tensorrt加速yolov5 cpu
时间: 2023-09-01 18:14:03 浏览: 159
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对于使用TensorRT加速Yolov5的CPU推理,你可以按照以下步骤进行操作:
1. 首先,确保你已经安装了TensorRT和Yolov5。你可以从NVIDIA官方网站下载并安装TensorRT,并从Yolov5的GitHub页面获取Yolov5的代码。
2. 在Yolov5的代码中,将模型转换为ONNX格式。你可以使用`export.py`脚本来完成这一步骤,具体命令如下:
```
python export.py --weights yolov5s.pt --img 640 --batch 1
```
这将生成一个名为`yolov5s.onnx`的ONNX模型文件。
3. 接下来,你需要使用TensorRT来优化这个ONNX模型。你可以使用TensorRT的Python API来完成这一步骤。下面是一个简单的示例代码:
```python
import tensorrt as trt
import onnx
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
def build_engine(onnx_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
builder.max_batch_size = 1
with open(onnx_path, 'rb') as model:
parser.parse(model.read())
return builder.build_cuda_engine(network)
def save_engine(engine, file_path):
serialized_engine = engine.serialize()
with open(file_path, 'wb') as f:
f.write(serialized_engine)
onnx_path = 'yolov5s.onnx'
engine_path = 'yolov5s.trt'
engine = build_engine(onnx_path)
save_engine(engine, engine_path)
```
这将生成一个名为`yolov5s.trt`的TensorRT引擎文件。
4. 现在,你可以使用TensorRT引擎来进行推理。下面是一个简单的示例代码:
```python
import tensorrt as trt
import pycuda.driver as cuda
import numpy as np
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
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_mem, device_mem))
else:
outputs.append((host_mem, device_mem))
return inputs, outputs, bindings, stream
def do_inference(engine, inputs, outputs, bindings, stream):
[cuda.memcpy_htod_async(inp, host, stream) for host, inp in inputs]
engine_context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
[cuda.memcpy_dtoh_async(host, out) for host, out in outputs]
stream.synchronize()
engine_path = 'yolov5s.trt'
with open(engine_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime:
engine = runtime.deserialize_cuda_engine(f.read())
inputs, outputs, bindings, stream = allocate_buffers(engine)
# 这里填入你的输入数据
input_data = np.random.random(size=(1, 3, 640, 640)).astype(np.float32)
inputs[0][0].host = input_data
with engine.create_execution_context() as engine_context:
do_inference(engine_context, inputs, outputs, bindings, stream)
output_data = outputs[0][0].host
```
这将生成一个名为`output_data`的推理结果。
这样,你就可以使用TensorRT加速Yolov5的CPU推理了。请注意,在从ONNX模型生成TensorRT引擎时,你可以根据自己的需求进行一些参数的调整,如最大工作空间大小和最大批处理大小。此外,你还可以根据自己的具体情况优化推理过程中的内存分配和数据传输。希望对你有所帮助!如果还有其他问题,请随时提问。
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