PSPNet | Pyramid Scene Parsing Network, CVPR2017 | Machine Learning library

Similar to the issue of The trained model can be deployed on the other platform without dependency of sagemaker or aws service?.

I have trained a model on AWS SageMaker by using the built-in algorithm Semantic Segmentation. This trained model named as model.tar.gz is stored on S3. So I want to download this file from S3 and then use it to make inference on my local PC without using AWS SageMaker anymore. Since the built-in algorithm Semantic Segmentation is built using the MXNet Gluon framework and the Gluon CV toolkit, so I try to refer the documentation of mxnet and gluon-cv to make inference on local PC.

It's easy to download this file from S3, and then I unzip this file to get three files:

1. hyperparams.json: includes the parameters for network architecture, data inputs, and training. Refer to Semantic Segmentation Hyperparameters.
2. model_algo-1
3. model_best.params

Both model_algo-1 and model_best.params are the trained models, and I think it's the output from net.save_parameters (Refer to Train the neural network). I can also load them with the function mxnet.ndarray.load.

Refer to Predict with a pre-trained model. I found there are two necessary things:

1. Reconstruct the network for making inference.
2. Load the trained parameters.

As for reconstructing the network for making inference, since I have used PSPNet from training, so I can use the class gluoncv.model_zoo.PSPNet to reconstruct the network. And I know how to use some services of AWS SageMaker, for example batch transform jobs, to make inference. I want to reproduce it on my local PC. If I use the class gluoncv.model_zoo.PSPNet to reconstruct the network, I can't make sure whether the parameters for this network are same those used on AWS SageMaker while making inference. Because I can't see the image 501404015308.dkr.ecr.ap-northeast-1.amazonaws.com/semantic-segmentation:latest in detail.

As for loading the trained parameters, I can use the load_parameters. But as for model_algo-1 and model_best.params, I don't know which one I should use.

I am currently trying to write a c++ wrapper for PSPNet's prediction (originally in Matlab). PSPNet runs on Caffe.

Situation: I have a trained caffe model, and would like to implement this wrapper to run the segmentation result when given an input. In this case, my crop_size is smaller than it's original size. Thus, it is being cropped manually to multiple 425x425 "frames" and fed forward into caffe net after the pre-processes in a for-loop.

Problem: However, net seems to only be running forward once despite being in a for loop. Supported by its processing time and output, refer below.

This is the incomplete code I am currently trying to work on: