File size: 4,409 Bytes
de6a540 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | 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://34.122.51.94:80"
API_KEY = None # teams insert their assigned token here
TASK_ID = "05-iar-attribution"
# ----------------------------
# 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 | Allowed labels: RAR, Taming, VAR, SD, outlier")
# ----------------------------
# 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())
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