build: bump version 0.2.0 → 0.3.0

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.0
+current_version = 0.3.0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.travis.yml

@@ -1,7 +1,11 @@
 dist: bionic
 sudo: false
 language: python
-python: 3.9
+python:
+  - 3.9
+  - 3.8
+  - 3.7
+  - 3.6
 cache:
   directories:
   - "$HOME/.cache/pip"
@@ -15,11 +19,26 @@ script:
 - ./tests/test_examples.sh examples/
 after_success:
 - bash <(curl -s https://codecov.io/bash)
-deploy:
-  provider: pypi
-  username: __token__
-  password:
-    secure: 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
-  on:
-    tags: true
-    skip_existing: true
+
+# Publish on PyPI
+jobs:
+  include:
+    - stage: build
+      python: 3.9
+      script: echo "Starting Pypi build"
+      deploy:
+        provider: pypi
+        username: __token__
+        distributions: "sdist bdist_wheel"
+        password:
+          secure: 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
+        on:
+          tags: true
+          skip_existing: true
+
+# The password is encrypted with:
+# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
+# See https://docs.travis-ci.com/user/deployment/pypi and
+# https://github.com/travis-ci/travis.rb#installation
+# for more details
+# Note: The encrypt command does not work well in ZSH.

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.2.0"
+release = "0.3.0"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/models/__init__.py

@@ -1,7 +1,5 @@
 """`models` plugin for the `prototorch` package."""
 
-from importlib.metadata import PackageNotFoundError, version
-
 from .callbacks import PrototypeConvergence, PruneLoserPrototypes
 from .cbc import CBC, ImageCBC
 from .glvq import (
@@ -23,4 +21,4 @@ from .probabilistic import CELVQ, PLVQ, RSLVQ, SLVQ
 from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
 from .vis import *
 
-__version__ = "0.2.0"
+__version__ = "0.3.0"

prototorch/models/abstract.py

@@ -1,7 +1,5 @@
 """Abstract classes to be inherited by prototorch models."""
 
-from typing import Final, final
-
 import pytorch_lightning as pl
 import torch
 import torchmetrics
@@ -43,7 +41,6 @@ class ProtoTorchBolt(pl.LightningModule):
         else:
             return optimizer
 
-    @final
     def reconfigure_optimizers(self):
         self.trainer.accelerator.setup_optimizers(self.trainer)
 
@@ -96,7 +93,7 @@ class UnsupervisedPrototypeModel(PrototypeModel):
             )
 
     def compute_distances(self, x):
-        protos = self.proto_layer()
+        protos = self.proto_layer().type_as(x)
         distances = self.distance_layer(x, protos)
         return distances
 
@@ -136,14 +133,14 @@ class SupervisedPrototypeModel(PrototypeModel):
 
     def forward(self, x):
         distances = self.compute_distances(x)
-        plabels = self.proto_layer.labels
+        _, plabels = self.proto_layer()
         winning = stratified_min_pooling(distances, plabels)
         y_pred = torch.nn.functional.softmin(winning)
         return y_pred
 
     def predict_from_distances(self, distances):
         with torch.no_grad():
-            plabels = self.proto_layer.labels
+            _, plabels = self.proto_layer()
             y_pred = self.competition_layer(distances, plabels)
         return y_pred
 
@@ -175,7 +172,7 @@ class NonGradientMixin(ProtoTorchMixin):
     """Mixin for custom non-gradient optimization."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.automatic_optimization: Final = False
+        self.automatic_optimization = False
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         raise NotImplementedError
@@ -183,7 +180,6 @@ class NonGradientMixin(ProtoTorchMixin):
 
 class ImagePrototypesMixin(ProtoTorchMixin):
     """Mixin for models with image prototypes."""
-    @final
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         """Constrain the components to the range [0, 1] by clamping after updates."""
         self.proto_layer.components.data.clamp_(0.0, 1.0)

prototorch/models/callbacks.py

@@ -55,7 +55,7 @@ class PruneLoserPrototypes(pl.Callback):
                 distribution = dict(zip(labels.tolist(), counts.tolist()))
                 if self.verbose:
                     print(f"Re-adding pruned prototypes...")
-                    print(f"{distribution=}")
+                    print(f"distribution={distribution}")
                 pl_module.add_prototypes(
                     distribution=distribution,
                     components_initializer=self.prototypes_initializer)
@@ -134,4 +134,4 @@ class GNGCallback(pl.Callback):
             pl_module.errors[
                 worst_neighbor] = errors[worst_neighbor] * self.reduction
 
-            trainer.accelerator_backend.setup_optimizers(trainer)
+            trainer.accelerator.setup_optimizers(trainer)

prototorch/models/glvq.py

@@ -55,7 +55,7 @@ class GLVQ(SupervisedPrototypeModel):
     def shared_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.compute_distances(x)
-        plabels = self.proto_layer.labels
+        _, plabels = self.proto_layer()
         loss = self.loss(out, y, plabels)
         return out, loss
 
