build: bump version 0.7.0 → 0.7.1

.bumpversion.cfg

@@ -1,15 +1,13 @@
 [bumpversion]
-current_version = 1.0.0a4
+current_version = 0.7.1
 commit = True
 tag = True
-parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)((?P<release>[a-zA-Z0-9_.-]+))?
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = 
+serialize = {major}.{minor}.{patch}
-	{major}.{minor}.{patch}-{release}
-	{major}.{minor}.{patch}
 message = build: bump version {current_version} → {new_version}
 
-[bumpversion:file:setup.py]
+[bumpversion:file:pyproject.toml]
 
-[bumpversion:file:./prototorch/models/__init__.py]
+[bumpversion:file:./src/prototorch/models/__init__.py]
 
 [bumpversion:file:./docs/source/conf.py]

.github/workflows/examples.yml

@@ -6,20 +6,20 @@ name: examples
 on:
   push:
     paths:
-      - 'examples/**.py'
+      - "examples/**.py"
 jobs:
   cpu:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+      - name: Set up Python 3.11
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: "3.10"
+          python-version: "3.11"
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-    - name: Run examples
+      - name: Run examples
-      run: |
+        run: |
-        ./tests/test_examples.sh examples/
+          ./tests/test_examples.sh examples/

.github/workflows/pythonapp.yml

@@ -6,70 +6,70 @@ name: tests
 on:
   push:
   pull_request:
-    branches: [ master ]
+    branches: [master]
 
 jobs:
   style:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+      - name: Set up Python 3.11
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: "3.10"
+          python-version: "3.11"
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-    - uses: pre-commit/action@v2.0.3
+      - uses: pre-commit/action@v3.0.0
   compatibility:
     needs: style
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         os: [ubuntu-latest, windows-latest]
         exclude:
-        - os: windows-latest
+          - os: windows-latest
-          python-version: "3.7"
+            python-version: "3.8"
-        - os: windows-latest
+          - os: windows-latest
-          python-version: "3.8"
+            python-version: "3.9"
-        - os: windows-latest
+          - os: windows-latest
-          python-version: "3.9"
+            python-version: "3.10"
 
     runs-on: ${{ matrix.os }}
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: ${{ matrix.python-version }}
+          python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-    - name: Test with pytest
+      - name: Test with pytest
-      run: |
+        run: |
-        pytest
+          pytest
   publish_pypi:
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
     needs: compatibility
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+      - name: Set up Python 3.11
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: "3.10"
+          python-version: "3.11"
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-        pip install wheel
+          pip install build
-    - name: Build package
+      - name: Build package
-      run: python setup.py sdist bdist_wheel
+        run: python -m build . -C verbose
-    - name: Publish a Python distribution to PyPI
+      - name: Publish a Python distribution to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@release/v1
-      with:
+        with:
-        user: __token__
+          user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
+          password: ${{ secrets.PYPI_API_TOKEN }}

.pre-commit-config.yaml

@@ -2,53 +2,53 @@
 # See https://pre-commit.com/hooks.html for more hooks
 
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
+  - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
+    rev: v4.4.0
-  hooks:
+    hooks:
-  - id: trailing-whitespace
+      - id: trailing-whitespace
-    exclude: (^\.bumpversion\.cfg$|cli_messages\.py)
+      - id: end-of-file-fixer
-  - id: end-of-file-fixer
+      - id: check-yaml
-  - id: check-yaml
+      - id: check-added-large-files
-  - id: check-added-large-files
+      - id: check-ast
-  - id: check-ast
+      - id: check-case-conflict
-  - id: check-case-conflict
 
-- repo: https://github.com/myint/autoflake
+  - repo: https://github.com/myint/autoflake
-  rev: v1.4
+    rev: v2.1.1
-  hooks:
+    hooks:
-  - id: autoflake
+      - id: autoflake
 
-- repo: http://github.com/PyCQA/isort
+  - repo: http://github.com/PyCQA/isort
-  rev: 5.10.1
+    rev: 5.12.0
-  hooks:
+    hooks:
-  - id: isort
+      - id: isort
 
-- repo: https://github.com/pre-commit/mirrors-mypy
+  - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.950
+    rev: v1.3.0
-  hooks:
+    hooks:
-  - id: mypy
+      - id: mypy
-    files: prototorch
+        files: prototorch
-    additional_dependencies: [types-pkg_resources]
+        additional_dependencies: [types-pkg_resources]
 
-- repo: https://github.com/pre-commit/mirrors-yapf
+  - repo: https://github.com/pre-commit/mirrors-yapf
-  rev: v0.32.0
+    rev: v0.32.0
-  hooks:
+    hooks:
-  - id: yapf
+      - id: yapf
+        additional_dependencies: ["toml"]
 
-- repo: https://github.com/pre-commit/pygrep-hooks
+  - repo: https://github.com/pre-commit/pygrep-hooks
-  rev: v1.9.0
+    rev: v1.10.0
-  hooks:
+    hooks:
-  - id: python-use-type-annotations
+      - id: python-use-type-annotations
-  - id: python-no-log-warn
+      - id: python-no-log-warn
-  - id: python-check-blanket-noqa
+      - id: python-check-blanket-noqa
 
-- repo: https://github.com/asottile/pyupgrade
+  - repo: https://github.com/asottile/pyupgrade
-  rev: v2.32.1
+    rev: v3.7.0
-  hooks:
+    hooks:
-  - id: pyupgrade
+      - id: pyupgrade
 
-- repo: https://github.com/si-cim/gitlint
+  - repo: https://github.com/si-cim/gitlint
-  rev: v0.15.2-unofficial
+    rev: v0.15.2-unofficial
-  hooks:
+    hooks:
-  - id: gitlint
+      - id: gitlint
-    args: [--contrib=CT1, --ignore=B6, --msg-filename]
+        args: [--contrib=CT1, --ignore=B6, --msg-filename]

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "1.0.0-a4"
+release = "0.7.1"
 
 # -- General configuration ---------------------------------------------------
 

examples/cbc_iris.py

@@ -1,12 +1,11 @@
 """CBC example using the Iris dataset."""
-
 import argparse
 import warnings
 
 import prototorch as pt
 import pytorch_lightning as pl
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import CBC, VisCBC2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -19,7 +18,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -53,8 +53,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/dynamic_pruning.py

@@ -7,13 +7,13 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     CELVQ,
     PruneLoserPrototypes,
     VisGLVQ2D,
 )
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -26,7 +26,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -83,8 +84,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             pruning,

examples/glvq_iris.py

@@ -7,8 +7,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import GLVQ, VisGLVQ2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.optim.lr_scheduler import ExponentialLR
 from torch.utils.data import DataLoader
@@ -21,7 +21,8 @@ if __name__ == "__main__":
     seed_everything(seed=4)
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -55,8 +56,10 @@ if __name__ == "__main__":
     vis = VisGLVQ2D(data=train_ds)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/gmlvq_iris.py

@@ -6,8 +6,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import GMLVQ, VisGMLVQ2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.optim.lr_scheduler import ExponentialLR
 from torch.utils.data import DataLoader
@@ -22,7 +22,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -59,8 +60,10 @@ if __name__ == "__main__":
     vis = VisGMLVQ2D(data=train_ds)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],
@@ -71,3 +74,5 @@ if __name__ == "__main__":
 
     # Training loop
     trainer.fit(model, train_loader)
+
+    torch.save(model, "iris.pth")

examples/gmlvq_mnist.py

@@ -6,13 +6,13 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     ImageGMLVQ,
     PruneLoserPrototypes,
     VisImgComp,
 )
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 from torchvision import transforms
@@ -26,7 +26,8 @@ if __name__ == "__main__":
     seed_everything(seed=4)
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -96,8 +97,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             pruning,

examples/gmlvq_spiral.py

@@ -6,13 +6,13 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     GMLVQ,
     PruneLoserPrototypes,
     VisGLVQ2D,
 )
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -25,7 +25,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -78,8 +79,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             es,

examples/gng_iris.py

@@ -7,8 +7,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import GrowingNeuralGas, VisNG2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -18,7 +18,8 @@ warnings.filterwarnings("ignore", category=UserWarning)
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Reproducibility
@@ -51,8 +52,10 @@ if __name__ == "__main__":
     vis = VisNG2D(data=train_loader)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/grlvq_iris.py

@@ -0,0 +1,77 @@
+"""GMLVQ example using the Iris dataset."""
+
+import argparse
+import warnings
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from lightning_fabric.utilities.seed import seed_everything
+from prototorch.models import GRLVQ, VisSiameseGLVQ2D
+from pytorch_lightning.utilities.warnings import PossibleUserWarning
+from torch.optim.lr_scheduler import ExponentialLR
+from torch.utils.data import DataLoader
+
+warnings.filterwarnings("ignore", category=PossibleUserWarning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
+if __name__ == "__main__":
+
+    # Reproducibility
+    seed_everything(seed=4)
+
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
+    args = parser.parse_args()
+
+    # Dataset
+    train_ds = pt.datasets.Iris([0, 1])
+
+    # Dataloaders
+    train_loader = DataLoader(train_ds, batch_size=64)
+
+    # Hyperparameters
+    hparams = dict(
+        input_dim=2,
+        distribution={
+            "num_classes": 3,
+            "per_class": 2
+        },
+        proto_lr=0.01,
+        bb_lr=0.01,
+    )
+
+    # Initialize the model
+    model = GRLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = VisSiameseGLVQ2D(data=train_ds)
+
+    # Setup trainer
+    trainer = pl.Trainer(
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
+        callbacks=[
+            vis,
+        ],
+        max_epochs=5,
+        log_every_n_steps=1,
+        detect_anomaly=True,
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)
+
+    torch.save(model, "iris.pth")

examples/gtlvq_mnist.py

@@ -6,13 +6,13 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     ImageGTLVQ,
     PruneLoserPrototypes,
     VisImgComp,
 )
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 from torchvision import transforms
@@ -27,7 +27,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -100,8 +101,10 @@ if __name__ == "__main__":
 
