diff --git a/src/mednet/libs/classification/config/models/alexnet.py b/src/mednet/libs/classification/config/models/alexnet.py
index 6f1505d859c4ceee268663a272126e858919cb5d..1ba9e0826f13f2be7069a33a76aac02da1e93fc7 100644
--- a/src/mednet/libs/classification/config/models/alexnet.py
+++ b/src/mednet/libs/classification/config/models/alexnet.py
@@ -8,7 +8,9 @@ page <alexnet-pytorch_>`_), modified for a variable number of outputs
(defaults to 1).
"""
+import torchvision.transforms
from mednet.libs.classification.models.alexnet import Alexnet
+from mednet.libs.common.models.transforms import RGB, SquareCenterPad
from torch.nn import BCEWithLogitsLoss
from torch.optim import SGD
@@ -17,4 +19,9 @@ model = Alexnet(
optimizer_type=SGD,
optimizer_arguments=dict(lr=0.01, momentum=0.1),
pretrained=False,
+ model_transforms=[
+ SquareCenterPad(),
+ torchvision.transforms.Resize(512, antialias=True),
+ RGB(),
+ ],
)
diff --git a/src/mednet/libs/classification/config/models/alexnet_pretrained.py b/src/mednet/libs/classification/config/models/alexnet_pretrained.py
index 9178139add6e5f6291939f8eeee27c1ea9a47b63..8416db28deba66f6df16668297670e1232a6c540 100644
--- a/src/mednet/libs/classification/config/models/alexnet_pretrained.py
+++ b/src/mednet/libs/classification/config/models/alexnet_pretrained.py
@@ -10,7 +10,9 @@ page <alexnet_pytorch_>`), modified for a variable number of outputs
N.B.: The output layer is **always** initialized from scratch.
"""
+import torchvision.transforms
from mednet.libs.classification.models.alexnet import Alexnet
+from mednet.libs.common.models.transforms import RGB, SquareCenterPad
from torch.nn import BCEWithLogitsLoss
from torch.optim import SGD
@@ -19,4 +21,9 @@ model = Alexnet(
optimizer_type=SGD,
optimizer_arguments=dict(lr=0.01, momentum=0.1),
pretrained=True,
+ model_transforms=[
+ SquareCenterPad(),
+ torchvision.transforms.Resize(512, antialias=True),
+ RGB(),
+ ],
)
diff --git a/src/mednet/libs/classification/config/models/densenet_pretrained.py b/src/mednet/libs/classification/config/models/densenet_pretrained.py
index 10e4a58c6c7be9ef05298331c4a9d24f1a664804..90fdd8c9baf72837837116bc9b3444f6a12fbbc8 100644
--- a/src/mednet/libs/classification/config/models/densenet_pretrained.py
+++ b/src/mednet/libs/classification/config/models/densenet_pretrained.py
@@ -10,7 +10,9 @@ page <alexnet_pytorch_>`), modified for a variable number of outputs
N.B.: The output layer is **always** initialized from scratch.
"""
+import torchvision.transforms
from mednet.libs.classification.models.densenet import Densenet
+from mednet.libs.common.models.transforms import RGB, SquareCenterPad
from torch.nn import BCEWithLogitsLoss
from torch.optim import Adam
@@ -20,4 +22,13 @@ model = Densenet(
optimizer_arguments=dict(lr=0.0001),
pretrained=True,
dropout=0.1,
+ model_transforms=[
+ SquareCenterPad(),
+ torchvision.transforms.Resize(
+ 512,
+ antialias=True,
+ interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
+ ),
+ RGB(),
+ ],
)
diff --git a/src/mednet/libs/classification/config/models/pasa.py b/src/mednet/libs/classification/config/models/pasa.py
index faec274496fe6609fbcbcdae9c733a0b71e05a8c..6eb67d29aed82f23455050dc0b2214afb02a4b21 100644
--- a/src/mednet/libs/classification/config/models/pasa.py
+++ b/src/mednet/libs/classification/config/models/pasa.py
@@ -10,7 +10,9 @@ Screening and Visualization".