@@ -112,7 +112,8 @@ class SiameseGLVQ(GLVQ):
         proto_opt = self.optimizer(self.proto_layer.parameters(),
                                    lr=self.hparams.proto_lr)
         # Only add a backbone optimizer if backbone has trainable parameters
-        if (bb_params := list(self.backbone.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:

prototorch/models/lvq.py

@@ -10,9 +10,7 @@ from .glvq import GLVQ
 class LVQ1(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.labels
-
+        protos, plables = self.proto_layer()
         x, y = train_batch
         dis = self.compute_distances(x)
         # TODO Vectorized implementation
@@ -30,8 +28,8 @@ class LVQ1(NonGradientMixin, GLVQ):
             self.proto_layer.load_state_dict({"_components": updated_protos},
                                              strict=False)
 
-        print(f"{dis=}")
-        print(f"{y=}")
+        print(f"dis={dis}")
+        print(f"y={y}")
         # Logging
         self.log_acc(dis, y, tag="train_acc")
 
@@ -41,8 +39,7 @@ class LVQ1(NonGradientMixin, GLVQ):
 class LVQ21(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 2.1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.labels
+        protos, plabels = self.proto_layer()
 
         x, y = train_batch
         dis = self.compute_distances(x)
@@ -99,8 +96,7 @@ class MedianLVQ(NonGradientMixin, GLVQ):
         return lower_bound
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.labels
+        protos, plabels = self.proto_layer()
 
         x, y = train_batch
         dis = self.compute_distances(x)

prototorch/models/probabilistic.py

@@ -20,7 +20,7 @@ class CELVQ(GLVQ):
     def shared_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.compute_distances(x)  # [None, num_protos]
-        plabels = self.proto_layer.labels
+        _, plabels = self.proto_layer()
         winning = stratified_min_pooling(out, plabels)  # [None, num_classes]
         probs = -1.0 * winning
         batch_loss = self.loss(probs, y.long())
@@ -54,7 +54,7 @@ class ProbabilisticLVQ(GLVQ):
     def training_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.forward(x)
-        plabels = self.proto_layer.labels
+        _, plabels = self.proto_layer()
         batch_loss = self.loss(out, y, plabels)
         loss = batch_loss.sum()
         return loss

prototorch/models/unsupervised.py

@@ -53,7 +53,7 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
         grid = self._grid.view(-1, 2)
         gd = squared_euclidean_distance(wp, grid)
         nh = torch.exp(-gd / self._sigma**2)
-        protos = self.proto_layer.components
+        protos = self.proto_layer()
         diff = x.unsqueeze(dim=1) - protos
         delta = self._lr * self.hparams.alpha * nh.unsqueeze(-1) * diff
         updated_protos = protos + delta.sum(dim=0)

prototorch/models/vis.py

@@ -251,8 +251,6 @@ class VisImgComp(Vis2DAbstract):
                                    size=self.embedding_data,
                                    replace=False)
             data = self.x_train[ind]
-            # print(f"{data.shape=}")
-            # print(f"{self.y_train[ind].shape=}")
             tb.add_embedding(data.view(len(ind), -1),
                              label_img=data,
                              global_step=None,

setup.py

@@ -22,7 +22,7 @@ with open("README.md", "r") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "prototorch>=0.6.0",
+    "prototorch>=0.7.0",
     "pytorch_lightning>=1.3.5",
     "torchmetrics",
 ]
@@ -53,7 +53,7 @@ ALL = CLI + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name=safe_name("prototorch_" + PLUGIN_NAME),
-    version="0.2.0",
+    version="0.3.0",
     description="Pre-packaged prototype-based "
     "machine learning models using ProtoTorch and PyTorch-Lightning.",
     long_description=long_description,
@@ -63,7 +63,7 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
-    python_requires=">=3.9",
+    python_requires=">=3.6",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "dev": DEV,
@@ -80,6 +80,9 @@ setup(
         "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.6",
         "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries",

tests/test_examples.sh

@@ -1,11 +1,27 @@
 #! /bin/bash
 
+
+# Read Flags
+gpu=0
+while [ -n "$1" ]; do
+	case "$1" in
+	    --gpu) gpu=1;;
+	    -g) gpu=1;;
+        *) path=$1;;
+	esac
+	shift
+done
+
+python --version
+echo "Using GPU: " $gpu
+
+# Loop
 failed=0
 
-for example in $(find $1 -maxdepth 1 -name "*.py")
+for example in $(find $path -maxdepth 1 -name "*.py")
 do
     echo  -n "$x" $example '... '
-    export DISPLAY= && python $example --fast_dev_run 1 &> run_log.txt
+    export DISPLAY= && python $example --fast_dev_run 1 --gpus $gpu &> run_log.txt
     if [[ $? -ne 0 ]]; then
         echo "FAILED!!"
         cat run_log.txt