From eb3ae3d272e922019c5d5bba69d5289d4b3a223f Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Mon, 30 Jan 2023 11:14:32 +0100
Subject: [PATCH] [tests] Fix docformatting
---
src/ptbench/configs/datasets/__init__.py | 2 --
src/ptbench/data/dataset.py | 5 -----
src/ptbench/data/loader.py | 4 ----
src/ptbench/data/transforms.py | 3 ++-
src/ptbench/data/utils.py | 2 --
src/ptbench/engine/evaluator.py | 3 ---
src/ptbench/engine/predictor.py | 1 -
src/ptbench/engine/trainer.py | 3 ---
src/ptbench/models/alexnet.py | 2 --
src/ptbench/models/densenet.py | 1 -
src/ptbench/models/densenet_rs.py | 2 --
src/ptbench/models/logistic_regression.py | 1 -
src/ptbench/models/pasa.py | 2 --
src/ptbench/models/signs_to_tb.py | 1 -
src/ptbench/scripts/aggregpred.py | 1 -
src/ptbench/scripts/compare.py | 1 -
src/ptbench/scripts/config.py | 15 ++++++++-------
src/ptbench/scripts/dataset.py | 4 ----
src/ptbench/scripts/evaluate.py | 1 -
src/ptbench/scripts/predtojson.py | 2 --
src/ptbench/scripts/train.py | 3 ---
src/ptbench/scripts/train_analysis.py | 2 --
src/ptbench/utils/checkpointer.py | 1 -
src/ptbench/utils/download.py | 1 -
src/ptbench/utils/measure.py | 4 ----
src/ptbench/utils/plot.py | 4 ----
src/ptbench/utils/rc.py | 1 -
src/ptbench/utils/resources.py | 8 --------
src/ptbench/utils/table.py | 1 -
tests/conftest.py | 2 --
30 files changed, 10 insertions(+), 73 deletions(-)
diff --git a/src/ptbench/configs/datasets/__init__.py b/src/ptbench/configs/datasets/__init__.py
index 1b074837..d5b0352a 100644
--- a/src/ptbench/configs/datasets/__init__.py
+++ b/src/ptbench/configs/datasets/__init__.py
@@ -49,7 +49,6 @@ def make_subset(samples, transforms=[], prefixes=[], suffixes=[]):
subset : :py:class:`ptbench.data.utils.SampleListDataset`
A pre-formatted dataset that can be fed to one of our engines
"""
-
from ...data.utils import SampleListDataset as wrapper
return wrapper(samples, prefixes + transforms + suffixes)
@@ -184,7 +183,6 @@ def get_samples_weights(dataset):
samples_weights : :py:class:`torch.Tensor`
the weights for all the samples in the dataset given as input
"""
-
samples_weights = []
if isinstance(dataset, torch.utils.data.ConcatDataset):
diff --git a/src/ptbench/data/dataset.py b/src/ptbench/data/dataset.py
index a87aaf0b..995f227c 100644
--- a/src/ptbench/data/dataset.py
+++ b/src/ptbench/data/dataset.py
@@ -102,7 +102,6 @@ class JSONDataset:
errors : int
Number of errors found
"""
-
logger.info("Checking dataset...")
errors = 0
for proto in self._protocols:
@@ -146,7 +145,6 @@ class JSONDataset:
A dictionary mapping subset names to lists of objects (respecting
the ``key``, ``data`` interface).
"""
-
fileobj = self._protocols[protocol]
if isinstance(fileobj, (str, bytes, pathlib.Path)):
if str(fileobj).endswith(".bz2"):
@@ -246,7 +244,6 @@ class CSVDataset:
errors : int
Number of errors found
"""
-
logger.info("Checking dataset...")
errors = 0
for name in self._subsets.keys():
@@ -277,7 +274,6 @@ class CSVDataset:
A dictionary mapping subset names to lists of objects (respecting
the ``key``, ``data`` interface).
"""
-
return {k: self.samples(k) for k in self._subsets.keys()}
def samples(self, subset):
@@ -301,7 +297,6 @@ class CSVDataset:
subset : list
A lists of objects (respecting the ``key``, ``data`` interface).
