diff --git a/src/mednet/libs/classification/models/alexnet.py b/src/mednet/libs/classification/models/alexnet.py
index b400a728d2219f19c8148bff1f39d1de88117833..320e850cd5c50bcf71a0671ef38a25f0d2faac5b 100644
--- a/src/mednet/libs/classification/models/alexnet.py
+++ b/src/mednet/libs/classification/models/alexnet.py
@@ -56,11 +56,11 @@ class Alexnet(ClassificationModel):
def __init__(
self,
- loss_type: torch.nn.Module = torch.nn.BCEWithLogitsLoss,
+ loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -68,20 +68,18 @@ class Alexnet(ClassificationModel):
num_classes: int = 1,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="alexnet",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "alexnet"
- self.num_classes = num_classes
-
self.pretrained = pretrained
# Load pretrained model
diff --git a/src/mednet/libs/classification/models/classification_model.py b/src/mednet/libs/classification/models/classification_model.py
index e2d4b3bebf7899522159e8dd1ae984e809ea58e6..51f6fa18a07b8fa886a2db0eeb0bb692e163cc3e 100644
--- a/src/mednet/libs/classification/models/classification_model.py
+++ b/src/mednet/libs/classification/models/classification_model.py
@@ -20,6 +20,8 @@ class ClassificationModel(Model):
Parameters
----------
+ name
+ Common name to give to models of this type.
loss_type
The loss to be used for training and evaluation.
@@ -49,6 +51,7 @@ class ClassificationModel(Model):
def __init__(
self,
+ name: str,
loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
@@ -60,6 +63,7 @@ class ClassificationModel(Model):
num_classes: int = 1,
):
super().__init__(
+ name,
loss_type,
loss_arguments,
optimizer_type,
diff --git a/src/mednet/libs/classification/models/cnn3d.py b/src/mednet/libs/classification/models/cnn3d.py
index 9a77092df9113f7083a7db31b191384a71546d19..64bf4893f16c89d6f3e1eae7d1deea5d330ac09c 100644
--- a/src/mednet/libs/classification/models/cnn3d.py
+++ b/src/mednet/libs/classification/models/cnn3d.py
@@ -65,6 +65,7 @@ class Conv3DNet(ClassificationModel):
num_classes: int = 1,
):
super().__init__(
+ name="cnn3d",
loss_type=loss_type,
loss_arguments=loss_arguments,
optimizer_type=optimizer_type,
diff --git a/src/mednet/libs/classification/models/densenet.py b/src/mednet/libs/classification/models/densenet.py
index e58446d1e04794fa4ad329050176385f21a3e798..e2e92793ad6a101d2fab498484a01d98e11c1aa9 100644
--- a/src/mednet/libs/classification/models/densenet.py
+++ b/src/mednet/libs/classification/models/densenet.py
@@ -56,11 +56,11 @@ class Densenet(ClassificationModel):
def __init__(
self,
- loss_type: torch.nn.Module = torch.nn.BCEWithLogitsLoss,
+ loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -69,20 +69,18 @@ class Densenet(ClassificationModel):
num_classes: int = 1,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="densenet-121",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "densenet-121"
- self.num_classes = num_classes
-
self.pretrained = pretrained
# Load pretrained model
diff --git a/src/mednet/libs/classification/models/logistic_regression.py b/src/mednet/libs/classification/models/logistic_regression.py
index f9fe1847be16de65dc3dfbd88223b89db6eeec97..d95bce0698be0b51ce86346a0e69f7426089c590 100644
--- a/src/mednet/libs/classification/models/logistic_regression.py
+++ b/src/mednet/libs/classification/models/logistic_regression.py
@@ -4,109 +4,59 @@
import typing
-import lightning.pytorch as pl
import torch
-import torch.nn as nn
+import torch.nn
+from .classification_model import ClassificationModel
-class LogisticRegression(pl.LightningModule):
+
+class LogisticRegression(ClassificationModel):
"""Logistic regression classifier with a single output.
