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pbizopoulospbizopoulos
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fix check-python-script version
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2 files changed

+124
-124
lines changed

2 files changed

+124
-124
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‎latex/python/main.py

Lines changed: 123 additions & 123 deletions
Original file line numberDiff line numberDiff line change
@@ -288,9 +288,6 @@ def forward(self: _DenseNet, signal: torch.Tensor) -> torch.Tensor:
288288

289289

290290
class _Hook:
291-
def __init__(self: _Hook) -> None:
292-
self.outputs: list[nn.Module] = []
293-
294291
def __call__(
295292
self: _Hook,
296293
_: nn.Module,
@@ -299,6 +296,9 @@ def __call__(
299296
) -> None:
300297
self.outputs.append(module_out)
301298

299+
def __init__(self: _Hook) -> None:
300+
self.outputs: list[nn.Module] = []
301+
302302

303303
class _LeNet2D(nn.Module):
304304
def __init__(self: _LeNet2D) -> None:
@@ -497,6 +497,12 @@ def __init__(
497497

498498

499499
class _UCIEpilepsy(Dataset[tuple[torch.Tensor, torch.Tensor]]):
500+
def __getitem__(
501+
self: _UCIEpilepsy,
502+
index: int,
503+
) -> tuple[torch.Tensor, torch.Tensor]:
504+
return (self.data[index], self.target[index])
505+
500506
def __init__(
501507
self: _UCIEpilepsy,
502508
num_samples: int,
@@ -543,12 +549,6 @@ def __init__(
543549
)
544550
self.data.unsqueeze_(1)
545551

546-
def __getitem__(
547-
self: _UCIEpilepsy,
548-
index: int,
549-
) -> tuple[torch.Tensor, torch.Tensor]:
550-
return (self.data[index], self.target[index])
551-
552552
def __len__(self: _UCIEpilepsy) -> int:
553553
return self.target.shape[0]
554554

@@ -640,120 +640,6 @@ def _densenet201(num_classes: int) -> nn.Module:
640640
)
641641

642642

643-
def _make_layers(cfg: list) -> nn.Module: # type: ignore[type-arg]
644-
layers: list[nn.Module] = []
645-
in_channels = 1
646-
for cfg_element in cfg:
647-
if cfg_element == "M":
648-
layers += [nn.MaxPool1d(kernel_size=2, stride=2)]
649-
else:
650-
conv1d = nn.Conv1d(in_channels, cfg_element, kernel_size=3, padding=1)
651-
layers += [conv1d, nn.ReLU()]
652-
in_channels = cfg_element
653-
return nn.Sequential(*layers)
654-
655-
656-
def _replace_last_layer(
657-
num_classes: int,
658-
model_base: nn.Module,
659-
model_file_name: str,
660-
) -> nn.Module:
661-
if model_file_name.startswith(("alexnet", "vgg")):
662-
model_base.classifier[-1] = nn.Linear(
663-
model_base.classifier[-1].in_features,
664-
num_classes,
665-
)
666-
elif model_file_name.startswith("resnet"):
667-
model_base.fc = nn.Linear(model_base.fc.in_features, num_classes)
668-
elif model_file_name.startswith("densenet"):
669-
model_base.classifier = nn.Linear(
670-
model_base.classifier.in_features,
671-
num_classes,
672-
)
673-
return model_base
674-
675-
676-
def _resnet101(num_classes: int) -> nn.Module:
677-
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 4, 23, 3])
678-
679-
680-
def _resnet152(num_classes: int) -> nn.Module:
681-
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 8, 36, 3])
682-
683-
684-
def _resnet18(num_classes: int) -> nn.Module:
685-
return _ResNet(_BasicBlock, num_classes, expansion=1, layers=[2, 2, 2, 2])
686-
687-
688-
def _resnet34(num_classes: int) -> nn.Module:
689-
return _ResNet(_BasicBlock, num_classes, expansion=1, layers=[3, 4, 6, 3])
690-
691-
692-
def _resnet50(num_classes: int) -> nn.Module:
693-
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 4, 6, 3])
694-
695-
696-
def _vgg11(num_classes: int) -> nn.Module:
697-
cfg = [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"]
698-
return _VGG(num_classes, _make_layers(cfg))
699-
700-
701-
def _vgg13(num_classes: int) -> nn.Module:
702-
cfg = [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"]
703-
return _VGG(num_classes, _make_layers(cfg))
704-
705-
706-
def _vgg16(num_classes: int) -> nn.Module:
707-
cfg = [
708-
64,
709-
64,
710-
"M",
711-
128,
712-
128,
713-
"M",
714-
256,
715-
256,
716-
256,
717-
"M",
718-
512,
719-
512,
720-
512,
721-
"M",
722-
512,
723-
512,
724-
512,
725-
"M",
726-
]
727-
return _VGG(num_classes, _make_layers(cfg))
728-
729-
730-
def _vgg19(num_classes: int) -> nn.Module:
731-
cfg = [
732-
64,
733-
64,
734-
"M",
735-
128,
736-
128,
737-
"M",
738-
256,
739-
256,
740-
256,
741-
256,
742-
"M",
743-
512,
744-
512,
745-
512,
746-
512,
747-
"M",
748-
512,
749-
512,
750-
512,
751-
512,
752-
"M",
753-
]
754-
return _VGG(num_classes, _make_layers(cfg))
755-
756-
757643
def _main() -> None: # noqa: C901,PLR0912,PLR0915
758644
if os.getenv("STAGE"):
759645
num_samples = 11500
@@ -1004,6 +890,120 @@ def _main() -> None: # noqa: C901,PLR0912,PLR0915
1004890
plt.close()
1005891