     # Setup trainer
     # using GPUs here is strongly recommended!
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             pruning,

examples/gtlvq_moons.py

@@ -7,9 +7,9 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import GTLVQ, VisGLVQ2D
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -19,7 +19,8 @@ warnings.filterwarnings("ignore", category=UserWarning)
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Reproducibility
@@ -61,8 +62,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             es,

examples/knn_iris.py

@@ -18,7 +18,8 @@ warnings.filterwarnings("ignore", category=PossibleUserWarning)
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -59,8 +60,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         max_epochs=1,
         callbacks=[
             vis,

examples/ksom_colors.py

@@ -7,10 +7,10 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from matplotlib import pyplot as plt
 from prototorch.models import KohonenSOM
 from prototorch.utils.colors import hex_to_rgb
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader, TensorDataset
 
@@ -58,7 +58,8 @@ class Vis2DColorSOM(pl.Callback):
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Reproducibility
@@ -104,8 +105,10 @@ if __name__ == "__main__":
     vis = Vis2DColorSOM(data=data)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         max_epochs=500,
         callbacks=[
             vis,

examples/lgmlvq_moons.py

@@ -7,9 +7,9 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import LGMLVQ, VisGLVQ2D
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -19,7 +19,8 @@ warnings.filterwarnings("ignore", category=UserWarning)
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Reproducibility
@@ -62,8 +63,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             es,

examples/lvqmln_iris.py

@@ -6,12 +6,12 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     LVQMLN,
     PruneLoserPrototypes,
     VisSiameseGLVQ2D,
 )
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -39,7 +39,8 @@ class Backbone(torch.nn.Module):
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -88,8 +89,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             pruning,

examples/median_lvq_iris.py

@@ -6,9 +6,9 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import MedianLVQ, VisGLVQ2D
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -20,7 +20,8 @@ if __name__ == "__main__":
     seed_everything(seed=4)
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -53,8 +54,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             es,

examples/ng_iris.py

@@ -6,8 +6,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import NeuralGas, VisNG2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
@@ -23,7 +23,8 @@ if __name__ == "__main__":
 
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Prepare and pre-process the dataset
@@ -60,8 +61,10 @@ if __name__ == "__main__":
     vis = VisNG2D(data=train_ds)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/rslvq_iris.py

@@ -6,8 +6,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import RSLVQ, VisGLVQ2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -17,7 +17,8 @@ warnings.filterwarnings("ignore", category=UserWarning)
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Reproducibility
@@ -54,8 +55,10 @@ if __name__ == "__main__":
     vis = VisGLVQ2D(data=train_ds)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/siamese_glvq_iris.py

@@ -6,8 +6,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import SiameseGLVQ, VisSiameseGLVQ2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -35,7 +35,8 @@ class Backbone(torch.nn.Module):
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -50,8 +51,7 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         distribution=[1, 2, 3],
-        proto_lr=0.01,
+        lr=0.01,
-        bb_lr=0.01,
     )
 
     # Initialize the backbone
@@ -69,8 +69,10 @@ if __name__ == "__main__":
     vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/siamese_gtlvq_iris.py

@@ -6,8 +6,8 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import SiameseGTLVQ, VisSiameseGLVQ2D
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.utils.data import DataLoader
 
@@ -35,7 +35,8 @@ class Backbone(torch.nn.Module):
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Dataset
@@ -50,8 +51,7 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         distribution=[1, 2, 3],
-        proto_lr=0.01,
+        lr=0.01,
-        bb_lr=0.01,
         input_dim=2,
         latent_dim=1,
     )
@@ -71,8 +71,10 @@ if __name__ == "__main__":
     vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
         ],

examples/warm_starting.py

@@ -6,6 +6,7 @@ import warnings
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from lightning_fabric.utilities.seed import seed_everything
 from prototorch.models import (
     GLVQ,
     KNN,
@@ -14,7 +15,6 @@ from prototorch.models import (
     VisGLVQ2D,
 )
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.utilities.warnings import PossibleUserWarning
 from torch.optim.lr_scheduler import ExponentialLR
 from torch.utils.data import DataLoader
@@ -27,7 +27,8 @@ if __name__ == "__main__":
     seed_everything(seed=4)
     # Command-line arguments
     parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
+    parser.add_argument("--gpus", type=int, default=0)
+    parser.add_argument("--fast_dev_run", type=bool, default=False)
     args = parser.parse_args()
 
     # Prepare the data
@@ -54,7 +55,9 @@ if __name__ == "__main__":
 
     # Setup trainer for GNG
     trainer = pl.Trainer(
-        max_epochs=1000,
+        accelerator="cpu",
+        max_epochs=50 if args.fast_dev_run else
+        1000,  # 10 epochs fast dev run reproducible DIV error.
         callbacks=[
             es,
         ],
@@ -108,8 +111,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
+    trainer = pl.Trainer(
-        args,
+        accelerator="cuda" if args.gpus else "cpu",
+        devices=args.gpus if args.gpus else "auto",
+        fast_dev_run=args.fast_dev_run,
         callbacks=[
             vis,
             pruning,

examples/y_architecture_example.py

@@ -1,100 +0,0 @@
-import prototorch as pt
-import pytorch_lightning as pl
-import torchmetrics
-from prototorch.core import SMCI
-from prototorch.y.callbacks import (
-    LogTorchmetricCallback,
-    PlotLambdaMatrixToTensorboard,
-    VisGMLVQ2D,
-)
-from prototorch.y.library.gmlvq import GMLVQ
-from pytorch_lightning.callbacks import EarlyStopping
-from torch.utils.data import DataLoader
-
-# ##############################################################################
-
-
-def main():
-    # ------------------------------------------------------------
-    # DATA
-    # ------------------------------------------------------------
-
-    # Dataset
-    train_ds = pt.datasets.Iris()
-
-    # Dataloader
-    train_loader = DataLoader(
-        train_ds,
-        batch_size=32,
-        num_workers=0,
-        shuffle=True,
-    )
-
-    # ------------------------------------------------------------
-    # HYPERPARAMETERS
-    # ------------------------------------------------------------
-
-    # Select Initializer
-    components_initializer = SMCI(train_ds)
-
-    # Define Hyperparameters
-    hyperparameters = GMLVQ.HyperParameters(
-        lr=dict(components_layer=0.1, _omega=0),
-        input_dim=4,
-        distribution=dict(
-            num_classes=3,
-            per_class=1,
-        ),
-        component_initializer=components_initializer,
-    )
-
-    # Create Model
-    model = GMLVQ(hyperparameters)
-
-    print(model.hparams)
-
-    # ------------------------------------------------------------
-    # TRAINING
-    # ------------------------------------------------------------
-
-    # Controlling Callbacks
-    stopping_criterion = LogTorchmetricCallback(
-        'recall',
-        torchmetrics.Recall,
-        num_classes=3,
-    )
-
-    es = EarlyStopping(
-        monitor=stopping_criterion.name,
-        mode="max",
-        patience=10,
-    )
-
-    # Visualization Callback
-    vis = VisGMLVQ2D(data=train_ds)
-
-    # Define trainer
-    trainer = pl.Trainer(callbacks=[
-        vis,
-        stopping_criterion,
-        es,
-        PlotLambdaMatrixToTensorboard(),
-    ], )
-
-    # Train
-    trainer.fit(model, train_loader)
-
-    # Manual save
-    trainer.save_checkpoint("./y_arch.ckpt")
-
-    # Load saved model
-    new_model = GMLVQ.load_from_checkpoint(
-        checkpoint_path="./y_arch.ckpt",
-        strict=True,
-    )
-
-    print(new_model.hparams)
-
-
-if __name__ == "__main__":
-    main()

prototorch/models/mixins.py

@@ -1,35 +0,0 @@
-import pytorch_lightning as pl
-import torch
-from prototorch.core.components import Components
-
-
-class ProtoTorchMixin(pl.LightningModule):
-    """All mixins are ProtoTorchMixins."""
-
-
-class NonGradientMixin(ProtoTorchMixin):
-    """Mixin for custom non-gradient optimization."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.automatic_optimization = False
-
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        raise NotImplementedError
-
-
-class ImagePrototypesMixin(ProtoTorchMixin):
-    """Mixin for models with image prototypes."""
-    proto_layer: Components
-    components: torch.Tensor
-
-    def on_train_batch_end(self, outputs, batch, batch_idx):
-        """Constrain the components to the range [0, 1] by clamping after updates."""
-        self.proto_layer.components.data.clamp_(0.0, 1.0)
-
-    def get_prototype_grid(self, num_columns=2, return_channels_last=True):
-        from torchvision.utils import make_grid
-        grid = make_grid(self.components, nrow=num_columns)
-        if return_channels_last:
-            grid = grid.permute((1, 2, 0))
-        return grid.cpu()