Parameters
----------
- train_loss
- The loss to be used during the training.
-
- .. warning::
-
- The loss should be set to always return batch averages (as opposed
- to the batch sum), as our logging system expects it so.
- validation_loss
- The loss to be used for validation (may be different from the training
- loss). If extra-validation sets are provided, the same loss will be
- used throughout.
+ loss_type
+ The loss to be used for training and evaluation.
.. warning::
The loss should be set to always return batch averages (as opposed
to the batch sum), as our logging system expects it so.
+ loss_arguments
+ Arguments to the loss.
optimizer_type
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ num_classes
+ Number of outputs (classes) for this model.
input_size
The number of inputs this classifer shall process.
"""
def __init__(
self,
- train_loss: torch.nn.Module = torch.nn.BCEWithLogitsLoss(),
- validation_loss: torch.nn.Module | None = None,
+ loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
+ loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {"lr": 1e-2},
+ num_classes: int = 1,
input_size: int = 14,
):
- super().__init__()
-
- self._train_loss = train_loss.to(self.device)
- self._validation_loss = (
- validation_loss if validation_loss is not None else train_loss
- ).to(self.device)
- self._optimizer_type = optimizer_type
- self._optimizer_arguments = optimizer_arguments
-
- self.name = "logistic-regression"
+ super().__init__(
+ name="logistic-regression",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=None,
+ scheduler_arguments={},
+ model_transforms=[],
+ augmentation_transforms=[],
+ num_classes=num_classes,
+ )
- self.linear = nn.Linear(input_size, 1)
+ self.linear = torch.nn.Linear(input_size, self.num_classes)
def forward(self, x):
return self.linear(self.normalizer(x))
-
- def training_step(self, batch, batch_idx):
- _input = batch[1]
- labels = batch[2]
-
- # Increase label dimension if too low
- # Allows single and multiclass usage
- if labels.ndim == 1:
- labels = torch.reshape(labels, (labels.shape[0], 1))
-
- # Forward pass on the network
- output = self(_input)
-
- # Manually move criterion to selected device, since not part of the model.
- self._train_loss = self._train_loss.to(self.device)
- training_loss = self._train_loss(output, labels.float())
-
- return {"loss": training_loss}
-
- def validation_step(self, batch, batch_idx, dataloader_idx=0):
- _input = batch[1]
- labels = batch[2]
-
- # Increase label dimension if too low
- # Allows single and multiclass usage
- if labels.ndim == 1:
- labels = torch.reshape(labels, (labels.shape[0], 1))
-
- # data forwarding on the existing network
- output = self(_input)
-
- # Manually move criterion to selected device, since not part of the model.
- self._validation_loss = self._validation_loss.to(self.device)
- validation_loss = self._validation_loss(output, labels.float())
-
- if dataloader_idx == 0:
- return {"validation_loss": validation_loss}
-
- return {f"extra_validation_loss_{dataloader_idx}": validation_loss}
-
- def predict_step(self, batch, batch_idx, dataloader_idx=0):
- outputs = self(batch[0])
- return torch.sigmoid(outputs)
-
- def configure_optimizers(self):
- return self._optimizer_type(
- self.parameters(),
- **self._optimizer_arguments,
- )
diff --git a/src/mednet/libs/classification/models/mlp.py b/src/mednet/libs/classification/models/mlp.py
index bd928410a3133141a0048de8537df4bf65cd8e49..b35ca1036607961a1cff3b4d68f170e58819833f 100644
--- a/src/mednet/libs/classification/models/mlp.py
+++ b/src/mednet/libs/classification/models/mlp.py
@@ -4,35 +4,32 @@
import typing
-import lightning.pytorch as pl
import torch
+import torch.nn
+from .classification_model import ClassificationModel
-class MultiLayerPerceptron(pl.LightningModule):
+
+class MultiLayerPerceptron(ClassificationModel):
"""MLP with a variable number of inputs and hidden neurons (single layer).