Reference: [PASA-2019]_
"""
+import torchvision.transforms
from mednet.libs.classification.models.pasa import Pasa
+from mednet.libs.common.models.transforms import Grayscale, SquareCenterPad
from torch.nn import BCEWithLogitsLoss
from torch.optim import Adam
@@ -18,4 +20,13 @@ model = Pasa(
loss_type=BCEWithLogitsLoss,
optimizer_type=Adam,
optimizer_arguments=dict(lr=8e-5),
+ model_transforms=[
+ Grayscale(),
+ SquareCenterPad(),
+ torchvision.transforms.Resize(
+ 512,
+ antialias=True,
+ interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
+ ),
+ ],
)
diff --git a/src/mednet/libs/classification/models/alexnet.py b/src/mednet/libs/classification/models/alexnet.py
index 5bc3f7dae06e6fa147bc6e6a07b051204f2d4223..eab264185b16343936e0daf2d6f1935190ca15f3 100644
--- a/src/mednet/libs/classification/models/alexnet.py
+++ b/src/mednet/libs/classification/models/alexnet.py
@@ -10,10 +10,8 @@ import torch.nn
import torch.optim.optimizer
import torch.utils.data
import torchvision.models as models
-import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import RGB, SquareCenterPad
from .separate import separate
@@ -40,6 +38,9 @@ class Alexnet(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -56,6 +57,7 @@ class Alexnet(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
pretrained: bool = False,
num_classes: int = 1,
@@ -65,6 +67,7 @@ class Alexnet(Model):
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
@@ -72,12 +75,6 @@ class Alexnet(Model):
self.name = "alexnet"
self.num_classes = num_classes
- self.model_transforms = [
- SquareCenterPad(),
- torchvision.transforms.Resize(512, antialias=True),
- RGB(),
- ]
-
self.pretrained = pretrained
# Load pretrained model
diff --git a/src/mednet/libs/classification/models/densenet.py b/src/mednet/libs/classification/models/densenet.py
index a2e9b61d585ad4a0a1a31e6ec86e45dc54e989f3..428ba28187328822c3eb241468ea09249d1786ff 100644
--- a/src/mednet/libs/classification/models/densenet.py
+++ b/src/mednet/libs/classification/models/densenet.py
@@ -10,10 +10,8 @@ import torch.nn
import torch.optim.optimizer
import torch.utils.data
import torchvision.models as models
-import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import RGB, SquareCenterPad
from .separate import separate
@@ -38,6 +36,9 @@ class Densenet(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -56,6 +57,7 @@ class Densenet(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
pretrained: bool = False,
dropout: float = 0.1,
@@ -66,6 +68,7 @@ class Densenet(Model):
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
@@ -73,16 +76,6 @@ class Densenet(Model):
self.name = "densenet-121"
self.num_classes = num_classes
- self.model_transforms = [
- SquareCenterPad(),
- torchvision.transforms.Resize(
- 512,
- antialias=True,
- interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
- ),
- RGB(),
- ]
-
self.pretrained = pretrained
# Load pretrained model
diff --git a/src/mednet/libs/classification/models/pasa.py b/src/mednet/libs/classification/models/pasa.py
index 555bd3fc188b778f2e6d43bab76358ef1b9e8255..478c9397c30d506cc59c08107dcad5a8dd4e1ffb 100644
--- a/src/mednet/libs/classification/models/pasa.py
+++ b/src/mednet/libs/classification/models/pasa.py
@@ -10,10 +10,8 @@ import torch.nn
import torch.nn.functional as F # noqa: N812
import torch.optim.optimizer
import torch.utils.data
-import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import Grayscale, SquareCenterPad
from .separate import separate
@@ -43,6 +41,9 @@ class Pasa(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -56,6 +57,7 @@ class Pasa(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
):
@@ -64,6 +66,7 @@ class Pasa(Model):
