Upload 4 files

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea740792eb2eef9c3372cb0621918c59dc59db2858cdb107d643c5f3eb41422d
+size 44772858

priv.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0bcff62dfaaf44cc9dc30d09f5c3bcdd931e1788cfafdd7e4ee768fb28f6dae7
+size 134968350

pub.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5dfa9e4f69301797806e60ae27b5301ec991749c561a97fb88e185c54b903458
+size 134712490

task_template.py

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+import os
+import sys
+import torch
+import pandas as pd
+import requests
+import random
+import argparse
+
+from pathlib import Path
+from torch.utils.data import Dataset
+from torchvision.models import resnet18
+
+
+# config
+PUB_PATH = "pub.pt"
+PRIV_PATH = "priv.pt"
+MODEL_PATH = "model.pt"
+OUTPUT_CSV = Path("submission.csv")
+
+BASE_URL = "http://35.192.205.84:80"
+API_KEY = "YOUR_API_KEY_HERE"
+TASK_ID = "your-mia-task-id"
+
+
+
+# dataset classes
+class TaskDataset(Dataset):
+    def __init__(self, transform=None):
+        self.ids = []
+        self.imgs = []
+        self.labels = []
+        self.transform = transform
+
+    def __getitem__(self, index):
+        id_ = self.ids[index]
+        img = self.imgs[index]
+        if self.transform is not None:
+            img = self.transform(img)
+        label = self.labels[index]
+        return id_, img, label
+
+    def __len__(self):
+        return len(self.ids)
+
+
+class MembershipDataset(TaskDataset):
+    def __init__(self, transform=None):
+        super().__init__(transform)
+        self.membership = []
+
+    def __getitem__(self, index):
+        id_, img, label = super().__getitem__(index)
+        return id_, img, label, self.membership[index]
+
+
+# load datasets
+print("Loading datasets...")
+pub_ds = torch.load(PUB_PATH, weights_only=False)
+priv_ds = torch.load(PRIV_PATH, weights_only=False)
+
+
+# load model
+print("Loading model...")
+model = resnet18(weights=None)
+model.conv1 = torch.nn.Conv2d(3, 64, 3, 1, 1, bias=False)
+model.maxpool = torch.nn.Identity()
+model.fc = torch.nn.Linear(512, 9)
+
+model.load_state_dict(torch.load(MODEL_PATH, map_location="cpu"))
+model.eval()
+
+
+# create random submission
+print("Creating random submission...")
+ids = [str(i) for i in priv_ds.ids]
+
+df = pd.DataFrame({
+    "id": ids,
+    "score": [random.random() for _ in ids]
+})
+
+df.to_csv(OUTPUT_CSV, index=False)
+print("Saved:", OUTPUT_CSV)
+
+
+# submit
+def die(msg):
+    print(msg, file=sys.stderr)
+    sys.exit(1)
+
+parser = argparse.ArgumentParser(description="Submit a JSONL file to the server.")
+args = parser.parse_args()
+
+submit_path = OUTPUT_CSV
+
+if not submit_path.exists():
+    die(f"File not found: {submit_path}")
+
+try:
+    with open(submit_path, "rb") as f:
+        resp = requests.post(
+            f"{BASE_URL}/submit/{TASK_ID}",
+            headers={"X-API-Key": API_KEY},
+            files={"file": (submit_path.name, f, "application/jsonl")},
+            timeout=(10, 600),
+        )
+    try:
+        body = resp.json()
+    except Exception:
+        body = {"raw_text": resp.text}
+
+    if resp.status_code == 413:
+        die("Upload rejected: file too large (HTTP 413).")
+
+    resp.raise_for_status()
+
+    print("Successfully submitted.")
+    print("Server response:", body)
+    submission_id = body.get("submission_id")
+    if submission_id:
+        print(f"Submission ID: {submission_id}")
+
+except requests.exceptions.RequestException as e:
+    detail = getattr(e, "response", None)
+    print(f"Submission error: {e}")
+    if detail is not None:
+        try:
+            print("Server response:", detail.json())
+        except Exception:
+            print("Server response (text):", detail.text)
+    sys.exit(1)