Upload task_template.py

task_template.py

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+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)
+    model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+    model.maxpool = nn.Identity()
+    model.fc = nn.Linear(model.fc.in_features, 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")
+
+model = make_model() 
+model.load_state_dict(state_dict, strict=True)
+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))
+
+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 359)
+-"score": Stealing confidence score for each image (float)
+"""
+
+# Example Submission:
+
+subset_ids = list(range(360))  
+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 360 rows**.
+- Each row corresponds to one unique `id` in the range **0–359** (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() == 359
+      id.unique().size == 360
+
+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 360
+- Any id is missing, duplicated, or outside [0, 359]
+- 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)
+