prototorch/y/__init__.py

@@ -1,23 +0,0 @@
-from .architectures.base import BaseYArchitecture
-from .architectures.comparison import (
-    OmegaComparisonMixin,
-    SimpleComparisonMixin,
-)
-from .architectures.competition import WTACompetitionMixin
-from .architectures.components import SupervisedArchitecture
-from .architectures.loss import GLVQLossMixin
-from .architectures.optimization import (
-    MultipleLearningRateMixin,
-    SingleLearningRateMixin,
-)
-
-__all__ = [
-    'BaseYArchitecture',
-    "OmegaComparisonMixin",
-    "SimpleComparisonMixin",
-    "SingleLearningRateMixin",
-    "MultipleLearningRateMixin",
-    "SupervisedArchitecture",
-    "WTACompetitionMixin",
-    "GLVQLossMixin",
-]

prototorch/y/architectures/base.py

@@ -1,226 +0,0 @@
-"""
-Proto Y Architecture
-
-Network architecture for Component based Learning.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Callable
-
-import pytorch_lightning as pl
-import torch
-from torchmetrics import Metric
-
-
-class BaseYArchitecture(pl.LightningModule):
-
-    @dataclass
-    class HyperParameters:
-        ...
-
-    # Fields
-    registered_metrics: dict[type[Metric], Metric] = {}
-    registered_metric_callbacks: dict[type[Metric], set[Callable]] = {}
-
-    # Type Hints for Necessary Fields
-    components_layer: torch.nn.Module
-
-    def __init__(self, hparams) -> None:
-        if type(hparams) is dict:
-            self.save_hyperparameters(hparams)
-            # TODO: => Move into Component Child
-            del hparams["initialized_proto_shape"]
-            hparams = self.HyperParameters(**hparams)
-        else:
-            self.save_hyperparameters(
-                hparams.__dict__,
-                ignore=["component_initializer"],
-            )
-
-        super().__init__()
-
-        # Common Steps
-        self.init_components(hparams)
-        self.init_latent(hparams)
-        self.init_comparison(hparams)
-        self.init_competition(hparams)
-
-        # Train Steps
-        self.init_loss(hparams)
-
-        # Inference Steps
-        self.init_inference(hparams)
-
-    # external API
-    def get_competition(self, batch, components):
-        latent_batch, latent_components = self.latent(batch, components)
-        # TODO: => Latent Hook
-        comparison_tensor = self.comparison(latent_batch, latent_components)
-        # TODO: => Comparison Hook
-        return comparison_tensor
-
-    def forward(self, batch):
-        if isinstance(batch, torch.Tensor):
-            batch = (batch, None)
-        # TODO: manage different datatypes?
-        components = self.components_layer()
-        # TODO: => Component Hook
-        comparison_tensor = self.get_competition(batch, components)
-        # TODO: => Competition Hook
-        return self.inference(comparison_tensor, components)
-
-    def predict(self, batch):
-        """
-        Alias for forward
-        """
-        return self.forward(batch)
-
-    def forward_comparison(self, batch):
-        if isinstance(batch, torch.Tensor):
-            batch = (batch, None)
-        # TODO: manage different datatypes?
-        components = self.components_layer()
-        # TODO: => Component Hook
-        return self.get_competition(batch, components)
-
-    def loss_forward(self, batch):
-        # TODO: manage different datatypes?
-        components = self.components_layer()
-        # TODO: => Component Hook
-        comparison_tensor = self.get_competition(batch, components)
-        # TODO: => Competition Hook
-        return self.loss(comparison_tensor, batch, components)
-
-    # Empty Initialization
-    # TODO: Docs
-    def init_components(self, hparams: HyperParameters) -> None:
-        ...
-
-    def init_latent(self, hparams: HyperParameters) -> None:
-        ...
-
-    def init_comparison(self, hparams: HyperParameters) -> None:
-        ...
-
-    def init_competition(self, hparams: HyperParameters) -> None:
-        ...
-
-    def init_loss(self, hparams: HyperParameters) -> None:
-        ...
-
-    def init_inference(self, hparams: HyperParameters) -> None:
-        ...
-
-    # Empty Steps
-    # TODO: Type hints
-    def components(self):
-        """
-        This step has no input.
-
-        It returns the components.
-        """
-        raise NotImplementedError(
-            "The components step has no reasonable default.")
-
-    def latent(self, batch, components):
-        """
-        The latent step receives the data batch and the components.
-        It can transform both by an arbitrary function.
-
-        It returns the transformed batch and components, each of the same length as the original input.
-        """
-        return batch, components
-
-    def comparison(self, batch, components):
-        """
-        Takes a batch of size N and the component set of size M.
-
-        It returns an NxMxD tensor containing D (usually 1) pairwise comparison measures.
-        """
-        raise NotImplementedError(
-            "The comparison step has no reasonable default.")
-
-    def competition(self, comparison_measures, components):
-        """
-        Takes the tensor of comparison measures.
-
-        Assigns a competition vector to each class.
-        """
-        raise NotImplementedError(
-            "The competition step has no reasonable default.")
-
-    def loss(self, comparison_measures, batch, components):
-        """
-        Takes the tensor of competition measures.
-
-        Calculates a single loss value
-        """
-        raise NotImplementedError("The loss step has no reasonable default.")
-
-    def inference(self, comparison_measures, components):
-        """
-        Takes the tensor of competition measures.
-
-        Returns the inferred vector.
-        """
-        raise NotImplementedError(
-            "The inference step has no reasonable default.")
-
-    # Y Architecture Hooks
-
-    # internal API, called by models and callbacks
-    def register_torchmetric(
-        self,
-        name: Callable,
-        metric: type[Metric],
-        **metric_kwargs,
-    ):
-        if metric not in self.registered_metrics:
-            self.registered_metrics[metric] = metric(**metric_kwargs)
-            self.registered_metric_callbacks[metric] = {name}
-        else:
-            self.registered_metric_callbacks[metric].add(name)
-
-    def update_metrics_step(self, batch):
-        # Prediction Metrics
-        preds = self(batch)
-
-        x, y = batch
-        for metric in self.registered_metrics:
-            instance = self.registered_metrics[metric].to(self.device)
-            instance(y, preds)
-
-    def update_metrics_epoch(self):
-        for metric in self.registered_metrics:
-            instance = self.registered_metrics[metric].to(self.device)
-            value = instance.compute()
-
-            for callback in self.registered_metric_callbacks[metric]:
-                callback(value, self)
-
-            instance.reset()
-
-    # Lightning Hooks
-
-    # Steps
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
-        self.update_metrics_step([torch.clone(el) for el in batch])
-
-        return self.loss_forward(batch)
-
-    def validation_step(self, batch, batch_idx):
-        return self.loss_forward(batch)
-
-    def test_step(self, batch, batch_idx):
-        return self.loss_forward(batch)
-
-    # Other Hooks
-    def training_epoch_end(self, outs) -> None:
-        self.update_metrics_epoch()
-
-    def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None:
-        checkpoint["hyper_parameters"] = {
-            'hparams': checkpoint["hyper_parameters"]
-        }
-        return super().on_save_checkpoint(checkpoint)

prototorch/y/architectures/comparison.py

@@ -1,112 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Callable, Dict
-
-import torch
-from prototorch.core.distances import euclidean_distance
-from prototorch.core.initializers import (
-    AbstractLinearTransformInitializer,
-    EyeLinearTransformInitializer,
-)
-from prototorch.nn.wrappers import LambdaLayer
-from prototorch.y.architectures.base import BaseYArchitecture
-from torch import Tensor
-from torch.nn.parameter import Parameter
-
-
-class SimpleComparisonMixin(BaseYArchitecture):
-    """
-    Simple Comparison
-
-    A comparison layer that only uses the positions of the components and the batch for dissimilarity computation.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        comparison_fn: The comparison / dissimilarity function to use. Default: euclidean_distance.
-        comparison_args: Keyword arguments for the comparison function. Default: {}.
-        """
-        comparison_fn: Callable = euclidean_distance
-        comparison_args: dict = field(default_factory=lambda: dict())
-
-        comparison_parameters: dict = field(default_factory=lambda: dict())
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_comparison(self, hparams: HyperParameters):
-        self.comparison_layer = LambdaLayer(
-            fn=hparams.comparison_fn,
-            **hparams.comparison_args,
-        )
-
-        self.comparison_kwargs: dict[str, Tensor] = dict()
-
-    def comparison(self, batch, components):
-        comp_tensor, _ = components
-        batch_tensor, _ = batch
-
-        comp_tensor = comp_tensor.unsqueeze(1)
-
-        distances = self.comparison_layer(
-            batch_tensor,
-            comp_tensor,
-            **self.comparison_kwargs,
-        )
-
-        return distances
-
-
-class OmegaComparisonMixin(SimpleComparisonMixin):
-    """
-    Omega Comparison
-
-    A comparison layer that uses the positions of the components and the batch for dissimilarity computation.
-    """
-
-    _omega: torch.Tensor
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(SimpleComparisonMixin.HyperParameters):
-        """
-        input_dim: Necessary Field: The dimensionality of the input.
-        latent_dim: The dimensionality of the latent space. Default: 2.
-        omega_initializer: The initializer to use for the omega matrix. Default: EyeLinearTransformInitializer.
-        """
-        input_dim: int | None = None
-        latent_dim: int = 2
-        omega_initializer: type[
-            AbstractLinearTransformInitializer] = EyeLinearTransformInitializer
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_comparison(self, hparams: HyperParameters) -> None:
-        super().init_comparison(hparams)
-
-        # Initialize the omega matrix
-        if hparams.input_dim is None:
-            raise ValueError("input_dim must be specified.")
-        else:
-            omega = hparams.omega_initializer().generate(
-                hparams.input_dim,
-                hparams.latent_dim,
-            )
-            self.register_parameter("_omega", Parameter(omega))
-            self.comparison_kwargs = dict(omega=self._omega)
-
-    # Properties
-    # ----------------------------------------------------------------------------------------------------
-    @property
-    def omega_matrix(self):
-        return self._omega.detach().cpu()
-
-    @property
-    def lambda_matrix(self):
-        omega = self._omega.detach()
-        lam = omega @ omega.T
-        return lam.detach().cpu()

