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import json
from pathlib import Path
import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer
from src.models.augment import augment, MAXLEN_TO_WINDOW
from src.models.dataset import deduplicate_positions, flatten_to_examples
from src.models.distillbert import reconstruct_triplets
from src.schemas.labels import MARKER_MODE, SENTIMENT_LABELS
BASE_TOKENIZER = "distilbert-base-uncased"
def build_tokenizer(mode: str):
tokenizer = AutoTokenizer.from_pretrained(BASE_TOKENIZER)
if mode == "marker":
tokenizer.add_special_tokens(
{"additional_special_tokens": [MARKER_MODE.entity_start, MARKER_MODE.entity_end]}
)
return tokenizer
def _softmax(logits: np.ndarray) -> np.ndarray:
exp = np.exp(logits - logits.max(axis=-1, keepdims=True))
return exp / exp.sum(axis=-1, keepdims=True)
def _tokenize_examples(
examples: list[dict], tokenizer, max_len: int,
) -> dict[str, np.ndarray]:
input_ids, attention_masks = [], []
for ex in examples:
seg_a = ex["seg_a"]
seg_b = ex["seg_b"]
if seg_b is None:
enc = tokenizer(
seg_a,
max_length=max_len,
truncation=True,
padding="max_length",
return_tensors="np",
)
else:
enc = tokenizer(
seg_a, seg_b,
max_length=max_len,
truncation="only_first",
padding="max_length",
return_tensors="np",
)
input_ids.append(enc["input_ids"][0])
attention_masks.append(enc["attention_mask"][0])
return {
"input_ids": np.array(input_ids, dtype=np.int64),
"attention_mask": np.array(attention_masks, dtype=np.int64),
}
def _run_batched(
session: ort.InferenceSession,
inputs: dict[str, np.ndarray],
batch_size: int,
) -> np.ndarray:
n = inputs["input_ids"].shape[0]
all_logits = []
for start in range(0, n, batch_size):
end = min(start + batch_size, n)
batch = {k: v[start:end] for k, v in inputs.items()}
logits = session.run(None, batch)[0]
all_logits.append(logits)
return np.concatenate(all_logits, axis=0)
def predict(
samples: list[dict],
session: ort.InferenceSession,
tokenizer,
mode: str,
max_len: int = 256,
batch_size: int = 32,
deduplicate: bool = False,
) -> list[dict]:
window_words = MAXLEN_TO_WINDOW[max_len]
augmented = augment(samples, window_words)
if deduplicate:
augmented = deduplicate_positions(augmented)
examples = flatten_to_examples(augmented, mode=mode)
if not examples:
return [{"id": s["id"], "entities": []} for s in samples]
inputs = _tokenize_examples(examples, tokenizer, max_len)
logits = _run_batched(session, inputs, batch_size)
sentiments = list(SENTIMENT_LABELS.classes)
probs = _softmax(logits)
if mode in ("marker", "qa_m"):
preds = np.argmax(probs, axis=-1)
max_probs = probs.max(axis=-1)
for ex, pred_id, conf in zip(examples, preds, max_probs):
ex["predicted_label"] = sentiments[int(pred_id)]
ex["confidence"] = float(conf)
else:
yes_probs = probs[:, 1]
preds3, _ = reconstruct_triplets(yes_probs, np.zeros_like(yes_probs))
triplet_idx = 0
i = 0
while i < len(examples) - 2:
pred_label = sentiments[preds3[triplet_idx]]
triplet_conf = float(yes_probs[i:i + 3].max())
for j in range(3):
examples[i + j]["predicted_label"] = pred_label
examples[i + j]["confidence"] = triplet_conf
triplet_idx += 1
i += 3
entity_preds: dict[tuple, tuple[str, float]] = {}
for ex in examples:
key = (ex["sample_id"], ex["entity_id"])
conf = ex.get("confidence", 0.0)
if key not in entity_preds or conf > entity_preds[key][1]:
entity_preds[key] = (ex["predicted_label"], conf)
results = []
for s in samples:
entities_out = []
for e in s["entities"]:
key = (s["id"], e["entity_id"])
label, _ = entity_preds.get(key, ("neutral", 0.0))
entities_out.append({
"entity_id": e["entity_id"],
"entity_text": e["entity_text"],
"classification": label,
})
results.append({"id": s["id"], "entities": entities_out})
return results
def main():
parser = argparse.ArgumentParser(description="Run ONNX inference on raw input JSON")
parser.add_argument("--onnx-path", required=True, help="Path to model.onnx")
parser.add_argument("--mode", required=True, choices=("marker", "qa_m", "qa_b"))
parser.add_argument("--data", required=True, help="Path to input JSON (assignment format)")
parser.add_argument("--output", default=None, help="Output JSON path (default: stdout)")
parser.add_argument("--max-len", type=int, default=256)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--deduplicate", action="store_true", help="Use one position per entity")
args = parser.parse_args()
mode = args.mode
tokenizer = build_tokenizer(mode)
session = ort.InferenceSession(args.onnx_path)
with open(args.data, "r", encoding="utf-8") as f:
samples = json.load(f)
results = predict(samples, session, tokenizer, mode, args.max_len, args.batch_size, deduplicate=args.deduplicate)
output_json = json.dumps(results, ensure_ascii=False, indent=2)
if args.output:
Path(args.output).write_text(output_json, encoding="utf-8")
print(f"Saved {len(results)} predictions to {args.output}")
else:
print(output_json)
if __name__ == "__main__":
main()
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