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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) |