task_template.py

import os
import sys
import requests

BASE_URL  = "http://35.192.205.84"
API_KEY   = "YOUR_API_KEY_HERE"

TASK_ID   = "11-duci"
FILE_PATH = "sample_submission.csv"


"""
MODEL_ID = model_{0i} ---> ResNet18
MODEL_ID = model_{1i} ---> ResNet50
MODEL_ID = model_{2i} ---> ResNet152
"""

# ── Model loading ──────────────────────────────────────────────────────────────
import torch
import torchvision.models as models

NUM_CLASSES = 100

ARCHITECTURE_MAP = {
    "0": models.resnet18,
    "1": models.resnet50,
    "2": models.resnet152,
}

def build_model(model_id: str) -> torch.nn.Module:
    """
    Build a model from a MODEL_ID string like 'model_00', 'model_12', etc.

    Naming convention:
        model_{arch}{instance}
        arch     0 → ResNet18  |  1 → ResNet50  |  2 → ResNet152
        instance 0, 1, 2  (three independently trained seeds per architecture)

    Examples:
        model_00, model_01, model_02  → ResNet18
        model_10, model_11, model_12  → ResNet50
        model_20, model_21, model_22  → ResNet152
    """
    # strip optional "model_" prefix
    key = model_id.removeprefix("model_")   # e.g. "12"
    arch_digit = key[0]                      # first digit encodes architecture

    if arch_digit not in ARCHITECTURE_MAP:
        raise ValueError(
            f"Unknown architecture digit '{arch_digit}' in model_id '{model_id}'. "
            f"Expected 0 (ResNet18), 1 (ResNet50), or 2 (ResNet152)."
        )

    model_fn = ARCHITECTURE_MAP[arch_digit]
    model = model_fn(weights=None, num_classes=NUM_CLASSES)
    return model


def load_model(model_id: str, weights_path: str,
               device: str = "cpu") -> torch.nn.Module:
    """
    Instantiate the correct architecture for *model_id* and load weights
    from *weights_path*.

    Args:
        model_id:     e.g. 'model_00', 'model_12', 'model_21'
        weights_path: path to the saved state-dict (.pth / .pt file)
        device:       'cpu', 'cuda', 'cuda:0', etc.

    Returns:
        model in eval mode with weights loaded.
    """
    model = build_model(model_id)
    state_dict = torch.load(weights_path, map_location=device)
    # support both raw state-dicts and checkpoint dicts
    if isinstance(state_dict, dict) and "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]
    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()
    return model


# ── Example usage ──────────────────────────────────────────────────────────────
# Replace the paths below with the actual locations of your weight files.
#
# MODEL_WEIGHTS = {
#     "model_00": "/path/to/weights/model_00.pth",
#     "model_01": "/path/to/weights/model_01.pth",
#     "model_02": "/path/to/weights/model_02.pth",
#     "model_10": "/path/to/weights/model_10.pth",
#     "model_11": "/path/to/weights/model_11.pth",
#     "model_12": "/path/to/weights/model_12.pth",
#     "model_20": "/path/to/weights/model_20.pth",
#     "model_21": "/path/to/weights/model_21.pth",
#     "model_22": "/path/to/weights/model_22.pth",
# }
#
# for model_id, path in MODEL_WEIGHTS.items():
#     model = load_model(model_id, path, device="cpu")
#     print(f"Loaded {model_id}: {model.__class__.__name__}")
# ──────────────────────────────────────────────────────────────────────────────



def die(msg):
    print(f"{msg}", file=sys.stderr)
    sys.exit(1)


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, "csv"),
        }
        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)