1006892

893+
def _make_layers(cfg: list) -> nn.Module: # type: ignore[type-arg]
894+
layers: list[nn.Module] = []
895+
in_channels = 1
896+
for cfg_element in cfg:
897+
if cfg_element == "M":
898+
layers += [nn.MaxPool1d(kernel_size=2, stride=2)]
899+
else:
900+
conv1d = nn.Conv1d(in_channels, cfg_element, kernel_size=3, padding=1)
901+
layers += [conv1d, nn.ReLU()]
902+
in_channels = cfg_element
903+
return nn.Sequential(*layers)
904+
905+
906+
def _replace_last_layer(
907+
num_classes: int,
908+
model_base: nn.Module,
909+
model_file_name: str,
910+
) -> nn.Module:
911+
if model_file_name.startswith(("alexnet", "vgg")):
912+
model_base.classifier[-1] = nn.Linear(
913+
model_base.classifier[-1].in_features,
914+
num_classes,
915+
)
916+
elif model_file_name.startswith("resnet"):
917+
model_base.fc = nn.Linear(model_base.fc.in_features, num_classes)
918+
elif model_file_name.startswith("densenet"):
919+
model_base.classifier = nn.Linear(
920+
model_base.classifier.in_features,
921+
num_classes,
922+
)
923+
return model_base
924+
925+
926+
def _resnet101(num_classes: int) -> nn.Module:
927+
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 4, 23, 3])
928+
929+
930+
def _resnet152(num_classes: int) -> nn.Module:
931+
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 8, 36, 3])
932+
933+
934+
def _resnet18(num_classes: int) -> nn.Module:
935+
return _ResNet(_BasicBlock, num_classes, expansion=1, layers=[2, 2, 2, 2])
936+
937+
938+
def _resnet34(num_classes: int) -> nn.Module:
939+
return _ResNet(_BasicBlock, num_classes, expansion=1, layers=[3, 4, 6, 3])
940+
941+
942+
def _resnet50(num_classes: int) -> nn.Module:
943+
return _ResNet(_Bottleneck, num_classes, expansion=4, layers=[3, 4, 6, 3])
944+
945+
946+
def _vgg11(num_classes: int) -> nn.Module:
947+
cfg = [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"]
948+
return _VGG(num_classes, _make_layers(cfg))
949+
950+
951+
def _vgg13(num_classes: int) -> nn.Module:
952+
cfg = [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"]
953+
return _VGG(num_classes, _make_layers(cfg))
954+
955+
956+
def _vgg16(num_classes: int) -> nn.Module:
957+
cfg = [
958+
64,
959+
64,
960+
"M",
961+
128,
962+
128,
963+
"M",
964+
256,
965+
256,
966+
256,
967+
"M",
968+
512,
969+
512,
970+
512,
971+
"M",
972+
512,
973+
512,
974+
512,
975+
"M",
976+
]
977+
return _VGG(num_classes, _make_layers(cfg))
978+
979+
980+
def _vgg19(num_classes: int) -> nn.Module:
981+
cfg = [
982+
64,
983+
64,
984+
"M",
985+
128,
986+
128,
987+
"M",
988+
256,
989+
256,
990+
256,
991+
256,
992+
"M",
993+
512,
994+
512,
995+
512,
996+
512,
997+
"M",
998+
512,
999+
512,
1000+
512,
1001+
512,
1002+
"M",
1003+
]
1004+
return _VGG(num_classes, _make_layers(cfg))
1005+
1006+
10071007
M = TypeVar("M", _BasicBlock, _Bottleneck)
10081008

10091009

‎latex/python/pyproject.toml

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ version = "0.0.0"
1111

1212
[project.optional-dependencies]
1313
dev = [
14-
"check-python-script@https://api.github.com/repos/pbizopoulos/check-python-script/tarball/1c91a46#subdirectory=python",
14+
"check-python-script@https://api.github.com/repos/pbizopoulos/check-python-script/tarball/25c772d#subdirectory=python",
1515
"coverage==7.5.3",
1616
"djlint==1.34.1",
1717
"mypy==1.10.0",

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