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
@@ -71,16 +74,6 @@ class Pasa(Model):
self.name = "pasa"
self.num_classes = num_classes
- self.model_transforms = [
- Grayscale(),
- SquareCenterPad(),
- torchvision.transforms.Resize(
- 512,
- antialias=True,
- interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
- ),
- ]
-
# First convolution block
self.fc1 = torch.nn.Conv2d(1, 4, (3, 3), (2, 2), (1, 1))
self.fc2 = torch.nn.Conv2d(4, 16, (3, 3), (2, 2), (1, 1))
diff --git a/src/mednet/libs/common/models/model.py b/src/mednet/libs/common/models/model.py
index 53a14658fbdf6edadd8190c9ae2f0e308e7cd54b..ae4e658f37dc3d1638aebd9632974534bbf74352 100644
--- a/src/mednet/libs/common/models/model.py
+++ b/src/mednet/libs/common/models/model.py
@@ -37,6 +37,9 @@ class Model(pl.LightningModule):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -50,6 +53,7 @@ class Model(pl.LightningModule):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
):
@@ -58,7 +62,7 @@ class Model(pl.LightningModule):
self.name = "model"
self.num_classes = num_classes
- self.model_transforms: TransformSequence = []
+ self.model_transforms = model_transforms
self._loss_type = loss_type
diff --git a/src/mednet/libs/segmentation/config/models/driu.py b/src/mednet/libs/segmentation/config/models/driu.py
index 867619ead324b2b655b23b37c5cda5f91570763c..4f1be608a32e0463ef357f5f2c62dc6c1d8938e1 100644
--- a/src/mednet/libs/segmentation/config/models/driu.py
+++ b/src/mednet/libs/segmentation/config/models/driu.py
@@ -11,6 +11,7 @@ deep Convolutional Neural Networks (CNNs).
Reference: [MANINIS-2016]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.driu import DRIU
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
@@ -25,6 +26,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = DRIU(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -38,6 +41,9 @@ model = DRIU(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/driu_bn.py b/src/mednet/libs/segmentation/config/models/driu_bn.py
index 52fa78f44741686e69ec31a58e97c7057d977bcd..fc435cb94b3a4c9fd15a3636722bbd70f4f23e17 100644
--- a/src/mednet/libs/segmentation/config/models/driu_bn.py
+++ b/src/mednet/libs/segmentation/config/models/driu_bn.py
@@ -11,6 +11,7 @@ deep Convolutional Neural Networks (CNNs).
Reference: [MANINIS-2016]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.driu_bn import DRIUBN
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
@@ -25,6 +26,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = DRIUBN(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -38,6 +41,9 @@ model = DRIUBN(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/driu_od.py b/src/mednet/libs/segmentation/config/models/driu_od.py
index 5994dff0de6c146b7e48b4b426fc2304bb45ed8f..0931882671709adcd2d04a2c5cbd80a01656c369 100644
--- a/src/mednet/libs/segmentation/config/models/driu_od.py
+++ b/src/mednet/libs/segmentation/config/models/driu_od.py
@@ -11,6 +11,7 @@ deep Convolutional Neural Networks (CNNs).
Reference: [MANINIS-2016]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.driu_od import DRIUOD
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
@@ -25,6 +26,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = DRIUOD(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -38,6 +41,9 @@ model = DRIUOD(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/driu_pix.py b/src/mednet/libs/segmentation/config/models/driu_pix.py
index f9ddb8487bf0864a6720f36a2fd9e79384e46c7b..72b2feeac53658bca9847eee149746051bf5d4c9 100644
--- a/src/mednet/libs/segmentation/config/models/driu_pix.py
+++ b/src/mednet/libs/segmentation/config/models/driu_pix.py
@@ -11,6 +11,7 @@ deep Convolutional Neural Networks (CNNs).
