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本文目录
- 1. 初始化
- 2. 保存onnx模型
- 3. 创建tensorrt引擎
- 4. 保存及载入引擎
- 5. 分配缓冲区
- 6. 前向推断
- 7. 矫正（Calibrator）
- 8. 具体的推断代码
- 9. 代码分析
- 10. 说明
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转载请注明出处：
https://www.cnblogs.com/darkknightzh/p/11332155.html
代码网址：
https://github.com/darkknightzh/TensorRT_pytorch
参考网址：
tensorrt安装包的sample/python目录
https://github.com/pytorch/examples/tree/master/mnist
此处代码使用的是tensorrt5.1.5
在安装完tensorrt之后，使用tensorrt主要包括下面几段代码：
1. 初始化
import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit # 此句代码中未使用，但是必须有。this is useful, otherwise stream = cuda.Stream() will cause 'explicit_context_dependent failed: invalid device context - no currently active context?'
如注解所示，import pycuda.autoinit这句话程序中未使用，但是必须包含，否则程序运行会出错。
2. 保存onnx模型
def saveONNX(model, filepath, c, h, w):
model = model.cuda()
dummy_input = torch.randn(1, c, h, w, device='cuda')
torch.onnx.export(model, dummy_input, filepath, verbose=True)
3. 创建tensorrt引擎
def build_engine(onnx_file_path):
TRT_LOGGER = trt.Logger(trt.Logger.WARNING) # INFO
# For more information on TRT basics, refer to the introductory samples.
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
if builder.platform_has_fast_fp16:
print('this card support fp16')
if builder.platform_has_fast_int8:
print('this card support int8')
builder.max_workspace_size = 1 << 30
with open(onnx_file_path, 'rb') as model:
parser.parse(model.read())
return builder.build_cuda_engine(network)
# This function builds an engine from a Caffe model.
def build_engine_int8(onnx_file_path, calib):
TRT_LOGGER = trt.Logger()
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
# We set the builder batch size to be the same as the calibrator's, as we use the same batches
# during inference. Note that this is not required in general, and inference batch size is
# independent of calibration batch size.
builder.max_batch_size = 1 # calib.get_batch_size()
builder.max_workspace_size = 1 << 30
builder.int8_mode = True
builder.int8_calibrator = calib
with open(onnx_file_path, 'rb') as model:
parser.parse(model.read()) # , dtype=trt.float32
return builder.build_cuda_engine(network)
4. 保存及载入引擎
def save_engine(engine, engine_dest_path):
buf = engine.serialize()
with open(engine_dest_path, 'wb') as f:
f.write(buf)
def load_engine(engine_path):
TRT_LOGGER = trt.Logger(trt.Logger.WARNING) # INFO
with open(engine_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
5. 分配缓冲区
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
def allocate_buffers(engine):
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
dtype = trt.nptype(engine.get_binding_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_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(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
6. 前向推断
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
7. 矫正（Calibrator）
使用tensorrt的int8时，需要矫正。具体可参见test_onnx_int8及calibrator.py。
8. 具体的推断代码
img_numpy = img.ravel().astype(np.float32) np.copyto(inputs[0].host, img_numpy) output = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream) output = [np.reshape(stage_i, (10)) for stage_i in output] # 有多个输出时遍历
9. 代码分析
程序中主要包括下面6个函数。
test_pytorch() # 测试pytorch模型的代码 export_onnx() # 导出pytorch模型到onnx模型 test_onnx_fp32() # 测试tensorrt的fp32模型（有保存引擎的代码） test_onnx_fp32_engine() # 测试tensorrt的fp32引擎的代码 test_onnx_int8() # 测试tensorrt的int8模型（有保存引擎的代码） test_onnx_int8_engine() # 测试tensorrt的int8引擎的代码
10. 说明
9的部分函数中，最开始有一句：
torch.load('mnist_cnn_3.pth') # 如果结果不对，加上这句话
因为有时候会碰到，不使用这句话，直接运行代码时，结果完全不正确;加上这句话之后，结果正确了。
具体原因为找到。。。也就先记在这里吧。