diff --git a/__init__.py b/__init__.py deleted file mode 100644 index 6705156..0000000 --- a/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -Edge_Inference_Service -工业级边缘AI推理服务 - -依赖安装: - pip install -r requirements.txt - -环境要求: - - Python 3.8+ - - CUDA 12.1 - - cuDNN 8.9.7 - - TensorRT 8.6.1 - -使用说明: - 1. 配置数据库连接 (config/settings.py 或环境变量) - 2. 配置Redis连接 - 3. 配置MQTT连接 - 4. 准备TensorRT引擎文件 (models/yolo11n.engine) - 5. 运行: python main.py - -作者: AI Edge Architecture Team -版本: 1.0.0 -""" - -__version__ = "1.0.0" -__author__ = "AI Edge Architecture Team" diff --git a/analyze_latency.py b/analyze_latency.py deleted file mode 100644 index 6f499f7..0000000 --- a/analyze_latency.py +++ /dev/null @@ -1,56 +0,0 @@ -"""详细延迟分析 - 简化版""" -import sys -import os -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import time -import numpy as np -import cv2 - -from config.settings import get_settings -from core.preprocessor import ImagePreprocessor - -settings = get_settings() -preprocessor = ImagePreprocessor(settings.inference) - -# 模拟 100 次推理 -img = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8) -roi_mock = type('ROI', (), {'x1': 300, 'y1': 100, 'x2': 1000, 'y2': 800, 'enabled': True})() - -times_preprocess = [] -times_single = [] -times_batch = [] - -for _ in range(100): - # 1. preprocess_single - start = time.perf_counter() - cropped = preprocessor.preprocess_single(img, roi_mock) - t = (time.perf_counter() - start) * 1000 - times_single.append(t) - - # 2. preprocess_batch (1→4) - start = time.perf_counter() - batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]]) - t = (time.perf_counter() - start) * 1000 - times_batch.append(t) - - # 3. 完整 preprocess (single + batch) - start = time.perf_counter() - cropped = preprocessor.preprocess_single(img, roi_mock) - batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]]) - t = (time.perf_counter() - start) * 1000 - times_preprocess.append(t) - -print("延迟分析 (100次平均):") -print(f" preprocess_single (ROI + resize): {np.mean(times_single):.2f}ms") -print(f" preprocess_batch (padding 1→4): {np.mean(times_batch):.2f}ms") -print(f" 完整预处理: {np.mean(times_preprocess):.2f}ms") -print() -print(f"TensorRT 推理 (batch=1): ~2.5ms (基准测试)") -print(f"TensorRT 推理 (batch=4): ~5.0ms (基准测试)") -print() -print("推算总延迟:") -print(f" 方案A (batch=1): {np.mean(times_single):.2f} + 2.5 + 后处理 ≈ 10-15ms") -print(f" 方案B (batch=4 实际只推理1帧): {np.mean(times_preprocess):.2f} + 5 + 后处理 ≈ 55-65ms") -print() -print("结论：延迟主要来自 batch padding 和不必要的 4帧推理开销") diff --git a/analyze_latency_batch1.py b/analyze_latency_batch1.py deleted file mode 100644 index 2f84f88..0000000 --- a/analyze_latency_batch1.py +++ /dev/null @@ -1,44 +0,0 @@ -"""延迟分析 - batch=1 优化后""" -import sys -import os -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import time -import numpy as np - -from config.settings import get_settings -from core.preprocessor import ImagePreprocessor, BatchPreprocessor - -settings = get_settings() -preprocessor = ImagePreprocessor(settings.inference) - -img = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8) -roi_mock = type('ROI', (), {'x1': 300, 'y1': 100, 'x2': 1000, 'y2': 800, 'enabled': True, 'roi_type': 0})() - -times_preprocess_single = [] -times_preprocess_batch = [] - -for _ in range(100): - # 1. preprocess_single - start = time.perf_counter() - cropped = preprocessor.preprocess_single(img, roi_mock) - t = (time.perf_counter() - start) * 1000 - times_preprocess_single.append(t) - - # 2. preprocess_batch (batch=1) - start = time.perf_counter() - batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]]) - t = (time.perf_counter() - start) * 1000 - times_preprocess_batch.append(t) - -print("延迟分析 (batch=1 优化后):") -print(f" preprocess_single: {np.mean(times_preprocess_single):.2f}ms") -print(f" preprocess_batch: {np.mean(times_preprocess_batch):.2f}ms") -print(f" 总预处理: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f}ms") -print() -print(f"TensorRT batch=1 推理: ~2.5ms") -print(f"TensorRT batch=4 推理: ~5.0ms") -print() -print("推算总延迟:") -print(f" batch=1: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f} + 2.5 ≈ 8-12ms") -print(f" batch=4: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f} + 5 ≈ 10-15ms") diff --git a/benchmark_trt.py b/benchmark_trt.py deleted file mode 100644 index c90fddc..0000000 --- a/benchmark_trt.py +++ /dev/null @@ -1,96 +0,0 @@ -"""TensorRT 纯推理延迟测试""" -import numpy as np -import tensorrt as trt -import pycuda.driver as cuda -import pycuda.autoinit -import time - -engine_path = './models/yolo11n.engine' - -with open(engine_path, 'rb') as f: - runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) - engine = runtime.deserialize_cuda_engine(f.read()) - -context = engine.create_execution_context() - -input_shape = (1, 3, 480, 480) -input_data = np.random.randn(*input_shape).astype(np.float32) - -context.set_input_shape('images', input_shape) - -output_shape = tuple(max(1, s) for s in engine.get_binding_shape(1)) -output_size = int(np.prod(output_shape)) - -h_input = cuda.pagelocked_empty(input_data.size, np.float32) -h_output = cuda.pagelocked_empty(output_size, np.float32) - -np.copyto(h_input, input_data.ravel()) - -d_input = cuda.mem_alloc(h_input.nbytes) -d_output = cuda.mem_alloc(h_output.nbytes) - -bindings = [int(d_input), int(d_output)] - -# Warmup -for _ in range(10): - cuda.memcpy_htod(d_input, h_input) - context.execute_v2(bindings=bindings) - cuda.memcpy_dtoh(h_output, d_output) - -# Benchmark -times = [] -for _ in range(100): - start = time.perf_counter() - cuda.memcpy_htod(d_input, h_input) - context.execute_v2(bindings=bindings) - cuda.memcpy_dtoh(h_output, d_output) - times.append((time.perf_counter() - start) * 1000) - -print(f'TensorRT 纯推理延迟 (batch=1):') -print(f' 平均: {np.mean(times):.2f}ms') -print(f' 中位数: {np.median(times):.2f}ms') -print(f' 最小: {np.min(times):.2f}ms') -print(f' 最大: {np.max(times):.2f}ms') -print(f' P95: {np.percentile(times, 95):.2f}ms') -print() - -# 再测试 batch=4 -print("测试 batch=4...") -input_shape_4 = (4, 3, 480, 480) -input_data_4 = np.random.randn(*input_shape_4).astype(np.float32) -context.set_input_shape('images', input_shape_4) - -output_shape_4 = (4, 84, 4725) -output_size_4 = int(np.prod(output_shape_4)) - -h_input_4 = cuda.pagelocked_empty(input_data_4.size, np.float32) -h_output_4 = cuda.pagelocked_empty(output_size_4, np.float32) - -np.copyto(h_input_4, input_data_4.ravel()) - -d_input_4 = cuda.mem_alloc(h_input_4.nbytes) -d_output_4 = cuda.mem_alloc(h_output_4.nbytes) - -bindings_4 = [int(d_input_4), int(d_output_4)] - -# Warmup -for _ in range(10): - cuda.memcpy_htod(d_input_4, h_input_4) - context.execute_v2(bindings=bindings_4) - cuda.memcpy_dtoh(h_output_4, d_output_4) - -# Benchmark -times_4 = [] -for _ in range(100): - start = time.perf_counter() - cuda.memcpy_htod(d_input_4, h_input_4) - context.execute_v2(bindings=bindings_4) - cuda.memcpy_dtoh(h_output_4, d_output_4) - times_4.append((time.perf_counter() - start) * 1000) - -print(f'TensorRT 纯推理延迟 (batch=4):') -print(f' 平均: {np.mean(times_4):.2f}ms') -print(f' 中位数: {np.median(times_4):.2f}ms') -print(f' 最小: {np.min(times_4):.2f}ms') -print(f' 最大: {np.max(times_4):.2f}ms') -print(f' P95: {np.percentile(times_4, 95):.2f}ms') diff --git a/build_engine.py b/build_engine.py deleted file mode 100644 index f9ce340..0000000 --- a/build_engine.py +++ /dev/null @@ -1,395 +0,0 @@ -""" -TensorRT Engine 生成脚本 (8GB显存优化版) - -功能： -- 将 YOLO11 模型转换为 TensorRT engine 文件 -- 针对 8GB 显存环境优化 -- 支持 FP16 精度、480x480 输入、动态 Batch(1-8) -- 启用所有高性能优化策略 - -使用方法： - python build_engine.py --download yolo11n --output models/yolo11n.engine [OPTIONS] - -示例： - python build_engine.py --download yolo11n --output models/yolo11n.engine --fp16 --batch 8 - -优化命令参数说明： - --min-batch 最小Batch大小 (默认: 1) - --opt-batch 优化Batch大小 (默认: 4) <-- TensorRT会针对此尺寸专门优化 - --max-batch 最大Batch大小 (默认: 8) - --workspace 工作空间大小MB (默认: 6144，即6GB) - --tactics 优化策略 (默认: +7，等价于 +CUBLAS,+CUBLAS_LT,+CUDNN) - --best 全局最优搜索 (默认: 启用) - --preview 预览特性 (默认: +faster_dynamic_shapes_0805) - - tactic_values: - CUBLAS = 1 - CUBLAS_LT = 2 - CUDNN = 4 - +7 = 全部启用""" - -import os -import sys -import argparse -from pathlib import Path - -try: - import tensorrt as trt - TRT_AVAILABLE = True -except ImportError: - TRT_AVAILABLE = False - trt = None - -TRT_LOGGER = trt.Logger(trt.Logger.WARNING) if TRT_AVAILABLE else None - - -def check_environment(): - """检查环境配置""" - print(f"\n{'='*60}") - print("环境检查") - print(f"{'='*60}") - - try: - import tensorrt as trt - print(f" TensorRT: {trt.__version__}") - except ImportError: - print(" TensorRT: 未安装") - - try: - import torch - print(f" PyTorch: {torch.__version__}") - print(f" CUDA: {torch.version.cuda if hasattr(torch, 'version') else 'N/A'}") - if torch.cuda.is_available(): - print(f" GPU: {torch.cuda.get_device_name(0)}") - print(f" GPU显存: {torch.cuda.get_device_properties(0).total_memory / (1024**3):.1f} GB") - except ImportError: - print(" PyTorch: 未安装") - - try: - from ultralytics import YOLO - print(" Ultralytics: 已安装") - except ImportError: - print(" Ultralytics: 未安装") - - -def download_model(model_type: str, save_dir: str = "./models") -> str: - """ - 下载 YOLO11 模型 - - Args: - model_type: 模型类型 (yolo11n, yolo11s, yolo11m, yolo11l, yolo11x) - save_dir: 保存目录 - - Returns: - 模型文件路径 - """ - from ultralytics import YOLO - - os.makedirs(save_dir, exist_ok=True) - - model_path = os.path.join(save_dir, f"{model_type}.pt") - - if os.path.exists(model_path): - print(f"模型已存在: {model_path}") - return model_path - - print(f"\n下载 YOLO11 {model_type} 模型...") - - model = YOLO(f"{model_type}.pt") - model.save(model_path) - - print(f"模型已保存: {model_path}") - return model_path - - -def export_onnx( - model_path: str, - output_path: str, - input_size: int = 480, - dynamic_batch: bool = True -) -> bool: - """ - 导出 ONNX 模型 - - Args: - model_path: 模型路径 - output_path: 输出路径 - input_size: 输入尺寸 - dynamic_batch: 是否使用动态Batch - - Returns: - 是否成功 - """ - from ultralytics import YOLO - - print(f"\n导出 ONNX 模型...") - print(f" 源模型: {model_path}") - print(f" 输出路径: {output_path}") - print(f" 输入尺寸: {input_size}x{input_size}") - print(f" 动态Batch: {'是' if dynamic_batch else '否'}") - - model = YOLO(model_path) - - try: - model.export( - format='onnx', - imgsz=input_size, - dynamic=dynamic_batch, - simplify=True, - opset=17 - ) - - if os.path.exists(output_path): - file_size = os.path.getsize(output_path) / (1024 * 1024) - print(f"\n✓ ONNX 导出成功!") - print(f" 文件: {output_path}") - print(f" 大小: {file_size:.2f} MB") - return True - else: - print("错误: ONNX 导出失败") - return False - - except Exception as e: - print(f"错误: ONNX 导出失败: {e}") - return False - - -def build_engine( - onnx_path: str, - engine_path: str, - input_size: int = 480, - min_batch: int = 1, - opt_batch: int = 4, - max_batch: int = 8, - fp16: bool = True, - workspace_mb: int = 6144, - tactic_sources: str = "+CUBLAS,+CUBLAS_LT,+CUDNN", - best: bool = True, - preview: str = "+faster_dynamic_shapes_0805" -) -> bool: - """ - 使用 TensorRT 构建 Engine（8GB显存优化版） - - Args: - onnx_path: ONNX 模型路径 - engine_path: 输出 engine 路径 - input_size: 输入尺寸 - min_batch: 最小 Batch 大小 - opt_batch: 优化 Batch 大小 (TensorRT 会针对此尺寸专门优化) - max_batch: 最大 Batch 大小 - fp16: 是否使用 FP16 - workspace_mb: 工作空间大小 (MB) - tactic_sources: 优化策略 - best: 是否全局最优搜索 - preview: 预览特性 - - Returns: - 是否成功 - """ - print(f"\n{'='*60}") - print("TensorRT Engine 构建 (8GB显存优化版)") - print(f"{'='*60}") - - if not os.path.exists(onnx_path): - print(f"错误: ONNX 模型不存在: {onnx_path}") - return False - - os.makedirs(os.path.dirname(engine_path), exist_ok=True) - - EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) - - with trt.Builder(TRT_LOGGER) as builder: - with builder.create_network(EXPLICIT_BATCH) as network: - with trt.OnnxParser(network, TRT_LOGGER) as parser: - print(f"加载 ONNX 模型: {onnx_path}") - with open(onnx_path, 'rb') as f: - if not parser.parse(f.read()): - print("错误: ONNX 解析失败") - for error in range(parser.num_errors): - print(f" {parser.get_error(error)}") - return False - - input_tensor = network.get_input(0) - input_name = input_tensor.name - input_shape = input_tensor.shape - - print(f"\n构建参数:") - print(f" 输入名称: {input_name}") - print(f" 输入形状: {input_shape}") - print(f" 最小Batch: {min_batch}") - print(f" 优化Batch: {opt_batch} <-- TensorRT会针对此尺寸专门优化!") - print(f" 最大Batch: {max_batch}") - print(f" 精度: {'FP16' if fp16 else 'FP32'}") - print(f" 工作空间: {workspace_mb} MB") - print(f" 优化策略: {tactic_sources}") - print(f" 全局最优: {'是' if best else '否'}") - print(f" 预览特性: {preview}") - - profile = builder.create_optimization_profile() - profile.set_shape( - input_name, - (min_batch, 3, input_size, input_size), - (opt_batch, 3, input_size, input_size), - (max_batch, 3, input_size, input_size) - ) - - config = builder.create_builder_config() - config.add_optimization_profile(profile) - - if fp16: - config.set_flag(trt.BuilderFlag.FP16) - - config.set_flag(trt.BuilderFlag.TF32) - - tactic_value = 0 - for source in tactic_sources.split(','): - source = source.strip() - if not source: - continue - - if source.startswith('+'): - name = source[1:] - if name.isdigit(): - tactic_value += int(name) - else: - name_upper = name.upper() - if name_upper == 'CUBLAS': - tactic_value += 1 - elif name_upper == 'CUBLAS_LT': - tactic_value += 2 - elif name_upper == 'CUDNN': - tactic_value += 4 - - if tactic_value > 0: - config.set_tactic_sources(tactic_value) - - # BENCHMARK flag 在 TRT 8.5+ 已废弃，tactic profiling 逻辑内部自动完成 - # config.set_flag(trt.BuilderFlag.BENCHMARK) - - config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace_mb * 1024 * 1024) - - print(f"\n正在构建 Engine，请稍候 (可能需要3-5分钟)...") - - serialized_engine = builder.build_serialized_network(network, config) - - if serialized_engine is None: - print("错误: Engine 构建失败") - return False - - with open(engine_path, 'wb') as f: - f.write(serialized_engine) - - if os.path.exists(engine_path): - file_size = os.path.getsize(engine_path) / (1024 * 1024) - print(f"\n✓ Engine 构建成功!") - print(f" 文件: {engine_path}") - print(f" 大小: {file_size:.2f} MB") - return True - else: - print("错误: Engine 保存失败") - return False - - -def main(): - parser = argparse.ArgumentParser( - description="TensorRT Engine 构建工具 (8GB显存优化版)", - formatter_class=argparse.RawDescriptionHelpFormatter - ) - parser.add_argument("--model", type=str, default="yolo11n.pt", - help="模型名称或路径 (默认: yolo11n.pt)") - parser.add_argument("--output", type=str, default="models/yolo11n.engine", - help="输出 engine 路径 (默认: models/yolo11n.engine)") - parser.add_argument("--download", type=str, default=None, - help="直接下载模型 (yolo11n/s/m/l/x)") - - group = parser.add_argument_group("构建参数") - group.add_argument("--input-size", type=int, default=480, - help="输入图像尺寸 (默认: 480)") - group.add_argument("--min-batch", type=int, default=1, - help="最小Batch大小 (默认: 1)") - group.add_argument("--opt-batch", type=int, default=4, - help="优化Batch大小，TensorRT针对此尺寸专门优化 (默认: 4)") - group.add_argument("--max-batch", type=int, default=8, - help="最大Batch大小 (默认: 8)") - group.add_argument("--no-fp16", dest="fp16", action="store_false", - help="禁用 FP16 精度") - parser.set_defaults(fp16=True) - - opt_group = parser.add_argument_group("8GB显存优化参数") - opt_group.add_argument("--workspace", type=int, default=6144, - help="工作空间大小 MB，8GB显存建议 6144 (默认: 6144)") - opt_group.add_argument("--tactics", type=str, - default="+CUBLAS,+CUBLAS_LT,+CUDNN", - help="优化策略 (默认: +CUBLAS,+CUBLAS_LT,+CUDNN)") - opt_group.add_argument("--best", action="store_true", default=True, - help="全局最优搜索 (默认: 启用)") - opt_group.add_argument("--no-best", dest="best", action="store_false", - help="禁用全局最优搜索") - opt_group.add_argument("--preview", type=str, - default="+faster_dynamic_shapes_0805", - help="预览特性 (默认: +faster_dynamic_shapes_0805)") - - args = parser.parse_args() - - check_environment() - - if not TRT_AVAILABLE: - print("\n错误: TensorRT 未安装") - print("请安装: pip install tensorrt") - return 1 - - try: - from ultralytics import YOLO - except ImportError: - print("\n错误: Ultralytics 未安装") - print("请安装: pip install ultralytics") - return 1 - - model_path = args.model - - if args.download: - model_path = download_model(args.download) - elif not os.path.exists(model_path): - if model_path.endswith('.pt') or model_path.endswith('.onnx'): - print(f"错误: 模型文件不存在: {model_path}") - return 1 - else: - model_path = download_model(model_path) - else: - print(f"\n使用本地模型: {model_path}") - - onnx_path = model_path - if model_path.endswith('.pt'): - onnx_path = model_path.replace('.pt', '.onnx') - success = export_onnx(model_path, onnx_path, args.input_size) - if not success: - return 1 - - success = build_engine( - onnx_path=onnx_path, - engine_path=args.output, - input_size=args.input_size, - min_batch=args.min_batch, - opt_batch=args.opt_batch, - max_batch=args.max_batch, - fp16=args.fp16, - workspace_mb=args.workspace, - tactic_sources=args.tactics, - best=args.best, - preview=args.preview - ) - - if success: - print(f"\n{'='*60}") - print("构建完成！") - print(f"{'='*60}") - print(f"Engine 文件: {args.output}") - print(f"\n推荐使用 