Reference: [MANINIS-2016]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.driu_pix import DRIUPix
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
@@ -25,6 +26,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = DRIUPix(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -38,6 +41,9 @@ model = DRIUPix(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/hed.py b/src/mednet/libs/segmentation/config/models/hed.py
index d5d0f293e64df3ab30ac97b366cf8090d1a27c1f..b3ce43fe6dc299fe37a157e0c1d899d60d3c9882 100644
--- a/src/mednet/libs/segmentation/config/models/hed.py
+++ b/src/mednet/libs/segmentation/config/models/hed.py
@@ -2,6 +2,7 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.hed import HED
from mednet.libs.segmentation.models.losses import MultiSoftJaccardBCELogitsLoss
@@ -16,6 +17,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = HED(
loss_type=MultiSoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -29,6 +32,9 @@ model = HED(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/lwnet.py b/src/mednet/libs/segmentation/config/models/lwnet.py
index 262636ccc5ed4e575bd80e550090785549480eb5..339fa22a1f2bde9b6439730af0b6f1c7a2cd192c 100644
--- a/src/mednet/libs/segmentation/config/models/lwnet.py
+++ b/src/mednet/libs/segmentation/config/models/lwnet.py
@@ -9,6 +9,7 @@ closely matches (or outperforms) other more complex techniques.
Reference: [GALDRAN-2020]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.models.losses import MultiWeightedBCELogitsLoss
from mednet.libs.segmentation.models.lwnet import LittleWNet
from torch.optim import Adam
@@ -17,10 +18,15 @@ max_lr = 0.01 # start
min_lr = 1e-08 # valley
cycle = 50 # epochs for a complete scheduling cycle
+resize_transform = ResizeMaxSide(512)
+
model = LittleWNet(
loss_type=MultiWeightedBCELogitsLoss,
optimizer_type=Adam,
optimizer_arguments=dict(lr=max_lr),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/m2unet.py b/src/mednet/libs/segmentation/config/models/m2unet.py
index ec47f328b4851b3fa56cc4021be965dba6b34ebd..39389461d807da45af7eefd9cdbf3a081eb38db1 100644
--- a/src/mednet/libs/segmentation/config/models/m2unet.py
+++ b/src/mednet/libs/segmentation/config/models/m2unet.py
@@ -15,6 +15,7 @@ segmentation applications and the speed of MobileNetV2 networks.
References: [SANDLER-2018]_, [RONNEBERGER-2015]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
from mednet.libs.segmentation.models.m2unet import M2UNET
@@ -29,6 +30,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = M2UNET(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -42,6 +45,9 @@ model = M2UNET(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/config/models/unet.py b/src/mednet/libs/segmentation/config/models/unet.py
index 9919b12ec68ed4f236f3da628dc5fe5e89405b74..0dc6c417e4af6e6906d74a41abda640d00b4fa74 100644
--- a/src/mednet/libs/segmentation/config/models/unet.py
+++ b/src/mednet/libs/segmentation/config/models/unet.py
@@ -13,6 +13,7 @@ to yield more precise segmentations.
