Python tensorboard.summary.custom_scalar_pb() Examples

def tb_layout():
    episode_rewards = layout_pb2.Category(
        title="Episode Reward",
        chart=gen_multiline_charts(
            [
                ("Shaped Reward", [r"shaping/eprewmean_true"]),
                ("Episode Length", [r"eplenmean"]),
                ("Sparse Reward", [r"shaping/epsparsemean"]),
                ("Dense Reward", [r"shaping/epdensemean"]),
                ("Dense Reward Annealing", [r"shaping/rew_anneal_c"]),
                ("Unshaped Reward", [r"ep_rewmean"]),
                ("Victim Action Noise", [r"shaping/victim_noise"]),
            ]
        ),
    )

    game_outcome = layout_pb2.Category(
        title="Game Outcomes",
        chart=gen_multiline_charts(
            [
                ("Agent 0 Win Proportion", [r"game_win0"]),
                ("Agent 1 Win Proportion", [r"game_win1"]),
                ("Tie Proportion", [r"game_tie"]),
                ("# of games", [r"game_total"]),
            ]
        ),
    )

    training = layout_pb2.Category(
        title="Training",
        chart=gen_multiline_charts(
            [
                ("Policy Loss", [r"policy_loss"]),
                ("Value Loss", [r"value_loss"]),
                ("Policy Entropy", [r"policy_entropy"]),
                ("Explained Variance", [r"explained_variance"]),
                ("Approx KL", [r"approxkl"]),
                ("Clip Fraction", [r"clipfrac"]),
            ]
        ),
    )

    # Intentionally unused:
    # + serial_timesteps (just total_timesteps / num_envs)
    # + time_elapsed (TensorBoard already logs wall-clock time)
    # + nupdates (this is already logged as step)
    time = layout_pb2.Category(
        title="Time",
        chart=gen_multiline_charts([("Total Timesteps", [r"total_timesteps"]), ("FPS", [r"fps"])]),
    )

    categories = [episode_rewards, game_outcome, training, time]
    return summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories)) 

def finalize_autosummaries() -> None:
    """Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
    """
    global _finalized
    tfutil.assert_tf_initialized()

    if _finalized:
        return None

    _finalized = True
    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])

    # Create summary ops.
    with tf.device(None), tf.control_dependencies(None):
        for name, vars_list in _vars.items():
            name_id = name.replace("/", "_")
            with tfutil.absolute_name_scope("Autosummary/" + name_id):
                moments = tf.add_n(vars_list)
                moments /= moments[0]
                with tf.control_dependencies([moments]):  # read before resetting
                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
                        mean = moments[1]
                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
                        tf.summary.scalar(name, mean)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)

    # Group by category and chart name.
    cat_dict = OrderedDict()
    for series_name in sorted(_vars.keys()):
        p = series_name.split("/")
        cat = p[0] if len(p) >= 2 else ""
        chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
        if cat not in cat_dict:
            cat_dict[cat] = OrderedDict()
        if chart not in cat_dict[cat]:
            cat_dict[cat][chart] = []
        cat_dict[cat][chart].append(series_name)

    # Setup custom_scalar layout.
    categories = []
    for cat_name, chart_dict in cat_dict.items():
        charts = []
        for chart_name, series_names in chart_dict.items():
            series = []
            for series_name in series_names:
                series.append(layout_pb2.MarginChartContent.Series(
                    value=series_name,
                    lower="xCustomScalars/" + series_name + "/margin_lo",
                    upper="xCustomScalars/" + series_name + "/margin_hi"))
            margin = layout_pb2.MarginChartContent(series=series)
            charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
        categories.append(layout_pb2.Category(title=cat_name, chart=charts))
    layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
    return layout 

def _dump_tensors(self):
        if not self._has_recorded_tensor:
            return

        layout_categories = []

        for scope in self._scope_tensor:
            chart = []
            for name in self._scope_tensor[scope]:
                chart.append(
                    layout_pb2.Chart(
                        title=name,
                        multiline=layout_pb2.MultilineChartContent(
                            tag=[r'name(?!.*margin.*)'.replace('name', name)
                                 ])))
            category = layout_pb2.Category(title=scope, chart=chart)
            layout_categories.append(category)

        if layout_categories:
            layout_proto_to_write = layout_pb2.Layout(
                category=layout_categories)

            try:
                # Load former layout_proto from self._layout_writer_dir.
                multiplexer = event_multiplexer.EventMultiplexer()
                multiplexer.AddRunsFromDirectory(self._layout_writer_dir)
                multiplexer.Reload()
                tensor_events = multiplexer.Tensors(
                    '.', metadata.CONFIG_SUMMARY_TAG)
                shutil.rmtree(self._layout_writer_dir)

                # Parse layout proto from disk.
                string_array = tf.make_ndarray(tensor_events[0].tensor_proto)
                content = np.asscalar(string_array)
                layout_proto_from_disk = layout_pb2.Layout()
                layout_proto_from_disk.ParseFromString(
                    tf.compat.as_bytes(content))

                # Merge two layout proto.
                merged_layout_json = merge(
                    json_format.MessageToJson(layout_proto_from_disk),
                    json_format.MessageToJson(layout_proto_to_write))
                merged_layout_proto = layout_pb2.Layout()
                json_format.Parse(str(merged_layout_json), merged_layout_proto)

                self._layout_writer = tf.summary.FileWriter(
                    self._layout_writer_dir)
                layout_summary = summary_lib.custom_scalar_pb(
                    merged_layout_proto)
                self._layout_writer.add_summary(layout_summary)
                self._layout_writer.close()
            except KeyError:
                # Write the current layout proto into disk
                # when there is no layout.
                self._layout_writer = tf.summary.FileWriter(
                    self._layout_writer_dir)
                layout_summary = summary_lib.custom_scalar_pb(
                    layout_proto_to_write)
                self._layout_writer.add_summary(layout_summary)
                self._layout_writer.close() 

def finalize_autosummaries() -> None:
    """Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
    """
    global _finalized
    tfutil.assert_tf_initialized()

    if _finalized:
        return None

    _finalized = True
    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])

