Add third-party-programs.txt file and update license (#132)

* Add third-party-programs.txt file and update license

* Fix precommit warnings

* Update third-party-programs.txt

* Update README.md

Co-authored-by: Samet Akcay <samet.akcay@intel.com>

README.md

@@ -49,7 +49,7 @@ You can get started with `anomalib` by just using pip.
 pip install anomalib
 ```
 
-> **_NOTE:_** Due to ongoing face pace of development, we encourage you to use editable install until we release v0.2.5. 
+> **_NOTE:_** Due to ongoing fast pace of development, we encourage you to use editable install until we release v0.2.5.
 
 ### Local Install
 It is highly recommended to use virtual environment when installing anomalib. For instance, with [anaconda](https://www.anaconda.com/products/individual), `anomalib` could be installed as,

anomalib/models/ganomaly/torch_model.py

@@ -1,22 +1,15 @@
+# Copyright (c) 2018-2022 Samet Akcay, Durham University, UK
+# SPDX-License-Identifier: MIT
+#
+# Copyright (C) 2020-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
 """Torch models defining encoder, decoder, Generator and Discriminator.
 
 Code adapted from https://github.com/samet-akcay/ganomaly.
 """
 
-# Copyright (C) 2020 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions
-# and limitations under the License.
-
 
 import math
 

anomalib/utils/callbacks/nncf_callback.py

@@ -92,6 +92,7 @@ def __init__(self, config: Union[ListConfig, DictConfig], dirpath: str, filename
         self.compression_scheduler: CompressionScheduler
 
     def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: Optional[str] = None) -> None:
+        # pylint: disable=unused-argument
         """Call when fit or test begins.
 
         Takes the pytorch model and wraps it using the compression controller so that it is ready for nncf fine-tuning.

tests/nightly/models/test_model_nightly.py

@@ -23,6 +23,7 @@
 from datetime import datetime
 from pathlib import Path
 from typing import Dict, List, Union
+
 import numpy as np
 import pandas as pd
 import torch
@@ -89,13 +90,19 @@ def _test_metrics(self, trainer, config, model, datamodule):
         threshold = thresholds[config.model.name][config.dataset.category]
         if "optimization" in config.keys() and config.optimization.nncf.apply:
             threshold = threshold.nncf
-        if not  (np.isclose(results["image_AUROC"], threshold["image_AUROC"], rtol=0.02) or (results["image_AUROC"] >= threshold["image_AUROC"])):
+        if not (
+            np.isclose(results["image_AUROC"], threshold["image_AUROC"], rtol=0.02)
+            or (results["image_AUROC"] >= threshold["image_AUROC"])
+        ):
             raise AssertionError(
                 f"results['image_AUROC']:{results['image_AUROC']} >= threshold['image_AUROC']:{threshold['image_AUROC']}"
             )
 
         if config.dataset.task == "segmentation":
-            if not (np.isclose(results["pixel_AUROC"] ,threshold["pixel_AUROC"], rtol=0.02) or (results["pixel_AUROC"] >= threshold["pixel_AUROC"])):
+            if not (
+                np.isclose(results["pixel_AUROC"], threshold["pixel_AUROC"], rtol=0.02)
+                or (results["pixel_AUROC"] >= threshold["pixel_AUROC"])
+            ):
                 raise AssertionError(
                     f"results['pixel_AUROC']:{results['pixel_AUROC']} >= threshold['pixel_AUROC']:{threshold['pixel_AUROC']}"
                 )
@@ -174,4 +181,3 @@ def test_model(self, path=get_dataset_path(), score_type=None):
                     job.result()
                 except Exception as e:
                     raise e
-

third-party-programs.txt

@@ -0,0 +1,20 @@
+
+Third Party Programs File
+
+This file contains the list of third party software ("third party programs")
+contained in the Intel software and their required notices and/or license
+terms. This third party software, even if included with the distribution of
+the Intel software, may be governed by separate license terms, including
+without limitation, third party license terms, other Intel software license
+terms, and open source software license terms. These separate license terms
+govern your use of the third party programs as set forth in the
+"third-party-programs.txt" or other similarly-named text file.
+
+Third party programs and their corresponding required notices and/or license
+terms are listed below.
+
+-------------------------------------------------------------
+
+1. Encoder, Decoder, Discriminator, Generator
+   Copyright (c) 2018-2022 Samet Akcay, Durham University, UK
+   SPDX-License-Identifier: MIT

tools/benchmarking/benchmark.py

@@ -236,7 +236,7 @@ def sweep(run_config: Union[DictConfig, ListConfig], device: int = 0, seed: int
     # Benchmarking entry point.
     # Spawn multiple processes one for cpu and rest for the number of gpus available in the system.
     # The idea is to distribute metrics collection over all the available devices.
-    
+
     print("Benchmarking started 🏃‍♂️. This will take a while ⏲ depending on your configuration.")
     distribute()
     print("Finished gathering results ⚡")