Parameters
----------
- train_loss
- The loss to be used during the training.
-
- .. warning::
-
- The loss should be set to always return batch averages (as opposed
- to the batch sum), as our logging system expects it so.
- validation_loss
- The loss to be used for validation (may be different from the training
- loss). If extra-validation sets are provided, the same loss will be
- used throughout.
+ loss_type
+ The loss to be used for training and evaluation.
.. warning::
The loss should be set to always return batch averages (as opposed
to the batch sum), as our logging system expects it so.
+ loss_arguments
+ Arguments to the loss.
optimizer_type
The type of optimizer to use for training.
optimizer_arguments
Arguments to the optimizer after ``params``.
+ num_classes
+ Number of outputs (classes) for this model.
input_size
The number of inputs this classifer shall process.
hidden_size
@@ -41,76 +38,30 @@ class MultiLayerPerceptron(pl.LightningModule):
def __init__(
self,
- train_loss: torch.nn.Module = torch.nn.BCEWithLogitsLoss(),
- validation_loss: torch.nn.Module | None = None,
+ loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
+ loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {"lr": 1e-2},
+ num_classes: int = 1,
input_size: int = 14,
hidden_size: int = 10,
):
- super().__init__()
-
- self._train_loss = train_loss.to(self.device)
- self._validation_loss = (
- validation_loss if validation_loss is not None else train_loss
- ).to(self.device)
- self._optimizer_type = optimizer_type
- self._optimizer_arguments = optimizer_arguments
-
- self.name = "mlp"
+ super().__init__(
+ name="mlp",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=None,
+ scheduler_arguments={},
+ model_transforms=[],
+ augmentation_transforms=[],
+ num_classes=num_classes,
+ )
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.nn.ReLU()
- self.fc2 = torch.nn.Linear(hidden_size, 1)
+ self.fc2 = torch.nn.Linear(hidden_size, self.num_classes)
def forward(self, x):
return self.fc2(self.relu(self.fc1(self.normalizer(x))))
-
- def training_step(self, batch, batch_idx):
- _input = batch[1]
- labels = batch[2]
-
- # Increase label dimension if too low
- # Allows single and multiclass usage
- if labels.ndim == 1:
- labels = torch.reshape(labels, (labels.shape[0], 1))
-
- # Forward pass on the network
- output = self(_input)
-
- # Manually move criterion to selected device, since not part of the model.
- self._train_loss = self._train_loss.to(self.device)
- training_loss = self._train_loss(output, labels.float())
-
- return {"loss": training_loss}
-
- def validation_step(self, batch, batch_idx, dataloader_idx=0):
- _input = batch[1]
- labels = batch[2]
-
- # Increase label dimension if too low
- # Allows single and multiclass usage
- if labels.ndim == 1:
- labels = torch.reshape(labels, (labels.shape[0], 1))
-
- # data forwarding on the existing network
- output = self(_input)
-
- # Manually move criterion to selected device, since not part of the model.
