def default_hook_generator()

in vissl/hooks/__init__.py [0:0]
60 lines of code
14 McCabe index (conditional complexity)

def default_hook_generator(cfg: AttrDict) -> List[ClassyHook]:
    """
    The utility function that prepares all the hoooks that will be used in training
    based on user selection. Some basic hooks are used by default.

    Optional hooks:
        - Tensorboard hook,
        - loss specific hooks (swav loss, deepcluster loss, moco loss) used only when the
          loss is being used
        - model complexity hook (if user wants to compute model flops, activations, params)
          enable the hook via HOOKS.MODEL_COMPLEXITY.COMPUTE_COMPLEXITY = True

    Returns:
        hooks (List(functions)): list containing the hook functions that will be used
    """
    hooks = []

    # conditionally add hooks based on use-case
    if cfg.HOOKS.PERF_STATS.MONITOR_PERF_STATS:
        perf_stat_freq = (
            cfg.HOOKS.PERF_STATS.PERF_STAT_FREQUENCY
            if cfg.HOOKS.PERF_STATS.PERF_STAT_FREQUENCY > 0
            else None
        )
        hooks.append(LogPerfTimeMetricsHook(perf_stat_freq))

    # add the loss hooks based on the loss being used
    hooks = add_loss_hooks(hooks, cfg.LOSS, cfg)

    if cfg.HOOKS.MODEL_COMPLEXITY.COMPUTE_COMPLEXITY:
        hooks.extend([SSLModelComplexityHook()])
    if cfg.HOOKS.LOG_GPU_STATS:
        hooks.extend([LogGpuStatsHook()])
    if cfg.HOOKS.MEMORY_SUMMARY.PRINT_MEMORY_SUMMARY:
        hooks.extend([LogGpuMemoryHook(cfg.HOOKS.MEMORY_SUMMARY.LOG_ITERATION_NUM)])
    if cfg.HOOKS.MEMORY_SUMMARY.DUMP_MEMORY_ON_EXCEPTION:
        hooks.append(DumpMemoryOnException())
    if cfg.HOOKS.TENSORBOARD_SETUP.USE_TENSORBOARD:
        assert is_tensorboard_available(), (
            "Tensorboard must be installed to use it. Please install tensorboard using:"
            "If pip environment: `pip install tensorboard` "
            "If using conda and you prefer conda install of tensorboard: "
            "`conda install -c conda-forge tensorboard`"
        )
        tb_hook = get_tensorboard_hook(cfg)
        hooks.extend([tb_hook])
    if cfg.MODEL.GRAD_CLIP.USE_GRAD_CLIP:
        hooks.extend(
            [
                GradClipHook(
                    norm_type=cfg.MODEL.GRAD_CLIP.NORM_TYPE,
                    max_norm=cfg.MODEL.GRAD_CLIP.MAX_NORM,
                )
            ]
        )

    # hooks that are used irrespective of workflow type
    rolling_btime_freq = (
        cfg.HOOKS.PERF_STATS.ROLLING_BTIME_FREQ
        if cfg.HOOKS.PERF_STATS.ROLLING_BTIME_FREQ > 0
        else None
    )

    if CudaSynchronizeHook.is_enabled(cfg.MODEL):
        hooks.append(CudaSynchronizeHook())

    if ProfilingHook.is_enabled(cfg.PROFILING):
        hooks.append(ProfilingHook(profiling_config=cfg.PROFILING))

    world_size = cfg.DISTRIBUTED.NUM_NODES * cfg.DISTRIBUTED.NUM_PROC_PER_NODE
    checkpoint_folder = get_checkpoint_folder(cfg)

    hooks.extend(
        [
            SetDataSamplerEpochHook(),
            FreezeParametersHook(),
            LogLossMetricsCheckpointHook(world_size),
            LogLossLrEtaHook(checkpoint_folder, rolling_btime_freq),
        ]
    )

    if cfg.METERS.model_output_mask:
        hooks.extend([ModelOutputMaskHook()])

    if cfg.HOOKS.CHECK_NAN:
        hooks.extend([CheckNanLossHook(), CheckNanModelOutputHook()])

    return hooks