"""
-
fileobj = self._subsets[subset]
if isinstance(fileobj, (str, bytes, pathlib.Path)):
with open(self._subsets[subset], newline="") as f:
diff --git a/src/ptbench/data/loader.py b/src/ptbench/data/loader.py
index 1e0f8923..2e03aa35 100644
--- a/src/ptbench/data/loader.py
+++ b/src/ptbench/data/loader.py
@@ -29,7 +29,6 @@ def load_pil(path):
image : PIL.Image.Image
A PIL image
"""
-
return PIL.Image.open(path)
@@ -49,7 +48,6 @@ def load_pil_baw(path):
image : PIL.Image.Image
A PIL image in grayscale mode
"""
-
return load_pil(path).convert("L")
@@ -69,7 +67,6 @@ def load_pil_rgb(path):
image : PIL.Image.Image
A PIL image in RGB mode
"""
-
return load_pil(path).convert("RGB")
@@ -99,7 +96,6 @@ def make_delayed(sample, loader, key=None):
In which ``key`` is as provided and ``data`` can be accessed to trigger
sample loading.
"""
-
return DelayedSample(
functools.partial(loader, sample),
key=key or sample["data"],
diff --git a/src/ptbench/data/transforms.py b/src/ptbench/data/transforms.py
index 91d56111..19b1cb6b 100644
--- a/src/ptbench/data/transforms.py
+++ b/src/ptbench/data/transforms.py
@@ -26,7 +26,8 @@ class SingleAutoLevel16to8:
This transform assumes that the input image is gray-scaled.
- To auto-level, we calculate the maximum and the minimum of the image, and
+ To auto-level, we calculate the maximum and the minimum of the
+ image, and
consider such a range should be mapped to the [0,255] range of the
destination image.
"""
diff --git a/src/ptbench/data/utils.py b/src/ptbench/data/utils.py
index b7437539..bc5bdbd4 100644
--- a/src/ptbench/data/utils.py
+++ b/src/ptbench/data/utils.py
@@ -59,7 +59,6 @@ class SampleListDataset(torch.utils.data.Dataset):
An optional list of transforms to set in the copy. If not
specified, use ``self.transforms``.
"""
-
return SampleListDataset(self._samples, transforms or self.transforms)
def random_permute(self, feature):
@@ -113,7 +112,6 @@ class SampleListDataset(torch.utils.data.Dataset):
The sample data: ``[key, image, label]``
"""
-
if isinstance(key, slice):
return [self[k] for k in range(*key.indices(len(self)))]
else: # we try it as an int
diff --git a/src/ptbench/engine/evaluator.py b/src/ptbench/engine/evaluator.py
index 3c0348c0..5e86ee85 100644
--- a/src/ptbench/engine/evaluator.py
+++ b/src/ptbench/engine/evaluator.py
@@ -37,7 +37,6 @@ def eer_threshold(neg, pos) -> float:
Threshold
"""
-
from scipy.interpolate import interp1d
from scipy.optimize import brentq
@@ -52,7 +51,6 @@ def eer_threshold(neg, pos) -> float:
def posneg(pred, gt, threshold):
"""Calculates true and false positives and negatives."""
-
# threshold
binary_pred = torch.gt(pred, threshold)
@@ -107,7 +105,6 @@ def sample_measures_for_threshold(pred, gt, threshold):
f1_score: float
"""
-
tp_tensor, fp_tensor, tn_tensor, fn_tensor = posneg(pred, gt, threshold)
# calc measures from scalars
diff --git a/src/ptbench/engine/predictor.py b/src/ptbench/engine/predictor.py
index 7becc675..27d9d214 100644
--- a/src/ptbench/engine/predictor.py
+++ b/src/ptbench/engine/predictor.py
@@ -72,7 +72,6 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
all_predictions : list
All the predictions associated with filename and groundtruth
"""
-
output_folder = os.path.join(output_folder, name)
logger.info(f"Output folder: {output_folder}")
diff --git a/src/ptbench/engine/trainer.py b/src/ptbench/engine/trainer.py
index 2c6be7e1..2e3d91a6 100644
--- a/src/ptbench/engine/trainer.py
+++ b/src/ptbench/engine/trainer.py
@@ -44,7 +44,6 @@ def torch_evaluation(model):
model : :py:class:`torch.nn.Module`
Network
"""
-
model.eval()
yield model
model.train()
