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"""
Benchmark inference latency: must be <50ms
"""
import time
import numpy as np
from pathlib import Path
from predict import BrainRobotPredictor
from src.utils import load_npz_data
def benchmark_latency(predictor, samples, n_trials=100):
"""
Measure average inference latency
Returns:
dict with latency statistics
"""
latencies = []
print(f"Running {n_trials} inference trials...")
for i in range(min(n_trials, len(samples))):
eeg, fnirs, _ = samples[i]
result = predictor.predict(eeg, fnirs)
latencies.append(result['latency_ms'])
latencies = np.array(latencies)
return {
'mean': latencies.mean(),
'std': latencies.std(),
'min': latencies.min(),
'max': latencies.max(),
'p50': np.percentile(latencies, 50),
'p95': np.percentile(latencies, 95),
'p99': np.percentile(latencies, 99),
}
def main():
# Load predictor
predictor = BrainRobotPredictor(model_dir='./models')
# Load test samples
data_dir = Path('./data/cache/robot_control')
samples = load_npz_data(data_dir)
# Benchmark
stats = benchmark_latency(predictor, samples, n_trials=100)
print("\n" + "="*50)
print("LATENCY BENCHMARK RESULTS")
print("="*50)
print(f"Mean: {stats['mean']:.2f} ms")
print(f"Std: {stats['std']:.2f} ms")
print(f"Min: {stats['min']:.2f} ms")
print(f"Max: {stats['max']:.2f} ms")
print(f"P50: {stats['p50']:.2f} ms")
print(f"P95: {stats['p95']:.2f} ms")
print(f"P99: {stats['p99']:.2f} ms")
print("="*50)
# Evaluation
target_latency = 50.0
if stats['p95'] < target_latency:
print(f"\n✓ PASSED: 95th percentile ({stats['p95']:.1f}ms) < {target_latency}ms")
else:
print(f"\n✗ FAILED: 95th percentile ({stats['p95']:.1f}ms) > {target_latency}ms")
print(" Consider simplifying model or reducing features")
if __name__ == "__main__":
main()