prototorch/y/architectures/competition.py

@@ -1,29 +0,0 @@
-from dataclasses import dataclass
-
-from prototorch.core.competitions import WTAC
-from prototorch.y.architectures.base import BaseYArchitecture
-
-
-class WTACompetitionMixin(BaseYArchitecture):
-    """
-    Winner Take All Competition
-
-    A competition layer that uses the winner-take-all strategy.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        No hyperparameters.
-        """
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_inference(self, hparams: HyperParameters):
-        self.competition_layer = WTAC()
-
-    def inference(self, comparison_measures, components):
-        comp_labels = components[1]
-        return self.competition_layer(comparison_measures, comp_labels)

prototorch/y/architectures/components.py

@@ -1,64 +0,0 @@
-from dataclasses import dataclass
-
-from prototorch.core.components import LabeledComponents
-from prototorch.core.initializers import (
-    AbstractComponentsInitializer,
-    LabelsInitializer,
-    ZerosCompInitializer,
-)
-from prototorch.y import BaseYArchitecture
-
-
-class SupervisedArchitecture(BaseYArchitecture):
-    """
-    Supervised Architecture
-
-    An architecture that uses labeled Components as component Layer.
-    """
-    components_layer: LabeledComponents
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters:
-        """
-        distribution: A valid prototype distribution. No default possible.
-        components_initializer: An implementation of AbstractComponentsInitializer. No default possible.
-        """
-        distribution: "dict[str, int]"
-        component_initializer: AbstractComponentsInitializer
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_components(self, hparams: HyperParameters):
-        if hparams.component_initializer is not None:
-            self.components_layer = LabeledComponents(
-                distribution=hparams.distribution,
-                components_initializer=hparams.component_initializer,
-                labels_initializer=LabelsInitializer(),
-            )
-            proto_shape = self.components_layer.components.shape[1:]
-            self.hparams["initialized_proto_shape"] = proto_shape
-        else:
-            # when restoring a checkpointed model
-            self.components_layer = LabeledComponents(
-                distribution=hparams.distribution,
-                components_initializer=ZerosCompInitializer(
-                    self.hparams["initialized_proto_shape"]),
-            )
-
-    # Properties
-    # ----------------------------------------------------------------------------------------------------
-    @property
-    def prototypes(self):
-        """
-        Returns the position of the prototypes.
-        """
-        return self.components_layer.components.detach().cpu()
-
-    @property
-    def prototype_labels(self):
-        """
-        Returns the labels of the prototypes.
-        """
-        return self.components_layer.labels.detach().cpu()

prototorch/y/architectures/loss.py

@@ -1,42 +0,0 @@
-from dataclasses import dataclass, field
-
-from prototorch.core.losses import GLVQLoss
-from prototorch.y.architectures.base import BaseYArchitecture
-
-
-class GLVQLossMixin(BaseYArchitecture):
-    """
-    GLVQ Loss
-
-    A loss layer that uses the Generalized Learning Vector Quantization (GLVQ) loss.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        margin: The margin of the GLVQ loss. Default: 0.0.
-        transfer_fn: Transfer function to use. Default: sigmoid_beta.
-        transfer_args: Keyword arguments for the transfer function. Default: {beta: 10.0}.
-        """
-        margin: float = 0.0
-
-        transfer_fn: str = "sigmoid_beta"
-        transfer_args: dict = field(default_factory=lambda: dict(beta=10.0))
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_loss(self, hparams: HyperParameters):
-        self.loss_layer = GLVQLoss(
-            margin=hparams.margin,
-            transfer_fn=hparams.transfer_fn,
-            **hparams.transfer_args,
-        )
-
-    def loss(self, comparison_measures, batch, components):
-        target = batch[1]
-        comp_labels = components[1]
-        loss = self.loss_layer(comparison_measures, target, comp_labels)
-        self.log('loss', loss)
-        return loss

prototorch/y/architectures/optimization.py

@@ -1,73 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Type
-
-import torch
-from prototorch.y import BaseYArchitecture
-from torch.nn.parameter import Parameter
-
-
-class SingleLearningRateMixin(BaseYArchitecture):
-    """
-    Single Learning Rate
-
-    All parameters are updated with a single learning rate.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        lr: The learning rate. Default: 0.1.
-        optimizer: The optimizer to use. Default: torch.optim.Adam.
-        """
-        lr: float = 0.1
-        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
-
-    # Hooks
-    # ----------------------------------------------------------------------------------------------------
-    def configure_optimizers(self):
-        return self.hparams.optimizer(self.parameters(),
-                                      lr=self.hparams.lr)  # type: ignore
-
-
-class MultipleLearningRateMixin(BaseYArchitecture):
-    """
-    Multiple Learning Rates
-
-    Define Different Learning Rates for different parameters.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        lr: The learning rate. Default: 0.1.
-        optimizer: The optimizer to use. Default: torch.optim.Adam.
-        """
-        lr: dict = field(default_factory=lambda: dict())
-        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
-
-    # Hooks
-    # ----------------------------------------------------------------------------------------------------
-    def configure_optimizers(self):
-        optimizers = []
-        for name, lr in self.hparams.lr.items():
-            if not hasattr(self, name):
-                raise ValueError(f"{name} is not a parameter of {self}")
-            else:
-                model_part = getattr(self, name)
-                if isinstance(model_part, Parameter):
-                    optimizers.append(
-                        self.hparams.optimizer(
-                            [model_part],
-                            lr=lr,  # type: ignore
-                        ))
-                elif hasattr(model_part, "parameters"):
-                    optimizers.append(
-                        self.hparams.optimizer(
-                            model_part.parameters(),
-                            lr=lr,  # type: ignore
-                        ))
-        return optimizers

