diff --git a/src/ptbench/configs/datasets/__init__.py b/src/ptbench/configs/datasets/__init__.py
index d5b0352ab4317b08057ee21750ad3bfe95ef54c3..400d54231f7f5dabe9dd5d5426f2f77dfbc21362 100644
--- a/src/ptbench/configs/datasets/__init__.py
+++ b/src/ptbench/configs/datasets/__init__.py
@@ -136,7 +136,6 @@ def make_dataset(
t_transforms += post_transforms
for key in subsets.keys():
-
retval[key] = make_subset(
subsets[key], transforms=transforms, suffixes=post_transforms
)
@@ -187,10 +186,8 @@ def get_samples_weights(dataset):
if isinstance(dataset, torch.utils.data.ConcatDataset):
for ds in dataset.datasets:
-
# Weighting only for binary labels
if isinstance(ds._samples[0].label, int):
-
# Groundtruth
targets = []
for s in ds._samples:
@@ -272,7 +269,6 @@ def get_positive_weights(dataset):
targets = []
if isinstance(dataset, torch.utils.data.ConcatDataset):
-
for ds in dataset.datasets:
for s in ds._samples:
targets.append(s.label)
diff --git a/src/ptbench/data/transforms.py b/src/ptbench/data/transforms.py
index 19b1cb6b3ab5972e25fb8d29540d8163399a91dd..c1f1f7d0a4457fb441ddfefa5bd53c9e57bd2a60 100644
--- a/src/ptbench/data/transforms.py
+++ b/src/ptbench/data/transforms.py
@@ -78,7 +78,6 @@ class ElasticDeformation:
def __call__(self, img):
if random.random() < self.p:
-
img = numpy.asarray(img)
assert img.ndim == 2
diff --git a/src/ptbench/engine/evaluator.py b/src/ptbench/engine/evaluator.py
index 5e86ee855aad524ab942b054952afac3000816b7..acbf7f3641b219e9212e024d0a4b1afe6ddf44f7 100644
--- a/src/ptbench/engine/evaluator.py
+++ b/src/ptbench/engine/evaluator.py
@@ -292,7 +292,6 @@ def run(
fig.savefig(fullpath)
if f1_thresh is not None and eer_thresh is not None:
-
# get the closest possible threshold we have
index = int(round(steps * f1_thresh))
f1_a_priori = data_df["f1_score"][index]
diff --git a/src/ptbench/engine/predictor.py b/src/ptbench/engine/predictor.py
index 27d9d2142f761b401f1709e85132fd01fde50349..6c4dd4af73bcd1ff868a2f20964f9d7980ddd2a5 100644
--- a/src/ptbench/engine/predictor.py
+++ b/src/ptbench/engine/predictor.py
@@ -114,7 +114,6 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
leave=False,
disable=None,
):
-
names = samples[0]
images = samples[1].to(
device=device, non_blocking=torch.cuda.is_available()
@@ -133,10 +132,8 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
topk = 1
for i in range(topk):
-
# Keep only "positive" signs
if probs[:, [i]] > 0.5:
-
# Grad-CAM
b = ids[:, [i]]
gcam.backward(ids=ids[:, [i]])
@@ -145,7 +142,6 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
)
for j in range(len(images)):
-
current_cam = regions[j, 0].cpu().numpy()
current_cam[current_cam < 0.75] = 0.0
current_cam[current_cam >= 0.75] = 1.0
@@ -158,14 +154,12 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
]
if len(cams_img) > 0:
-
# Convert original image tensor into PIL Image
original_image = transforms.ToPILImage(mode="RGB")(
images[0]
)
for sign_id, label_prob in cams_img.items():
-
label = label_prob[0]
# Create the colored overlay for current sign
@@ -222,7 +216,6 @@ def run(model, data_loader, name, device, output_folder, grad_cams=False):
fig.savefig(cam_filename)
with torch.no_grad():
-
start_time = time.perf_counter()
outputs = model(images)
probabilities = torch.sigmoid(outputs)
diff --git a/src/ptbench/engine/trainer.py b/src/ptbench/engine/trainer.py
index 2e3d91a60387edffea0abdcf85eea54cc97fc32d..8cc72514f863e1f7d8447ebd964429c6fbeeef55 100644
--- a/src/ptbench/engine/trainer.py
+++ b/src/ptbench/engine/trainer.py
@@ -225,7 +225,6 @@ def train_epoch(loader, model, optimizer, device, criterion, batch_chunk_count):
for idx, samples in enumerate(
tqdm(loader, desc="train", leave=False, disable=None)
):
-
images = samples[1].to(
device=device, non_blocking=torch.cuda.is_available()
)
@@ -310,7 +309,6 @@ def validate_epoch(loader, model, device, criterion, pbar_desc):
samples_in_batch = []
with torch.no_grad(), torch_evaluation(model):
-
for samples in tqdm(loader, desc=pbar_desc, leave=False, disable=None):
images = samples[1].to(
device=device,
@@ -599,7 +597,6 @@ def run(
leave=False,
disable=None,
):
-
with ResourceMonitor(
interval=monitoring_interval,
has_gpu=(device.type == "cuda"),
diff --git a/src/ptbench/scripts/aggregpred.py b/src/ptbench/scripts/aggregpred.py
index 9c7a082a21e7f0faa0fa6dbeb33779d78438d4f2..a6d1974769f707292ee41e0f0faba9017183414e 100644
--- a/src/ptbench/scripts/aggregpred.py
+++ b/src/ptbench/scripts/aggregpred.py
@@ -46,7 +46,6 @@ def aggregpred(label_path, output_folder) -> None:
# loads all data
series = []
for predictions_path in label_path:
-
# Load predictions
logger.info(f"Loading predictions from {predictions_path}...")
