duan8/resnet

resnet

ResNet-18 and ResNet-50 model from "Deep Residual Learning for Image Recognition" https://arxiv.org/pdf/1512.03385.pdf

For the Pytorch implementation, you can refer to pytorchx/resnet

Wide Resnet-50 model from "Wide Residual Networks" https://arxiv.org/pdf/1605.07146.pdf . For the Pytorch implementation, you can refer to BlueMirrors/torchtrtz

Following tricks are used in this resnet, nothing special, residual connection and batchnorm are used.

• Batchnorm layer, implemented with scale layer.

TensorRT C++ API

// 1a. generate resnet18.wts,resnet34.wts or resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)

// 1b. generate wide_resnet50.wts from [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz)

// 2. put resnet18.wts,resnet34 or resnet50.wts into tensorrtx/resnet

// 3. build and run

cd tensorrtx/resnet

mkdir build

cd build

cmake ..

make

sudo ./resnet18 -s   // serialize model to plan file i.e. 'resnet18.engine'
sudo ./resnet18 -d   // deserialize plan file and run inference

or
sudo ./resnet34 -s   // serialize model to plan file i.e. 'resnet34.engine'
sudo ./resnet34 -d   // deserialize plan file and run inference

or

sudo ./resnet50 -s   // serialize model to plan file i.e. 'resnet50.engine'
sudo ./resnet50 -d   // deserialize plan file and run inference

or

sudo ./resnext50 -s   // serialize model to plan file i.e. 'resnext50.engine'
sudo ./resnext50 -d   // deserialize plan file and run inference

or

sudo ./wide_resnet50 -s   // serialize model to plan file i.e. 'wide_resnet50.engine'
sudo ./wide_resnet50 -d   // deserialize plan file and run inference


// 4. see if the output is same as 
- [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet) - for resnet18, resnet34, resnet50, resnext50
- [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz) - for wide_resnet50

TensorRT Python API

# 1a. generate resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet)
# 1b. generate wide_resnet50.wts from [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz)

# 2. put resnet50.wts or wide_resnet50.wts into tensorrtx/resnet

# 3. install Python dependencies (tensorrt/pycuda/numpy)

cd tensorrtx/resnet

python resnet50.py -s   // serialize model to plan file i.e. 'resnet50.engine'
python resnet50.py -d   // deserialize plan file and run inference

or 

python wide_resnet50.py -s   // serialize model to plan file i.e. 'wide_resnet50.engine'
python wide_resnet50.py -d   // deserialize plan file and run inference

# 4. see if the output is same as 
- pytorchx/resnet - for resnet50
- BlueMirrors/torchtrtz - for wide_resnet50