From bfc106ab413c9b0245556a9280db81f0d16ceb4b Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Fri, 21 Jul 2023 20:35:02 +0200
Subject: [PATCH] [tests] Some reformatting to make black happy
---
src/ptbench/data/image_utils.py | 16 +++++++++-------
src/ptbench/utils/checkpointer.py | 10 +++++++---
tests/test_ch.py | 4 ++--
tests/test_mc.py | 15 +++++----------
4 files changed, 23 insertions(+), 22 deletions(-)
diff --git a/src/ptbench/data/image_utils.py b/src/ptbench/data/image_utils.py
index ed284afc..b1c9d82e 100644
--- a/src/ptbench/data/image_utils.py
+++ b/src/ptbench/data/image_utils.py
@@ -31,14 +31,16 @@ class SingleAutoLevel16to8:
).convert("L")
-def remove_black_borders(img: PIL.Image.Image, threshold: int=0) -> PIL.Image.Image:
- """Remove black borders of CXR
+def remove_black_borders(
+ img: PIL.Image.Image, threshold: int = 0
+) -> PIL.Image.Image:
+ """Remove black borders of CXR.
Parameters
----------
- img
+ img
A PIL image
- threshold
+ threshold
Threshold value from which borders are considered black.
Defaults to 0.
@@ -49,10 +51,10 @@ def remove_black_borders(img: PIL.Image.Image, threshold: int=0) -> PIL.Image.Im
img = numpy.asarray(img)
- if len(img.shape) == 2: # single channel
+ if len(img.shape) == 2: # single channel
mask = numpy.asarray(img) > threshold
return PIL.Image.fromarray(img[numpy.ix_(mask.any(1), mask.any(0))])
-
+
elif len(img.shape) == 3 and img.shape[2] == 3:
r_mask = img[:, :, 0] > threshold
g_mask = img[:, :, 1] > threshold
@@ -60,7 +62,7 @@ def remove_black_borders(img: PIL.Image.Image, threshold: int=0) -> PIL.Image.Im
mask = r_mask | g_mask | b_mask
return PIL.Image.fromarray(img[numpy.ix_(mask.any(1), mask.any(0))])
-
+
else:
raise NotImplementedError
diff --git a/src/ptbench/utils/checkpointer.py b/src/ptbench/utils/checkpointer.py
index 5c2f272c..318811b0 100644
--- a/src/ptbench/utils/checkpointer.py
+++ b/src/ptbench/utils/checkpointer.py
@@ -1,11 +1,13 @@
import logging
import os
-
import typing
+
logger = logging.getLogger(__name__)
-def get_checkpoint(output_folder: str, resume_from: typing.Literal["last", "best"] | str | None) -> str | None :
+def get_checkpoint(
+ output_folder: str, resume_from: typing.Literal["last", "best"] | str | None
+) -> str | None:
"""Gets a checkpoint file.
Can return the best or last checkpoint, or a checkpoint at a specific path.
@@ -56,7 +58,9 @@ def get_checkpoint(output_folder: str, resume_from: typing.Literal["last", "best
elif resume_from is None:
if os.path.isfile(last_checkpoint_path):
checkpoint_file = last_checkpoint_path
- logger.info(f"Found existing checkpoint {last_checkpoint_path}. Loading.")
+ logger.info(
+ f"Found existing checkpoint {last_checkpoint_path}. Loading."
+ )
else:
return None
diff --git a/tests/test_ch.py b/tests/test_ch.py
index b28c81e9..c678e087 100644
--- a/tests/test_ch.py
+++ b/tests/test_ch.py
@@ -128,8 +128,8 @@ def test_loading():
assert isinstance(data, torch.Tensor)
- assert data.size(0) == 3 # check 3 channels
- assert data.size(1) == data.size(2) # check square image
+ assert data.size(0) == 3 # check 3 channels
+ assert data.size(1) == data.size(2) # check square image
assert (
torchvision.transforms.ToPILImage()(data).mode == "RGB"
diff --git a/tests/test_mc.py b/tests/test_mc.py
index 2fcd14ac..25bd4709 100644
--- a/tests/test_mc.py
+++ b/tests/test_mc.py
@@ -10,7 +10,6 @@ import pytest
def test_protocol_consistency():
-
# Default protocol
datamodule = importlib.import_module(
"ptbench.data.montgomery.default"
@@ -126,12 +125,12 @@ def test_loading():
assert isinstance(data, torch.Tensor)
- assert data.size(0) == 1 # check single channel
- assert data.size(1) == data.size(2) # check square image
+ assert data.size(0) == 1 # check single channel
+ assert data.size(1) == data.size(2) # check square image
assert (
- torchvision.transforms.ToPILImage()(data).mode == "L"
- ) # Check colors
+ torchvision.transforms.ToPILImage()(data).mode == "L"
+ ) # Check colors
assert "label" in metadata
assert metadata["label"] in [0, 1] # Check labels
@@ -145,10 +144,7 @@ def test_loading():
raw_data_loader = datamodule.raw_data_loader
# Need to use private function so we can limit the number of samples to use
- dataset = _DelayedLoadingDataset(
- subset["train"][:limit],
- raw_data_loader
- )
+ dataset = _DelayedLoadingDataset(subset["train"][:limit], raw_data_loader)
for s in dataset:
_check_sample(s)
@@ -188,4 +184,3 @@ def test_check():
)
== 0
)
-
--
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