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Getting Started with TensorRTx

We use a lenet5 demo to explain how we implement DL network in TensorRTx.

1. Run lenet5 in pytorch

Clone the wang-xinyu/pytorchx in your machine. Enter lenet folder.

And of course you should install pytorch first.

git clone https://github.com/wang-xinyu/pytorchx
cd pytorchx/lenet

Run lenet5.py to generate lenet5.pth which is the pytorch serialized model. The lenet5 arch is defined in lenet5.py.

python lenet5.py

Run inference.py to generate lenet5.wts, which is weights file for tensorrt.

python inference.py

You should see the output from terminal like this, the output of lenet5 is [[0.0950, 0.0998, 0.1101, 0.0975, 0.0966, 0.1097, 0.0948, 0.1056, 0.0992, 0.0917]], shape is [1, 10].

cuda device count:  2
input:  torch.Size([1, 1, 32, 32])
conv1 torch.Size([1, 6, 28, 28])
pool1:  torch.Size([1, 6, 14, 14])
conv2 torch.Size([1, 16, 10, 10])
pool2 torch.Size([1, 16, 5, 5])
view:  torch.Size([1, 400])
fc1:  torch.Size([1, 120])
lenet out: tensor([[0.0950, 0.0998, 0.1101, 0.0975, 0.0966, 0.1097, 0.0948, 0.1056, 0.0992,
         0.0917]], device='cuda:0', grad_fn=<SoftmaxBackward>)

2. Run lenet5 in tensorrt

Clone the wang-xinyu/tensorrtx in your machine. Enter lenet folder, copy lenet5.wts generated above, and cmake&make c++ code.

And of course you should install cuda/cudnn/tensorrt first.

git clone https://github.com/wang-xinyu/tensorrtx
cd tensorrtx/lenet
cp [PATH-OF-pytorchx]/pytorchx/lenet/lenet5.wts .
mkdir build
cd build
cmake ..
make

If the make succeed, the executable lenet will generated.

Run lenet to build tensorrt engine and serialize it to file lenet5.engine.

./lenet -s

Deserialize the engine and run inference.

./lenet -d

You should see the output like this,

Output:

0.0949623, 0.0998472, 0.110072, 0.0975036, 0.0965564, 0.109736, 0.0947979, 0.105618, 0.099228, 0.0916792,

3. Compare the two output

As the input to pytorch and tensorrt are same, i.e. a [1,1,32,32] all ones tensor.

So the output should be same, otherwise there must be something wrong.

The pytorch output is 
0.0950, 0.0998, 0.1101, 0.0975, 0.0966, 0.1097, 0.0948, 0.1056, 0.0992, 0.0917

The tensorrt output is 
0.0949623, 0.0998472, 0.110072, 0.0975036, 0.0965564, 0.109736, 0.0947979, 0.105618, 0.099228, 0.0916792

Same! exciting, isn't it?

The .wts content format

The .wts is plain text file.

For example the lenet5.wts, part content are shown below.

The first line is a number, indicate how many lines it has, excluding itself.

And then each line is

[weight name] [value count = N] [value1] [value2], ..., [valueN]

The value is in HEX format.

10
conv1.weight 150 be40ee1b bd20bab8 bdc4bc53 .......
conv1.bias 6 bd327058 .......
conv2.weight 2400 3c6f2220 3c693090 ......
conv2.bias 16 bd183967 bcb1ac8a .......
fc1.weight 48000 3c162c20 bd25196a ......
fc1.bias 120 3d3c3d49 bc64b948 ......
fc2.weight 10080 bce095a4 3d33b9dc ......
fc2.bias 84 bc71eaa0 3d9b276c ....... 
fc3.weight 840 3c252870 3d855351 .......
fc3.bias 10 bdbe4bb8 3b119ee0 ......