File size: 8,909 Bytes
9cd7b60 9b11d6f 9cd7b60 c16bb67 9cd7b60 c16bb67 9cd7b60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 | import os
import sys
import json
import time
import argparse
import numpy as np
import torch
from transformers import PreTrainedTokenizerFast
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from model import CompactGenderClassifier
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
PROJECT_ROOT = os.path.abspath(os.path.join(SCRIPT_DIR, ".."))
def find_first_existing(*paths):
for p in paths:
if os.path.exists(p):
return p
return paths[0]
CONFIG_PATH = find_first_existing(
os.path.join(SCRIPT_DIR, "config.json"),
os.path.join(SCRIPT_DIR, "config", "model_config.json"),
os.path.join(PROJECT_ROOT, "config", "model_config.json"),
os.path.join(PROJECT_ROOT, "config.json")
)
TOKENIZER_DIR = find_first_existing(
os.path.join(SCRIPT_DIR, "tokenizer"),
os.path.join(PROJECT_ROOT, "models", "tokenizer"),
SCRIPT_DIR
)
ONNX_PATH = find_first_existing(
os.path.join(SCRIPT_DIR, "model_int8.onnx"),
os.path.join(PROJECT_ROOT, "models", "model_int8.onnx"),
os.path.join(SCRIPT_DIR, "models", "model_int8.onnx")
)
WEIGHTS_PATH = find_first_existing(
os.path.join(SCRIPT_DIR, "model_weights.pt"),
os.path.join(PROJECT_ROOT, "models", "model_weights.pt"),
os.path.join(SCRIPT_DIR, "models", "model_weights.pt")
)
TITLE_PREFIXES = {
'mr', 'mrs', 'ms', 'miss', 'dr', 'prof', 'sri', 'shri', 'smt', 'master',
'md', 'mohd', 'sk', 'sekh', 'sheikh', 'syed', 'kazi', 'adv', 'er', 'ca'
}
class GenderClassifier:
"""
Dedicated binary Gender predictor (MALE vs FEMALE).
Supports ultra-fast ONNX INT8 CPU inference or PyTorch.
"""
def __init__(self, use_onnx=True):
if not os.path.exists(CONFIG_PATH):
raise FileNotFoundError(f"Configuration not found: {CONFIG_PATH}. Please train the model first.")
with open(CONFIG_PATH, "r", encoding="utf-8") as f:
self.cfg = json.load(f)
self.max_length = self.cfg.get("max_length", 20)
self.labels = {int(k): v for k, v in self.cfg.get("labels", {"0": "MALE", "1": "FEMALE"}).items()}
# Load Tokenizer
tok_json = os.path.join(TOKENIZER_DIR, "tokenizer.json")
self.tokenizer = PreTrainedTokenizerFast(
tokenizer_file=tok_json,
do_lower_case=True,
pad_token="[PAD]",
unk_token="[UNK]",
cls_token="[CLS]",
sep_token="[SEP]",
mask_token="[MASK]"
)
self.use_onnx = use_onnx and os.path.exists(ONNX_PATH)
if self.use_onnx:
import onnxruntime
session_opts = onnxruntime.SessionOptions()
session_opts.intra_op_num_threads = 1
session_opts.inter_op_num_threads = 1
session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
self.session = onnxruntime.InferenceSession(ONNX_PATH, session_opts)
self.backend_name = "ONNX Runtime INT8 (Single-Thread)"
else:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = CompactGenderClassifier(
vocab_size=self.cfg["vocab_size"],
emb_dim=self.cfg["emb_dim"],
hidden_dim=self.cfg["hidden_dim"],
num_layers=self.cfg["num_layers"],
num_heads=self.cfg["num_heads"],
max_len=self.max_length,
dropout=0.0
).to(self.device)
self.model.load_state_dict(torch.load(WEIGHTS_PATH, map_location=self.device, weights_only=True))
self.model.eval()
self.backend_name = f"PyTorch ({self.device})"
def parse_name_tokens(self, text):
words = [w for w in str(text).strip().split() if len(w) > 0]
if not words:
return "", []
idx = 0
while idx < len(words):
cleaned = "".join([c for c in words[idx].lower() if c.isalpha()])
if cleaned in TITLE_PREFIXES:
idx += 1
else:
break
remaining = words[idx:]
if not remaining:
return words[0], []
return remaining[0], remaining[1:]