@@ -217,7 +216,6 @@ def train_epoch(loader, model, optimizer, device, criterion, batch_chunk_count):
A floating-point value corresponding the weighted average of this
epoch's loss
"""
-
losses_in_epoch = []
samples_in_epoch = []
losses_in_batch = []
@@ -308,7 +306,6 @@ def validate_epoch(loader, model, device, criterion, pbar_desc):
A floating-point value corresponding the weighted average of this
epoch's loss
"""
-
batch_losses = []
samples_in_batch = []
diff --git a/src/ptbench/models/alexnet.py b/src/ptbench/models/alexnet.py
index 7248d306..ea096ecb 100644
--- a/src/ptbench/models/alexnet.py
+++ b/src/ptbench/models/alexnet.py
@@ -44,7 +44,6 @@ class Alexnet(nn.Module):
tensor : :py:class:`torch.Tensor`
"""
-
return self.model_ft(x)
@@ -56,7 +55,6 @@ def build_alexnet(pretrained=False):
module : :py:class:`torch.nn.Module`
"""
-
model = Alexnet(pretrained=pretrained)
model = [("normalizer", TorchVisionNormalizer()), ("model", model)]
model = nn.Sequential(OrderedDict(model))
diff --git a/src/ptbench/models/densenet.py b/src/ptbench/models/densenet.py
index 2b28be03..7a98acac 100644
--- a/src/ptbench/models/densenet.py
+++ b/src/ptbench/models/densenet.py
@@ -54,7 +54,6 @@ def build_densenet(pretrained=False, nb_channels=3):
module : :py:class:`torch.nn.Module`
"""
-
model = Densenet(pretrained=pretrained)
model = [
("normalizer", TorchVisionNormalizer(nb_channels=nb_channels)),
diff --git a/src/ptbench/models/densenet_rs.py b/src/ptbench/models/densenet_rs.py
index a5836930..c4448fbc 100644
--- a/src/ptbench/models/densenet_rs.py
+++ b/src/ptbench/models/densenet_rs.py
@@ -40,7 +40,6 @@ class DensenetRS(nn.Module):
tensor : :py:class:`torch.Tensor`
"""
-
return self.model_ft(x)
@@ -52,7 +51,6 @@ def build_densenetrs():
module : :py:class:`torch.nn.Module`
"""
-
model = DensenetRS()
model = [("normalizer", TorchVisionNormalizer()), ("model", model)]
model = nn.Sequential(OrderedDict(model))
diff --git a/src/ptbench/models/logistic_regression.py b/src/ptbench/models/logistic_regression.py
index 7b001061..7e7818c7 100644
--- a/src/ptbench/models/logistic_regression.py
+++ b/src/ptbench/models/logistic_regression.py
@@ -41,7 +41,6 @@ def build_logistic_regression(input_size):
module : :py:class:`torch.nn.Module`
"""
-
model = LogisticRegression(input_size)
model.name = "logistic_regression"
return model
diff --git a/src/ptbench/models/pasa.py b/src/ptbench/models/pasa.py
index f0f66727..10e6cede 100644
--- a/src/ptbench/models/pasa.py
+++ b/src/ptbench/models/pasa.py
@@ -82,7 +82,6 @@ class PASA(nn.Module):
tensor : :py:class:`torch.Tensor`
"""
-
# First convolution block
_x = x
x = F.relu(self.batchNorm2d_4(self.fc1(x))) # 1st convolution
@@ -137,7 +136,6 @@ def build_pasa():
module : :py:class:`torch.nn.Module`
"""
-
model = PASA()
model = [
("normalizer", TorchVisionNormalizer(nb_channels=1)),
diff --git a/src/ptbench/models/signs_to_tb.py b/src/ptbench/models/signs_to_tb.py
index 1958282d..f3b3d5ea 100644
--- a/src/ptbench/models/signs_to_tb.py
+++ b/src/ptbench/models/signs_to_tb.py
@@ -48,7 +48,6 @@ def build_signs_to_tb(input_size, hidden_size):
module : :py:class:`torch.nn.Module`
"""
-
model = SignsToTB(input_size, hidden_size)
model.name = "signs_to_tb"
return model
diff --git a/src/ptbench/scripts/aggregpred.py b/src/ptbench/scripts/aggregpred.py
index 2d9aeac4..cf43f3d0 100644
--- a/src/ptbench/scripts/aggregpred.py
+++ b/src/ptbench/scripts/aggregpred.py
@@ -36,7 +36,6 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
@verbosity_option(logger=logger, expose_value=False)
def aggregpred(label_path, output_folder) -> None:
"""Aggregate multiple predictions csv files into one."""