prototorch/y/callbacks.py

@@ -1,218 +0,0 @@
-import warnings
-from typing import Optional, Type
-
-import numpy as np
-import pytorch_lightning as pl
-import torch
-import torchmetrics
-from matplotlib import pyplot as plt
-from prototorch.models.vis import Vis2DAbstract
-from prototorch.utils.utils import mesh2d
-from prototorch.y.architectures.base import BaseYArchitecture
-from prototorch.y.library.gmlvq import GMLVQ
-from pytorch_lightning.loggers import TensorBoardLogger
-
-DIVERGING_COLOR_MAPS = [
-    'PiYG',
-    'PRGn',
-    'BrBG',
-    'PuOr',
-    'RdGy',
-    'RdBu',
-    'RdYlBu',
-    'RdYlGn',
-    'Spectral',
-    'coolwarm',
-    'bwr',
-    'seismic',
-]
-
-
-class LogTorchmetricCallback(pl.Callback):
-
-    def __init__(
-        self,
-        name,
-        metric: Type[torchmetrics.Metric],
-        on="prediction",
-        **metric_kwargs,
-    ) -> None:
-        self.name = name
-        self.metric = metric
-        self.metric_kwargs = metric_kwargs
-        self.on = on
-
-    def setup(
-        self,
-        trainer: pl.Trainer,
-        pl_module: BaseYArchitecture,
-        stage: Optional[str] = None,
-    ) -> None:
-        if self.on == "prediction":
-            pl_module.register_torchmetric(
-                self,
-                self.metric,
-                **self.metric_kwargs,
-            )
-        else:
-            raise ValueError(f"{self.on} is no valid metric hook")
-
-    def __call__(self, value, pl_module: BaseYArchitecture):
-        pl_module.log(self.name, value)
-
-
-class LogConfusionMatrix(LogTorchmetricCallback):
-
-    def __init__(
-        self,
-        num_classes,
-        name="confusion",
-        on='prediction',
-        **kwargs,
-    ):
-        super().__init__(
-            name,
-            torchmetrics.ConfusionMatrix,
-            on=on,
-            num_classes=num_classes,
-            **kwargs,
-        )
-
-    def __call__(self, value, pl_module: BaseYArchitecture):
-        fig, ax = plt.subplots()
-        ax.imshow(value.detach().cpu().numpy())
-
-        # Show all ticks and label them with the respective list entries
-        # ax.set_xticks(np.arange(len(farmers)), labels=farmers)
-        # ax.set_yticks(np.arange(len(vegetables)), labels=vegetables)
-
-        # Rotate the tick labels and set their alignment.
-        plt.setp(
-            ax.get_xticklabels(),
-            rotation=45,
-            ha="right",
-            rotation_mode="anchor",
-        )
-
-        # Loop over data dimensions and create text annotations.
-        for i in range(len(value)):
-            for j in range(len(value)):
-                text = ax.text(
-                    j,
-                    i,
-                    value[i, j].item(),
-                    ha="center",
-                    va="center",
-                    color="w",
-                )
-
-        ax.set_title(self.name)
-        fig.tight_layout()
-
-        pl_module.logger.experiment.add_figure(
-            tag=self.name,
-            figure=fig,
-            close=True,
-            global_step=pl_module.global_step,
-        )
-
-
-class VisGLVQ2D(Vis2DAbstract):
-
-    def visualize(self, pl_module):
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        x_train, y_train = self.x_train, self.y_train
-        ax = self.setup_ax()
-        self.plot_protos(ax, protos, plabels)
-        if x_train is not None:
-            self.plot_data(ax, x_train, y_train)
-            mesh_input, xx, yy = mesh2d(
-                np.vstack([x_train, protos]),
-                self.border,
-                self.resolution,
-            )
-        else:
-            mesh_input, xx, yy = mesh2d(protos, self.border, self.resolution)
-        _components = pl_module.components_layer.components
-        mesh_input = torch.from_numpy(mesh_input).type_as(_components)
-        y_pred = pl_module.predict(mesh_input)
-        y_pred = y_pred.cpu().reshape(xx.shape)
-        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-
-
-class VisGMLVQ2D(Vis2DAbstract):
-
-    def __init__(self, *args, ev_proj=True, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.ev_proj = ev_proj
-
-    def visualize(self, pl_module):
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        x_train, y_train = self.x_train, self.y_train
-        device = pl_module.device
-        omega = pl_module._omega.detach()
-        lam = omega @ omega.T
-        u, _, _ = torch.pca_lowrank(lam, q=2)
-        with torch.no_grad():
-            x_train = torch.Tensor(x_train).to(device)
-            x_train = x_train @ u
-            x_train = x_train.cpu().detach()
-        if self.show_protos:
-            with torch.no_grad():
-                protos = torch.Tensor(protos).to(device)
-                protos = protos @ u
-                protos = protos.cpu().detach()
-        ax = self.setup_ax()
-        self.plot_data(ax, x_train, y_train)
-        if self.show_protos:
-            self.plot_protos(ax, protos, plabels)
-
-
-class PlotLambdaMatrixToTensorboard(pl.Callback):
-
-    def __init__(self, cmap='seismic') -> None:
-        super().__init__()
-        self.cmap = cmap
-
-        if self.cmap not in DIVERGING_COLOR_MAPS and type(self.cmap) is str:
-            warnings.warn(
-                f"{self.cmap} is not a diverging color map. We recommend to use one of the following: {DIVERGING_COLOR_MAPS}"
-            )
-
-    def on_train_start(self, trainer, pl_module: GMLVQ):
-        self.plot_lambda(trainer, pl_module)
-
-    def on_train_epoch_end(self, trainer, pl_module: GMLVQ):
-        self.plot_lambda(trainer, pl_module)
-
-    def plot_lambda(self, trainer, pl_module: GMLVQ):
-
-        self.fig, self.ax = plt.subplots(1, 1)
-
-        # plot lambda matrix
-        l_matrix = pl_module.lambda_matrix
-
-        # normalize lambda matrix
-        l_matrix = l_matrix / torch.max(torch.abs(l_matrix))
-
-        # plot lambda matrix
-        self.ax.imshow(l_matrix.detach().numpy(), self.cmap, vmin=-1, vmax=1)
-
-        self.fig.colorbar(self.ax.images[-1])
-
-        # add title
-        self.ax.set_title('Lambda Matrix')
-
-        # add to tensorboard
-        if isinstance(trainer.logger, TensorBoardLogger):
-            trainer.logger.experiment.add_figure(
-                f"lambda_matrix",
-                self.fig,
-                trainer.global_step,
-            )
-        else:
-            warnings.warn(
-                f"{self.__class__.__name__} is not compatible with {trainer.logger.__class__.__name__} as logger. Use TensorBoardLogger instead."
-            )

prototorch/y/library/__init__.py

@@ -1,7 +0,0 @@
-from .glvq import GLVQ
-from .gmlvq import GMLVQ
-
-__all__ = [
-    "GLVQ",
-    "GMLVQ",
-]

prototorch/y/library/glvq.py

@@ -1,35 +0,0 @@
-from dataclasses import dataclass
-
-from prototorch.y import (
-    SimpleComparisonMixin,
-    SingleLearningRateMixin,
-    SupervisedArchitecture,
-    WTACompetitionMixin,
-)
-from prototorch.y.architectures.loss import GLVQLossMixin
-
-
-class GLVQ(
-        SupervisedArchitecture,
-        SimpleComparisonMixin,
-        GLVQLossMixin,
-        WTACompetitionMixin,
-        SingleLearningRateMixin,
-):
-    """
-    Generalized Learning Vector Quantization (GLVQ)
-
-    A GLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
-    """
-
-    @dataclass
-    class HyperParameters(
-            SimpleComparisonMixin.HyperParameters,
-            SingleLearningRateMixin.HyperParameters,
-            GLVQLossMixin.HyperParameters,
-            WTACompetitionMixin.HyperParameters,
-            SupervisedArchitecture.HyperParameters,
-    ):
-        """
-        No hyperparameters.
-        """

prototorch/y/library/gmlvq.py

@@ -1,50 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Callable
-
-import torch
-from prototorch.core.distances import omega_distance
-from prototorch.y import (
-    GLVQLossMixin,
-    MultipleLearningRateMixin,
-    OmegaComparisonMixin,
-    SupervisedArchitecture,
-    WTACompetitionMixin,
-)
-
-
-class GMLVQ(
-        SupervisedArchitecture,
-        OmegaComparisonMixin,
-        GLVQLossMixin,
-        WTACompetitionMixin,
-        MultipleLearningRateMixin,
-):
-    """
-    Generalized Matrix Learning Vector Quantization (GMLVQ)
-
-    A GMLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
-    """
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(
-            MultipleLearningRateMixin.HyperParameters,
-            OmegaComparisonMixin.HyperParameters,
-            GLVQLossMixin.HyperParameters,
-            WTACompetitionMixin.HyperParameters,
-            SupervisedArchitecture.HyperParameters,
-    ):
-        """
-        comparison_fn: The comparison / dissimilarity function to use. Override Default: omega_distance.
-        comparison_args: Keyword arguments for the comparison function. Override Default: {}.
-        """
-        comparison_fn: Callable = omega_distance
-        comparison_args: dict = field(default_factory=lambda: dict())
-        optimizer: type[torch.optim.Optimizer] = torch.optim.Adam
-
-        lr: dict = field(default_factory=lambda: dict(
-            components_layer=0.1,
-            _omega=0.5,
-        ))

pyproject.toml

@@ -0,0 +1,90 @@
+
+[project]
+name = "prototorch-models"
+version = "0.7.1"
+description = "Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning."
+authors = [
+    { name = "Jensun Ravichandran", email = "jjensun@gmail.com" },
+    { name = "Alexander Engelsberger", email = "engelsbe@hs-mittweida.de" },
+]
+dependencies = ["lightning>=2.0.0", "prototorch>=0.7.5"]
+requires-python = ">=3.8"
+readme = "README.md"
+license = { text = "MIT" }
+classifiers = [
+    "Development Status :: 2 - Pre-Alpha",
+    "Environment :: Plugins",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Education",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Natural Language :: English",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+]
+
+[project.urls]
+Homepage = "https://github.com/si-cim/prototorch_models"
+Downloads = "https://github.com/si-cim/prototorch_models.git"
+
+[project.optional-dependencies]
+dev = ["bumpversion", "pre-commit", "yapf", "toml"]
+examples = ["matplotlib", "scikit-learn"]
+ci = ["pytest", "pre-commit"]
+docs = [
+    "recommonmark",
+    "nbsphinx",
+    "sphinx",
+    "sphinx_rtd_theme",
+    "sphinxcontrib-bibtex",
+    "sphinxcontrib-katex",
+    "ipykernel",
+]
+all = [
+    "bumpversion",
+    "pre-commit",
+    "yapf",
+    "toml",
+    "pytest",
+    "matplotlib",
+    "scikit-learn",
+    "recommonmark",
+    "nbsphinx",
+    "sphinx",
+    "sphinx_rtd_theme",
+    "sphinxcontrib-bibtex",
+    "sphinxcontrib-katex",
+    "ipykernel",
+]
+
+[build-system]
+requires = ["setuptools>=61", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.yapf]
+based_on_style = "pep8"
+spaces_before_comment = 2
+split_before_logical_operator = true
+
+[tool.pylint]
+disable = ["too-many-arguments", "too-few-public-methods", "fixme"]
+
+[tool.isort]
+profile = "hug"
+src_paths = ["isort", "test"]
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 3
+use_parentheses = true
+line_length = 79
+
+[tool.mypy]
+explicit_package_bases = true
+namespace_packages = true

