Ya1

.gitignore

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+/scratch.ipynb
+/__pycache__
+/data

caltech_data.py

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+import torch
+from torchvision.transforms import v2
+from torch.utils.data import random_split,DataLoader
+import os
+import urllib.request
+import tarfile
+from typing import Optional, Callable, Any
+from tqdm import tqdm
+from torchvision.datasets import ImageFolder
+from torchvision.datasets.folder import default_loader
+
+
+data_dir = os.path.join(os.getcwd(),"data")
+
+PATH_TO_CALTECH256 = os.path.join(data_dir,"caltech256")
+
+# PATH_TO_CALTECH256 = "/mnt/769EC2439EC1FB9D/vsc_projs/caltech256"
+
+
+
+class CustomCaltech256(ImageFolder):
+    def __init__(
+        self,
+        root: str,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        loader: Callable[[str], Any] = default_loader,
+        download: bool = False,
+        custom_url: Optional[str] = None,
+        filename: str = "256_ObjectCategories.tar",
+    ):
+        self.root = os.path.expanduser(root)
+        self.custom_url = custom_url or (
+            "https://data.caltech.edu/records/nyy15-4j048/files/256_ObjectCategories.tar?download=1"
+        )
+        self.filename = filename
+        self.filepath = os.path.join(self.root, self.filename)
+        self.data_folder = os.path.join(self.root, "256_ObjectCategories")
+
+        if download:
+            self._download()
+
+        super().__init__(
+            root=self.data_folder,
+            transform=transform,
+            target_transform=target_transform,
+            loader=loader
+        )
+
+    def _download(self):
+        if os.path.isdir(self.data_folder):
+            print("✅ Caltech-256 already extracted.")
+            return
+
+        os.makedirs(self.root, exist_ok=True)
+
+        if not os.path.isfile(self.filepath):
+            print("⬇️ Downloading Caltech-256...")
+
+            def progress_hook(t):
+                last_b = [0]
+                def update_to(block_num=1, block_size=1, total_size=None):
+                    if total_size is not None:
+                        t.total = total_size
+                    downloaded = block_num * block_size
+                    t.update(downloaded - last_b[0])
+                    last_b[0] = downloaded
+                return update_to
+
+            with tqdm(unit='B', unit_scale=True, unit_divisor=1024, miniters=1, desc=self.filename) as t:
+                urllib.request.urlretrieve(self.custom_url, self.filepath, reporthook=progress_hook(t))
+
+            print("✅ Download complete.")
+
+        print("📦 Extracting Caltech-256...")
+        with tarfile.open(self.filepath, "r") as tar:
+            tar.extractall(path=self.root)
+        print("✅ Extraction complete.")
+
+
+transforms = v2.Compose([
+    v2.PILToTensor(),
+    v2.ToDtype(torch.float32,scale=True),
+    v2.Resize(256),
+    v2.CenterCrop(224),
+    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),    #imagenet norm values...
+
+    # augments if to be added
+    v2.AutoAugment()
+
+])
+
+caltech256 = CustomCaltech256(
+    root=PATH_TO_CALTECH256,
+    transform=transforms,
+    download=True,
+)
+train_data,val_data = random_split(caltech256,[27607,3000])     # ~90/10
+
+def get_caltech_train_loader(batch_size,shuffle=True,num_workers=4):
+    return DataLoader(train_data,batch_size=batch_size,shuffle=shuffle,num_workers=num_workers,pin_memory=True)
+
+def get_caltech_val_loader(batch_size,shuffle=True,num_workers=4):
+    return DataLoader(val_data,batch_size=batch_size,shuffle=shuffle,num_workers=num_workers,pin_memory=True)
+
+

cifar_data.py

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+
+import torch
+from torchvision import datasets
+from torchvision.transforms import v2
+from torch.utils.data import random_split,DataLoader
+import os
+
+
+data_dir = os.path.join(os.getcwd(),"data")
+
+if os.path.isdir(os.path.join(data_dir,"cifar100")):
+    PATH_TO_CIFAR100 = os.path.join(data_dir,"cifar100")
+else:
+    os.makedirs(os.path.join(data_dir,"cifar100"))
+    PATH_TO_CIFAR100 = os.path.join(data_dir,"cifar100")
+
+# PATH_TO_CIFAR100 = "/mnt/769EC2439EC1FB9D/vsc_projs/cifar100"
+
+transforms = v2.Compose([
+    v2.PILToTensor(),
+    v2.ToDtype(torch.float32,scale=True),
+    v2.Resize(224),
+    v2.Normalize(mean=(0.5071, 0.4867, 0.4408),std=(0.2675, 0.2565, 0.2761)),    #cifar100 norm values...
+
+    # augments if to be added
+    v2.AutoAugment()
+
+])
+
+
+cifar100 = datasets.CIFAR100(root=PATH_TO_CIFAR100,download=True,transform=transforms)
+train_data, val_data = random_split(dataset=cifar100, lengths=[45000,5000])     #90/10
+
+
+def get_cifar_train_loader(batch_size,shuffle=True,num_workers=4):
+    return DataLoader(dataset=train_data,batch_size=batch_size,shuffle=shuffle,num_workers=num_workers,pin_memory=True)
+
+def get_cifar_val_loader(batch_size,shuffle=True,num_workers=4):
+    return DataLoader(dataset=val_data,batch_size=batch_size,shuffle=shuffle,num_workers=num_workers,pin_memory=True)
+
+
+
+