-
import os
import re
import shutil
diff --git a/src/ptbench/scripts/compare.py b/src/ptbench/scripts/compare.py
index 5b5e006d..779c21ca 100644
--- a/src/ptbench/scripts/compare.py
+++ b/src/ptbench/scripts/compare.py
@@ -15,7 +15,6 @@ def _validate_threshold(t, dataset):
Returns parsed threshold.
"""
-
if t is None:
return t
diff --git a/src/ptbench/scripts/config.py b/src/ptbench/scripts/config.py
index 00fe26ab..edb47b2d 100644
--- a/src/ptbench/scripts/config.py
+++ b/src/ptbench/scripts/config.py
@@ -21,10 +21,14 @@ def _retrieve_entry_points(
) -> typing.Iterable[importlib.metadata.EntryPoint]:
"""Wraps various entry-point retrieval mechanisms.
- For Python 3.9 and 3.10, :py:func:`importlib.metadata.entry_points()`
- returns a dictionary keyed by entry-point group names. From Python 3.10
- onwards, one may pass the ``group`` keyword to that function to enable
- pre-filtering, or use the ``select()`` method on the returned value, which
+ For Python 3.9 and 3.10,
+ :py:func:`importlib.metadata.entry_points()`
+ returns a dictionary keyed by entry-point group names. From Python
+ 3.10
+ onwards, one may pass the ``group`` keyword to that function to
+ enable
+ pre-filtering, or use the ``select()`` method on the returned value,
+ which
is no longer a dictionary.
For anything before Python 3.8, you must use the backported library
@@ -70,7 +74,6 @@ def config():
@verbosity_option(logger=logger)
def list(verbose) -> None:
"""Lists configuration files installed."""
-
entry_points = _retrieve_entry_points("ptbench.config")
entry_point_dict = {k.name: k for k in entry_points}
@@ -156,7 +159,6 @@ def list(verbose) -> None:
@verbosity_option(logger=logger)
def describe(name, verbose) -> None:
"""Describes a specific configuration file."""
-
entry_points = _retrieve_entry_points("ptbench.config")
entry_point_dict = {k.name: k for k in entry_points}
@@ -206,7 +208,6 @@ def describe(name, verbose) -> None:
@verbosity_option(logger=logger, expose_value=False)
def copy(source, destination) -> None:
"""Copy a specific configuration resource so it can be modified locally."""
-
import shutil
entry_points = _retrieve_entry_points("ptbench.config")
diff --git a/src/ptbench/scripts/dataset.py b/src/ptbench/scripts/dataset.py
index d5c277c5..0a27ce56 100644
--- a/src/ptbench/scripts/dataset.py
+++ b/src/ptbench/scripts/dataset.py
@@ -17,7 +17,6 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
def _get_supported_datasets():
"""Returns a list of supported dataset names."""
-
basedir = importlib.resources.files(__name__.split(".", 1)[0]).joinpath(
"data"
)
@@ -36,7 +35,6 @@ def _get_installed_datasets() -> dict[str, str]:
* group(0): the name of the key for the dataset directory
* group("name"): the short name for the dataset
"""
-
from ..utils.rc import load_rc
return dict(load_rc().get("datadir", {}))
@@ -78,7 +76,6 @@ def dataset() -> None:
@verbosity_option(logger=logger, expose_value=False)
def list():
"""Lists all supported and configured datasets."""
-
supported = _get_supported_datasets()
installed = _get_installed_datasets()
@@ -129,7 +126,6 @@ def list():
@verbosity_option(logger=logger, expose_value=False)
def check(dataset, limit):
"""Checks file access on one or more datasets."""
-
import importlib
to_check = _get_installed_datasets()
diff --git a/src/ptbench/scripts/evaluate.py b/src/ptbench/scripts/evaluate.py
index 85b20b11..5100f79d 100644
--- a/src/ptbench/scripts/evaluate.py
+++ b/src/ptbench/scripts/evaluate.py
@@ -15,7 +15,6 @@ def _validate_threshold(t, dataset):
Returns parsed threshold.
"""
-
if t is None:
return 0.5
diff --git a/src/ptbench/scripts/predtojson.py b/src/ptbench/scripts/predtojson.py
index ce9a23be..0e90343a 100644
--- a/src/ptbench/scripts/predtojson.py
+++ b/src/ptbench/scripts/predtojson.py
@@ -38,7 +38,6 @@ def _load(data):
def _to_double_tensor(col):
"""Converts a column in a dataframe to a tensor array."""