trtexec 测试性能:") - print(f" trtexec --loadEngine={args.output} --streams=8 --iterations=100") - return 0 - else: - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/check_engine.py b/check_engine.py deleted file mode 100644 index 690e595..0000000 --- a/check_engine.py +++ /dev/null @@ -1,43 +0,0 @@ -"""检查 TensorRT Engine 的实际 shape""" -import sys -import os -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -try: - import tensorrt as trt - - engine_path = "./models/yolo11n.engine" - - with open(engine_path, "rb") as f: - runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) - engine = runtime.deserialize_cuda_engine(f.read()) - - print("=" * 60) - print("Engine Binding Information") - print("=" * 60) - - for i in range(engine.num_bindings): - name = engine.get_binding_name(i) - shape = engine.get_binding_shape(i) - dtype = trt.nptype(engine.get_binding_dtype(i)) - is_input = engine.binding_is_input(i) - - size = trt.volume(shape) - - print(f"\nBinding {i}:") - print(f" Name: {name}") - print(f" Shape: {shape}") - print(f" Dtype: {dtype}") - print(f" Size: {size}") - print(f" Is Input: {is_input}") - - if is_input: - print(f" Total Elements: {size}") - print(f" Expected Batch Size: {shape[0] if len(shape) > 0 else 'N/A'}") - - print("\n" + "=" * 60) - -except Exception as e: - print(f"Error: {e}") - import traceback - traceback.print_exc() diff --git a/check_engine_output.py b/check_engine_output.py deleted file mode 100644 index 425583c..0000000 --- a/check_engine_output.py +++ /dev/null @@ -1,53 +0,0 @@ -"""检查 TensorRT Engine 输出的实际 shape""" -import sys -import os -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import numpy as np - -try: - import tensorrt as trt - import pycuda.driver as cuda - import pycuda.autoinit - - engine_path = "./models/yolo11n.engine" - - with open(engine_path, "rb") as f: - runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) - engine = runtime.deserialize_cuda_engine(f.read()) - - context = engine.create_execution_context() - - print("=" * 60) - print("Engine Binding Information") - print("=" * 60) - - for i in range(engine.num_bindings): - name = engine.get_binding_name(i) - shape = engine.get_binding_shape(i) - dtype = trt.nptype(engine.get_binding_dtype(i)) - is_input = engine.binding_is_input(i) - - print(f"\nBinding {i}: {name}") - print(f" Shape: {shape}") - print(f" Dtype: {dtype}") - print(f" Is Input: {is_input}") - - print("\n" + "=" * 60) - - input_shape = engine.get_binding_shape(0) - output_shape = engine.get_binding_shape(1) - - print(f"Input shape: {input_shape}") - print(f"Output shape: {output_shape}") - - input_size = np.prod([max(1, s) for s in input_shape]) - output_size = np.prod([max(1, s) for s in output_shape]) - - print(f"Input size: {input_size}") - print(f"Output size: {output_size}") - -except Exception as e: - print(f"Error: {e}") - import traceback - traceback.print_exc() diff --git a/debug_output_shape.py b/debug_output_shape.py deleted file mode 100644 index 69afc38..0000000 --- a/debug_output_shape.py +++ /dev/null @@ -1,69 +0,0 @@ -"""调试 TensorRT 输出 shape""" -import sys -import os -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import numpy as np - -try: - import tensorrt as trt - import pycuda.driver as cuda - import pycuda.autoinit - - engine_path = "./models/yolo11n.engine" - - with open(engine_path, "rb") as f: - runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) - engine = runtime.deserialize_cuda_engine(f.read()) - - context = engine.create_execution_context() - - input_shape = (1, 3, 480, 480) - input_data = np.random.randn(*input_shape).astype(np.float32) - - input_binding_idx = 0 - output_binding_idx = 1 - - output_shape = engine.get_binding_shape(output_binding_idx) - print(f"Engine 定义 output shape: {output_shape}") - - context.set_input_shape(engine.get_binding_name(0), input_shape) - - output_size = int(np.prod([max(1, s) for s in output_shape])) - - h_input = cuda.pagelocked_empty(input_data.size, np.float32) - h_output = cuda.pagelocked_empty(output_size, np.float32) - - np.copyto(h_input, input_data.ravel()) - - d_input = cuda.mem_alloc(h_input.nbytes) - d_output = cuda.mem_alloc(h_output.nbytes) - - bindings = [int(d_input), int(d_output)] - - cuda.memcpy_htod(d_input, h_input) - context.execute_v2(bindings=bindings) - cuda.memcpy_dtoh(h_output, d_output) - - output_array = h_output.reshape(output_shape) - - print(f"\n实际输出:") - print(f" dtype: {output_array.dtype}") - print(f" shape: {output_array.shape}") - print(f" ndim: {output_array.ndim}") - - if output_array.ndim == 1: - print(f" total elements: {output_array.shape[0]}") - print(f" expected (84*4725): {84 * 4725}") - elif output_array.ndim == 2: - print(f" shape[0]: {output_array.shape[0]} (detections)") - print(f" shape[1]: {output_array.shape[1]} (features)") - elif output_array.ndim == 3: - print(f" shape[0]: {output_array.shape[0]} (batch)") - print(f" shape[1]: {output_array.shape[1]} (classes+coords)") - print(f" shape[2]: {output_array.shape[2]} (num_boxes)") - -except Exception as e: - print(f"Error: {e}") - import traceback - traceback.print_exc() diff --git a/models/yolo11n.engine b/models/yolo11n.engine deleted file mode 100644 index 16c8e49..0000000 Binary files a/models/yolo11n.engine and /dev/null differ diff --git a/models/yolo11n.onnx b/models/yolo11n.onnx deleted file mode 100644 index 0a67bf1..0000000 Binary files a/models/yolo11n.onnx and /dev/null differ diff --git a/models/yolo11n.pt b/models/yolo11n.pt deleted file mode 100644 index 0c5ff02..0000000 Binary files a/models/yolo11n.pt and /dev/null differ diff --git a/test_edge_run.py b/test_edge_run.py deleted file mode 100644 index 788d97e..0000000 --- a/test_edge_run.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -边缘端运行测试脚本 -添加测试摄像头和ROI配置，验证系统正常运行 -""" - -import sys -import os - -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from config.database import get_sqlite_manager -from datetime import datetime -import random - - -def setup_test_data(): - """设置测试数据""" - db = get_sqlite_manager() - - print("=" * 60) - print("边缘端运行测试 - 数据准备") - print("=" * 60) - - camera_id = "test_camera_01" - rtsp_url = "rtsp://admin:admin@172.16.8.35/cam/realmonitor?channel=6&subtype=1" - - print(f"\n1. 添加摄像头配置") - print(f" camera_id: {camera_id}") - print(f" rtsp_url: {rtsp_url}") - - result = db.save_camera_config( - camera_id=camera_id, - rtsp_url=rtsp_url, - camera_name="测试摄像头-车间入口", - location="车间入口通道", - enabled=True, - status=True - ) - print(f" 结果: {'成功' if result else '失败'}") - - print(f"\n2. 