def evaluate()

in src/evaluate.py [0:0]

• 37 lines of code
• 15 McCabe index (conditional complexity)

def evaluate(train_config, reference_video, evaluations):
    if not hasattr(train_config, 'outDir'):
        train_config.outDir = train_config.logDir

    if "opt" in evaluations and not train_config.config_file.trainWithGTDepth:
        print(f"Rendering _opt.mp4")
        train_config.store_camera_options()
        train_config.config_file.camPath = "cam_path"
        train_config.config_file.camType = "PredefinedCamera"
        train_config.config_file.videoFrames = -1
        render_video(train_config, vid_name="_opt", out_dir=train_config.outDir)
        train_config.restore_camera_options()

    # get network size
    if "complexity" in evaluations:
        get_network_size(train_config)

    with torch.cuda.device(train_config.device):
        torch.cuda.empty_cache()

    # get image data
    if "images" in evaluations:
        generate_data(train_config, evaluations)

    with torch.cuda.device(train_config.device):
        torch.cuda.empty_cache()

    # get video data
    if "videos" in evaluations and not train_config.config_file.trainWithGTDepth:
        generate_data(train_config, evaluations, reference_video)

    if "output_videos" in evaluations and not train_config.config_file.trainWithGTDepth:
        # Overwrite the "video" names in the train_config with the requested video names
        if train_config.evaluation_cam_path is not None and len(train_config.evaluation_cam_path) > 0:
            for cam_path in train_config.evaluation_cam_path:
                print(f"Rendering output video {cam_path}")
                train_config.store_camera_options()
                train_config.config_file.camPath = cam_path
                train_config.config_file.camType = "PredefinedCamera"
                train_config.config_file.videoFrames = -1
                render_video(train_config, vid_name=cam_path, out_dir=train_config.outDir)
                train_config.restore_camera_options()
        else:
            print("Warning: output_videos was supplied for evaluation but no camera path (--camPath) was supplied!")

    if "export" in evaluations:
        export_onnx(train_config=train_config, out_dir=os.path.join(train_config.outDir, 'exported_model'))

    # copy opt.txt to eval folder to signify completed evaluation
    if os.path.exists(os.path.join(train_config.logDir, "opt.txt")):
        copyfile(os.path.join(train_config.logDir, "opt.txt"), os.path.join(train_config.outDir, "eval", "opt.txt"))