def softmax(self, logits):
e_x = np.exp(logits - np.max(logits, axis=-1, keepdims=True))
return e_x / np.sum(e_x, axis=-1, keepdims=True)
def predict(self, texts):
if isinstance(texts, str):
texts = [texts]
parsed_items = [self.parse_name_tokens(t) for t in texts]
query_names = [given if given else raw for raw, (given, _) in zip(texts, parsed_items)]
enc = self.tokenizer(
query_names,
padding="max_length",
max_length=self.max_length,
truncation=True,
return_tensors="np"
)
input_ids = enc["input_ids"].astype(np.int32)
attention_mask = enc["attention_mask"].astype(np.int32)
t0 = time.time()
if self.use_onnx:
feeds = {
"input_ids": input_ids,
"attention_mask": attention_mask
}
logits = self.session.run(None, feeds)[0]
else:
with torch.no_grad():
ids_t = torch.from_numpy(input_ids).to(self.device)
mask_t = torch.from_numpy(attention_mask).to(self.device)
logits = self.model(ids_t, mask_t).cpu().numpy()
elapsed_ms = (time.time() - t0) * 1000 / len(texts)
probs = self.softmax(logits)
results = []
for i, text in enumerate(texts):
p = probs[i]
given_name, surnames = parsed_items[i]
pred_idx = int(np.argmax(p))
label = self.labels.get(pred_idx, "UNKNOWN")
conf = float(p[pred_idx])
results.append({
"text": text,
"gender": label,
"confidence": conf,
"given_name": given_name,
"surnames": surnames,
"raw_probs": {
"male": float(p[0]),
"female": float(p[1])
},
"latency_ms": elapsed_ms
})
return results
def main():
parser = argparse.ArgumentParser(description="CompactGenderClassifier Prediction Tool")
parser.add_argument("names", nargs="*", help="Name string(s) to classify as positional arguments")
parser.add_argument("--text", nargs="+", help="Name string(s) to classify")
parser.add_argument("-i", "--interactive", action="store_true", help="Start interactive CLI loop")
parser.add_argument("--use-pytorch", action="store_true", help="Force using PyTorch model instead of ONNX")
args = parser.parse_args()
clf = GenderClassifier(use_onnx=not args.use_pytorch)
print(f"--> Initialized {clf.backend_name}")
if args.interactive:
print("\n===============================================================")
print("CompactGenderClassifier Interactive Mode")
print("Type a name and press Enter. Type 'exit' or 'q' to quit.")
print("===============================================================\n")
while True:
try:
raw = input("Enter Name > ").strip()
if not raw:
continue
if raw.lower() in ["exit", "quit", "q"]:
print("Exiting interactive mode.")
break
r = clf.predict(raw)[0]
probs = r["raw_probs"]
given_str = f" [Given Name: {r['given_name']}]" if r["surnames"] else ""
print(f"--> Result: {r['gender']} ({r['confidence']*100:.1f}%) | Male: {probs['male']*100:.1f}% | Female: {probs['female']*100:.1f}%{given_str}\n")
except (KeyboardInterrupt, EOFError):
print("\nExiting interactive mode.")
break
return
input_names = args.names if args.names else args.text
if input_names:
names = input_names
else:
names = [
"Supratim",
"Sayantika",
"Supratim Mondal",
"Sayantika Biswas",
"Alexander Hamilton",
"Priya Sharma",
"John Smith",
"Emily Watson",
"Dr. Rahul Verma",
"Ms. Fatima Sheikh"
]
print("\n" + "=" * 95)
print(f"{'Input Name':<24} | {'Gender':<8} | {'Conf':<6} | {'Given Name':<14} | {'Probabilities'}")
print("=" * 95)
results = clf.predict(names)
for r in results:
probs = r['raw_probs']
prob_str = f"Male: {probs['male']*100:.1f}% | Female: {probs['female']*100:.1f}%"
print(f"{r['text']:<24} | {r['gender']:<8} | {r['confidence']*100:5.1f}% | {r['given_name']:<14} | {prob_str}")
print("=" * 95)
if __name__ == "__main__":
main()
|