This Sample Inference Application supports:
- connect to annInferenceServer
- queries number of GPUs on the server
- queries pre-loaded neural network models on the server
- upload pre-trained CAFFE models (optional)
- specify input width and height dimensions
- browse and pick deploy.prototxt and weights.caffemodel files
- specify input preprocesor values for normalization (if needed)
- specify input channel order (RGB or BGR)
- optionally, you can publish your neural network for use by others
- select "PublishAs"
- give a name to your model
- type password (default passowrd of
annInferenceServer
is "radeon")
- pick a CNN model, if upload option is not used
- run inference on images from a folder
- browse and pick labels (i.e., synset words for each output label)
- browse and pick input images to be classified
- optionally, pick list of input images from a .txt or .csv file
- optionally, specify max number of images to be processed from folder or image list
If you want to run a simple test, use annInferenceApp.py (a python script) to simply pick a network model to classify local images.
% python annInferenceApp.py [-v] -host:<hostname> -port:<port> -model:<modelName>
[-synset:<synset.txt>] [-output:<output.csv>]
<folder>|<file(s)>