62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import time
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import os
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from paddle import core
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from paddleocr import PaddleOCRVL
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IMAGE_PATH = "images/名片02.jpg"
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WARMUP_ROUNDS = 0
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BENCHMARK_ROUNDS = 1
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# ── 线程配置 ──
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# 可通过环境变量 PADDLE_THREADS 覆盖，否则使用逻辑核心数
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DEFAULT_THREADS = int(os.environ.get("PADDLE_THREADS", os.cpu_count() or 4))
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core.set_num_threads(DEFAULT_THREADS)
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print(f"[Threads] oneDNN compiled, using {DEFAULT_THREADS} threads (CPU cores: {os.cpu_count()})")
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# ── 模型初始化计时 ──
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print("=" * 60)
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print("初始化模型...")
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t0 = time.perf_counter()
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pipeline = PaddleOCRVL(pipeline_version="v1.6")
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t_init = time.perf_counter() - t0
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print(f"[OK] 模型初始化耗时: {t_init:.2f}s")
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print("=" * 60)
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# ── 推理 Benchmark ──
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print(f"\n开始 OCR 识别: {IMAGE_PATH}")
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print(f"预热 {WARMUP_ROUNDS} 轮 + 正式测试 {BENCHMARK_ROUNDS} 轮\n")
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# 预热
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for i in range(WARMUP_ROUNDS):
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print(f" 预热 {i + 1}/{WARMUP_ROUNDS}...")
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_ = pipeline.predict(IMAGE_PATH)
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# 正式计时
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times = []
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for i in range(BENCHMARK_ROUNDS):
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print(f" 推理 {i + 1}/{BENCHMARK_ROUNDS}...", end=" ", flush=True)
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t0 = time.perf_counter()
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result = pipeline.predict(IMAGE_PATH)
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elapsed = time.perf_counter() - t0
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times.append(elapsed)
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print(f"{elapsed:.2f}s")
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print("\n" + "=" * 60)
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print("[Benchmark]")
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print(f" 图片尺寸: {result[0]['width']} x {result[0]['height']}")
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print(f" 检测文本块: {len(result[0]['layout_det_res']['boxes'])} 个")
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print(f" 识别文本块: {len(result[0]['parsing_res_list'])} 个")
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print(f" 推理次数: {BENCHMARK_ROUNDS}")
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print(f" 最快: {min(times):.2f}s")
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print(f" 最慢: {max(times):.2f}s")
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print(f" 平均: {sum(times) / len(times):.2f}s")
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if len(times) > 1:
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print(f" 标准差: {(sum((t - sum(times) / len(times)) ** 2 for t in times) / len(times)) ** 0.5:.2f}s")
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print("=" * 60)
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# ── 输出识别结果 ──
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print("\n[识别结果]\n")
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for item in result:
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for block in item["parsing_res_list"]:
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print(f" [{block.label}] ({block.bbox})")
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print(f" {block.content}\n") |