-
pattern = re.compile(" +")
return col.apply(lambda cell: numpy.array(eval(pattern.sub(",", cell))))
@@ -82,7 +81,6 @@ def _load(data):
@verbosity_option(logger=logger, expose_value=False)
def predtojson(label_path, output_folder) -> None:
"""Convert predictions to dataset."""
-
import os
import shutil
diff --git a/src/ptbench/scripts/train.py b/src/ptbench/scripts/train.py
index d14c133d..754e603c 100644
--- a/src/ptbench/scripts/train.py
+++ b/src/ptbench/scripts/train.py
@@ -30,7 +30,6 @@ def setup_pytorch_device(name):
device : :py:class:`torch.device`
The pytorch device to use, pre-configured (and checked)
"""
-
import torch
if name.startswith("cuda:"):
@@ -74,7 +73,6 @@ def set_seeds(value, all_gpus):
If set, then reset the seed on all GPUs available at once. This is
normally **not** what you want if running on a single GPU
"""
-
import random
import numpy.random
@@ -100,7 +98,6 @@ def set_reproducible_cuda():
Reference: `PyTorch page for reproducibility
<https://pytorch.org/docs/stable/notes/randomness.html>`_.
"""
-
import torch.backends.cudnn
# ensure to use only optimization algos for cuda that are known to have
diff --git a/src/ptbench/scripts/train_analysis.py b/src/ptbench/scripts/train_analysis.py
index f5d0bc32..f32c801a 100644
--- a/src/ptbench/scripts/train_analysis.py
+++ b/src/ptbench/scripts/train_analysis.py
@@ -30,7 +30,6 @@ def _loss_evolution(df):
matplotlib.figure.Figure: Figure to be displayed or saved to file
"""
-
import numpy
figure = plt.figure()
@@ -91,7 +90,6 @@ def _hardware_utilisation(df, const):
matplotlib.figure.Figure: figure to be displayed or saved to file
"""
-
figure = plt.figure()
axes = figure.gca()
diff --git a/src/ptbench/utils/checkpointer.py b/src/ptbench/utils/checkpointer.py
index 8daf8a3e..36d10e5a 100644
--- a/src/ptbench/utils/checkpointer.py
+++ b/src/ptbench/utils/checkpointer.py
@@ -66,7 +66,6 @@ class Checkpointer:
partial : :py:class:`bool`, Optional
If True, loading is not strict and only the model is loaded
"""
-
if f is None:
f = self.last_checkpoint()
diff --git a/src/ptbench/utils/download.py b/src/ptbench/utils/download.py
index 9c780c7e..911d5f91 100644
--- a/src/ptbench/utils/download.py
+++ b/src/ptbench/utils/download.py
@@ -30,7 +30,6 @@ def download_to_tempfile(url, progress=False):
f : :py:func:`tempfile.NamedTemporaryFile`
A named temporary file that contains the downloaded URL
"""
-
file_size = 0
response = urllib.request.urlopen(url)
meta = response.info()
diff --git a/src/ptbench/utils/measure.py b/src/ptbench/utils/measure.py
index 7b03cc9c..f0031c0c 100644
--- a/src/ptbench/utils/measure.py
+++ b/src/ptbench/utils/measure.py
@@ -35,7 +35,6 @@ def tricky_division(n, d):
Returns 0.0 in case of a division by zero
"""
-
return n / (d + (d == 0))
@@ -114,7 +113,6 @@ def base_measures(tp, fp, tn, fn):
one needs to consider the true abscence of annotations in a region as
part of the measure.
"""
-
return (
tricky_division(tp, tp + fp), # precision
tricky_division(tp, tp + fn), # recall
@@ -227,7 +225,6 @@ def beta_credible_region(successes, failures, lambda_, coverage):
lower, upper: float
The lower and upper bounds of the credible region
"""
-
# we return the equally-tailed range
right = (1.0 - coverage) / 2 # half-width in each side
lower = scipy.special.betaincinv(
@@ -359,7 +356,6 @@ def bayesian_measures(tp, fp, tn, fn, lambda_, coverage):
better proxy if one needs to consider the true abscence of annotations
in a region as part of the measure.
