Upload task_template.py

task_template.py

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+import torch
+import requests
+import sys
+import os
+import numpy as np
+
+# --------------------------------
+# AUXILIARY DATASET
+# --------------------------------
+
+"""
+Auxiliary dataset contents:
+
+-"images": Tensor containing the 1000 images, has shape (1000, 3, 32, 32)
+-"labels": List of true labels for the images, has shape (1000)
+"""
+
+# Load the dataset
+dataset = torch.load("auxiliary_dataset.pt", weights_only=False)
+
+print("Images shape:", dataset["images"].shape)
+print("Labels shape:", dataset["labels"].shape)
+print("First 10 labels:", dataset["labels"][:10])
+print("First image tensor:", dataset["images"][:1])
+
+# --------------------------------
+# SUBMISSION FORMAT
+# --------------------------------
+
+"""
+The submission must be a .npz file of the following format:
+
+-"images": Tensor containing the reconstructed images with shape (100, 3, 32, 32).
+    These images do not have to be in any particular order.
+    The dtype must be float32, with values in [0, 1] range.
+"""
+
+# Example Submission:
+
+reconstructed_images = torch.randint(0, 256, size=(100, 3, 32, 32), dtype=torch.uint8)
+
+reconstructed_images = reconstructed_images.float() / 255.0  # normalize to [0, 1] range
+
+images_np = reconstructed_images.detach().cpu().numpy()
+
+np.savez_compressed("example_submission.npz", images=images_np)
+
+# --------------------------------
+# SUBMISSION PROCESS
+# --------------------------------
+
+"""
+Data Reconstruction Task — Participant Submission Guide
+========================================================
+
+You will upload a single **.npz** file that contains ONLY an array named **'images'**.
+The evaluator will load your file, run shape/dtype checks,
+and then score it by comparing to the training dataset.
+
+Follow these rules carefully to avoid automatic rejection.
+
+1) File format
+--------------
+- **Extension:** `.npz` (NumPy compressed archive)
+- **Content:** must contain exactly one required key: `'images'`
+- **Max file size:** 200 MB (hard limit). Larger files are rejected.
+
+2) Array requirements
+---------------------
+Let `G` be the ground-truth tensor loaded:
+
+- **Shape:** `images.shape` must match `G["images"].shape` **exactly**.
+  - If `G["images"]` is `(N, 3, H, W)`, your array must also be `(N, 3, H, W)`.
+  - No extra samples; no fewer; no different dimensions.
+- **Dtype:** `images.dtype` must match `G["images"].dtype` **exactly**.
+  - If the GT uses `float32`, you must submit `float32`.
+  - Safe cast example: `images = np.asarray(images, dtype=np.float32)`
+- **Contiguity:** The server will convert to a contiguous Torch tensor; standard NumPy arrays are fine.
+
+
+3) Typical failure messages & what they mean
+--------------------------------------------
+- "File must be .npz and contain an 'images' array."
+  → Wrong extension or missing `'images'` key.
+- "File too large: X bytes (limit 209715200)."
+  → Your file exceeds 200 MB.
+- "Failed to read .npz: ..."
+  → The file is corrupted or not a valid `.npz` created with `allow_pickle=False`.
+- "Failed to convert 'images' to torch tensor: ..."
+  → Your `'images'` array has an unsupported dtype or structure (e.g., object array).
+- "Submitted images must have shape (N, C, H, W), but got (...)."
+  → Shape mismatch with the ground-truth images.
+- "Submitted images must be of type torch.float32, but got torch.float64."
+  → Dtype mismatch with the ground-truth images.
+"""
+
+BASE_URL  = "http://35.192.205.84:80"
+API_KEY  = "YOUR_API_KEY_HERE"
+
+TASK_ID = "12-data-reconstruction"
+
+# Path to the .npz file containing the images you want to get logits for
+
+QUERY_PATH = "/PATH/FILE.npz"
+
+# Path to the .npz file you want to send
+
+FILE_PATH = "/PATH/FILE.npz"
+
+
+GET_LOGITS = False      # set True to get logits from the API
+SUBMIT     = False      # set True to submit your solution
+
+
+def die(msg):
+    print(f"{msg}", file=sys.stderr)
+    sys.exit(1)
+
+if GET_LOGITS:
+
+    with open(QUERY_PATH, "rb") as f:
+        files = {"npz": (QUERY_PATH, f, "application/octet-stream")}
+        response = requests.post(
+            f"{BASE_URL}/{TASK_ID}/logits",
+            files=files,
+            headers={"X-API-Key": API_KEY},
+        )
+
+    if response.status_code == 200:
+        data = response.json()
+        print("Request successful")
+        print(data)
+
+    else:
+        print("Request failed")
+        print("Status code:", response.status_code)
+        print("Detail:", response.text)
+
+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 = {
+                "file": (os.path.basename(FILE_PATH), f, "application/octet-stream"),
+            }
+            resp = requests.post(
+                f"{BASE_URL}/submit/{TASK_ID}",
+                headers={"X-API-Key": API_KEY},
+                files=files,
+                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()
+
+        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)