| Argument list: | |
| * ```model_path``` Path to the TF SavedModel | |
| * ```save_path``` Path to save the PyTorch state dict | |
| * ```--statedict``` Export to state dict instead of TorchScript | |
| * ```--fp32``` Save weights at full precision | |
| * ```--skiptest``` Skip testing and save model immediately instead | |
| * ```--gpu``` Whether to attempt to use GPU for testing | |
| # Testing exported model | |
| The following script creates an MP4 video of interpolated frames between 2 input images: | |
| ``` | |
| python inference.py "model_path" "img1" "img2" [--save_path SAVE_PATH] [--gpu] [--fp16] [--frames FRAMES] [--fps FPS] | |
| ``` | |
| * ```model_path``` Path to the exported TorchScript checkpoint | |
| * ```img1``` Path to the first image | |
| * ```img2``` Path to the second image | |
| * ```--save_path SAVE_PATH``` Path to save the interpolated frames as a video, if absent it will be saved in the same directory as ```img1``` is located and named ```output.mp4``` | |
| * ```--gpu``` Whether to attempt to use GPU for predictions | |
| * ```--fp16``` Whether to use fp16 for calculations, speeds inference up on GPUs with tensor cores | |
| * ```--frames FRAMES``` Number of frames to interpolate between the input images | |
| * ```--fps FPS``` FPS of the output video |