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submission.py

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+import os
+import sys
+import requests
+
+"""
+Submission script for the Robustness task.
+
+Submission Requirements (read carefully to avoid automatic rejection):
+
+1. FILE FORMAT
+----------------
+- The file must be a PyTorch state dict saved as a .pt file.
+- Save only the state dict, not the full model instance:
+      torch.save(model.state_dict(), "model.pt")  # correct
+      torch.save(model, "model.pt")               # wrong
+
+2. MODEL ARCHITECTURE
+----------------------
+- You must specify the model architecture using the model-name field.
+- Allowed values: resnet18, resnet34, resnet50
+- The architecture must match the state dict you are submitting.
+
+3. MODEL REQUIREMENTS
+----------------------
+- Input shape must be (1, 3, 32, 32)
+- Output shape must be (1, 9)
+- The final fc layer must be replaced to output 9 classes
+
+4. EVALUATION
+--------------
+- Your model must achieve clean accuracy greater than 50% to be accepted.
+- Submissions below this threshold will be automatically rejected.
+- Score = 0.5 * clean accuracy + 0.5 * robustness accuracy
+
+5. TECHNICAL LIMITS
+--------------------
+- Only one submission per group every 60 minutes.
+- If your submission fails due to an error on your side, the cooldown is 2 minutes.
+
+Your submission will fail if:
+- The file is not a valid .pt state dict
+- The model-name does not match the submitted state dict
+- The output shape is not (1, 9)
+- The input shape is not (1, 3, 32, 32)
+- Clean accuracy is below 50%
+"""
+
+BASE_URL = "http://34.63.153.158"
+API_KEY = "YOUR_API_KEY_HERE"  # replace with your actual API key
+
+MODEL_PATH = "PATH/TO/YOUR/MODEL.pt"  # replace with your actual model path
+MODEL_NAME = "resnet18"  # replace with your actual model architecture - resnet18, resnet34, or resnet50
+
+SUBMIT = True  # 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(MODEL_PATH):
+        die(f"File not found: {MODEL_PATH}")
+
+    try:
+        with open(MODEL_PATH, "rb") as f:
+            files = {"file": (os.path.basename(MODEL_PATH), f, "application/x-pytorch")}
+            resp = requests.post(
+                f"{BASE_URL}/robustness",
+                headers={"X-API-Key": API_KEY},
+                files=files,
+                data={"model-name": MODEL_NAME},
+                timeout=(10, 120),
+            )
+
+        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()
+
+        print("Successfully submitted.")
+        print("Server response:", body)
+
+    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)

task_template.py

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+import torch
+import torch.nn as nn
+import numpy as np
+from torch.utils.data import DataLoader, TensorDataset
+from torchvision.models import resnet18, resnet34, resnet50
+
+# the dataset is provided as a .npz file (compressed numpy archive)
+# it contains two arrays:
+# images: uint8 array of shape (N, 3, 32, 32), values in [0, 255]
+# labels: integer class labels in range [0, 8]
+# we divide images by 255.0 to get float values in [0, 1]
+
+data = np.load("train.npz")
+images = torch.from_numpy(data["images"]).float() / 255.0
+labels = torch.from_numpy(data["labels"]).long()
+
+dataset = TensorDataset(images, labels)
+loader = DataLoader(dataset, batch_size=256, shuffle=True)
+
+print("Dataset size:", len(dataset))
+print("Image shape:", images.shape)
+print("Label range:", labels.min().item(), "to", labels.max().item())
+
+NUM_CLASSES = 9
+
+# pick one of: resnet18, resnet34, resnet50
+model = resnet18(weights=None)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+
+# resnet34 example
+# model = resnet34(weights=None)
+# model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+
+# resnet50 example
+# model = resnet50(weights=None)
+# model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+
+# sanity check -- output shape must be (1, 9)
+model.eval()
+with torch.no_grad():
+    out = model(torch.randn(1, 3, 32, 32))
+print("Output shape:", out.shape)
+
+# save only the state dict, not the full model instance
+torch.save(model.state_dict(), "model.pt")

train.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e21c23fb3f019cfbbfc59465d0612f4d1fb460016a06727f73ceb53c7da40aab
+size 126604007