 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.978\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m237\u001b[0m - \u001b[34m\u001b[1mClasses in Session 0's Test Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.026\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m236\u001b[0m - \u001b[34m\u001b[1mSplitting test data for 5 sessions\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.027\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m239\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 1\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.027\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m244\u001b[0m - \u001b[34m\u001b[1mOld classes end at 50 (exc), New classes start at 50 (inc) and end at 60 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.031\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m257\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 1. There are 5000 samples, and 5000 labels. There are 50 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.033\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m258\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:42.979\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m242\u001b[0m - \u001b[34m\u001b[1mSplitting test data for 5 sessions\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.980\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m245\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 1\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.980\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m250\u001b[0m - \u001b[34m\u001b[1mOld classes end at 50 (exc), New classes start at 50 (inc) and end at 60 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.986\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m263\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 1. There are 5000 samples, and 5000 labels. There are 50 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.988\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m264\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.035\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 1. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.036\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m274\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's NEW Dataset: tensor([50, 51, 52, 53, 54, 55, 56, 57, 58, 59])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.042\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 1. There are 6000 samples, and 6000 labels. There are 60 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.044\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m285\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:42.990\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m279\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 1. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.991\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m280\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's NEW Dataset: tensor([50, 51, 52, 53, 54, 55, 56, 57, 58, 59])\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.996\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m290\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 1. There are 6000 samples, and 6000 labels. There are 60 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.997\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m291\u001b[0m - \u001b[34m\u001b[1mClasses in Session 1's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.044\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m239\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 2\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.045\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m244\u001b[0m - \u001b[34m\u001b[1mOld classes end at 60 (exc), New classes start at 60 (inc) and end at 70 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.054\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m257\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 2. There are 6000 samples, and 6000 labels. There are 60 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.055\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m258\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:42.998\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m245\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 2\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:42.998\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m250\u001b[0m - \u001b[34m\u001b[1mOld classes end at 60 (exc), New classes start at 60 (inc) and end at 70 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.004\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m263\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 2. There are 6000 samples, and 6000 labels. There are 60 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.005\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m264\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.057\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 2. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.058\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m274\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's NEW Dataset: tensor([60, 61, 62, 63, 64, 65, 66, 67, 68, 69])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.063\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 2. There are 7000 samples, and 7000 labels. There are 70 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.065\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m285\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.007\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m279\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 2. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.008\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m280\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's NEW Dataset: tensor([60, 61, 62, 63, 64, 65, 66, 67, 68, 69])\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.013\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m290\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 2. There are 7000 samples, and 7000 labels. There are 70 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.014\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m291\u001b[0m - \u001b[34m\u001b[1mClasses in Session 2's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.065\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m239\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 3\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.066\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m244\u001b[0m - \u001b[34m\u001b[1mOld classes end at 70 (exc), New classes start at 70 (inc) and end at 80 