Delete task_template.py

task_template.py

@@ -1,134 +0,0 @@
-import csv
-import random
-import zipfile
-import requests
-from pathlib import Path
-from collections import Counter
-
-import torch
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms, models, datasets
-from PIL import Image
-
-
-# ----------------------------
-# CONFIG
-# ----------------------------
-ZIP_FILE = "Dataset.zip"     # Path to dataset zip
-DATASET_DIR = Path("dataset")  # Unzipped folder
-SUBMISSION_FILE = "submission.csv"
-LABELS = ["RAR", "Taming", "VAR", "SD", "outlier"] # Donot change this
-
-# Leaderboard submission
-SERVER_URL = "http://34.122.51.94:9090"
-TOKEN = None  # teams insert their assigned token here
-
-
-# ----------------------------
-# UNZIP DATASET
-# ----------------------------
-if not DATASET_DIR.exists():
-    print("Unzipping dataset...")
-    with zipfile.ZipFile(ZIP_FILE, "r") as zip_ref:
-        zip_ref.extractall(".")
-else:
-    print("Dataset already extracted.")
-
-
-# ----------------------------
-# TRANSFORMS
-# ----------------------------
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-])
-
-
-# ----------------------------
-# DATASETS & DATALOADERS
-# ----------------------------
-print("Loading datasets...")
-
-train_dataset = datasets.ImageFolder(root=DATASET_DIR / "train", transform=transform)
-val_dataset   = datasets.ImageFolder(root=DATASET_DIR / "val", transform=transform)
-
-# Custom dataset for unlabeled test images
-class TestDataset(Dataset):
-    def __init__(self, root, transform=None):
-        self.root = Path(root)
-        self.files = sorted(list(self.root.glob("*.*")))  # all files
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.files)
-
-    def __getitem__(self, idx):
-        img_path = self.files[idx]
-        image = Image.open(img_path).convert("RGB")
-        if self.transform:
-            image = self.transform(image)
-        return {"image": image, "image_name": img_path.name}
-
-test_dataset = TestDataset(DATASET_DIR / "test", transform=transform)
-
-train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-val_loader   = DataLoader(val_dataset, batch_size=32, shuffle=False)
-test_loader  = DataLoader(test_dataset, batch_size=32, shuffle=False)
-
-# Print classes and per-class counts for train/val
-def _print_class_stats(name: str, ds):
-    counts = Counter(getattr(ds, "targets", []))
-    print(f"{name} classes: {ds.classes}")
-    for cls, idx in ds.class_to_idx.items():
-        print(f"  {cls}: {counts.get(idx, 0)}")
-
-_print_class_stats("Train", train_dataset)
-_print_class_stats("Val", val_dataset)
-
-print(f"Train size: {len(train_dataset)} | Val size: {len(val_dataset)} | Test size: {len(test_dataset)}")
-
-
-# ----------------------------
-# EXAMPLE MODEL (ResNet18)
-# ----------------------------
-print("Building dummy model...")
-model = models.resnet18(weights=None, num_classes=len(LABELS))  # untrained
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
-
-
-# ----------------------------
-# DUMMY INFERENCE ON TEST / DUMMY SUBMISSION
-# ----------------------------
-print("Generating random predictions for submission...")
-preds = []
-for batch in test_loader:
-    for fname in batch["image_name"]:
-        label = random.choice(LABELS)  # random baseline
-        preds.append([fname, label])
-
-# ----------------------------
-# SAVE SUBMISSION
-# ----------------------------
-with open(SUBMISSION_FILE, "w", newline="", encoding="utf-8") as f:
-    writer = csv.writer(f)
-    writer.writerow(["image_name", "label"])
-    writer.writerows(preds)
-
-print(f"Saved submission file to {SUBMISSION_FILE}")
-print("   Format: image_name,label | Allowed labels: RAR, Taming, VAR, SD, outlier")
-
-
-# ----------------------------
-# SUBMIT TO LEADERBOARD SERVER
-# ----------------------------
-if TOKEN is None:
-    print("No TOKEN provided. Please set your team TOKEN in this script to submit.")
-else:
-    print("Submitting to leaderboard server...")
-    response = requests.post(
-        SERVER_URL,
-        files={"file": open(SUBMISSION_FILE, "rb")},
-        headers={"token": TOKEN}
-    )
-    print("Server response:", response.json())