pred_data = pandas.read_csv(predictions_path)
diff --git a/src/ptbench/scripts/compare.py b/src/ptbench/scripts/compare.py
index f5ff1725cd38cbaff7a90df97387df2534204d4c..95223a37a6ecb0ef5fdfa42a255786ed1642e132 100644
--- a/src/ptbench/scripts/compare.py
+++ b/src/ptbench/scripts/compare.py
@@ -62,7 +62,6 @@ def _load(data, threshold):
# loads all data
retval = {}
for name, predictions_path in data.items():
-
# Load predictions
logger.info(f"Loading predictions from {predictions_path}...")
pred_data = pandas.read_csv(predictions_path)
diff --git a/src/ptbench/scripts/config.py b/src/ptbench/scripts/config.py
index 6ecd6a8aeeaa63cf60ae009c81e5f7602fe60f03..13ef09df8a182707448b7df47b9175a3306d112a 100644
--- a/src/ptbench/scripts/config.py
+++ b/src/ptbench/scripts/config.py
@@ -96,7 +96,6 @@ def list(verbose) -> None:
entry_points_by_module[k][name] = ep
for config_type in sorted(entry_points_by_module):
-
# calculates the longest config name so we offset the printing
longest_name_length = max(
len(k) for k in entry_points_by_module[config_type].keys()
diff --git a/src/ptbench/scripts/predict.py b/src/ptbench/scripts/predict.py
index e57e227f46fc90a89fd393558b8987aaa7e5d148..18e57c3d5c6f5e019536e902cb338b4f04481073 100644
--- a/src/ptbench/scripts/predict.py
+++ b/src/ptbench/scripts/predict.py
@@ -150,7 +150,6 @@ def predict(
pdf.close()
for k, v in dataset.items():
-
if k.startswith("_"):
logger.info(f"Skipping dataset '{k}' (not to be evaluated)")
continue
@@ -186,7 +185,6 @@ def predict(
all_mse = []
for f in range(nb_features):
-
v_original = copy.deepcopy(v)
# Randomly permute feature values from all samples
diff --git a/src/ptbench/scripts/predtojson.py b/src/ptbench/scripts/predtojson.py
index 361d2316887c972e6f573fb3e5acfe8967b744d1..549aa3a9c38c7ce41f9f0d3c1846d362541ec824 100644
--- a/src/ptbench/scripts/predtojson.py
+++ b/src/ptbench/scripts/predtojson.py
@@ -44,7 +44,6 @@ def _load(data):
# loads all data
retval = {}
for name, predictions_path in data.items():
-
# Load predictions
logger.info(f"Loading predictions from {predictions_path}...")
pred_data = pandas.read_csv(predictions_path)
@@ -110,7 +109,6 @@ def predtojson(label_path, output_folder) -> None:
logger.info("Saving JSON file...")