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.076\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m257\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 3. There are 7000 samples, and 7000 labels. There are 70 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.078\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m258\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.015\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m245\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 3\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.015\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m250\u001b[0m - \u001b[34m\u001b[1mOld classes end at 70 (exc), New classes start at 70 (inc) and end at 80 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.026\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m263\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 3. There are 7000 samples, and 7000 labels. There are 70 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.027\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m264\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.079\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 3. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.080\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m274\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's NEW Dataset: tensor([70, 71, 72, 73, 74, 75, 76, 77, 78, 79])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.088\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 3. There are 8000 samples, and 8000 labels. There are 80 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.089\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m285\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.029\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m279\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 3. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.030\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m280\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's NEW Dataset: tensor([70, 71, 72, 73, 74, 75, 76, 77, 78, 79])\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.037\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m290\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 3. There are 8000 samples, and 8000 labels. There are 80 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.038\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m291\u001b[0m - \u001b[34m\u001b[1mClasses in Session 3's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.090\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m239\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 4\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.090\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m244\u001b[0m - \u001b[34m\u001b[1mOld classes end at 80 (exc), New classes start at 80 (inc) and end at 90 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.102\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m257\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 4. There are 8000 samples, and 8000 labels. There are 80 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.104\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m258\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.039\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m245\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 4\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.039\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m250\u001b[0m - \u001b[34m\u001b[1mOld classes end at 80 (exc), New classes start at 80 (inc) and end at 90 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.052\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m263\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 4. There are 8000 samples, and 8000 labels. There are 80 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.054\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m264\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.106\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 4. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.107\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m274\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's NEW Dataset: tensor([80, 81, 82, 83, 84, 85, 86, 87, 88, 89])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.114\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 4. There are 9000 samples, and 9000 labels. There are 90 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.116\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m285\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.055\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m279\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 4. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.056\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m280\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's NEW Dataset: tensor([80, 81, 82, 83, 84, 85, 86, 87, 88, 89])\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.064\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m290\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 4. There are 9000 samples, and 9000 labels. There are 90 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.065\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m291\u001b[0m - \u001b[34m\u001b[1mClasses in Session 4's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.117\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m239\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 5\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.117\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m244\u001b[0m - \u001b[34m\u001b[1mOld classes end at 90 (exc), New classes start at 90 (inc) and end at 100 (exc)\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.132\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m257\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 5. There are 9000 samples, and 9000 labels. There are 90 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.134\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m258\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.066\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m245\u001b[0m - \u001b[34m\u001b[1mSplitting test data for session 5\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.066\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m250\u001b[0m - \u001b[34m\u001b[1mOld classes end at 90 (exc), New classes start at 90 (inc) and end at 100 (exc)\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.080\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m263\u001b[0m - \u001b[34m\u001b[1mCreating OLD dataset for session 5. There are 9000 samples, and 9000 labels. There are 90 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.082\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m264\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's OLD Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.136\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 5. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.137\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m274\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's NEW Dataset: tensor([90, 91, 92, 93, 94, 95, 96, 97, 98, 99])\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.150\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 5. There are 10000 samples, and 10000 labels. There are 100 different classes\u001b[0m\n",
-      "\u001b[32m2024-12-02 15:25:44.151\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m285\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "\u001b[32m2024-12-03 12:06:43.084\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m279\u001b[0m - \u001b[34m\u001b[1mCreating NEW dataset for session 5. There are 1000 samples, and 1000 labels. There are 10 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.084\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m280\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's NEW Dataset: tensor([90, 91, 92, 93, 94, 95, 96, 97, 98, 99])\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.093\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m290\u001b[0m - \u001b[34m\u001b[1mCreating ALL dataset for session 5. There are 10000 samples, and 10000 labels. There are 100 different classes\u001b[0m\n",
+      "\u001b[32m2024-12-03 12:06:43.094\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mentcl.data.cifar100\u001b[0m:\u001b[36m_split_test_data_for_sessions\u001b[0m:\u001b[36m291\u001b[0m - \u001b[34m\u001b[1mClasses in Session 5's ALL Dataset: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
@@ -192,20 +190,20 @@
     "seed(8008135)\n",
     "from entcl.models.model import ENTCLModel\n",
     "from entcl.models.linear_head import LinearHead\n",
-    "from entcl.data.cifar100 import CIFAR100Dataset\n",
+    "from entcl.data.cifar100feats import CIFAR100FeatureDataset\n",
     "import torch\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
-    "device = torch.device('cpu')\n",
+    "device = torch.device('cuda:0')\n",
     "eps = 1e-8\n",
     "\n",
     "pretrained_model = ENTCLModel(LinearHead(768, 50), backbone_version=1)\n",
     "pretrained_model.head.load_state_dict(torch.load('/cl/entcl_LFS/experiments/dino_nosched_bb/session_0/head_s0_ep99.pt'))\n",
     "pretrained_model = pretrained_model.to(device)\n",
     "\n",
-    "dataset_master = CIFAR100Dataset()\n",
+    "dataset_master = CIFAR100FeatureDataset(backbone=)\n",
     "dataset = dataset_master.get_dataset(1)"
    ]
   },
@@ -218,13 +216,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "119c4a9fd83d4d23a73c1af59a9d43d4",
+       "model_id": "cba9e1a8a0eb49eb881081a14a2fd8ca",
        "version_major": 2,
        "version_minor": 0
       },
@@ -234,6 +232,20 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
@@ -295,12 +307,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -335,12 +347,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -368,13 +380,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "926a9415c404460c9a45984d3350c36e",
+       "model_id": "0299d4800a744441ad923b5d9530a21c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -385,11 +397,25 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n",
+      "/root/miniconda3/envs/entcl/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy of the model on the testset: 85.20%\n"
+      "Accuracy of the model on the testset: 86.96%\n"
      ]
     }
    ],
@@ -427,27 +453,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Input X contains infinity or a value too large for dtype('float32').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 21\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# GMM 2: energy\u001b[39;00m\n\u001b[1;32m     20\u001b[0m energy_gmm \u001b[38;5;241m=\u001b[39m GaussianMixture(n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8008135\u001b[39m, max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m, init_params\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m'\u001b[39m, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-4\u001b[39m)\n\u001b[0;32m---> 21\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menergy_cluster\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43menergy_gmm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43menergy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m soft_clusters \u001b[38;5;241m=\u001b[39m energy_gmm\u001b[38;5;241m.\u001b[39mpredict_proba(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     24\u001b[0m mean_0 \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menergy_cluster\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1471\u001b[0m     )\n\u001b[1;32m   1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/mixture/_base.py:212\u001b[0m, in \u001b[0;36mBaseMixture.fit_predict\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_predict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    186\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Estimate model parameters using X and predict the labels for X.\u001b[39;00m\n\u001b[1;32m    187\u001b[0m \n\u001b[1;32m    188\u001b[0m \u001b[38;5;124;03m    The method fits the model n_init times and sets the parameters with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;124;03m        Component labels.\u001b[39;00m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 212\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components:\n\u001b[1;32m    214\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    215\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected n_samples >= n_components \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    216\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got n_components = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    217\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mX\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    218\u001b[0m         )\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/base.py:633\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    631\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 633\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[1;32m    635\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/utils/validation.py:1064\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1058\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1059\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1060\u001b[0m         \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m   1061\u001b[0m     )\n\u001b[1;32m   1063\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m-> 1064\u001b[0m     \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m        \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[1;32m   1072\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_numpy_namespace(xp):\n\u001b[1;32m   1073\u001b[0m         \u001b[38;5;66;03m# only make a copy if `array` and `array_orig` may share memory`\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/utils/validation.py:123\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 123\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    130\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/sklearn/utils/validation.py:172\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    158\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    171\u001b[0m     )\n\u001b[0;32m--> 172\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
