From 367ca62fe92e3ee2f466b352a4e82111a24f1be7 Mon Sep 17 00:00:00 2001
From: dcarron <daniel.carron@idiap.ch>
Date: Wed, 26 Jul 2023 12:42:46 +0200
Subject: [PATCH] Update train_analysis, extract values from tensorboard logs
---
src/ptbench/scripts/experiment.py | 14 ++
src/ptbench/scripts/train_analysis.py | 198 ++++++++++----------------
src/ptbench/utils/tensorboard.py | 55 +++++++
3 files changed, 143 insertions(+), 124 deletions(-)
create mode 100644 src/ptbench/utils/tensorboard.py
diff --git a/src/ptbench/scripts/experiment.py b/src/ptbench/scripts/experiment.py
index 3c43db6a..bbfe5b86 100644
--- a/src/ptbench/scripts/experiment.py
+++ b/src/ptbench/scripts/experiment.py
@@ -297,8 +297,22 @@ def experiment(
resume_from=resume_from,
balance_classes=balance_classes,
)
+
logger.info("Ended training")
+ logger.info("Started train analysis")
+ from .train_analysis import train_analysis
+
+ logdir = os.path.join(train_output_folder, "logs")
+ output_pdf = os.path.join(train_output_folder, "train_analysis.pdf")
+ ctx.invoke(
+ train_analysis,
+ logdir=logdir,
+ output_pdf=output_pdf,
+ )
+
+ logger.info("Ended train analysis")
+
logger.info("Started predicting")
from .predict import predict
diff --git a/src/ptbench/scripts/train_analysis.py b/src/ptbench/scripts/train_analysis.py
index f33cd9ab..4061164f 100644
--- a/src/ptbench/scripts/train_analysis.py
+++ b/src/ptbench/scripts/train_analysis.py
@@ -8,126 +8,79 @@ import os
import click
import matplotlib.pyplot as plt
+import pandas
from clapper.click import ConfigCommand, ResourceOption, verbosity_option
from clapper.logging import setup
+from matplotlib.ticker import MaxNLocator
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
-def _loss_evolution(df):
- """Plots the loss evolution over time (epochs)
+def create_figures(df: pandas.DataFrame) -> list[plt.figure]:
+ """Generates figures for each metric in the dataframe.
+
+ Each row of the dataframe correspond to an epoch and each column to a metric.
+ It is assumed that some metric names are of the form <metric>/<subset>.
+ All subsets for a metric will be displayed on the same figure.
Parameters
----------
- df : pandas.DataFrame
- dataframe containing the training logs
-
+ df:
+ Pandas dataframe containing the data to plot.
Returns
-------
- matplotlib.figure.Figure: Figure to be displayed or saved to file
+ figures:
+ List of matplotlib figures, one per metric.
"""
- import numpy
-
- figure = plt.figure()
- axes = figure.gca()
-
- axes.plot(df.epoch.values, df.loss.values, label="Training")
- if "validation_loss" in df.columns:
- axes.plot(
- df.epoch.values, df.validation_loss.values, label="Validation"
- )
- # shows a red dot on the location with the minima on the validation set
- lowest_index = numpy.argmin(df["validation_loss"])
-
- axes.plot(
- df.epoch.values[lowest_index],
- df.validation_loss[lowest_index],
- "mo",
- label=f"Lowest validation ({df.validation_loss[lowest_index]:.3f}@{df.epoch[lowest_index]})",
- )
-
- if "extra_validation_losses" in df.columns:
- # These losses are in array format. So, we read all rows, then create a
- # 2d array. We transpose the array to iterate over each column and
- # plot the losses individually. They are numbered from 1.
- df["extra_validation_losses"] = df["extra_validation_losses"].apply(
- lambda x: numpy.fromstring(x.strip("[]"), sep=" ")
- )
- losses = numpy.vstack(df.extra_validation_losses.values).T
- for n, k in enumerate(losses):
- axes.plot(df.epoch.values, k, label=f"Extra validation {n+1}")
-
- axes.set_title("Loss over time")
- axes.set_xlabel("Epoch")
- axes.set_ylabel("Loss")
-
- axes.legend(loc="best")
- axes.grid(alpha=0.3)
- figure.set_layout_engine("tight")
-
- return figure
-
-
-def _hardware_utilisation(df, const):
- """Plot the CPU utilisation over time (epochs).
- Parameters
- ----------
+ figures = []
- df : pandas.DataFrame
- dataframe containing the training logs
+ labels = sorted(df.columns)
+ from collections import defaultdict
- const : dict
- training and hardware constants
+ # Dict of metric: subset. Subset can be None.