    # Create summary ops.
    with tf.device(None), tf.control_dependencies(None):
        for name, vars_list in _vars.items():
            name_id = name.replace("/", "_")
            with tfutil.absolute_name_scope("Autosummary/" + name_id):
                moments = tf.add_n(vars_list)
                moments /= moments[0]
                with tf.control_dependencies([moments]):  # read before resetting
                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
                        mean = moments[1]
                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
                        tf.summary.scalar(name, mean)
                        if enable_custom_scalars:
                            tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
                            tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)

    # Setup layout for custom scalars.
    layout = None
    if enable_custom_scalars:
        cat_dict = OrderedDict()
        for series_name in sorted(_vars.keys()):
            p = series_name.split("/")
            cat = p[0] if len(p) >= 2 else ""
            chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
            if cat not in cat_dict:
                cat_dict[cat] = OrderedDict()
            if chart not in cat_dict[cat]:
                cat_dict[cat][chart] = []
            cat_dict[cat][chart].append(series_name)
        categories = []
        for cat_name, chart_dict in cat_dict.items():
            charts = []
            for chart_name, series_names in chart_dict.items():
                series = []
                for series_name in series_names:
                    series.append(layout_pb2.MarginChartContent.Series(
                        value=series_name,
                        lower="xCustomScalars/" + series_name + "/margin_lo",
                        upper="xCustomScalars/" + series_name + "/margin_hi"))
                margin = layout_pb2.MarginChartContent(series=series)
                charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
            categories.append(layout_pb2.Category(title=cat_name, chart=charts))
        layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
    return layout 

def finalize_autosummaries() -> None:
    """Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
    """
    global _finalized
    tfutil.assert_tf_initialized()

    if _finalized:
        return None

    _finalized = True
    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])

    # Create summary ops.
    with tf.device(None), tf.control_dependencies(None):
        for name, vars_list in _vars.items():
            name_id = name.replace("/", "_")
            with tfutil.absolute_name_scope("Autosummary/" + name_id):
                moments = tf.add_n(vars_list)
                moments /= moments[0]
                with tf.control_dependencies([moments]):  # read before resetting
                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
                        mean = moments[1]
                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
                        tf.summary.scalar(name, mean)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)

    # Group by category and chart name.
    cat_dict = OrderedDict()
    for series_name in sorted(_vars.keys()):
        p = series_name.split("/")
        cat = p[0] if len(p) >= 2 else ""
        chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
        if cat not in cat_dict:
            cat_dict[cat] = OrderedDict()
        if chart not in cat_dict[cat]:
            cat_dict[cat][chart] = []
        cat_dict[cat][chart].append(series_name)

    # Setup custom_scalar layout.
    categories = []
    for cat_name, chart_dict in cat_dict.items():
        charts = []
        for chart_name, series_names in chart_dict.items():
            series = []
            for series_name in series_names:
                series.append(layout_pb2.MarginChartContent.Series(
                    value=series_name,
                    lower="xCustomScalars/" + series_name + "/margin_lo",
                    upper="xCustomScalars/" + series_name + "/margin_hi"))
            margin = layout_pb2.MarginChartContent(series=series)
            charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
        categories.append(layout_pb2.Category(title=cat_name, chart=charts))
    layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
    return layout 

def finalize_autosummaries() -> None:
    """Create the necessary ops to include autosummaries in TensorBoard report.
    Note: This should be done only once per graph.
    """
    global _finalized
    tfutil.assert_tf_initialized()

    if _finalized:
        return None

    _finalized = True
    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])

    # Create summary ops.
    with tf.device(None), tf.control_dependencies(None):
        for name, vars_list in _vars.items():
            name_id = name.replace("/", "_")
            with tfutil.absolute_name_scope("Autosummary/" + name_id):
                moments = tf.add_n(vars_list)
                moments /= moments[0]
                with tf.control_dependencies([moments]):  # read before resetting
                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
                        mean = moments[1]
                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
                        tf.summary.scalar(name, mean)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
                        tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)

    # Group by category and chart name.
    cat_dict = OrderedDict()
    for series_name in sorted(_vars.keys()):
        p = series_name.split("/")
        cat = p[0] if len(p) >= 2 else ""
        chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
        if cat not in cat_dict:
            cat_dict[cat] = OrderedDict()
        if chart not in cat_dict[cat]:
            cat_dict[cat][chart] = []
        cat_dict[cat][chart].append(series_name)

    # Setup custom_scalar layout.
    categories = []
    for cat_name, chart_dict in cat_dict.items():
        charts = []
        for chart_name, series_names in chart_dict.items():
            series = []
            for series_name in series_names:
                series.append(layout_pb2.MarginChartContent.Series(
                    value=series_name,
                    lower="xCustomScalars/" + series_name + "/margin_lo",
                    upper="xCustomScalars/" + series_name + "/margin_hi"))
            margin = layout_pb2.MarginChartContent(series=series)
            charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
        categories.append(layout_pb2.Category(title=cat_name, chart=charts))
    layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
    return layout