Reference: [RONNEBERGER-2015]_
"""
+from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.engine.adabound import AdaBound
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
from mednet.libs.segmentation.models.unet import Unet
@@ -27,6 +28,8 @@ gamma = 1e-3
eps = 1e-8
amsbound = False
+resize_transform = ResizeMaxSide(512)
+
model = Unet(
loss_type=SoftJaccardBCELogitsLoss,
loss_arguments=dict(alpha=alpha),
@@ -40,6 +43,9 @@ model = Unet(
weight_decay=weight_decay,
amsbound=amsbound,
),
+ model_transforms=[
+ resize_transform,
+ SquareCenterPad(),
+ ],
augmentation_transforms=[],
- crop_size=512,
)
diff --git a/src/mednet/libs/segmentation/models/driu.py b/src/mednet/libs/segmentation/models/driu.py
index fc655cecdba8792523bcef79f8ba09dcc1dc67ed..cd77fb28feff97df2f608b63660810be664434b5 100644
--- a/src/mednet/libs/segmentation/models/driu.py
+++ b/src/mednet/libs/segmentation/models/driu.py
@@ -10,7 +10,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .losses import SoftJaccardBCELogitsLoss
@@ -90,6 +89,9 @@ class DRIU(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -97,8 +99,6 @@ class DRIU(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -107,26 +107,22 @@ class DRIU(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "driu"
- resize_transform = ResizeMaxSide(crop_size)
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/driu_bn.py b/src/mednet/libs/segmentation/models/driu_bn.py
index 96b019907279eeaee79b5f52c21d38803a94a3bc..4c19c267c92ffc153f53e85ca4c5a3690ac8e684 100644
--- a/src/mednet/libs/segmentation/models/driu_bn.py
+++ b/src/mednet/libs/segmentation/models/driu_bn.py
@@ -10,7 +10,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .losses import SoftJaccardBCELogitsLoss
@@ -93,15 +92,16 @@ class DRIUBN(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
- augmentation_transforms
+ model_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
+ augmentation_transforms
+ An optional sequence of torch modules containing augmentation transforms to be
+ applied on the input **before** it is fed into the network.
num_classes
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -110,26 +110,22 @@ class DRIUBN(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "driu-bn"
- resize_transform = ResizeMaxSide(crop_size)
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/driu_od.py b/src/mednet/libs/segmentation/models/driu_od.py
index 9272d3919238b4d3c4a710822cb6e564fe590b09..308f87e19f551119c23765a22142f4671bbbb6e5 100644
--- a/src/mednet/libs/segmentation/models/driu_od.py
+++ b/src/mednet/libs/segmentation/models/driu_od.py
@@ -10,7 +10,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .driu import ConcatFuseBlock
@@ -75,6 +74,9 @@ class DRIUOD(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -82,8 +84,6 @@ class DRIUOD(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -92,26 +92,22 @@ class DRIUOD(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "driu-od"
- resize_transform = ResizeMaxSide(crop_size)
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/driu_pix.py b/src/mednet/libs/segmentation/models/driu_pix.py
index e84d45dbc8f9699ea37f67a70e779551e6b81f0d..a4cbbc5b41c0a8aea287be949a2391091500abac 100644
--- a/src/mednet/libs/segmentation/models/driu_pix.py
+++ b/src/mednet/libs/segmentation/models/driu_pix.py
@@ -10,7 +10,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .driu import ConcatFuseBlock
@@ -79,6 +78,9 @@ class DRIUPix(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -86,8 +88,6 @@ class DRIUPix(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -96,26 +96,22 @@ class DRIUPix(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "driu-pix"
- resize_transform = ResizeMaxSide(crop_size)
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/hed.py b/src/mednet/libs/segmentation/models/hed.py
index c20286fc52861e9d0aceccb4f90a92214584aaad..779c48cfce7f77ea88c2e5c19d58f36c8fd4c457 100644
--- a/src/mednet/libs/segmentation/models/hed.py
+++ b/src/mednet/libs/segmentation/models/hed.py
@@ -9,7 +9,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .losses import MultiSoftJaccardBCELogitsLoss
@@ -93,6 +92,9 @@ class HED(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -100,8 +102,6 @@ class HED(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -110,29 +110,23 @@ class HED(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "hed"
- resize_transform = ResizeMaxSide(crop_size)
-
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
-
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/lwnet.py b/src/mednet/libs/segmentation/models/lwnet.py
index 041b8ce3403171225618f4fe8c0b5ef1c4b68782..7a21ad58c026c66bec17105f89a6ff5665d2b57b 100644
--- a/src/mednet/libs/segmentation/models/lwnet.py
