python模型部署_pytorch模型部署到移动端（使用腾讯TNN）
记录分为
pytorch2onnx
onnx2tnn
tnn结果验证
移动端(安卓)使用
1、pytorch2onnx
环境：
pytorch 1.4.0
onnx 1.6.0 (转换)
onnxruntime 1.3.0 (测试)
onnx-simplifier 0.2.9 (模型量化，不执行后续报错了，我测试是这样的)
转换代码：
import onnx
import torch
from test_net import TestModel
import numpy as np
import cv2
if 1:
torch_model = TestModel("model.pt")
torch_model.eval()
batch_size = 1 #批处理大小
input_shape = (3,384,384) #输入数据
# set the model to inference mode
# torch_model.eval()
x = torch.randn(batch_size,*input_shape)# 生成张量
export_onnx_file = "./model.onnx"# 目的ONNX文件名
torch.onnx.export(torch_model,
x,
export_onnx_file,
export_params=True,
opset_version=11,
do_constant_folding=True, # wether to execute constant folding for optimization
input_names = ['input'], # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes={'input' : {0 : 'batch_size'}, # variable lenght axes
'output' : {0 : 'batch_size'}}
)
print ('get onnx ok!')
利用 onnxruntime 测试转换的模型:
import onnxruntime
import imageio
import time
(width, height) = (384,384)
cap = cv2.VideoCapture(0)
while 1:
ret,img = cap.read()
time_start = time.time()
if img is None:
print('no image input!')
break
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
in_height ,in_width ,_ = img.shape
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
img_resized = (
torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
)
value = img_resized.unsqueeze(0)
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
ort_session = onnxruntime.InferenceSession("model.onnx")
ort_inputs = {ort_session.get_inputs()[0].name: (to_numpy(value)).astype(np.float32)}
#Actual: (N11onnxruntime17PrimitiveDataTypeIdEE) , expected: (N11onnxruntime17PrimitiveDataTypeIfEE)
#传入数据类型不对
ort_outs = ort_session.run(None, ort_inputs)
result = ort_outs[0][0, 0, :, :]
result = np.array(result)
print (result.shape)
reslut_resized = cv2.resize(
result, (in_width, in_height), interpolation=cv2.INTER_AREA
)
print('cost : %.3f (s)'%(time.time() - time_start))
cv2.namedWindow('re',2)
cv2.imshow('re',reslut_resized)
if cv2.waitKey(1) ==27:
break
cap.release()
cv2.destroyAllWindows()
模型简化操作：
python -m onnxsim model10.onnx model_sim.onnx --input-shape 1,3,384,384
2、onnx2tnn
下载tnn源码，https://github.com/Tencent/TNN ；进入
~/TNN-master/tools/onnx2tnn/onnx-converter 文件夹，运行 ./build 进行编译。
2.运行命令进行转换
python onnx2tnn.py model/model_sim.onnx -version=algo_version -optimize=1
0.----onnx version:1.6.0
结果为：
algo_optimize 1
onnx_net_opt_path /home/jiang/TNN-master/tools/onnx2tnn/onnx-converter/model/model_sim.opt.onnx
1.----onnx_optimizer: /home/jiang/TNN-master/tools/onnx2tnn/onnx-converter/model/model_sim.onnx
/home/jiang/TNN-master/tools/onnx2tnn/onnx-converter
----load onnx model: /home/jiang/TNN-master/tools/onnx2tnn/onnx-converter/model/model_sim.onnx
----onnxsim.simplify error: You'd better check the result with Netron
----onnxsim.simplify error:
----export optimized onnx model: /home/jiang/TNN-master/tools/onnx2tnn/onnx-converter/model/model_sim.opt.onnx
----export optimized onnx model done
2.----onnx2tnn: /home/jiang/TNN-master/tools/onnx2tnn/onnx-converter/model/model_sim.opt.onnx
get_node_attr_ai [Line 116] name :546
get_node_attr_ai [Line 116] name :585
get_node_attr_ai [Line 116] name :624
get_node_attr_ai [Line 116] name :663
get_node_attr_ai [Line 116] name :693
TNNLayerParam [Line 61] resize: coordinate_transformation_mode(pytorch_half_pixel) is not supported, result may be different.
3.----onnx2tnn status: 0
出现了错误----onnxsim.simplify error: You’d better check the result with Netron
----onnxsim.simplify error: 。
参数 algo_optimize=0即不进行优化就不保存转换成功。
3、tnn结果验证
先要安装Android环境；
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