setup.cfg

@@ -1,23 +0,0 @@
-[yapf]
-based_on_style = pep8
-spaces_before_comment = 2
-split_before_logical_operator = true
-
-[pylint]
-disable =
-	too-many-arguments,
-	too-few-public-methods,
-	fixme,
-
-
-[pycodestyle]
-max-line-length = 79
-
-[isort]
-profile = hug
-src_paths = isort, test
-multi_line_output = 3
-include_trailing_comma = True
-force_grid_wrap = 3
-use_parentheses = True
-line_length = 79

setup.py

@@ -1,99 +0,0 @@
-"""
-
- ######
- #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
- #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
- ######  #    # #    #   #   #    #   #   #    # #    # #      ######
- #       #####  #    #   #   #    #   #   #    # #####  #      #    #
- #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
- #       #    #  ####    #    ####    #    ####  #    #  ####  #    #Plugin
-
-ProtoTorch models Plugin Package
-"""
-from pkg_resources import safe_name
-from setuptools import find_namespace_packages, setup
-
-PLUGIN_NAME = "models"
-
-PROJECT_URL = "https://github.com/si-cim/prototorch_models"
-DOWNLOAD_URL = "https://github.com/si-cim/prototorch_models.git"
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-INSTALL_REQUIRES = [
-    "prototorch>=0.7.3",
-    "pytorch_lightning>=1.6.0",
-    "torchmetrics",
-    "protobuf<3.20.0",
-]
-CLI = [
-    "jsonargparse",
-]
-DEV = [
-    "bumpversion",
-    "pre-commit",
-]
-DOCS = [
-    "recommonmark",
-    "sphinx",
-    "nbsphinx",
-    "ipykernel",
-    "sphinx_rtd_theme",
-    "sphinxcontrib-katex",
-    "sphinxcontrib-bibtex",
-]
-EXAMPLES = [
-    "matplotlib",
-    "scikit-learn",
-]
-TESTS = [
-    "codecov",
-    "pytest",
-]
-ALL = CLI + DEV + DOCS + EXAMPLES + TESTS
-
-setup(
-    name=safe_name("prototorch_" + PLUGIN_NAME),
-    version="1.0.0-a4",
-    description="Pre-packaged prototype-based "
-    "machine learning models using ProtoTorch and PyTorch-Lightning.",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    author="Alexander Engelsberger",
-    author_email="engelsbe@hs-mittweida.de",
-    url=PROJECT_URL,
-    download_url=DOWNLOAD_URL,
-    license="MIT",
-    python_requires=">=3.7",
-    install_requires=INSTALL_REQUIRES,
-    extras_require={
-        "dev": DEV,
-        "examples": EXAMPLES,
-        "tests": TESTS,
-        "all": ALL,
-    },
-    classifiers=[
-        "Development Status :: 2 - Pre-Alpha",
-        "Environment :: Plugins",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Education",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: MIT License",
-        "Natural Language :: English",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.7",
-        "Operating System :: OS Independent",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        "Topic :: Software Development :: Libraries",
-        "Topic :: Software Development :: Libraries :: Python Modules",
-    ],
-    entry_points={
-        "prototorch.plugins": f"{PLUGIN_NAME} = prototorch.{PLUGIN_NAME}"
-    },
-    packages=find_namespace_packages(include=["prototorch.*"]),
-    zip_safe=False,
-)

src/prototorch/models/__init__.py

@@ -36,4 +36,4 @@ from .unsupervised import (
 )
 from .vis import *
 
-__version__ = "1.0.0-a4"
+__version__ = "0.7.1"

src/prototorch/models/abstract.py

@@ -22,16 +22,7 @@ from prototorch.nn.wrappers import LambdaLayer
 
 
 class ProtoTorchBolt(pl.LightningModule):
-    """All ProtoTorch models are ProtoTorch Bolts.
+    """All ProtoTorch models are ProtoTorch Bolts."""
-
-    hparams:
-        - lr: learning rate
-
-    kwargs:
-        - optimizer: optimizer class
-        - lr_scheduler: learning rate scheduler class
-        - lr_scheduler_kwargs: learning rate scheduler kwargs
-    """
 
     def __init__(self, hparams, **kwargs):
         super().__init__()
@@ -74,18 +65,13 @@ class ProtoTorchBolt(pl.LightningModule):
 
 
 class PrototypeModel(ProtoTorchBolt):
-    """Abstract Prototype Model
-
-    kwargs:
-        - distance_fn: distance function
-    """
     proto_layer: AbstractComponents
 
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
 
         distance_fn = kwargs.get("distance_fn", euclidean_distance)
-        self.distance_layer = LambdaLayer(distance_fn)
+        self.distance_layer = LambdaLayer(distance_fn, name="distance_fn")
 
     @property
     def num_prototypes(self):
@@ -200,20 +186,63 @@ class SupervisedPrototypeModel(PrototypeModel):
 
     def log_acc(self, distances, targets, tag):
         preds = self.predict_from_distances(distances)
-        accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
+        accuracy = torchmetrics.functional.accuracy(
-        # `.int()` because FloatTensors are assumed to be class probabilities
+            preds.int(),
+            targets.int(),
+            "multiclass",
+            num_classes=self.num_classes,
+        )
 
-        self.log(tag,
+        self.log(
-                 accuracy,
+            tag,
-                 on_step=False,
+            accuracy,
-                 on_epoch=True,
+            on_step=False,
-                 prog_bar=True,
+            on_epoch=True,
-                 logger=True)
+            prog_bar=True,
+            logger=True,
+        )
 
     def test_step(self, batch, batch_idx):
         x, targets = batch
 
         preds = self.predict(x)
-        accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
+        accuracy = torchmetrics.functional.accuracy(
+            preds.int(),
+            targets.int(),
+            "multiclass",
+            num_classes=self.num_classes,
+        )
 
         self.log("test_acc", accuracy)
+
+
+class ProtoTorchMixin:
+    """All mixins are ProtoTorchMixins."""
+
+
+class NonGradientMixin(ProtoTorchMixin):
+    """Mixin for custom non-gradient optimization."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.automatic_optimization = False
+
+    def training_step(self, train_batch, batch_idx):
+        raise NotImplementedError
+
+
+class ImagePrototypesMixin(ProtoTorchMixin):
+    """Mixin for models with image prototypes."""
+    proto_layer: Components
+    components: torch.Tensor
+
+    def on_train_batch_end(self, outputs, batch, batch_idx):
+        """Constrain the components to the range [0, 1] by clamping after updates."""
+        self.proto_layer.components.data.clamp_(0.0, 1.0)
+
+    def get_prototype_grid(self, num_columns=2, return_channels_last=True):
+        from torchvision.utils import make_grid
+        grid = make_grid(self.components, nrow=num_columns)
+        if return_channels_last:
+            grid = grid.permute((1, 2, 0))
+        return grid.cpu()

src/prototorch/models/callbacks.py

@@ -40,8 +40,8 @@ class PruneLoserPrototypes(pl.Callback):
             return None
 
         ratios = pl_module.prototype_win_ratios.mean(dim=0)
-        to_prune_tensor = torch.arange(len(ratios))[ratios < self.threshold]
+        to_prune = torch.arange(len(ratios))[ratios < self.threshold]
-        to_prune = to_prune_tensor.tolist()
+        to_prune = to_prune.tolist()
         prune_labels = pl_module.prototype_labels[to_prune]
         if self.prune_quota_per_epoch > 0:
             to_prune = to_prune[:self.prune_quota_per_epoch]

src/prototorch/models/cbc.py

@@ -1,5 +1,4 @@
 import torch
-import torch.nn.functional as F
 import torchmetrics
 from prototorch.core.competitions import CBCC
 from prototorch.core.components import ReasoningComponents
@@ -8,13 +7,12 @@ from prototorch.core.losses import MarginLoss
 from prototorch.core.similarities import euclidean_similarity
 from prototorch.nn.wrappers import LambdaLayer
 
+from .abstract import ImagePrototypesMixin
 from .glvq import SiameseGLVQ
-from .mixins import ImagePrototypesMixin
 
 
 class CBC(SiameseGLVQ):
     """Classification-By-Components."""
-    proto_layer: ReasoningComponents
 
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, skip_proto_layer=True, **kwargs)
@@ -24,7 +22,7 @@ class CBC(SiameseGLVQ):
         reasonings_initializer = kwargs.get("reasonings_initializer",
                                             RandomReasoningsInitializer())
         self.components_layer = ReasoningComponents(
-            self.hparams["distribution"],
+            self.hparams.distribution,
             components_initializer=components_initializer,
             reasonings_initializer=reasonings_initializer,
         )
@@ -34,7 +32,7 @@ class CBC(SiameseGLVQ):
         # Namespace hook
         self.proto_layer = self.components_layer
 