"""
-
return (
beta_credible_region(tp, fp, lambda_, coverage), # precision
beta_credible_region(tp, fn, lambda_, coverage), # recall
diff --git a/src/ptbench/utils/plot.py b/src/ptbench/utils/plot.py
index e71532f3..fd6bc1e8 100644
--- a/src/ptbench/utils/plot.py
+++ b/src/ptbench/utils/plot.py
@@ -47,7 +47,6 @@ def _precision_recall_canvas(title=None):
axes : matplotlib.figure.Axes
An axis set where to precision-recall plots should be added to
"""
-
fig, axes1 = plt.subplots(1)
# Names and bounds
@@ -133,7 +132,6 @@ def precision_recall_f1iso(data):
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
-
lines = ["-", "--", "-.", ":"]
colors = [
"#1f77b4",
@@ -211,7 +209,6 @@ def roc_curve(data, title=None):
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
-
fig, axes = plt.subplots(1)
# Names and bounds
@@ -293,7 +290,6 @@ def relevance_analysis_plot(data, title=None):
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
-
fig, axes = plt.subplots(1, 1, figsize=(6, 6))
# Names and bounds
diff --git a/src/ptbench/utils/rc.py b/src/ptbench/utils/rc.py
index 9f6bf41f..25049ce1 100644
--- a/src/ptbench/utils/rc.py
+++ b/src/ptbench/utils/rc.py
@@ -7,5 +7,4 @@ from exposed.rc import UserDefaults
def load_rc() -> UserDefaults:
"""Returns global configuration variables."""
-
return UserDefaults("ptbench.toml")
diff --git a/src/ptbench/utils/resources.py b/src/ptbench/utils/resources.py
index d146b53c..d9a1baf6 100644
--- a/src/ptbench/utils/resources.py
+++ b/src/ptbench/utils/resources.py
@@ -53,7 +53,6 @@ def run_nvidia_smi(query, rename=None):
returns ``None``. Percentage information is left alone,
memory information is transformed to gigabytes (floating-point).
"""
-
if _nvidia_smi is not None:
if rename is None:
@@ -100,7 +99,6 @@ def gpu_constants():
* ``memory.total``, as ``gpu_memory_total`` (transformed to gigabytes,
:py:class:`float`)
"""
-
return run_nvidia_smi(
("gpu_name", "driver_version", "memory.total"),
("gpu_name", "gpu_driver_version", "gpu_memory_total"),
@@ -130,7 +128,6 @@ def gpu_log():
* ``utilization.gpu``, as ``gpu_percent``,
(:py:class:`float`, in percent)
"""
-
retval = run_nvidia_smi(
(
"memory.total",
@@ -168,7 +165,6 @@ def cpu_constants():
in gigabytes
1. ``cpu_count`` (:py:class:`int`): number of logical CPUs available
"""
-
return (
("cpu_memory_total", psutil.virtual_memory().total / GB),
("cpu_count", psutil.cpu_count(logical=True)),
@@ -215,7 +211,6 @@ class CPULogger:
5. ``cpu_open_files`` (:py:class:`int`): total number of open files by
self and children
"""
-
# check all cluster components and update process list
# done so we can keep the cpu_percent() initialization
stored_children = set(self.cluster[1:])
@@ -296,7 +291,6 @@ class _InformationGatherer:
def summary(self):
"""Returns the current data."""
-
if len(self.data[0]) == 0:
self.logger.error("CPU/GPU logger was not able to collect any data")
retval = []
@@ -330,7 +324,6 @@ def _monitor_worker(interval, has_gpu, main_pid, stop, queue, logging_level):
logging_level: int
The logging level to use for logging from launched processes
"""
-
logger = multiprocessing.log_to_stderr(level=logging_level)
ra = _InformationGatherer(has_gpu, main_pid, logger)
@@ -397,7 +390,6 @@ class ResourceMonitor:
def __enter__(self):
"""Starts the monitoring process."""
-
self.monitor.start()
return self
diff --git a/src/ptbench/utils/table.py b/src/ptbench/utils/table.py
index 78d321a6..cb4594b9 100644
--- a/src/ptbench/utils/table.py
+++ b/src/ptbench/utils/table.py
@@ -44,7 +44,6 @@ def performance_table(data, fmt):
table : str
A table in a specific format
"""
-
headers = [
"Dataset",
"T",
diff --git a/tests/conftest.py b/tests/conftest.py
index f268377b..f1aa8c92 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -10,13 +10,11 @@ import pytest
@pytest.fixture
def datadir(request) -> pathlib.Path:
"""Returns the directory in which the test is sitting."""
-
return pathlib.Path(request.module.__file__).parents[0] / "data"
def pytest_configure(config):
"""This function is run once for pytest setup."""
-
config.addinivalue_line(
"markers",
"skip_if_rc_var_not_set(name): this mark skips the test if a certain "
--
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