A pure numpy-based inference framework for CNN. The infCNN supports the inference of model trained on pytorch.
In general, all the elements used in the inference of CNN are divided into op and layer. op contains no trainable weights such as "relu, sigmoid, softmax, maxpool, flatten", layer contains trainable weights such as "conv2d, dense". The op and layer implemented are very few now.
Example is shown in net.py, which shows the inference of a LeNet CNN. Weights converted from pytorch model are loaded here. To be noted that net.py should align with train/lenet.py since they should have the same model. Just replacing nn.Conv2d/Linear with inferCNN.Conv2d/Dense will work.
The training on pytorch is regular, which can be seen in train/ folder. The weights of CNN are exported to .mat file.
The plugin example for mnist is in plgs/ folder