| 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 |
| import torchvision.transforms as transforms |
|
|
|
|
| |
| BASE = Path(__file__).parent |
| PUB_PATH = BASE / "pub.pt" |
| PRIV_PATH = BASE / "priv.pt" |
| MODEL_PATH = BASE / "model.pt" |
| OUTPUT_CSV = BASE / "submission.csv" |
|
|
| BASE_URL = "http://34.63.153.158" |
| API_KEY = "YOUR_API_KEY_HERE" |
| TASK_ID = "01-mia" |
|
|
|
|
|
|
| |
| 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] |
|
|
|
|
| |
| print("Loading datasets...") |
| pub_ds = torch.load(PUB_PATH, weights_only=False) |
| priv_ds = torch.load(PRIV_PATH, weights_only=False) |
|
|
|
|
| |
| MEAN = [0.7406, 0.5331, 0.7059] |
| STD = [0.1491, 0.1864, 0.1301] |
|
|
| transform = transforms.Compose([ |
| transforms.Resize(32), |
| transforms.Normalize(mean=MEAN, std=STD), |
| ]) |
|
|
| pub_ds.transform = transform |
| priv_ds.transform = transform |
|
|
|
|
| |
| 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() |
|
|
|
|
| |
| 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) |
|
|
|
|
| |
| def die(msg): |
| print(msg, file=sys.stderr) |
| sys.exit(1) |
|
|
| parser = argparse.ArgumentParser(description="Submit a CSV 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/csv")}, |
| 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) |
