Source code for catalyst.contrib.utils.plotly

from typing import Dict, List, Optional, Union
from collections import defaultdict
from pathlib import Path

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot

from .tools.tensorboard import SummaryItem, SummaryReader

def _get_tensorboard_scalars(
    logdir: Union[str, Path], metrics: Optional[List[str]], step: str
) -> Dict[str, List]:
    summary_reader = SummaryReader(logdir, types=["scalar"])

    items = defaultdict(list)
    for item in summary_reader:
        if step in item.tag and (
            metrics is None or any(m in item.tag for m in metrics)
    return items

def _get_scatter(scalars: List[SummaryItem], name: str) -> go.Scatter:
    xs = [s.step for s in scalars]
    ys = [s.value for s in scalars]
    return go.Scatter(x=xs, y=ys, name=name)

[docs]def plot_tensorboard_log( logdir: Union[str, Path], step: Optional[str] = "batch", metrics: Optional[List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, ) -> None: """@TODO: Docs. Contribution is welcome.""" init_notebook_mode() logdir = Path(logdir) logdirs = {"_log", ""): x for x in logdir.glob("**/*") if x.is_dir() and str(x).endswith("_log") } scalars_per_loader = { key: _get_tensorboard_scalars(inner_logdir, metrics, step) for key, inner_logdir in logdirs.items() } scalars_per_metric = defaultdict(dict) for key, value in scalars_per_loader.items(): for key2, value2 in value.items(): scalars_per_metric[key2][key] = value2 for metric_name, metric_logs in scalars_per_metric.items(): metric_data = [] for key, value in metric_logs.items(): try: data_ = _get_scatter(value, f"{key}/{metric_name}") metric_data.append(data_) except: # noqa: E722 pass layout = go.Layout( title=metric_name, height=height, width=width, yaxis={"hoverformat": ".5f"}, ) iplot(go.Figure(data=metric_data, layout=layout))
__all__ = ["plot_tensorboard_log"]