Update task_template.py

task_template.py

@@ -1,135 +1,135 @@
-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 = ["var16", "var20", "var24", "var30", "rarb", "rarl", "rarxl", "rarxxl", "outlier"] # Donot change this
-
-# Leaderboard submission
-SERVER_URL = "http://34.122.51.94:80"
-API_KEY = None  # teams insert their assigned token here
-TASK_ID = "15-model-tracer"
-
-# ----------------------------
-# UNZIP DATASET
-# ----------------------------
-if not DATASET_DIR.exists():
-    print("Unzipping dataset...")
-    with zipfile.ZipFile(ZIP_FILE, "r") as zip_ref:
-        zip_ref.extractall(DATASET_DIR)
-else:
-    print("Dataset already extracted.")
-
-
-# ----------------------------
-# TRANSFORMS
-# ----------------------------
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-])
-
-
-# ----------------------------s
-# 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, num_workers=4)
-val_loader   = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
-test_loader  = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
-
-# 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")
-
-
-# ----------------------------
-# SUBMIT TO LEADERBOARD SERVER
-# ----------------------------
-if API_KEY 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(
-        f"{SERVER_URL}/submit/{TASK_ID}",
-        files={"file": open(SUBMISSION_FILE, "rb")},
-        headers={"X-API-Key": API_KEY},
-    )
-    print("Server response:", response.json())
+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 = ["var16", "var20", "var24", "var30", "rarb", "rarl", "rarxl", "rarxxl", "outlier"] # Donot change this
+
+# Leaderboard submission
+SERVER_URL = "http://35.192.205.84:80"
+API_KEY = None  # teams insert their assigned token here
+TASK_ID = "15-model-tracer"
+
+# ----------------------------
+# UNZIP DATASET
+# ----------------------------
+if not DATASET_DIR.exists():
+    print("Unzipping dataset...")
+    with zipfile.ZipFile(ZIP_FILE, "r") as zip_ref:
+        zip_ref.extractall(DATASET_DIR)
+else:
+    print("Dataset already extracted.")
+
+
+# ----------------------------
+# TRANSFORMS
+# ----------------------------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+
+# ----------------------------s
+# 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, num_workers=4)
+val_loader   = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
+test_loader  = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
+
+# 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")
+
+
+# ----------------------------
+# SUBMIT TO LEADERBOARD SERVER
+# ----------------------------
+if API_KEY 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(
+        f"{SERVER_URL}/submit/{TASK_ID}",
+        files={"file": open(SUBMISSION_FILE, "rb")},
+        headers={"X-API-Key": API_KEY},
+    )
+    print("Server response:", response.json())