+ metrics_groups = defaultdict(list)
+ for label in labels:
+ # Separate the name of the subset from the metric
+ split_label = label.rsplit("/", 1)
+ metric = split_label[0]
+ subset = split_label[1] if len(split_label) > 1 else None
+ metrics_groups[metric].append(subset)
- Returns
- -------
+ for metric, subsets in metrics_groups.items():
+ figure = plt.figure()
+ axes = figure.gca()
- matplotlib.figure.Figure: figure to be displayed or saved to file
- """
- figure = plt.figure()
- axes = figure.gca()
-
- cpu_percent = df.cpu_percent.values / const["cpu_count"]
- cpu_memory = 100 * df.cpu_rss / const["cpu_memory_total"]
-
- axes.plot(
- df.epoch.values,
- cpu_percent,
- label=f"CPU usage (cores: {const['cpu_count']})",
- )
- axes.plot(
- df.epoch.values,
- cpu_memory,
- label=f"CPU memory (total: {const['cpu_memory_total']:.1f} Gb)",
- )
- if "gpu_percent" in df:
- axes.plot(
- df.epoch.values,
- df.gpu_percent.values,
- label=f"GPU usage (type: {const['gpu_name']})",
- )
- if "gpu_memory_percent" in df:
- axes.plot(
- df.epoch.values,
- df.gpu_memory_percent.values,
- label=f"GPU memory (total: {const['gpu_memory_total']:.1f} Gb)",
- )
- axes.set_title("Hardware utilisation over time")
- axes.set_xlabel("Epoch")
- axes.set_ylabel("Relative utilisation (%)")
- axes.set_ylim([0, 100])
-
- axes.legend(loc="best")
- axes.grid(alpha=0.3)
- figure.set_layout_engine("tight")
-
- return figure
+ for subset in subsets:
+ if subset is None:
+ axes.plot(
+ df["step"].values,
+ df[metric],
+ label=metric,
+ )
+ else:
+ axes.plot(
+ df["step"].values,
+ df[metric + "/" + subset],
+ label=subset,
+ )
+
+ axes.xaxis.set_major_locator(MaxNLocator(integer=True))
+ axes.set_title(metric)
+ axes.set_xlabel("Epoch")
+
+ axes.legend(loc="best")
+ axes.grid(alpha=0.3)
+ figure.set_layout_engine("tight")
+
+ figures.append(figure)
+
+ return figures
@click.command(
@@ -140,17 +93,12 @@ def _hardware_utilisation(df, const):
.. code:: sh
- ptbench train-analysis -vv log.csv constants.csv
-
+ ptbench train-analysis -vv results/logs
""",
)
@click.argument(
- "log",
- type=click.Path(dir_okay=False, exists=True),
-)
-@click.argument(
- "constants",
- type=click.Path(dir_okay=False, exists=True),
+ "logdir",
+ type=click.Path(dir_okay=True, exists=True),
)
@click.option(
"--output-pdf",
@@ -162,33 +110,35 @@ def _hardware_utilisation(df, const):
)
@verbosity_option(logger=logger, cls=ResourceOption, expose_value=False)
def train_analysis(
- log,
- constants,
- output_pdf,
+ logdir: str,
+ output_pdf: str,
**_,
-):
- """Analyzes the training logs for loss evolution and resource
- utilisation."""
+) -> None:
+ """Creates a plot for each metric in the training logs and saves them in a
+ pdf file.
+
+ Parameters
+ ----------
+
+ logdir:
+ Directory containing tensorboard event files.
- import pandas
+ output_pdf:
+ The pdf file in which to save the plots.
+ """
from matplotlib.backends.backend_pdf import PdfPages
- constants = pandas.read_csv(constants)
- constants = dict(zip(constants.keys(), constants.values[0]))
- data = pandas.read_csv(log)
+ from ..utils.tensorboard import get_scalars
+
+ data = get_scalars(logdir)
# makes sure the directory to save the output PDF is there
dirname = os.path.dirname(os.path.realpath(output_pdf))
if not os.path.exists(dirname):
os.makedirs(dirname)
- # now, do the analysis
with PdfPages(output_pdf) as pdf:
- figure = _loss_evolution(data)
- pdf.savefig(figure)
- plt.close(figure)
-
- figure = _hardware_utilisation(data, constants)
- pdf.savefig(figure)
- plt.close(figure)
+ for figure in create_figures(data):
+ pdf.savefig(figure)
+ plt.close(figure)
diff --git a/src/ptbench/utils/tensorboard.py b/src/ptbench/utils/tensorboard.py
new file mode 100644
index 00000000..6cd0d1e6
--- /dev/null
+++ b/src/ptbench/utils/tensorboard.py
@@ -0,0 +1,55 @@
+import glob
+import os
+
+from collections import defaultdict
+from typing import Any
+
+import pandas
+
+from tensorboard.backend.event_processing.event_accumulator import (
+ EventAccumulator,
+)
+
+
+def get_scalars(logdir: str) -> pandas.DataFrame:
+ """Returns scalars stored in tensorboard event files.
+
+ Parameters
+ ----------
+
+ logdir:
+ Directory containing the event files.
+
+ Returns
+ -------
+
+ data:
+ Pandas dataframe containing the results. Rows correspond to an epoch, columns to the metrics.
+ """
+ tensorboard_logs = sorted(
+ glob.glob(os.path.join(logdir, "events.out.tfevents.*"))
+ )
+
+ data: dict[str, dict[str, Any]] = defaultdict(dict)
+ headers = {"step"}
+
+ for logfile in tensorboard_logs:
+ event_accumulator = EventAccumulator(logfile)
+ event_accumulator.Reload()
+
+ tags = event_accumulator.Tags()
+ # Can cause issues if different logfiles don't have the same tags
+
+ for scalar_tag in tags["scalars"]:
+ headers.add(scalar_tag)
+ tag_list = event_accumulator.Scalars(scalar_tag)
+ for tag_data in tag_list:
+ _ = tag_data.wall_time
+ step = tag_data.step
+ value = tag_data.value
+
+ data[step]["step"] = step
+ data[step][scalar_tag] = value
+
+ data = pandas.DataFrame.from_dict(data, orient="index")
+ return data
--
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