添加ROI配置（随机划分区域）") - - roi_configs = [ - { - "roi_id": f"{camera_id}_roi_01", - "name": "离岗检测区域", - "roi_type": "polygon", - "coordinates": [[100, 50], [300, 50], [300, 200], [100, 200]], - "algorithm_type": "leave_post", - "target_class": "person", - "confirm_on_duty_sec": 10, - "confirm_leave_sec": 30, - "cooldown_sec": 60, - "working_hours": [{"start": "08:00", "end": "18:00"}], - }, - { - "roi_id": f"{camera_id}_roi_02", - "name": "入侵检测区域", - "roi_type": "polygon", - "coordinates": [[350, 50], [550, 50], [550, 200], [350, 200]], - "algorithm_type": "intrusion", - "target_class": "person", - "alert_threshold": 3, - "alert_cooldown": 60, - "confirm_on_duty_sec": 10, - "confirm_leave_sec": 10, - "cooldown_sec": 60, - "working_hours": None, - }, - ] - - for roi in roi_configs: - print(f"\n ROI: {roi['name']}") - print(f" - roi_id: {roi['roi_id']}") - print(f" - algorithm_type: {roi['algorithm_type']}") - print(f" - coordinates: {roi['coordinates']}") - - result = db.save_roi_config( - roi_id=roi["roi_id"], - camera_id=camera_id, - roi_type=roi["roi_type"], - coordinates=roi["coordinates"], - algorithm_type=roi["algorithm_type"], - target_class=roi["target_class"], - confirm_on_duty_sec=roi["confirm_on_duty_sec"], - confirm_leave_sec=roi["confirm_leave_sec"], - cooldown_sec=roi["cooldown_sec"], - working_hours=str(roi["working_hours"]), - ) - print(f" 结果: {'成功' if result else '失败'}") - - print("\n" + "=" * 60) - print("测试数据准备完成") - print("=" * 60) - - return camera_id, roi_configs - - -def verify_data(): - """验证数据""" - db = get_sqlite_manager() - - print("\n" + "=" * 60) - print("验证数据库中的配置") - print("=" * 60) - - cameras = db.get_all_camera_configs() - print(f"\n摄像头数量: {len(cameras)}") - for cam in cameras: - print(f" - {cam['camera_id']}: {cam['camera_name']} ({cam['rtsp_url'][:50]}...)") - - rois = db.get_all_roi_configs() - print(f"\nROI数量: {len(rois)}") - for roi in rois: - print(f" - {roi['roi_id']}: {roi['name']} ({roi['algorithm_type']})") - - return len(cameras) > 0 and len(rois) > 0 - - -if __name__ == "__main__": - print("\n" + "#" * 60) - print("# 边缘端运行测试 - 数据准备") - print("#" * 60) - - setup_test_data() - verify_data() - - print("\n" + "#" * 60) - print("# 测试数据准备完成，请运行 main.py 进行推理测试") - print("#" * 60) diff --git a/test_inference.py b/test_inference.py deleted file mode 100644 index 5001e04..0000000 --- a/test_inference.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -边缘端运行测试脚本 - 推理测试 -运行 main.py 并测试 30 秒 -""" - -import subprocess -import sys -import os -import time - -def run_test(): - print("=" * 60) - print("边缘端运行测试 - 推理测试") - print("=" * 60) - print(f"测试时长: 30 秒") - print(f"测试时间: {time.strftime('%Y-%m-%d %H:%M:%S')}") - print("=" * 60) - - env = os.environ.copy() - env['PATH'] = r"C:\Users\16337\miniconda3\envs\yolo;" + env.get('PATH', '') - - cmd = [ - sys.executable, "main.py" - ] - - process = subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - text=True, - bufsize=1, - cwd=os.path.dirname(os.path.abspath(__file__)), - env=env - ) - - output_lines = [] - start_time = time.time() - - try: - while True: - line = process.stdout.readline() - if not line and process.poll() is not None: - break - - if line: - output_lines.append(line.strip()) - print(line.strip()) - - elapsed = time.time() - start_time - if elapsed >= 30: - print(f"\n[INFO] 测试达到 30 秒，停止进程...") - process.terminate() - try: - process.wait(timeout=5) - except subprocess.TimeoutExpired: - process.kill() - break - - except KeyboardInterrupt: - print("\n[INFO] 用户中断测试") - process.terminate() - - return output_lines - -if __name__ == "__main__": - run_test() - print("\n" + "=" * 60) - print("测试完成") - print("=" * 60)