- self._validation_loss = self._validation_loss.to(self.device)
- validation_loss = self._validation_loss(output, labels.float())
-
- if dataloader_idx == 0:
- return {"validation_loss": validation_loss}
-
- return {f"extra_validation_loss_{dataloader_idx}": validation_loss}
-
- def predict_step(self, batch, batch_idx, dataloader_idx=0):
- outputs = self(batch[0])
- return torch.sigmoid(outputs)
-
- def configure_optimizers(self):
- return self._optimizer_type(
- self.parameters(),
- **self._optimizer_arguments,
- )
diff --git a/src/mednet/libs/classification/models/pasa.py b/src/mednet/libs/classification/models/pasa.py
index 8eea2c38e3bff899ac9686b6aba695df04c687b9..6f0c7c75763034dd462c749c9b958d790fcc4d2f 100644
--- a/src/mednet/libs/classification/models/pasa.py
+++ b/src/mednet/libs/classification/models/pasa.py
@@ -56,31 +56,29 @@ class Pasa(ClassificationModel):
def __init__(
self,
- loss_type: torch.nn.Module = torch.nn.BCEWithLogitsLoss,
+ loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="pasa",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "pasa"
- self.num_classes = num_classes
-
# 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/engine/trainer.py b/src/mednet/libs/common/engine/trainer.py
index 5d3f2a0de703aa97faa055b2c269d1e3b816eba1..63d916bc6043f76ac27f52b99309953c463d0fe1 100644
--- a/src/mednet/libs/common/engine/trainer.py
+++ b/src/mednet/libs/common/engine/trainer.py
@@ -73,8 +73,6 @@ def run(
output_folder.mkdir(parents=True, exist_ok=True)
- model.configure_losses()
-
from .loggers import CustomTensorboardLogger
log_dir = "logs"
diff --git a/src/mednet/libs/common/models/model.py b/src/mednet/libs/common/models/model.py
index 669ba6c18b35faf30876780c14fa30f6368846ef..cad520975ce784f6183c30b427c0db08ebfaeaab 100644
--- a/src/mednet/libs/common/models/model.py
+++ b/src/mednet/libs/common/models/model.py
@@ -2,6 +2,7 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
+import copy
import logging
import typing
@@ -25,6 +26,8 @@ class Model(pl.LightningModule):
Parameters
----------
+ name
+ Common name to give to models of this type.
loss_type
The loss to be used for training and evaluation.
@@ -54,6 +57,7 @@ class Model(pl.LightningModule):
def __init__(
self,
+ name: str,
loss_type: type[torch.nn.Module] = torch.nn.BCEWithLogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
@@ -66,25 +70,21 @@ class Model(pl.LightningModule):
):
super().__init__()
- self.name = "model"
+ self.name = name
self.num_classes = num_classes
-
self.model_transforms = model_transforms
-
self._loss_type = loss_type
-
- self._train_loss_arguments = loss_arguments
-
- self._validation_loss_arguments = loss_arguments
-
+ self._train_loss_arguments = copy.deepcopy(loss_arguments)
+ self._validation_loss_arguments = copy.deepcopy(loss_arguments)
self._optimizer_type = optimizer_type
self._optimizer_arguments = optimizer_arguments
-
self._scheduler_type = scheduler_type
self._scheduler_arguments = scheduler_arguments
-
self.augmentation_transforms = augmentation_transforms
+ # initializes losses from input arguments
+ self._configure_losses()
+
@property
def augmentation_transforms(self) -> torchvision.transforms.Compose:
return self._augmentation_transforms
@@ -149,8 +149,14 @@ class Model(pl.LightningModule):
def predict_step(self, batch, batch_idx, dataloader_idx=0):
raise NotImplementedError
- def configure_losses(self):
+ def _configure_losses(self):
+ """Create loss objects for train and validation."""
+
+ logger.info(f"Configuring train loss ({self._train_loss_arguments})...")
self._train_loss = self._loss_type(**self._train_loss_arguments)
+ logger.info(
+ f"Configuring validation loss ({self._validation_loss_arguments})..."
+ )
self._validation_loss = self._loss_type(**self._validation_loss_arguments)
def configure_optimizers(self):
@@ -195,10 +201,8 @@ class Model(pl.LightningModule):
]
)
- if self._train_loss is not None:
- self._train_loss.to(*args, **kwargs)
- if self._validation_loss is not None:
- self._validation_loss.to(*args, **kwargs)
+ self._train_loss.to(*args, **kwargs)
+ self._validation_loss.to(*args, **kwargs)
return self
@@ -213,23 +217,36 @@ class Model(pl.LightningModule):
try:
getattr(self._loss_type(), "pos_weight")
+
except AttributeError:
logger.warning(
f"Loss {self._loss_type} does not posess a 'pos_weight' attribute and will not be balanced."
)
+
else:
- logger.info(f"Balancing training loss {self._loss_type}.")
train_weights = get_positive_weights(datamodule.train_dataloader())
self._train_loss_arguments["pos_weight"] = train_weights
+ logger.info(
+ f"Balanced training loss {self._loss_type}: "
+ f"`pos_weight={train_weights.item():.3f}`."
+ )
- logger.info(f"Balancing validation loss {self._loss_type}.")
if "validation" in datamodule.val_dataloader().keys():
validation_weights = get_positive_weights(
datamodule.val_dataloader()["validation"]
)
else:
logger.warning(
- "Datamodule does not contain a validation dataloader. The training dataloader will be used instead."
+ "Datamodule does not contain a validation dataloader. "
+ "The training dataloader will be used instead."
)
validation_weights = get_positive_weights(datamodule.train_dataloader())
+
self._validation_loss_arguments["pos_weight"] = validation_weights
+ logger.info(
+ f"Balanced validation loss {self._loss_type}: "
+ f"`pos_weight={validation_weights.item():.3f}`."
+ )
+
+ # re-instantiates losses for train and validation
+ self._configure_losses()
diff --git a/src/mednet/libs/segmentation/models/driu.py b/src/mednet/libs/segmentation/models/driu.py
index 295713cb5ec4c22ae4ab391a88d21af41cf6d592..40d097096179005e3cd2608ee2b6d52197ced218 100644
--- a/src/mednet/libs/segmentation/models/driu.py
+++ b/src/mednet/libs/segmentation/models/driu.py
@@ -8,6 +8,7 @@ import typing
import torch
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from .backbones.vgg import vgg16_for_segmentation
@@ -43,7 +44,7 @@ class DRIUHead(torch.nn.Module):
Number of channels for each feature map that is returned from backbone.
"""
- def __init__(self, in_channels_list=None):
+ def __init__(self, in_channels_list):
super().__init__()
(
in_conv_1_2_16,
@@ -106,11 +107,11 @@ class DRIU(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -118,17 +119,17 @@ class DRIU(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="driu",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "driu"
self.pretrained = pretrained
diff --git a/src/mednet/libs/segmentation/models/driu_bn.py b/src/mednet/libs/segmentation/models/driu_bn.py
index bacbf2913f02b54f5b00b31e35600416e6a59e9b..9e32925e1db8bf824dd7359b1c2ceac2ac8ea5a8 100644
--- a/src/mednet/libs/segmentation/models/driu_bn.py
+++ b/src/mednet/libs/segmentation/models/driu_bn.py
@@ -8,6 +8,7 @@ import typing
import torch
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from .backbones.vgg import vgg16_for_segmentation
@@ -46,7 +47,7 @@ class DRIUBNHead(torch.nn.Module):
Number of channels for each feature map that is returned from backbone.
"""
- def __init__(self, in_channels_list=None):
+ def __init__(self, in_channels_list):
super().__init__()
(
in_conv_1_2_16,
@@ -109,11 +110,11 @@ class DRIUBN(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -121,17 +122,17 @@ class DRIUBN(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="driu-bn",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "driu-bn"
self.pretrained = pretrained
diff --git a/src/mednet/libs/segmentation/models/driu_od.py b/src/mednet/libs/segmentation/models/driu_od.py
index af4727c556b112020739a37337f724afe3ed1b24..2336869e5f5e881affecb7ecb981224831fe0447 100644
--- a/src/mednet/libs/segmentation/models/driu_od.py
+++ b/src/mednet/libs/segmentation/models/driu_od.py
@@ -28,7 +28,7 @@ class DRIUODHead(torch.nn.Module):
Number of channels for each feature map that is returned from backbone.
