diff --git a/pixi.lock b/pixi.lock
index f1f59c107545011a830adfa6be089503c6ff8a69..525cd3bcb0e267b1d4b61d4f309957c7c0f5c52a 100644
--- a/pixi.lock
+++ b/pixi.lock
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+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.12-he940a02_1.conda
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- - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.0-pyhff2d567_0.conda
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- size: 3458445
- timestamp: 1715681264937
+ size: 4363507
+ timestamp: 1719150878323
- kind: conda
name: wayland
version: 1.23.0
@@ -19477,6 +20279,22 @@ packages:
purls: []
size: 30550
timestamp: 1677037030945
+- kind: conda
+ name: xorg-xf86vidmodeproto
+ version: 2.3.1
+ build: h7f98852_1002
+ build_number: 1002
+ subdir: linux-64
+ url: https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2
+ sha256: 43398aeacad5b8753b7a1c12cb6bca36124e0c842330372635879c350c430791
+ md5: 3ceea9668625c18f19530de98b15d5b0
+ depends:
+ - libgcc-ng >=9.3.0
+ license: MIT
+ license_family: MIT
+ purls: []
+ size: 23875
+ timestamp: 1620067286978
- kind: conda
name: xorg-xproto
version: 7.0.31
@@ -19566,36 +20384,39 @@ packages:
- kind: conda
name: zfp
version: 1.0.1
- build: h59595ed_0
- subdir: linux-64
- url: https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h59595ed_0.conda
- sha256: 52c3bb8ab48892a2851e84764b0d35589434aebebe7941d44d9aeffde53c26d3
- md5: fd486bffbf0d6841cf1456a8f2e3a995
+ build: ha84d530_1
+ build_number: 1
+ subdir: osx-arm64
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/zfp-1.0.1-ha84d530_1.conda
+ sha256: 55fd874ea43a96880a79fd18cde5e7a61b234d9065fe4c5b423dfbc09e5d1bf2
+ md5: 588dc431a2f805060225549c4c99225e
depends:
- - _openmp_mutex >=4.5
- - libgcc-ng >=12
- - libstdcxx-ng >=12
+ - __osx >=11.0
+ - libcxx >=16
+ - llvm-openmp >=16.0.6
license: BSD-3-Clause
license_family: BSD
purls: []
- size: 278045
- timestamp: 1702765714384
+ size: 196636
+ timestamp: 1719230614632
- kind: conda
name: zfp
version: 1.0.1
- build: ha8f4885_0
- subdir: osx-arm64
- url: https://conda.anaconda.org/conda-forge/osx-arm64/zfp-1.0.1-ha8f4885_0.conda
- sha256: 3b81ddab41d7174125f90739035784d8d8ff2e47b92906593a84a0858ce56090
- md5: ffa2d992521c35ea31a217e138f01b8b
+ build: hac33072_1
+ build_number: 1
+ subdir: linux-64
+ url: https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-hac33072_1.conda
+ sha256: 0f39a8089dd152419ec98d437f3910811fe0150261481a4e5a45bcbba5f318e2
+ md5: df96b7266e49529d82de467b23977452
depends:
- - libcxx >=15
- - llvm-openmp >=16.0.6
+ - _openmp_mutex >=4.5
+ - libgcc-ng >=12
+ - libstdcxx-ng >=12
license: BSD-3-Clause
license_family: BSD
purls: []
- size: 196830
- timestamp: 1702766075336
+ size: 276454
+ timestamp: 1719230451754
- kind: conda
name: zipp
version: 3.19.2
@@ -19679,6 +20500,51 @@ packages:
purls: []
size: 105002
timestamp: 1718853554747
+- kind: conda
+ name: zstandard
+ version: 0.22.0
+ build: py311h4a6b76e_1
+ build_number: 1
+ subdir: osx-arm64
+ url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.22.0-py311h4a6b76e_1.conda
+ sha256: b7b86382936a604d58af2706933c743c124bd9078054e13e6b76d57807596613
+ md5: 34b2bd2270fd11f9926e0498c0c87a05
+ depends:
+ - __osx >=11.0
+ - cffi >=1.11
+ - python >=3.11,<3.12.0a0
+ - python >=3.11,<3.12.0a0 *_cpython
+ - python_abi 3.11.* *_cp311
+ - zstd >=1.5.6,<1.5.7.0a0
+ - zstd >=1.5.6,<1.6.0a0
+ license: BSD-3-Clause
+ license_family: BSD
+ purls:
+ - pkg:pypi/zstandard?source=conda-forge-mapping
+ size: 332160
+ timestamp: 1718866671859
+- kind: conda
+ name: zstandard
+ version: 0.22.0
+ build: py311hb6f056b_1
+ build_number: 1
+ subdir: linux-64
+ url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.22.0-py311hb6f056b_1.conda
+ sha256: 9980740f9b5f9028288c71b750d6a6691d0676ae50db97866d9ee34ab51e8b16
+ md5: 72e84ef20a510ab5fca1f3d80a16e9e2
+ depends:
+ - cffi >=1.11
+ - libgcc-ng >=12
+ - python >=3.11,<3.12.0a0
+ - python_abi 3.11.* *_cp311
+ - zstd >=1.5.6,<1.5.7.0a0
+ - zstd >=1.5.6,<1.6.0a0
+ license: BSD-3-Clause
+ license_family: BSD
+ purls:
+ - pkg:pypi/zstandard?source=conda-forge-mapping
+ size: 415158
+ timestamp: 1718866512189
- kind: conda
name: zstandard
version: 0.22.0
diff --git a/pyproject.toml b/pyproject.toml
index 4fe3774f480f0af9f53bf74218ef27693170fc4c..b190ca3b282739950ce395fa23a76488fa2217c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,6 +48,7 @@ dependencies = [
"tensorboard",
"torchvision~=0.17.2",
"torch~=2.2.2",
+ "torchio",
"tqdm",
"versioningit",
]
@@ -104,7 +105,7 @@ tensorboard = "*"