with open(output_file, "a+", newline="") as f:
-
f.write("{")
for i, (name, value) in enumerate(data.items()):
if i > 0:
diff --git a/src/ptbench/scripts/train.py b/src/ptbench/scripts/train.py
index e69e81cf1fbcedc7f5c57e74e74975e64e8aa2ee..e3d9b04345fc43a7959ec6b5169383066d348a35 100644
--- a/src/ptbench/scripts/train.py
+++ b/src/ptbench/scripts/train.py
@@ -458,7 +458,6 @@ def train(
valid_loader = None
if validation_dataset is not None:
-
# Redefine a weighted valid criterion if possible
if (
isinstance(criterion_valid, torch.nn.BCEWithLogitsLoss)
diff --git a/src/ptbench/scripts/train_analysis.py b/src/ptbench/scripts/train_analysis.py
index 5baf36383e97c171d9ff20fc2445ff8b12edf461..8eddb18aa1446cf7f32179f454e044d9fa7d4144 100644
--- a/src/ptbench/scripts/train_analysis.py
+++ b/src/ptbench/scripts/train_analysis.py
@@ -185,7 +185,6 @@ def train_analysis(
# now, do the analysis
with PdfPages(output_pdf) as pdf:
-
figure = _loss_evolution(data)
pdf.savefig(figure)
plt.close(figure)
diff --git a/src/ptbench/utils/plot.py b/src/ptbench/utils/plot.py
index fd6bc1e8b1246082960c3ab153f7d2d4f32aa84f..410a086af4e6a48c6f4cc96f260569f165b82d2b 100644
--- a/src/ptbench/utils/plot.py
+++ b/src/ptbench/utils/plot.py
@@ -149,11 +149,9 @@ def precision_recall_f1iso(data):
linecycler = cycle(lines)
with _precision_recall_canvas(title=None) as (fig, axes):
-
legend = []
for name, value in data.items():
-
df = value["df"]
# plots Recall/Precision curve
@@ -249,7 +247,6 @@ def roc_curve(data, title=None):
legend = []
for name, value in data.items():
-
df = value["df"]
# plots roc curve
diff --git a/src/ptbench/utils/resources.py b/src/ptbench/utils/resources.py
index d9a1baf63eb6a9dc39ed69c08344d8a0cae292c6..be23ee452a1823555220c5d92d80a2f7c6a9223f 100644
--- a/src/ptbench/utils/resources.py
+++ b/src/ptbench/utils/resources.py
@@ -54,7 +54,6 @@ def run_nvidia_smi(query, rename=None):
memory information is transformed to gigabytes (floating-point).
"""
if _nvidia_smi is not None:
-
if rename is None:
rename = query
else:
diff --git a/src/ptbench/utils/summary.py b/src/ptbench/utils/summary.py
index 9b8156457080808a284da53d547ff9559a7a5df7..2f7d468c5a26439c3415326be383c9df9af66a09 100644
--- a/src/ptbench/utils/summary.py
+++ b/src/ptbench/utils/summary.py
@@ -9,6 +9,40 @@ from functools import reduce
from torch.nn.modules.module import _addindent
+# ignore this space!
+def _repr(model):
+ # We treat the extra repr like the sub-module, one item per line
+ extra_lines = []
+ extra_repr = model.extra_repr()
+ # empty string will be split into list ['']
+ if extra_repr:
+ extra_lines = extra_repr.split("\n")
+ child_lines = []
+ total_params = 0
+ for key, module in model._modules.items():
+ mod_str, num_params = _repr(module)
+ mod_str = _addindent(mod_str, 2)
+ child_lines.append("(" + key + "): " + mod_str)
+ total_params += num_params
+ lines = extra_lines + child_lines
+
+ for _, p in model._parameters.items():
+ if hasattr(p, "dtype"):
+ total_params += reduce(lambda x, y: x * y, p.shape)
+
+ main_str = model._get_name() + "("
+ if lines:
+ # simple one-liner info, which most builtin Modules will use
+ if len(extra_lines) == 1 and not child_lines:
+ main_str += extra_lines[0]
+ else:
+ main_str += "\n " + "\n ".join(lines) + "\n"
+
+ main_str += ")"
+ main_str += f", {total_params:,} params"
+ return main_str, total_params
+
+
def summary(model):
"""Counts the number of parameters in each model layer.
@@ -27,37 +61,4 @@ def summary(model):
nparam : int
number of parameters
"""
- # ignore this space!
- def _repr(model):
- # We treat the extra repr like the sub-module, one item per line
- extra_lines = []
- extra_repr = model.extra_repr()
- # empty string will be split into list ['']
- if extra_repr:
- extra_lines = extra_repr.split("\n")
- child_lines = []
- total_params = 0
- for key, module in model._modules.items():
- mod_str, num_params = _repr(module)
- mod_str = _addindent(mod_str, 2)
- child_lines.append("(" + key + "): " + mod_str)
- total_params += num_params
- lines = extra_lines + child_lines
-
- for _, p in model._parameters.items():
- if hasattr(p, "dtype"):
- total_params += reduce(lambda x, y: x * y, p.shape)
-
- main_str = model._get_name() + "("
- if lines:
- # simple one-liner info, which most builtin Modules will use
- if len(extra_lines) == 1 and not child_lines:
- main_str += extra_lines[0]
- else:
- main_str += "\n " + "\n ".join(lines) + "\n"
-
- main_str += ")"
- main_str += f", {total_params:,} params"
- return main_str, total_params
-
return _repr(model)
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 5a95c42a5282baca805af7ebcef9a3f377055ac2..6507e3d09e2959bbf952a01a44a9a65b4147b413 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -191,13 +191,11 @@ def test_train_pasa_montgomery(temporary_basedir, montgomery_datadir):
# Temporarily modifies Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.train import train
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "results")
result = runner.invoke(
train,
@@ -252,17 +250,14 @@ def test_train_pasa_montgomery(temporary_basedir, montgomery_datadir):
def test_predict_pasa_montgomery(
temporary_basedir, montgomery_datadir, datadir
):
-
# Temporarily modifies Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.predict import predict
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "predictions")
result = runner.invoke(
predict,
@@ -312,13 +307,11 @@ def test_predtojson(datadir, temporary_basedir, montgomery_datadir):
# Temporarily modify Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.predtojson import predtojson
runner = CliRunner()
with stdout_logging() as buf:
-
predictions = str(datadir / "test_predictions.csv")
output_folder = str(temporary_basedir / "pred_to_json")
result = runner.invoke(
@@ -357,13 +350,11 @@ def test_evaluate_pasa_montgomery(temporary_basedir, montgomery_datadir):
# Temporarily modify Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.evaluate import evaluate
runner = CliRunner()
with stdout_logging() as buf:
-
prediction_folder = str(temporary_basedir / "predictions")
output_folder = str(temporary_basedir / "evaluations")
result = runner.invoke(
@@ -411,13 +402,11 @@ def test_compare_pasa_montgomery(temporary_basedir, montgomery_datadir):
# Temporarily modify Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.compare import compare
runner = CliRunner()
with stdout_logging() as buf:
-
predictions_folder = str(temporary_basedir / "predictions")
output_folder = str(temporary_basedir / "comparisons")
result = runner.invoke(
@@ -461,7 +450,6 @@ def test_train_signstotb_montgomery_rs(temporary_basedir, datadir):
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "results")
result = runner.invoke(
train,
@@ -514,7 +502,6 @@ def test_predict_signstotb_montgomery_rs(temporary_basedir, datadir):
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "predictions")
result = runner.invoke(
predict,
@@ -563,7 +550,6 @@ def test_train_logreg_montgomery_rs(temporary_basedir, datadir):
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "results")
result = runner.invoke(
train,
@@ -616,7 +602,6 @@ def test_predict_logreg_montgomery_rs(temporary_basedir, datadir):
runner = CliRunner()
with stdout_logging() as buf:
-
output_folder = str(temporary_basedir / "predictions")
result = runner.invoke(
predict,
@@ -657,13 +642,11 @@ def test_aggregpred(temporary_basedir, montgomery_datadir):
# Temporarily modify Montgomery datadir if need be
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.scripts.aggregpred import aggregpred
runner = CliRunner()
with stdout_logging() as buf:
-
predictions = str(
temporary_basedir / "predictions" / "train" / "predictions.csv"
)
diff --git a/tests/test_config.py b/tests/test_config.py
index f20d7ba6d7b6e273c59a6a8bc97ad1318ea659f8..6493892baea443210cc8c24410b07ffdb753d2d1 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -64,7 +64,6 @@ def test_get_samples_weights(montgomery_datadir):
# Temporarily modify Montgomery datadir
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.configs.datasets.montgomery.default import dataset
train_samples_weights = get_samples_weights(
@@ -92,7 +91,6 @@ def test_get_samples_weights_concat(montgomery_datadir):
# Temporarily modify Montgomery datadir
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.configs.datasets.montgomery.default import dataset
train_dataset = ConcatDataset(
@@ -133,7 +131,6 @@ def test_get_positive_weights(montgomery_datadir):
# Temporarily modify Montgomery datadir
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.configs.datasets.montgomery.default import dataset
train_positive_weights = get_positive_weights(
@@ -211,7 +208,6 @@ def test_get_positive_weights_concat(montgomery_datadir):
# Temporarily modify Montgomery datadir
new_value = {"datadir.montgomery": str(montgomery_datadir)}
with rc_context(**new_value):
-
from ptbench.configs.datasets.montgomery.default import dataset
train_dataset = ConcatDataset(
diff --git a/tests/test_data_utils.py b/tests/test_data_utils.py
index 557b74b58b2bd98a5ae89df0148826a74cf7d2a1..d21d050af41d3fb2bad01c773e3fe71bfad0cb64 100644
--- a/tests/test_data_utils.py
+++ b/tests/test_data_utils.py
@@ -26,7 +26,6 @@ def test_random_permute():
nb_equal = 0.0
for k, s in enumerate(test_set._samples):
-
if original[k] == s.data["data"][2]:
nb_equal += 1
else: