[RC Release] - v0.4.0rc1 (#887)

* Changed the version number

* Remove v0.4.0 references from the code base and the readme file

* Updated the changelog

* Remove configs directory. Will be added once CLI is ready

CHANGELOG.md

@@ -4,11 +4,10 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
-## [Unreleased]
+## [v0.4.0rc1] - 2023-02-07
 
 ### Added
 
-- Bump up PyTorch Lightning version to v.1.9.\* (<https://github.com/openvinotoolkit/anomalib/pull/870>)
 - Add ShanghaiTech Campus video anomaly detection dataset (<https://github.com/openvinotoolkit/anomalib/pull/869>)
 - Add `pyupgrade` to `pre-commit` configs, and refactor based on `pyupgrade` and `refurb` (<https://github.com/openvinotoolkit/anomalib/pull/845>)
 - Add [CFA](https://arxiv.org/abs/2206.04325) model implementation (<https://github.com/openvinotoolkit/anomalib/pull/783>)
@@ -30,6 +29,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Bump PyTorch Lightning version to v.1.9.\* (<https://github.com/openvinotoolkit/anomalib/pull/870>)
 - Make input image normalization and center cropping configurable from config (https://github.com/openvinotoolkit/anomalib/pull/822)
 - Improve flexibility and configurability of subset splitting (https://github.com/openvinotoolkit/anomalib/pull/822)
 - Switch to new datamodules design (https://github.com/openvinotoolkit/anomalib/pull/822)

README.md

@@ -76,8 +76,6 @@ pip install -e .
 
 # Training
 
-## ⚠️ Anomalib < v.0.4.0
-
 By default [`python tools/train.py`](https://github.com/openvinotoolkit/anomalib/blob/main/tools/train.py)
 runs [PADIM](https://arxiv.org/abs/2011.08785) model on `leather` category from the [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) dataset.
 
@@ -134,24 +132,11 @@ Tips:
 
 The backbone can be set in the config file, two examples below.
 
-Anomalib < v.0.4.0
-
 ```yaml
 model:
   name: cflow
   backbone: wide_resnet50_2
   pre_trained: true
-Anomalib > v.0.4.0 Beta - Subject to Change
-```
-
-Anomalib >= v.0.4.0
-
-```yaml
-model:
-  class_path: anomalib.models.Cflow
-  init_args:
-    backbone: wide_resnet50_2
-    pre_trained: true
 ```
 
 ## Custom Dataset
@@ -187,26 +172,8 @@ dataset:
     random_tile_count: 16
 ```
 
-## ⚠️ Anomalib > v.0.4.0 Beta - Subject to Change
-
-We introduce a new CLI approach that uses [PyTorch Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html). To train a model using the new CLI, one would call the following:
-
-```bash
-anomalib fit --config <path/to/new/config/file>
-```
-
-For instance, to train a [PatchCore](https://github.com/openvinotoolkit/anomalib/tree/main/anomalib/models/patchcore) model, the following command would be run:
-
-```bash
-anomalib fit --config ./configs/model/patchcore.yaml
-```
-
-The new CLI approach offers a lot more flexibility, details of which are explained in the [documentation](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html).
-
 # Inference
 
-## ⚠️ Anomalib < v.0.4.0
-
 Anomalib includes multiple tools, including Lightning, Gradio, and OpenVINO inferencers, for performing inference with a trained model.
 
 The following command can be used to run PyTorch Lightning inference from the command line:

anomalib/__init__.py

@@ -3,4 +3,4 @@
 # Copyright (C) 2022 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "0.4.0dev"
+__version__ = "0.4.0rc1"

anomalib/config/config.py

@@ -294,7 +294,7 @@ def get_configurable_parameters(
 
     # thresholding
     if "metrics" in config.keys():
-        # NOTE: Deprecate this after v0.4.0.
+        # NOTE: Deprecate this once the new CLI is implemented.
         if "adaptive" in config.metrics.threshold.keys():
             warn(
                 DeprecationWarning(

anomalib/models/components/feature_extractors/timm.py

@@ -112,6 +112,6 @@ class FeatureExtractor(TimmFeatureExtractor):
     def __init__(self, *args, **kwargs):
         logger.warning(
             "FeatureExtractor is deprecated. Use TimmFeatureExtractor instead."
-            " Both FeatureExtractor and TimmFeatureExtractor will be removed in version 2023.1"
+            " Both FeatureExtractor and TimmFeatureExtractor will be removed in a future release."
         )
         super().__init__(*args, **kwargs)