torchvision = { version = "~=0.17.2", channel = "pytorch" }
tqdm = "*"
versioningit = "*"
-torchio = ">=0.19.7,<0.20"
+torchio = "*"
[tool.pixi.feature.self.pypi-dependencies]
mednet = { path = ".", editable = true }
@@ -248,7 +249,7 @@ densenet = "mednet.libs.classification.config.models.densenet"
densenet-pretrained = "mednet.libs.classification.config.models.densenet_pretrained"
# 3D models
-cnn3d = "mednet.config.models.cnn3d"
+cnn3d = "mednet.libs.classification.config.models.cnn3d"
# lists of data augmentations
affine = "mednet.libs.common.config.augmentations.affine"
diff --git a/src/mednet/libs/classification/config/data/visceral/default.py b/src/mednet/libs/classification/config/data/visceral/default.py
index 46942bb7d2015a9223a9069d7f1d166af3a4c5c7..5dc0bbd18315852234fbe0146694d7924b44a0be 100644
--- a/src/mednet/libs/classification/config/data/visceral/default.py
+++ b/src/mednet/libs/classification/config/data/visceral/default.py
@@ -3,12 +3,11 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""VISCERAL dataset for 3D organ classification.
-Database reference:
-
-See :py:class:`mednet.config.data.visceral.datamodule.DataModule` for
-technical details.
+See
+:py:class:`mednet.libs.classification.config.data.visceral.datamodule.DataModule`
+for technical details.
"""
-from mednet.config.data.visceral.datamodule import DataModule
+from mednet.libs.classification.config.data.visceral.datamodule import DataModule
datamodule = DataModule("default.json")
diff --git a/src/mednet/libs/classification/config/models/cnn3d.py b/src/mednet/libs/classification/config/models/cnn3d.py
index e0559d7de03d81d1f858a04dee5ca837f34c536a..2c956834e59090107a060fac63d388fc63d972a2 100644
--- a/src/mednet/libs/classification/config/models/cnn3d.py
+++ b/src/mednet/libs/classification/config/models/cnn3d.py
@@ -1,9 +1,9 @@
# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
-"""Simple CNN for 3D organ classification, to be trained from scratch."""
+"""Simple CNN for 3D image classification, to be trained from scratch."""
-from mednet.models.cnn3d import Conv3DNet
+from mednet.libs.classification.models.cnn3d import Conv3DNet
from torch.nn import BCEWithLogitsLoss
from torch.optim import Adam
diff --git a/src/mednet/libs/classification/scripts/upload.py b/src/mednet/libs/classification/scripts/upload.py
index 1224edf62024090e1dca8a731654f3d38212f488..5b653ce1d0b109f4ed90edca456404f447e3a875 100644
--- a/src/mednet/libs/classification/scripts/upload.py
+++ b/src/mednet/libs/classification/scripts/upload.py
@@ -14,7 +14,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
@click.command(
- entry_point_group="mednet.config",
+ entry_point_group="mednet.libs.classification.config",
cls=ConfigCommand,
epilog="""Examples:
diff --git a/src/mednet/libs/segmentation/scripts/upload.py b/src/mednet/libs/segmentation/scripts/upload.py
index bd523442a773d159ea4758a91cee047d6b166351..0de94332a9154033d3fc6955ccfec3a1a3bf38e2 100644
--- a/src/mednet/libs/segmentation/scripts/upload.py
+++ b/src/mednet/libs/segmentation/scripts/upload.py
@@ -14,7 +14,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
@click.command(
- entry_point_group="mednet.config",
+ entry_point_group="mednet.libs.segmentation.config",
cls=ConfigCommand,
epilog="""Examples:
diff --git a/tests/classification/test_montgomery_shenzhen_indian_tbx11k.py b/tests/classification/test_montgomery_shenzhen_indian_tbx11k.py
index fd9c88cfa87eb50423ab5116f3069f7ae92dfe20..5500d3e0c5207712aaa0d8dadffc61013678e91b 100644
--- a/tests/classification/test_montgomery_shenzhen_indian_tbx11k.py
+++ b/tests/classification/test_montgomery_shenzhen_indian_tbx11k.py
@@ -115,7 +115,7 @@ def test_split_consistency(name: str, tbx11k_name: str):
def test_batch_uniformity(tbx11k_name: str, dataset: str):
combined = importlib.import_module(
f".{tbx11k_name}",
- "mednet.config.data.montgomery_shenzhen_indian_tbx11k",
+ "mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k",
).datamodule
combined.model_transforms = [] # should be done before setup()
diff --git a/tests/classification/test_visceral.py b/tests/classification/test_visceral.py
index 77d0e94bf2d3b624497e675284d29a98c9439540..58ecd6bd2a6e1cab5d9a343171f60d550c17bf8b 100644
--- a/tests/classification/test_visceral.py
+++ b/tests/classification/test_visceral.py
@@ -28,7 +28,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.visceral", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.visceral", f"{split}.json"),
lengths=lenghts,
prefixes=("16/10000"),
possible_labels=(0, 1),