import numpy as np import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit import time import torch # 1. 确定batch size大小，与导出的trt模型保持一致 BATCH_SIZE = 32 # 2. 选择是否采用FP16精度，与导出的trt模型保持一致 USE_FP16 = True target_dtype = np.float16 if USE_FP16 else np.float32 # 3. 创建Runtime，加载TRT引擎 f = open("resnet_engine.trt", "rb") # 读取trt模型 runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) # 创建一个Runtime(传入记录器Logger) engine = runtime.deserialize_cuda_engine(f.read()) # 从文件中加载trt引擎 context = engine.create_execution_context() # 创建context # 4. 分配input和output内存 input_batch = np.random.randn(BATCH_SIZE, 224, 224, 3).astype(target_dtype) output = np.empty([BATCH_SIZE, 1000], dtype = target_dtype) d_input = cuda.mem_alloc(1 * input_batch.nbytes) d_output = cuda.mem_alloc(1 * output.nbytes) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # 5. 创建predict函数 def predict(batch): # result gets copied into output # transfer input data to device cuda.memcpy_htod_async(d_input, batch, stream) # execute model context.execute_async_v2(bindings, stream.handle, None) # 此处采用异步推理。如果想要同步推理，需将execute_async_v2替换成execute_v2 # transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # syncronize threads stream.synchronize() return output # 6. 调用predict函数进行推理，并记录推理时间 def preprocess_input(input): # input_batch无法直接传给模型，还需要做一定的预处理 # 此处可以添加一些其它的预处理操作（如标准化、归一化等） result = torch.from_numpy(input).transpose(0,2).transpose(1,2) # 利用torch中的transpose,使(224,224,3)——>(3,224,224) return np.array(result, dtype=target_dtype) preprocessed_inputs = np.array([preprocess_input(input) for input in input_batch]) # (BATCH_SIZE,224,224,3)——>(BATCH_SIZE,3,224,224) print("Warming up...") pred = predict(preprocessed_inputs) print("Done warming up!") t0 = time.time() pred = predict(preprocessed_inputs) t = time.time() - t0 print("Prediction cost {:.4f}s".format(t)) 请将这部分代码，改成可以输入电脑摄像头视频的

这段代码是用Python语言实现的，用于引入一些常用的库，例如numpy、tensorrt、pycuda等等。同时，还引入了torch库。其中，numpy是Python中用于快速处理大型数组和矩阵计算的库；tensorrt是NVIDIA推出的深度学习推理引擎，可以将训练好的模型转化为高效的推理模型；pycuda是一个Python接口，用于与CUDA（Compute Unified Device Architecture）进行交互，使得Python能够利用GPU进行高性能计算；torch是一个基于Python的科学计算包，主要面向深度学习领域。

相关问题

tensorrt提速yolov5

为了提高YOLOv5的推理速度，可以使用TensorRT进行加速。以下是使用TensorRT加速YOLOv5的步骤：

1. 首先，需要安装TensorRT并设置环境变量。可以参考NVIDIA官方文档进行安装和设置。

2. 接下来，需要将YOLOv5模型导出为ONNX格式。可以使用官方提供的export.py脚本进行导出。例如，导出yolov5s模型可以使用以下命令：

   python export.py --weights yolov5s.pt --img 640 --batch 1 --include torchscript onnx --optimize --dynamic

这将在模型位置处生成yolov5s.onnx文件。

3. 然后，可以使用TensorRT进行优化和加速。可以使用以下代码进行加载和优化：

   import tensorrt as trt

   TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

   def build_engine(onnx_file_path):
       with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
           builder.max_workspace_size = 1 << 30 # 1GB
           builder.max_batch_size = 1
           builder.fp16_mode = True
           with open(onnx_file_path, 'rb') as model:
               parser.parse(model.read())
           engine = builder.build_cuda_engine(network)
           return engine

这将返回一个已经优化的TensorRT引擎。

4. 最后，可以使用以下代码进行推理：

   import pycuda.driver as cuda
   import pycuda.autoinit
   import numpy as np
   import cv2

   def allocate_buffers(engine):
       inputs = []
       outputs = []
       bindings = []
       for binding in engine:
           size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
           dtype = trt.nptype(engine.get_binding_dtype(binding))
           # Allocate host and device buffers
           host_mem = cuda.pagelocked_empty(size, dtype)
           device_mem = cuda.mem_alloc(host_mem.nbytes)
           # Append the device buffer to device bindings.
           bindings.append(int(device_mem))
           # Append to the appropriate list.
           if engine.binding_is_input(binding):
               inputs.append({'host': host_mem, 'device': device_mem})
      

Tensorrt 可以将PyTorch中的神经网络模型转换为onnx 格式吗

是的，TensorRT可以将PyTorch中的神经网络模型转换为ONNX格式。TensorRT提供了一个Python API，您可以使用它来将PyTorch模型转换为ONNX格式，然后使用TensorRT将其优化为适用于GPU加速推理的序列化引擎。具体步骤如下：

1. 将PyTorch模型转换为ONNX格式：

import torch
import onnx

# Load the PyTorch model
model = torch.load('model.pt')

# Convert the PyTorch model to ONNX
dummy_input = torch.randn(1, 3, 224, 224)
input_names = ['input']
output_names = ['output']
onnx_path = 'model.onnx'
torch.onnx.export(model, dummy_input, onnx_path, verbose=False, input_names=input_names, output_names=output_names)

2. 使用TensorRT将ONNX模型优化为序列化引擎：

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

# Load the ONNX model
onnx_path = 'model.onnx'
onnx_model = onnx.load(onnx_path)

# Create a TensorRT builder and network
trt_logger = trt.Logger(trt.Logger.WARNING)
trt_builder = trt.Builder(trt_logger)
trt_network = trt_builder.create_network()

# Create an ONNX parser to parse the ONNX model into the TensorRT network
onnx_parser = trt.OnnxParser(trt_network, trt_logger)
onnx_parser.parse(onnx_model.SerializeToString())

# Set the maximum batch size and maximum workspace size
trt_builder.max_batch_size = 1
trt_builder.max_workspace_size = 1 << 30

# Build the TensorRT engine from the TensorRT network
trt_engine = trt_builder.build_cuda_engine(trt_network)

# Serialize the TensorRT engine to a file
trt_engine_path = 'model.engine'
with open(trt_engine_path, 'wb') as f:
    f.write(trt_engine.serialize())

3. 使用TensorRT引擎进行推理：

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np

# Load the serialized TensorRT engine
trt_engine_path = 'model.engine'
with open(trt_engine_path, 'rb') as f:
    trt_engine_data = f.read()

# Create a TensorRT runtime and deserialize the TensorRT engine
trt_logger = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(trt_logger)
trt_engine = trt_runtime.deserialize_cuda_engine(trt_engine_data)

# Create a TensorRT execution context
trt_context = trt_engine.create_execution_context()

# Allocate GPU memory for the input and output tensors
input_shape = (1, 3, 224, 224)
output_shape = (1, 1000)
input_dtype = np.float32
output_dtype = np.float32
input_size = np.product(input_shape) * np.dtype(input_dtype).itemsize
output_size = np.product(output_shape) * np.dtype(output_dtype).itemsize
input_gpu = cuda.mem_alloc(input_size)
output_gpu = cuda.mem_alloc(output_size)

# Create a CUDA stream
stream = cuda.Stream()

# Initialize the input tensor with random data
input_cpu = np.random.rand(*input_shape).astype(input_dtype)
cuda.memcpy_htod_async(input_gpu, input_cpu, stream)

# Run inference on the TensorRT engine
trt_context.execute_async(1, [int(input_gpu), int(output_gpu)], stream.handle, None)

# Copy the output tensor back to the CPU
output_cpu = np.empty(output_shape, dtype=output_dtype)
cuda.memcpy_dtoh_async(output_cpu, output_gpu, stream)

# Synchronize the CUDA stream
stream.synchronize()

# Print the output tensor
print(output_cpu)