Download only rf toolbox5/3/2023 ![]() Here is a simple example: import torchvision from rfa_toolbox import create_graph_from_pytorch_model, visualize_architecture model = torchvision. The simplest way of importing a model is by directly extracting the compute-graph from the PyTorch-implementation of your model. There are multiple ways to import your model into RFA-Toolbox for analysis, with additional ways being added in future Since such a layer is probably not operating and maximum efficiency.īeing able to detect these types of inefficiencies is especially useful if you plan to train your model on resolutions that are substantially lower than theĪs an alternative, you can also use the graph from RFA-Toolbox to hook RFA-toolbox more directly into your program. ![]() In edge case scenarios, where the receptive field expands of the boundaries of the image on some but not all tensor-axis, the layer will be marked yellow, The visualization will automatically mark layers predicted to be unproductive red and critical layers, that are potentially unproductive orange. To visualize your architecture using GraphViz. You can do this simply by importing your architecture into the format of RFA-Toolbox and then use the in-build functions This library allows you to look for certain inefficiencies withing your convolutional neural network setup without Receptive Field Analysis (RFA) is a simple yet effective way to optimize the efficiency of any neural architecture without Pip install rfa_toolbox What is Receptive Field Analysis? Using receptive field analysis (RFA) and create graph visualizations of your architecture. This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures
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