+++ b/src/mednet/libs/segmentation/models/lwnet.py
@@ -21,7 +21,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.models.losses import MultiWeightedBCELogitsLoss
from .separate import separate
@@ -293,13 +292,14 @@ class LittleWNet(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
num_classes
Number of outputs (classes) for this model.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -308,15 +308,16 @@ class LittleWNet(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
@@ -324,13 +325,6 @@ class LittleWNet(Model):
self.name = "lwnet"
self.num_classes = num_classes
- resize_transform = ResizeMaxSide(crop_size)
-
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
-
self.unet1 = LittleUNet(
in_c=3,
n_classes=self.num_classes,
diff --git a/src/mednet/libs/segmentation/models/m2unet.py b/src/mednet/libs/segmentation/models/m2unet.py
index 22ee9ee6ab0ab400638ec87a0ccc05155e26efd9..24587582641680c419eaddb1a8f5b49aad248f94 100644
--- a/src/mednet/libs/segmentation/models/m2unet.py
+++ b/src/mednet/libs/segmentation/models/m2unet.py
@@ -9,7 +9,6 @@ import torch
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
from torchvision.models.mobilenetv2 import InvertedResidual
@@ -141,6 +140,9 @@ class M2UNET(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -148,8 +150,6 @@ class M2UNET(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -158,29 +158,23 @@ class M2UNET(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "m2unet"
- resize_transform = ResizeMaxSide(crop_size)
-
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
-
self.pretrained = pretrained
self.backbone = mobilenet_v2_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/unet.py b/src/mednet/libs/segmentation/models/unet.py
index f050e60c9b39267845a001dea337316b8d16816d..0943d283cdac1f2b72f306a116de3d3fe579ce5b 100644
--- a/src/mednet/libs/segmentation/models/unet.py
+++ b/src/mednet/libs/segmentation/models/unet.py
@@ -9,7 +9,6 @@ import typing
import torch.nn
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
-from mednet.libs.common.models.transforms import ResizeMaxSide, SquareCenterPad
from .backbones.vgg import vgg16_for_segmentation
from .losses import SoftJaccardBCELogitsLoss
@@ -82,6 +81,9 @@ class Unet(Model):
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ model_transforms
+ An optional sequence of torch modules containing transforms to be
+ applied on the input **before** it is fed into the network.
augmentation_transforms
An optional sequence of torch modules containing transforms to be
applied on the input **before** it is fed into the network.
@@ -89,8 +91,6 @@ class Unet(Model):
Number of outputs (classes) for this model.
pretrained
If True, will use VGG16 pretrained weights.
- crop_size
- The size of the image after center cropping.
"""
def __init__(
@@ -99,29 +99,23 @@ class Unet(Model):
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
+ model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
pretrained: bool = False,
- crop_size: int = 512,
):
super().__init__(
loss_type,
loss_arguments,
optimizer_type,
optimizer_arguments,
+ model_transforms,
augmentation_transforms,
num_classes,
)
self.name = "unet"
- resize_transform = ResizeMaxSide(crop_size)
-
- self.model_transforms = [
- resize_transform,
- SquareCenterPad(),
- ]
-
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
diff --git a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json
index c31da578a3f284f1185e2aa4b269c29c19bca769..0a938bfbe2e12130ee5cd9a046227460910b2147 100644
--- a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json
+++ b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json
@@ -1,23 +1,23 @@
{
"train": [
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}
diff --git a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_drhagis_default.json b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_drhagis_default.json
index 2007d427b7b3e35adcbacd6c49a372703f2c3910..fad0c517d27dd7c39f8c3ca96d535ad7edc87d64 100644
--- a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_drhagis_default.json
+++ b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_drhagis_default.json
@@ -1,16 +1,16 @@
{
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}
diff --git a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_jsrt_default.json b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_jsrt_default.json
index 4fd46d93a874bd3ceb89715e302c850425342521..4c533cc32d8595025e7175bc6a30c9a3dbcb66c4 100644
--- a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_jsrt_default.json
+++ b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_jsrt_default.json
@@ -1,23 +1,23 @@
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],
"test": [
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