feat: updates to datasets/-loaders

CNN-model/dataset_loaders/Dataset.py

@@ -0,0 +1,26 @@
+from abc import abstractmethod, ABC
+from os.path import join, curdir
+from typing import Callable
+
+from torch.utils.data import Dataset as TorchDataset
+
+"""
+Author: Tibo De Peuter
+"""
+class Dataset(TorchDataset, ABC):
+    """Abstract base class for datasets."""
+    @abstractmethod
+    def __init__(self, root: str, transform: Callable = None):
+        """
+        :param root: Relative path to the dataset root directory
+        """
+        self._root: str = join(curdir, 'data', root)
+        self.transform = transform
+        self.dataset = None
+
+    @property
+    def root(self):
+        return self._root
+
+    def __len__(self):
+        return len(self.dataset)

CNN-model/dataset_loaders/EnWik9.py

@@ -0,0 +1,25 @@
+from datasets import load_dataset
+from os.path import curdir, join
+from .Dataset import Dataset
+from torch.utils.data import TensorDataset
+from typing import Callable
+
+
+class EnWik9DataSet(Dataset):
+    def __init__(self, root: str = "data", transform: Callable = None):
+        super().__init__(root, transform)
+
+        path = join(curdir, root)
+        self._root = path
+
+        data = load_dataset("haukur/enwik9", cache_dir=path, split="train")
+        text = data["text"]
+        self.dataset = TensorDataset(text)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        if self.transform is not None:
+            return self.transform(self.dataset[idx])
+        return self.dataset[idx]

CNN-model/dataset_loaders/LoremIpsumDataset.py

@@ -0,0 +1,35 @@
+from typing import Callable
+
+import torch
+from os.path import curdir, join
+from lorem.text import TextLorem
+from .Dataset import Dataset
+
+
+class LoremIpsumDataset(Dataset):
+    def __init__(self, root: str = "data", transform: Callable = None):
+        super().__init__(root, transform)
+
+        # Generate text and convert to bytes
+        _lorem = TextLorem()
+        _text = ' '.join(_lorem._word() for _ in range(512))
+
+        path = join(curdir, "data")
+        self._root = path
+        # Convert text to bytes (UTF-8 encoded)
+        self.dataset = torch.tensor([ord(c) for c in list(_text)], dtype=torch.long)
+
+        sequence_count = self.dataset.shape[0] // 128 # how many vectors of 128 elements can we make
+        self.dataset = self.dataset[:sequence_count * 128]
+        self.dataset = self.dataset.view(-1, 128)
+
+        print(self.dataset.shape)
+
+    def __len__(self):
+        # Number of possible sequences of length sequence_length
+        return self.dataset.size(0)
+
+    def __getitem__(self, idx):
+        if self.transform is not None:
+            return self.transform(self.dataset[idx])
+        return self.dataset[idx]

CNN-model/dataset_loaders/__init__.py

@@ -0,0 +1,3 @@
+from .EnWik9 import EnWik9DataSet
+from .LoremIpsumDataset import LoremIpsumDataset
+from .Dataset import Dataset

CNN-model/datasets/EnWik9.py

@@ -1,11 +0,0 @@
-from datasets import load_dataset
-from os.path import curdir, join
-
-class EnWik9DataSet:
-    def __init__(self):
-        path = join(curdir, "data")
-        self.data = load_dataset("haukur/enwik9", cache_dir=path, split="train")
-
-
-    def __len__(self):
-        return len(self.data)

CNN-model/datasets/LoremIpsumDataset.py

@@ -1,5 +0,0 @@
-import lorem
-
-class LoremIpsumDataset:
-    def __init__(self):
-        self.data = lorem.text(paragraphs=100)

CNN-model/datasets/__init__.py

@@ -1,2 +0,0 @@
-from EnWik9 import EnWik9DataSet
-from LoremIpsumDataset import LoremIpsumDataset

CNN-model/main_cnn.py

@@ -2,10 +2,10 @@ from argparse import ArgumentParser
 from math import ceil
 
 import torch
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
 
-from datasets import EnWik9DataSet, LoremIpsumDataset
-from trainers import OptunaTrainer, Trainer
+from dataset_loaders import EnWik9DataSet, LoremIpsumDataset, Dataset
+from trainers import OptunaTrainer, Trainer, FullTrainer
 
 BATCH_SIZE = 64
 DEVICE = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
@@ -21,9 +21,9 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     if args.method == "train":
-        dataset = EnWik9DataSet()
+        dataset: Dataset = EnWik9DataSet(transform=lambda x: x.to(DEVICE))
     elif args.method == "optuna":
-        dataset = LoremIpsumDataset()
+        dataset: Dataset = LoremIpsumDataset(transform=lambda x: x.to(DEVICE))
     else:
         raise ValueError(f"Unknown method: {args.method}")
 
@@ -31,9 +31,8 @@ if __name__ == "__main__":
     training_size = ceil(0.8 * dataset_length)
 
     print(f"training set size = {training_size}, validation set size {dataset_length - training_size}")
-    data = dataset.data["text"]
 
-    train_set, validate_set = torch.utils.data.random_split(TensorDataset(data),
+    train_set, validate_set = torch.utils.data.random_split(dataset,
                                                             [training_size, dataset_length - training_size])
     training_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
     validation_loader = DataLoader(validate_set, batch_size=BATCH_SIZE, shuffle=False)
@@ -43,7 +42,7 @@ if __name__ == "__main__":
     if args.model_path is not None:
         model = torch.load(args.model_path)
 
-    trainer: Trainer = OptunaTrainer() if args.method == "optuna" else None
+    trainer: Trainer = OptunaTrainer() if args.method == "optuna" else FullTrainer()
 
     trainer.execute(
         model=model,

CNN-model/trainers/FullTrainer.py

@@ -4,9 +4,9 @@ import torch
 from torch import nn as nn
 from torch.utils.data import DataLoader
 
-from trainer import Trainer
-from train import train
-from ..utils import print_losses
+from .trainer import Trainer
+from .train import train
+from utils import print_losses
 
 class FullTrainer(Trainer):
     def execute(

CNN-model/trainers/OptunaTrainer.py

@@ -6,9 +6,9 @@ import torch
 from torch import nn as nn
 from torch.utils.data import DataLoader
 
-from trainer import Trainer
-from ..model.cnn import CNNPredictor
-from train import train
+from .trainer import Trainer
+from model.cnn import CNNPredictor
+from .train import train
 
 
 def create_model(trial: tr.Trial, vocab_size: int = 256):

CNN-model/trainers/__init__.py

@@ -1,2 +1,3 @@
-from OptunaTrainer import OptunaTrainer
-from trainer import Trainer
+from .OptunaTrainer import OptunaTrainer
+from .FullTrainer import FullTrainer
+from .trainer import Trainer