-        self.loss = MarginLoss(self.hparams["margin"])
+        self.loss = MarginLoss(self.hparams.margin)
 
     def forward(self, x):
         components, reasonings = self.components_layer()
@@ -46,25 +44,31 @@ class CBC(SiameseGLVQ):
         probs = self.competition_layer(detections, reasonings)
         return probs
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         y_pred = self(x)
         num_classes = self.num_classes
-        y_true = F.one_hot(y.long(), num_classes=num_classes)
+        y_true = torch.nn.functional.one_hot(y.long(), num_classes=num_classes)
         loss = self.loss(y_pred, y_true).mean()
         return y_pred, loss
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
-        y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
+        y_pred, train_loss = self.shared_step(batch, batch_idx)
         preds = torch.argmax(y_pred, dim=1)
-        accuracy = torchmetrics.functional.accuracy(preds.int(),
+        accuracy = torchmetrics.functional.accuracy(
-                                                    batch[1].int())
+            preds.int(),
-        self.log("train_acc",
+            batch[1].int(),
-                 accuracy,
+            "multiclass",
-                 on_step=False,
+            num_classes=self.num_classes,
-                 on_epoch=True,
+        )
-                 prog_bar=True,
+        self.log(
-                 logger=True)
+            "train_acc",
+            accuracy,
+            on_step=False,
+            on_epoch=True,
+            prog_bar=True,
+            logger=True,
+        )
         return train_loss
 
     def predict(self, x):

src/prototorch/models/extras.py

@@ -39,7 +39,7 @@ def ltangent_distance(x, y, omegas):
     :param `torch.tensor` omegas: Three dimensional matrix
     :rtype: `torch.tensor`
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     p = torch.eye(omegas.shape[-2], device=omegas.device) - torch.bmm(
         omegas, omegas.permute([0, 2, 1]))
     projected_x = x @ p

src/prototorch/models/glvq.py

@@ -17,9 +17,8 @@ from prototorch.core.transforms import LinearTransform
 from prototorch.nn.wrappers import LambdaLayer, LossLayer
 from torch.nn.parameter import Parameter
 
-from .abstract import SupervisedPrototypeModel
+from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
 from .extras import ltangent_distance, orthogonalization
-from .mixins import ImagePrototypesMixin
 
 
 class GLVQ(SupervisedPrototypeModel):
@@ -47,24 +46,19 @@ class GLVQ(SupervisedPrototypeModel):
     def initialize_prototype_win_ratios(self):
         self.register_buffer(
             "prototype_win_ratios",
-            torch.zeros(self.num_prototypes, device=self.device),
+            torch.zeros(self.num_prototypes, device=self.device))
-        )
 
     def on_train_epoch_start(self):
         self.initialize_prototype_win_ratios()
 
     def log_prototype_win_ratios(self, distances):
         batch_size = len(distances)
-        prototype_wc = torch.zeros(
+        prototype_wc = torch.zeros(self.num_prototypes,
-            self.num_prototypes,
+                                   dtype=torch.long,
-            dtype=torch.long,
+                                   device=self.device)
-            device=self.device,
+        wi, wc = torch.unique(distances.min(dim=-1).indices,
-        )
+                              sorted=True,
-        wi, wc = torch.unique(
+                              return_counts=True)
-            distances.min(dim=-1).indices,
-            sorted=True,
-            return_counts=True,
-        )
         prototype_wc[wi] = wc
         prototype_wr = prototype_wc / batch_size
         self.prototype_win_ratios = torch.vstack([
@@ -72,27 +66,29 @@ class GLVQ(SupervisedPrototypeModel):
             prototype_wr,
         ])
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         out = self.compute_distances(x)
         _, plabels = self.proto_layer()
         loss = self.loss(out, y, plabels)
         return out, loss
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
-        out, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
+        out, train_loss = self.shared_step(batch, batch_idx)
         self.log_prototype_win_ratios(out)
         self.log("train_loss", train_loss)
         self.log_acc(out, batch[-1], tag="train_acc")
         return train_loss
 
     def validation_step(self, batch, batch_idx):
+        # `model.eval()` and `torch.no_grad()` handled by pl
         out, val_loss = self.shared_step(batch, batch_idx)
         self.log("val_loss", val_loss)
         self.log_acc(out, batch[-1], tag="val_acc")
         return val_loss
 
     def test_step(self, batch, batch_idx):
+        # `model.eval()` and `torch.no_grad()` handled by pl
         out, test_loss = self.shared_step(batch, batch_idx)
         self.log_acc(out, batch[-1], tag="test_acc")
         return test_loss
@@ -113,43 +109,19 @@ class SiameseGLVQ(GLVQ):
 
     """
 
-    def __init__(
+    def __init__(self,
-            self,
+                 hparams,
-            hparams,
+                 backbone=torch.nn.Identity(),
-            backbone=torch.nn.Identity(),
+                 both_path_gradients=False,
-            both_path_gradients=False,
+                 **kwargs):
-            **kwargs,
-    ):
         distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
         self.backbone = backbone
         self.both_path_gradients = both_path_gradients
 
-    def configure_optimizers(self):
-        proto_opt = self.optimizer(
-            self.proto_layer.parameters(),
-            lr=self.hparams["proto_lr"],
-        )
-        # Only add a backbone optimizer if backbone has trainable parameters
-        bb_params = list(self.backbone.parameters())
-        if (bb_params):
-            bb_opt = self.optimizer(bb_params, lr=self.hparams["bb_lr"])
-            optimizers = [proto_opt, bb_opt]
-        else:
-            optimizers = [proto_opt]
-        if self.lr_scheduler is not None:
-            schedulers = []
-            for optimizer in optimizers:
-                scheduler = self.lr_scheduler(optimizer,
-                                              **self.lr_scheduler_kwargs)
-                schedulers.append(scheduler)
-            return optimizers, schedulers
-        else:
-            return optimizers
-
     def compute_distances(self, x):
         protos, _ = self.proto_layer()
-        x, protos = [arr.view(arr.size(0), -1) for arr in (x, protos)]
+        x, protos = (arr.view(arr.size(0), -1) for arr in (x, protos))
         latent_x = self.backbone(x)
 
         bb_grad = any([el.requires_grad for el in self.backbone.parameters()])
@@ -213,9 +185,12 @@ class GRLVQ(SiameseGLVQ):
         self.register_parameter("_relevances", Parameter(relevances))
 
         # Override the backbone
-        self.backbone = LambdaLayer(lambda x: x @ torch.diag(self._relevances),
+        self.backbone = LambdaLayer(self._apply_relevances,
                                     name="relevance scaling")
 
+    def _apply_relevances(self, x):
+        return x @ torch.diag(self._relevances)
+
     @property
     def relevance_profile(self):
         return self._relevances.detach().cpu()
@@ -270,19 +245,11 @@ class GMLVQ(GLVQ):
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
 
         # Additional parameters
-        omega_initializer = kwargs.get(
+        omega_initializer = kwargs.get("omega_initializer",
-            "omega_initializer",
+                                       EyeLinearTransformInitializer())
-            EyeLinearTransformInitializer(),
+        omega = omega_initializer.generate(self.hparams["input_dim"],
-        )
+                                           self.hparams["latent_dim"])
-        omega = omega_initializer.generate(
-            self.hparams["input_dim"],
-            self.hparams["latent_dim"],
-        )
         self.register_parameter("_omega", Parameter(omega))
-        self.backbone = LambdaLayer(
-            lambda x: x @ self._omega,
-            name="omega matrix",
-        )
 
     @property
     def omega_matrix(self):

src/prototorch/models/knn.py

@@ -34,7 +34,7 @@ class KNN(SupervisedPrototypeModel):
             labels_initializer=LiteralLabelsInitializer(targets))
         self.competition_layer = KNNC(k=self.hparams.k)
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         return 1  # skip training step
 
     def on_train_batch_start(self, train_batch, batch_idx):

src/prototorch/models/lvq.py

@@ -1,21 +1,20 @@
 """LVQ models that are optimized using non-gradient methods."""
 
 import logging
-from collections import OrderedDict
 
 from prototorch.core.losses import _get_dp_dm
 from prototorch.nn.activations import get_activation
 from prototorch.nn.wrappers import LambdaLayer
 
+from .abstract import NonGradientMixin
 from .glvq import GLVQ
-from .mixins import NonGradientMixin
 
 
 class LVQ1(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 1."""
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
-        protos, plabels = self.proto_layer()
+        protos, plables = self.proto_layer()
         x, y = train_batch
         dis = self.compute_distances(x)
         # TODO Vectorized implementation
@@ -29,11 +28,9 @@ class LVQ1(NonGradientMixin, GLVQ):
             else:
                 shift = protos[w] - xi
             updated_protos = protos + 0.0
-            updated_protos[w] = protos[w] + (self.hparams["lr"] * shift)
+            updated_protos[w] = protos[w] + (self.hparams.lr * shift)
-            self.proto_layer.load_state_dict(
+            self.proto_layer.load_state_dict({"_components": updated_protos},
-                OrderedDict(_components=updated_protos),
+                                             strict=False)
-                strict=False,
-            )
 
         logging.debug(f"dis={dis}")
         logging.debug(f"y={y}")
@@ -46,7 +43,7 @@ class LVQ1(NonGradientMixin, GLVQ):
 class LVQ21(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 2.1."""
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         protos, plabels = self.proto_layer()
 
         x, y = train_batch
@@ -61,12 +58,10 @@ class LVQ21(NonGradientMixin, GLVQ):
             shiftp = xi - protos[wp]
             shiftn = protos[wn] - xi
             updated_protos = protos + 0.0
-            updated_protos[wp] = protos[wp] + (self.hparams["lr"] * shiftp)
+            updated_protos[wp] = protos[wp] + (self.hparams.lr * shiftp)
-            updated_protos[wn] = protos[wn] + (self.hparams["lr"] * shiftn)
+            updated_protos[wn] = protos[wn] + (self.hparams.lr * shiftn)
-            self.proto_layer.load_state_dict(
+            self.proto_layer.load_state_dict({"_components": updated_protos},
-                OrderedDict(_components=updated_protos),
+                                             strict=False)
-                strict=False,
-            )
 