-      "\u001b[0;31mValueError\u001b[0m: Input X contains infinity or a value too large for dtype('float32')."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.mixture import GaussianMixture\n",
     "# GMM 1: entropy\n",
@@ -493,23 +501,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Entropy-based Known Correct/Total (Accuracy%) 574/1000 (57.4000%)\n",
-      "Entropy-based Novel Correct/Total (Accuracy%) 3704/4000 (92.6000%)\n",
+      "Entropy-based Known Correct/Total (Accuracy%) 538/1000 (53.8000%)\n",
+      "Entropy-based Novel Correct/Total (Accuracy%) 3278/4000 (81.9500%)\n",
       "\n",
-      "Energy-based Known Correct/Total (Accuracy%) 891/1000 (89.1000%)\n",
-      "Energy-based Novel Correct/Total (Accuracy%) 2639/4000 (65.9750%)\n"
+      "Energy-based Known Correct/Total (Accuracy%) 801/1000 (80.1000%)\n",
+      "Energy-based Novel Correct/Total (Accuracy%) 2369/4000 (59.2250%)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -519,7 +527,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -562,15 +570,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Known Correct/Total (Accuracy%) 686/1000 (68.6000%)\n",
-      "Novel Correct/Total (Accuracy%) 3504/4000 (87.6000%)\n"
+      "Known Correct/Total (Accuracy%) 617/1000 (61.7000%)\n",
+      "Novel Correct/Total (Accuracy%) 3093/4000 (77.3250%)\n"
      ]
     }
    ],
@@ -608,15 +616,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Known Correct/Total (Accuracy%) 610/1000 (61.0000%)\n",
-      "Novel Correct/Total (Accuracy%) 3640/4000 (91.0000%)\n"
+      "Known Correct/Total (Accuracy%) 582/1000 (58.2000%)\n",
+      "Novel Correct/Total (Accuracy%) 3154/4000 (78.8500%)\n"
      ]
     }
    ],
@@ -675,7 +683,27 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Means 0, -1, 1: 0.06324542313814163, 0.5723875164985657, 1.943918228149414\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/4139231074.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cluster\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cluster'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrename_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Means 0, -1, 1: {df[df['cluster'] == 0]['entropy'].mean()}, {df[df['cluster'] == -1]['entropy'].mean()}, {df[df['cluster'] == 1]['entropy'].mean()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mknown\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"true_type\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cluster\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mnovel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"true_type\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cluster\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Non -1 Known Correct/Total (Accuracy%) {known[known['cluster'] == 0].shape[0]}/{known.shape[0]} ({ 100 * (known[known['cluster'] == 0].shape[0]/known.shape[0]):.4f}%)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/entcl/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1575\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1576\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1577\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1578\u001b[0m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1579\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1580\u001b[0m         )\n",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+     ]
+    }
+   ],
    "source": [
     "df = master_df.copy()\n",
     "\n",
@@ -685,7 +713,7 @@
     "df[\"cluster\"] = gmm.fit_predict(df['entropy'].values.reshape(-1, 1))\n",
     "soft_clusters = gmm.predict_proba(df['entropy'].values.reshape(-1, 1))\n",
     "\n",
-    "cluster_means = df.group_by('cluster')['entropy'].mean()\n",
+    "cluster_means = df.groupby('cluster')['entropy'].mean()\n",
     "sorted_clusters = cluster_means.sort_values().index\n",
     "\n",
     "rename_mapping = {sorted_clusters[0]: 0, sorted_clusters[1]: -1, sorted_clusters[2]: 1}\n",
@@ -694,8 +722,10 @@
     "\n",
     "print(f\"Means 0, -1, 1: {df[df['cluster'] == 0]['entropy'].mean()}, {df[df['cluster'] == -1]['entropy'].mean()}, {df[df['cluster'] == 1]['entropy'].mean()}\")\n",
     "\n",
-    "known = df[df[\"true_type\"] == 0 and df[\"cluster\"] != -1]\n",
-    "novel = df[df[\"true_type\"] == 1 and df[\"cluster\"] != -1]\n",
+    "known = df[df[\"true_type\"] == 0]\n",
+    "known = known[known[\"cluster\"] != -1]\n",
+    "novel = df[df[\"true_type\"] == 1]\n",
+    "novel = novel[novel[\"cluster\"] != -1]\n",
     "\n",
     "print(f\"Non -1 Known Correct/Total (Accuracy%) {known[known['cluster'] == 0].shape[0]}/{known.shape[0]} ({ 100 * (known[known['cluster'] == 0].shape[0]/known.shape[0]):.4f}%)\")\n",
     "print(f\"Non -1 Novel Correct/Total (Accuracy%) {novel[novel['cluster'] == 1].shape[0]}/{novel.shape[0]} ({ 100 * (novel[novel['cluster'] == 1].shape[0]/novel.shape[0]):.4f}%)\")\n",
@@ -725,7 +755,7 @@
     "# for the feature distance sorting of cluster -1, we need an exemplar set. We will randomly sample 32 images per class from the session 0 training dataset\n",
     "session_0_trainset = dataset_master.get_dataset(session=0)\n",
     "\n",
-    "labels_np = session_0_trainset.tensor_dataset.tensors[1].cpu().numpy()\n",
+    "labels_np = session_0_trainset.tensors[1].cpu().numpy()\n",
     "\n",
     "samples_per_label = 32\n",
     "\n",
@@ -737,7 +767,7 @@
     "    label_indices = np.where(labels_np == label)[0]\n",
     "    sample_indices.extend(np.random.choice(label_indices, samples_per_label, replace=False))\n",
     "\n",
-    "subset = torch.utils.data.Subset(session_0_trainset.tensor_dataset, sample_indices)\n",
+    "subset = torch.utils.data.Subset(session_0_trainset, sample_indices)\n",
     "subset_loader = torch.utils.data.DataLoader(subset, batch_size=512, shuffle=False, num_workers=4, pin_memory=True)\n",
     "\n",
     "# get the features, logits, entropies and energies of the exemplar set\n",
-- 
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