Delete task_template.py

task_template.py

@@ -1,186 +0,0 @@
-import os
-import sys
-import requests
-from pathlib import Path
-import torch
-import torch.nn as nn
-from torchvision import datasets, transforms
-from torchvision.models import resnet18
-from safetensors.torch import load_file
-import pandas as pd
-
-# --------------------------------
-# LOADING A MODEL (EXAMPLE: TARGET MODEL)
-# --------------------------------
-
-def make_model():
-    model = resnet18(weights=None)  # Initialize with random weights
-    model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) # Adjust for 32x32 input
-    model.maxpool = nn.Identity()  # Remove maxpool for 32x32 input
-    model.fc = nn.Linear(model.fc.in_features, 100)  # Adjust for CIFAR-100 
-    return model
-
-checkpoint_path = "path/to/your/model_checkpoint.safetensors"  # Replace with your model checkpoint path 
-state_dict = load_file(checkpoint_path, device="cpu")  # Load the checkpoint using safetensors
-
-model = make_model()  # Create model instance
-model.load_state_dict(state_dict, strict=True)  # Load weights into model
-model.eval()
-
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5071, 0.4867, 0.4408),
-                         (0.2675, 0.2565, 0.2761)),
-])
-
-data_root = "path/to/cifar100"  # Replace with your CIFAR-100 dataset path, or where it should be downloaded
-dataset = datasets.CIFAR100(root=data_root, train=False, download=True, transform=transform)
-x, y = dataset[0]  # Example: get the first image and label
-
-with torch.no_grad():
-    logits = model(x.unsqueeze(0))  # Add batch dimension
-
-print("True label:", y)
-print("Logits shape:", logits.shape)  # Should be [1, 100] for CIFAR-100
-print("Logits:", logits)
-
-# # --------------------------------
-# # SUBMISSION FORMAT
-# # --------------------------------
-
-"""
-The submission must be a .csv file with the following format:
-
--"id": ID of the subset (from 0 to 239)
--"score": Stealing confidence score for each image (float)
-"""
-
-# Example Submission:
-
-subset_ids = list(range(240))  
-confidence_scores = torch.rand(len(subset_ids)).tolist()
-submission_df = pd.DataFrame({
-    "id": subset_ids,
-    "score": confidence_scores
-})
-submission_df.to_csv("example_submission.csv", index=None)
-
-# --------------------------------
-# SUBMISSION PROCESS
-# --------------------------------
-
-"""
-Example submission script for the Stolen Model Detection Task.
-
-Submission Requirements (read carefully to avoid automatic rejection):
-
-1. CSV FORMAT
-----------------
-- The file **must be a CSV** with extension `.csv`.
-- It must contain **exactly two columns**, named:
-      id, score
-  → Column names must match exactly (lowercase, no extra spaces).
-  → Column order does not matter, but both must be present.
-
-2. ROW COUNT AND IDENTIFIERS
--------------------------------
-- Your file must contain **exactly 240 rows**.
-- Each row corresponds to one unique `id` in the range **0–239** (inclusive).
-- Every id must appear **exactly once**.
-- Do **not** add, remove, or rename any IDs.
-- Do **not** include duplicates or missing entries.
-- The evaluator checks:
-      id.min() == 0
-      id.max() == 239
-      id.unique().size == 240
-
-3. STEALING CONFIDENCE SCORES
-----------------------
-- The `score` column must contain **numeric values** representing your model’s predicted confidence
-  that the corresponding subset is a **stolen** model.
-
-  Examples of valid score values:
-    - Probabilities: values in [0.0, 1.0]
-    - Raw model scores: any finite numeric values (will be ranked for TPR@FPR=0.05)
-
-- Do **not** submit string labels like "yes"/"no" or "stolen"/"not stolen".
-- The evaluator converts your `score` column to numeric using `pd.to_numeric()`.
-  → Any non-numeric, NaN, or infinite entries will cause automatic rejection.
-
-4. TECHNICAL LIMITS
-----------------------
-- Maximum file size: **20 MB**
-- Encoding: UTF-8 recommended.
-- Avoid extra columns, blank lines, or formulas.
-- Ensure all values are numeric and finite.
-- Supported data types: int, float (e.g., float32, float64)
-
-5. VALIDATION SUMMARY
-------------------------
-Your submission will fail if:
-- Columns don’t match exactly ("id", "score")
-- Row count differs from 240
-- Any id is missing, duplicated, or outside [0, 239]
-- Any score value is NaN, Inf, or non-numeric
-- File is too large or not a valid CSV
-
-One key metric is computed:
-  1. **TPR@FPR=0.05 (True Positive Rate at False Positive Rate = 0.05)** 
-  — measures the ability to correctly identify stolen models while keeping the false positive rate at 5%.
-"""
-
-BASE_URL  = "http://35.192.205.84:80"
-API_KEY   = "YOUR_API_KEY_HERE"  # replace with your actual API key
-
-TASK_ID   = "19-stolen-model-detection"
-FILE_PATH = "PATH/TO/YOUR/SUBMISSION.csv"  # replace with your actual file path
-
-SUBMIT     = False  # Set to True to enable submission
-
-def die(msg):
-    print(f"{msg}", file=sys.stderr)
-    sys.exit(1)
-
-if SUBMIT:
-    if not os.path.isfile(FILE_PATH):
-        die(f"File not found: {FILE_PATH}")
-
-    try:
-        with open(FILE_PATH, "rb") as f:
-            files = {
-                # (fieldname) -> (filename, fileobj, content_type)
-                "file": (os.path.basename(FILE_PATH), f, "csv"),
-            }
-            resp = requests.post(
-                f"{BASE_URL}/submit/{TASK_ID}",
-                headers={"X-API-Key": API_KEY},
-                files=files,
-                timeout=(10, 120),  # (connect timeout, read timeout)
-            )
-        # Helpful output even on non-2xx
-        try:
-            body = resp.json()
-        except Exception:
-            body = {"raw_text": resp.text}
-
-        if resp.status_code == 413:
-            die("Upload rejected: file too large (HTTP 413). Reduce size and try again.")
-
-        resp.raise_for_status()
-
-        submission_id = body.get("submission_id")
-        print("Successfully submitted.")
-        print("Server response:", body)
-        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)
-