diff --git a/pyproject.toml b/pyproject.toml
index 0f1681ca11af5d77db441936b94570df5197f4fc..e6cf3efb889070181fbc604570d7ebfa8ecb2c81 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -423,6 +423,7 @@ visceral = "mednet.config.data.visceral.default"
# models
driu = "mednet.libs.segmentation.config.models.driu"
+hed = "mednet.libs.segmentation.config.models.hed"
lwnet = "mednet.libs.segmentation.config.models.lwnet"
unet = "mednet.libs.segmentation.config.models.unet"
diff --git a/src/mednet/libs/segmentation/config/models/hed.py b/src/mednet/libs/segmentation/config/models/hed.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c88c9c262c03bd184c3da971af6e35e348e0b6d
--- /dev/null
+++ b/src/mednet/libs/segmentation/config/models/hed.py
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from mednet.libs.segmentation.engine.adabound import AdaBound
+from mednet.libs.segmentation.models.hed import HED
+from mednet.libs.segmentation.models.losses import MultiSoftJaccardBCELogitsLoss
+
+lr = 0.001
+alpha = 0.7
+betas = (0.9, 0.999)
+eps = 1e-08
+weight_decay = 0
+final_lr = 0.1
+gamma = 1e-3
+eps = 1e-8
+amsbound = False
+crop_size = 544
+
+model = HED(
+ loss_type=MultiSoftJaccardBCELogitsLoss,
+ loss_arguments=dict(alpha=alpha),
+ optimizer_type=AdaBound,
+ optimizer_arguments=dict(
+ lr=lr,
+ betas=betas,
+ final_lr=final_lr,
+ gamma=gamma,
+ eps=eps,
+ weight_decay=weight_decay,
+ amsbound=amsbound,
+ ),
+ augmentation_transforms=[],
+ crop_size=crop_size,
+)
diff --git a/src/mednet/libs/segmentation/models/hed.py b/src/mednet/libs/segmentation/models/hed.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbefa8a44501219ff500a2092b833d83de55f414
--- /dev/null
+++ b/src/mednet/libs/segmentation/models/hed.py
@@ -0,0 +1,188 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import logging
+import typing
+
+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
+from .make_layers import UpsampleCropBlock, conv_with_kaiming_uniform
+
+logger = logging.getLogger("mednet")
+
+
+class ConcatFuseBlock(torch.nn.Module):
+ """Take in five feature maps with one channel each, concatenates thems and
+ applies a 1x1 convolution with 1 output channel.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.conv = conv_with_kaiming_uniform(5, 1, 1, 1, 0)
+
+ def forward(self, x1, x2, x3, x4, x5):
+ x_cat = torch.cat([x1, x2, x3, x4, x5], dim=1)
+ return self.conv(x_cat)
+
+
+class HEDHead(torch.nn.Module):
+ """HED head module.
+
+ Parameters
+ ----------
+ in_channels_list : list
+ Number of channels for each feature map that is returned from backbone.
+ """
+
+ def __init__(self, in_channels_list=None):
+ super().__init__()
+ (
+ in_conv_1_2_16,
+ in_upsample2,
+ in_upsample_4,
+ in_upsample_8,
+ in_upsample_16,
+ ) = in_channels_list
+
+ self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16, 1, 3, 1, 1)
+ # Upsample
+ self.upsample2 = UpsampleCropBlock(in_upsample2, 1, 4, 2, 0)
+ self.upsample4 = UpsampleCropBlock(in_upsample_4, 1, 8, 4, 0)
+ self.upsample8 = UpsampleCropBlock(in_upsample_8, 1, 16, 8, 0)
+ self.upsample16 = UpsampleCropBlock(in_upsample_16, 1, 32, 16, 0)
+ # Concat and Fuse
+ self.concatfuse = ConcatFuseBlock()
+
+ def forward(self, x):
+ hw = x[0]
+ conv1_2_16 = self.conv1_2_16(x[1])
+ upsample2 = self.upsample2(x[2], hw)
+ upsample4 = self.upsample4(x[3], hw)
+ upsample8 = self.upsample8(x[4], hw)
+ upsample16 = self.upsample16(x[5], hw)
+ concatfuse = self.concatfuse(
+ conv1_2_16, upsample2, upsample4, upsample8, upsample16
+ )
+
+ return (upsample2, upsample4, upsample8, upsample16, concatfuse)
+
+
+class HED(Model):
+ """Implementation of the HED model.
+
+ Parameters
+ ----------
+ 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``.
+ 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.
+ pretrained
+ If True, will use VGG16 pretrained weights.
+ crop_size
+ The size of the image after center cropping.
+ """
+
+ def __init__(
+ self,
+ loss_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] = {},
+ augmentation_transforms: TransformSequence = [],
+ num_classes: int = 1,
+ pretrained: bool = False,
+ crop_size: int = 544,
+ ):
+ super().__init__(
+ loss_type,
+ loss_arguments,
+ optimizer_type,
+ optimizer_arguments,
+ 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(
+ pretrained=self.pretrained,
+ return_features=[3, 8, 14, 22, 29],
+ )
+
+ self.head = HEDHead([64, 128, 256, 512, 512])
+
+ def forward(self, x):
+ if self.normalizer is not None:
+ x = self.normalizer(x)
+ x = self.backbone(x)
+ return self.head(x)
+
+ def set_normalizer(self, dataloader: torch.utils.data.DataLoader) -> None:
+ """Initialize the normalizer for the current model.
+
+ This function is NOOP if ``pretrained = True`` (normalizer set to
+ imagenet weights, during contruction).
+
+ Parameters
+ ----------
+ dataloader
+ A torch Dataloader from which to compute the mean and std.
+ Will not be used if the model is pretrained.
+ """
+ if self.pretrained:
+ from mednet.libs.common.models.normalizer import make_imagenet_normalizer
+
+ logger.warning(
+ f"ImageNet pre-trained {self.name} model - NOT "
+ f"computing z-norm factors from train dataloader. "
+ f"Using preset factors from torchvision.",
+ )
+ self.normalizer = make_imagenet_normalizer()
+ else:
+ self.normalizer = None
+
+ def training_step(self, batch, batch_idx):
+ images = batch[0]
+ ground_truths = batch[1]["target"]
+ masks = batch[1]["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]
+ ground_truths = batch[1]["target"]
+ masks = batch[1]["mask"]
+
+ outputs = self(images)
+ return self._validation_loss(outputs, ground_truths, masks)