         # Logging
         self.log_acc(dis, y, tag="train_acc")
@@ -85,17 +80,14 @@ class MedianLVQ(NonGradientMixin, GLVQ):
         super().__init__(hparams, **kwargs)
 
         self.transfer_layer = LambdaLayer(
-            get_activation(self.hparams["transfer_fn"]))
+            get_activation(self.hparams.transfer_fn))
 
     def _f(self, x, y, protos, plabels):
         d = self.distance_layer(x, protos)
-        dp, dm = _get_dp_dm(d, y, plabels, with_indices=False)
+        dp, dm = _get_dp_dm(d, y, plabels)
         mu = (dp - dm) / (dp + dm)
-        negative_mu = -1.0 * mu
+        invmu = -1.0 * mu
-        f = self.transfer_layer(
+        f = self.transfer_layer(invmu, beta=self.hparams.transfer_beta) + 1.0
-            negative_mu,
-            beta=self.hparams["transfer_beta"],
-        ) + 1.0
         return f
 
     def expectation(self, x, y, protos, plabels):
@@ -108,7 +100,7 @@ class MedianLVQ(NonGradientMixin, GLVQ):
         lower_bound = (gamma * f.log()).sum()
         return lower_bound
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         protos, plabels = self.proto_layer()
 
         x, y = train_batch
@@ -126,10 +118,8 @@ class MedianLVQ(NonGradientMixin, GLVQ):
                 _lower_bound = self.lower_bound(x, y, _protos, plabels, gamma)
                 if _lower_bound > lower_bound:
                     logging.debug(f"Updating prototype {i} to data {k}...")
-                    self.proto_layer.load_state_dict(
+                    self.proto_layer.load_state_dict({"_components": _protos},
-                        OrderedDict(_components=_protos),
+                                                     strict=False)
-                        strict=False,
-                    )
                     break
 
         # Logging

src/prototorch/models/probabilistic.py

@@ -21,7 +21,7 @@ class CELVQ(GLVQ):
         # Loss
         self.loss = torch.nn.CrossEntropyLoss()
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         out = self.compute_distances(x)  # [None, num_protos]
         _, plabels = self.proto_layer()
@@ -63,7 +63,7 @@ class ProbabilisticLVQ(GLVQ):
         prediction[confidence < self.rejection_confidence] = -1
         return prediction
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
         x, y = batch
         out = self.forward(x)
         _, plabels = self.proto_layer()
@@ -123,7 +123,7 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
         self.loss = torch.nn.KLDivLoss()
 
     # FIXME
-    # def training_step(self, batch, batch_idx, optimizer_idx=None):
+    # def training_step(self, batch, batch_idx):
     #     x, y = batch
     #     y_pred = self(x)
     #     batch_loss = self.loss(y_pred, y)

src/prototorch/models/unsupervised.py

@@ -6,10 +6,9 @@ from prototorch.core.competitions import wtac
 from prototorch.core.distances import squared_euclidean_distance
 from prototorch.core.losses import NeuralGasEnergy
 
-from .abstract import UnsupervisedPrototypeModel
+from .abstract import NonGradientMixin, UnsupervisedPrototypeModel
 from .callbacks import GNGCallback
 from .extras import ConnectionTopology
-from .mixins import NonGradientMixin
 
 
 class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
@@ -64,7 +63,7 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
             strict=False,
         )
 
-    def training_epoch_end(self, training_step_outputs):
+    def on_training_epoch_end(self, training_step_outputs):
         self._sigma = self.hparams.sigma * np.exp(
             -self.current_epoch / self.trainer.max_epochs)
 

src/prototorch/models/vis.py

@@ -1,6 +1,5 @@
 """Visualization Callbacks."""
 
-import os
 import warnings
 from typing import Sized
 
@@ -33,10 +32,6 @@ class Vis2DAbstract(pl.Callback):
                  tensorboard=False,
                  show_last_only=False,
                  pause_time=0.1,
-                 save=False,
-                 save_dir="./img",
-                 fig_size=(5, 4),
-                 dpi=500,
                  block=False):
         super().__init__()
 
@@ -80,16 +75,8 @@ class Vis2DAbstract(pl.Callback):
         self.tensorboard = tensorboard
         self.show_last_only = show_last_only
         self.pause_time = pause_time
-        self.save = save
-        self.save_dir = save_dir
-        self.fig_size = fig_size
-        self.dpi = dpi
         self.block = block
 
-        if save:
-            if not os.path.exists(save_dir):
-                os.makedirs(save_dir)
-
     def precheck(self, trainer):
         if self.show_last_only:
             if trainer.current_epoch != trainer.max_epochs - 1:
@@ -138,11 +125,6 @@ class Vis2DAbstract(pl.Callback):
     def log_and_display(self, trainer, pl_module):
         if self.tensorboard:
             self.add_to_tensorboard(trainer, pl_module)
-        if self.save:
-            plt.tight_layout()
-            self.fig.set_size_inches(*self.fig_size, forward=False)
-            plt.savefig(f"{self.save_dir}/{trainer.current_epoch}.png",
-                        dpi=self.dpi)
         if self.show:
             if not self.block:
                 plt.pause(self.pause_time)

tests/test_models.py

@@ -1,195 +1,193 @@
 """prototorch.models test suite."""
 
-import prototorch as pt
+import prototorch.models
-import pytest
-import torch
 
 
 def test_glvq_model_build():
-    model = pt.models.GLVQ(
+    model = prototorch.models.GLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_glvq1_model_build():
-    model = pt.models.GLVQ1(
+    model = prototorch.models.GLVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_glvq21_model_build():
-    model = pt.models.GLVQ1(
+    model = prototorch.models.GLVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_gmlvq_model_build():
-    model = pt.models.GMLVQ(
+    model = prototorch.models.GMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 2,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_grlvq_model_build():
-    model = pt.models.GRLVQ(
+    model = prototorch.models.GRLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_gtlvq_model_build():
-    model = pt.models.GTLVQ(
+    model = prototorch.models.GTLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_lgmlvq_model_build():
-    model = pt.models.LGMLVQ(
+    model = prototorch.models.LGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_image_glvq_model_build():
-    model = pt.models.ImageGLVQ(
+    model = prototorch.models.ImageGLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_image_gmlvq_model_build():
-    model = pt.models.ImageGMLVQ(
+    model = prototorch.models.ImageGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 16,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_image_gtlvq_model_build():
-    model = pt.models.ImageGMLVQ(
+    model = prototorch.models.ImageGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 16,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_siamese_glvq_model_build():
-    model = pt.models.SiameseGLVQ(
+    model = prototorch.models.SiameseGLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_siamese_gmlvq_model_build():
-    model = pt.models.SiameseGMLVQ(
+    model = prototorch.models.SiameseGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_siamese_gtlvq_model_build():
-    model = pt.models.SiameseGTLVQ(
+    model = prototorch.models.SiameseGTLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_knn_model_build():
-    train_ds = pt.datasets.Iris(dims=[0, 2])
+    train_ds = prototorch.datasets.Iris(dims=[0, 2])
-    model = pt.models.KNN(dict(k=3), data=train_ds)
+    model = prototorch.models.KNN(dict(k=3), data=train_ds)
 
 
 def test_lvq1_model_build():
-    model = pt.models.LVQ1(
+    model = prototorch.models.LVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_lvq21_model_build():
-    model = pt.models.LVQ21(
+    model = prototorch.models.LVQ21(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_median_lvq_model_build():
-    model = pt.models.MedianLVQ(
+    model = prototorch.models.MedianLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_celvq_model_build():
-    model = pt.models.CELVQ(
+    model = prototorch.models.CELVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_rslvq_model_build():
-    model = pt.models.RSLVQ(
+    model = prototorch.models.RSLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_slvq_model_build():
-    model = pt.models.SLVQ(
+    model = prototorch.models.SLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_growing_neural_gas_model_build():
-    model = pt.models.GrowingNeuralGas(
+    model = prototorch.models.GrowingNeuralGas(
         {"num_prototypes": 5},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_kohonen_som_model_build():
-    model = pt.models.KohonenSOM(
+    model = prototorch.models.KohonenSOM(
         {"shape": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_neural_gas_model_build():
-    model = pt.models.NeuralGas(
+    model = prototorch.models.NeuralGas(
         {"num_prototypes": 5},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )