yolov8fps代码

### YOLOv8 FPS 实现代码示例 以下是基于 PyTorch 和 TensorRT 的 YOLOv8 推理性能测试代码示例。此代码可用于计算不同精度模式下的推理速度。 #### 使用 PyTorch 测试 FPS 以下是一个简单的 Python 脚本，用于测量 YOLOv8 模型在 PyTorch 中的平均 FPS： ```python import time from ultralytics import YOLO def calculate_fps_pytorch(model, images_path, num_images=10000): model = model.cuda() # 将模型加载到 GPU 上 start_time = time.time() for _ in range(num_images): results = model(images_path) # 假设 `images_path` 是输入图像路径 end_time = time.time() total_inference_time = end_time - start_time fps = num_images / total_inference_time return fps if __name__ == "__main__": model = YOLO('yolov8x.pt') # 加载预训练模型 pytorch_fps = calculate_fps_pytorch(model, 'test_image.jpg', num_images=10000) print(f"PyTorch FPS: {pytorch_fps:.2f}") ``` 上述脚本通过循环调用模型预测来模拟批量推理过程，并记录总耗时以计算 FPS[^1]。 --- #### 使用 TensorRT FP16 测试 FPS TensorRT 提供了更高的推理效率，特别是对于 FP16 精度的支持。下面是如何利用 TensorRT 进行 FPS 计算的一个例子： ```python import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit import numpy as np import time TRT_LOGGER = trt.Logger(trt.Logger.WARNING) def load_engine(engine_file_path): with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime: engine = runtime.deserialize_cuda_engine(f.read()) return engine def allocate_buffers(engine): inputs = [] outputs = [] bindings = [] stream = cuda.Stream() for binding in engine: size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size dtype = trt.nptype(engine.get_binding_dtype(binding)) host_mem = cuda.pagelocked_empty(size, dtype) device_mem = cuda.mem_alloc(host_mem.nbytes) bindings.append(int(device_mem)) if engine.binding_is_input(binding): inputs.append({'host': host_mem, 'device': device_mem}) else: outputs.append({'host': host_mem, 'device': device_mem}) return inputs, outputs, bindings, stream def do_inference(context, bindings, inputs, outputs, stream, batch_size=1): [cuda.memcpy_htod_async(inp['device'], inp['host'], stream) for inp in inputs] context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle) [cuda.memcpy_dtoh_async(out['host'], out['device'], stream) for out in outputs] stream.synchronize() def calculate_fps_tensorrt(engine_file, input_data, num_images=10000): engine = load_engine(engine_file) context = engine.create_execution_context() inputs, outputs, bindings, stream = allocate_buffers(engine) start_time = time.time() for _ in range(num_images): do_inference(context, bindings, inputs, outputs, stream) end_time = time.time() total_inference_time = end_time - start_time fps = num_images / total_inference_time return fps if __name__ == "__main__": fp16_engine_fps = calculate_fps_tensorrt('yolov8x_fp16.engine', None, num_images=10000) print(f"TensorRT FP16 FPS: {fp16_engine_fps:.2f}") ``` 该脚本展示了如何加载 TensorRT 引擎并执行推理操作，从而获得更高性能的 FPS 结果。 --- #### 使用 TensorRT INT8/Mixed Precision 测试 FPS 对于 INT8 或混合精度 (Mixed Precision)，可以采用类似的流程，只需调整引擎文件路径即可。例如，将 `'yolov8x_fp16.engine'` 替换为 `'yolov8x_int8.engine'` 或其他对应类型的引擎文件名。 --- ### 关于损失函数优化的影响 除了硬件加速外，改进损失函数也可以提升模型的整体表现。例如，在 YOLOv8 中引入 MPDIoU（Modified Point-to-Distance IoU）作为回归损失函数能够显著提高检测精度和收敛速度[^2]。 ---