"""
- def __init__(self, in_channels_list=None):
+ def __init__(self, in_channels_list):
super().__init__()
(
in_upsample2,
@@ -91,11 +91,11 @@ class DRIUOD(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -103,17 +103,17 @@ class DRIUOD(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="driu-od",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "driu-od"
self.pretrained = pretrained
diff --git a/src/mednet/libs/segmentation/models/driu_pix.py b/src/mednet/libs/segmentation/models/driu_pix.py
index e9800984a884106e36a2aa96238bad48b784dc49..42436fcc1d5eb72c7b5d7b7511561207a5a3c68f 100644
--- a/src/mednet/libs/segmentation/models/driu_pix.py
+++ b/src/mednet/libs/segmentation/models/driu_pix.py
@@ -8,6 +8,7 @@ import typing
import torch
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from .backbones.vgg import vgg16_for_segmentation
@@ -28,7 +29,7 @@ class DRIUPIXHead(torch.nn.Module):
Number of channels for each feature map that is returned from backbone.
"""
- def __init__(self, in_channels_list=None):
+ def __init__(self, in_channels_list):
super().__init__()
(
in_conv_1_2_16,
@@ -95,11 +96,11 @@ class DRIUPix(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -107,17 +108,17 @@ class DRIUPix(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="driu-pix",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "driu-pix"
self.pretrained = pretrained
diff --git a/src/mednet/libs/segmentation/models/hed.py b/src/mednet/libs/segmentation/models/hed.py
index 12a7c591aec5d75fb971b9265a27ade8cf51806f..cd99707da2194659b2f5c5621bac4ea05015b970 100644
--- a/src/mednet/libs/segmentation/models/hed.py
+++ b/src/mednet/libs/segmentation/models/hed.py
@@ -7,6 +7,7 @@ import typing
import torch
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from .backbones.vgg import vgg16_for_segmentation
@@ -40,7 +41,7 @@ class HEDHead(torch.nn.Module):
Number of channels for each feature map that is returned from backbone.
"""
- def __init__(self, in_channels_list=None):
+ def __init__(self, in_channels_list):
super().__init__()
(
in_conv_1_2_16,
@@ -109,11 +110,11 @@ class HED(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = MultiSoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = MultiSoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -121,19 +122,18 @@ class HED(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="hed",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "hed"
-
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 9442c2c92c1e1d1f32d2e02406a9101e2688796a..5cd491b04d392bbf8579bbca3525e4ea6f46bd45 100644
--- a/src/mednet/libs/segmentation/models/lwnet.py
+++ b/src/mednet/libs/segmentation/models/lwnet.py
@@ -310,31 +310,29 @@ class LittleWNet(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = MultiWeightedBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = MultiWeightedBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
num_classes: int = 1,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="lwnet",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "lwnet"
- self.num_classes = num_classes
-
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 fcca7d6072a88ad6bc6bccd4932eef2a1b0a153f..8735956595b3737b7320a7f76ac26db37fe35c0b 100644
--- a/src/mednet/libs/segmentation/models/m2unet.py
+++ b/src/mednet/libs/segmentation/models/m2unet.py
@@ -7,6 +7,7 @@ import typing
import torch
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.segmentation.models.losses import SoftJaccardBCELogitsLoss
from torchvision.models.mobilenetv2 import InvertedResidual
@@ -157,11 +158,11 @@ class M2UNET(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -169,19 +170,18 @@ class M2UNET(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="m2unet",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "m2unet"
-
self.pretrained = pretrained
self.backbone = mobilenet_v2_for_segmentation(
diff --git a/src/mednet/libs/segmentation/models/segmentation_model.py b/src/mednet/libs/segmentation/models/segmentation_model.py
index 8ffb75ac7fed41340991deef8636acaa9f2fcbae..dae5cde663ad0aebef3302b8a68210efc396beac 100644
--- a/src/mednet/libs/segmentation/models/segmentation_model.py
+++ b/src/mednet/libs/segmentation/models/segmentation_model.py
@@ -21,6 +21,8 @@ class SegmentationModel(Model):
Parameters
----------
+ name
+ Common name to give to models of this type.
loss_type
The loss to be used for training and evaluation.
@@ -50,6 +52,7 @@ class SegmentationModel(Model):
def __init__(
self,
+ name: str,
loss_type: type[torch.nn.Module] = MultiWeightedBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
@@ -61,6 +64,7 @@ class SegmentationModel(Model):
num_classes: int = 1,
):
super().__init__(
+ name,
loss_type,
loss_arguments,
optimizer_type,
diff --git a/src/mednet/libs/segmentation/models/unet.py b/src/mednet/libs/segmentation/models/unet.py
index cb1e34cfa7c07fcbafa43a59269412703f4be3ef..f4d64f01658c9303ad39a71bcc1065d1e0ecd2fc 100644
--- a/src/mednet/libs/segmentation/models/unet.py
+++ b/src/mednet/libs/segmentation/models/unet.py
@@ -7,6 +7,7 @@ import logging
import typing
import torch.nn
+import torch.utils.data
from mednet.libs.common.data.typing import TransformSequence
from .backbones.vgg import vgg16_for_segmentation
@@ -28,7 +29,7 @@ class UNetHead(torch.nn.Module):
If True, upsample using PixelShuffleICNR.
"""
- def __init__(self, in_channels_list: list[int] = None, pixel_shuffle=False):
+ def __init__(self, in_channels_list: list[int], pixel_shuffle=False):
super().__init__()
# number of channels
c_decode1, c_decode2, c_decode3, c_decode4, c_decode5 = in_channels_list
@@ -98,11 +99,11 @@ class Unet(SegmentationModel):
def __init__(
self,
- loss_type: torch.nn.Module = SoftJaccardBCELogitsLoss,
+ loss_type: type[torch.nn.Module] = SoftJaccardBCELogitsLoss,
loss_arguments: dict[str, typing.Any] = {},
optimizer_type: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_arguments: dict[str, typing.Any] = {},
- scheduler_type: type[torch.optim.lr_scheduler] = None,
+ scheduler_type: type[torch.optim.lr_scheduler.LRScheduler] | None = None,
scheduler_arguments: dict[str, typing.Any] = {},
model_transforms: TransformSequence = [],
augmentation_transforms: TransformSequence = [],
@@ -110,19 +111,18 @@ class Unet(SegmentationModel):
pretrained: bool = False,
):
super().__init__(
- loss_type,
- loss_arguments,
- optimizer_type,
- optimizer_arguments,
- scheduler_type,
- scheduler_arguments,
- model_transforms,
- augmentation_transforms,
- num_classes,
+ name="unet",
+ loss_type=loss_type,
+ loss_arguments=loss_arguments,
+ optimizer_type=optimizer_type,
+ optimizer_arguments=optimizer_arguments,
+ scheduler_type=scheduler_type,
+ scheduler_arguments=scheduler_arguments,
+ model_transforms=model_transforms,
+ augmentation_transforms=augmentation_transforms,
+ num_classes=num_classes,
)
- self.name = "unet"
-
self.pretrained = pretrained
self.backbone = vgg16_for_segmentation(
@@ -160,26 +160,3 @@ class Unet(SegmentationModel):
self.normalizer = make_imagenet_normalizer()
else:
super().set_normalizer(dataloader)
-
- def training_step(self, batch, batch_idx):
- images = batch[0]["image"]
- ground_truths = batch[0]["target"]
- masks = batch[0]["mask"]
-
- outputs = self(self._augmentation_transforms(images))
- return self._train_loss(outputs, ground_truths, masks)
-
- def validation_step(self, batch, batch_idx):
- images = batch[0]["image"]
- ground_truths = batch[0]["target"]
- masks = batch[0]["mask"]
-
- outputs = self(images)
- return self._validation_loss(outputs, ground_truths, masks)
-
- def predict_step(self, batch, batch_idx, dataloader_idx=0):
- output = self(batch[0]["image"])[1]
- return torch.sigmoid(output)
-
- def configure_optimizers(self):
- return self._optimizer_type(self.parameters(), **self._optimizer_arguments)