From 02a2856f06516500d9ca04d4f920a9fa80e5012b Mon Sep 17 00:00:00 2001 From: Robert Muchsel Date: Mon, 13 Nov 2023 16:32:36 -0600 Subject: [PATCH 01/27] Update documentation and distiller version (#268) --- README.md | 8 ++++---- README.pdf | Bin 6777160 -> 6777350 bytes distiller | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index dbdf3c2eb..8dbe7cf2a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -October 23, 2023 +November 13, 2023 ADI’s MAX78000/MAX78002 project is comprised of five repositories: @@ -13,7 +13,7 @@ ADI’s MAX78000/MAX78002 project is comprised of five repositories: 4. The synthesis repository, which is used to *convert a trained model into C code* using the “izer” tool: [ai8x-synthesis](https://github.com/MaximIntegratedAI/ai8x-synthesis) **(described in this document)** 5. The reference design repository, which contains host applications and sample applications for reference designs such as [MAXREFDES178 (Cube Camera)](https://www.analog.com/en/design-center/reference-designs/maxrefdes178.html): - [refdes](https://github.com/MaximIntegratedAI/refdes) + [refdes](https://github.com/Analog-Devices-MSDK/refdes) *Note: Examples for EVkits and Feather boards are part of the MSDK* _Open the `.md` version of this file in a markdown enabled viewer, for example Typora (). @@ -1164,7 +1164,7 @@ The MAX78000 hardware does not support arbitrary network parameters. Specificall * `Flatten` functionality is available to convert 2D input data for use by fully connected layers, see [Fully Connected Layers](#fully-connected-linear-layers). - * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384. Pooling cannot be used at the same time as flattening. + * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. * Element-wise operators support from 2 up to 16 inputs. @@ -1259,7 +1259,7 @@ The MAX78002 hardware does not support arbitrary network parameters. Specificall * The maximum number of input neurons is 1024, and the maximum number of output neurons is 1024 (16 each per processor used). * `Flatten` functionality is available to convert 2D input data for use by fully connected layers, see [Fully Connected Layers](#fully-connected-linear-layers). - * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384. Pooling cannot be used at the same time as flattening. + * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. * Element-wise operators support from 2 up to 16 inputs. * Element-wise operators can be chained in-flight with pooling and 2D convolution (where the order of pooling and element-wise operations can be swapped). * For convenience, a `Softmax` operator is supported in software. diff --git a/README.pdf b/README.pdf index d83d8c355969e411f47c35cd61311d2ae1f0db81..944c6fe876c39b8ffaf14e52374c2569c0f83a1d 100644 GIT binary patch delta 93206 zcmY(~Lv*f97bf7?)`@M~ww-*jZ992m+qQFJ+t!I~+g9iA9`)dw)v9OJplUt$-c@7s zIiR77IiT@^0A^M~etsBd7bjCgTNn@EYMnJF+*Wkoo7&T^W@YuN7c%?E;#GNC`$FUY zhCSme3Mj)CS4rtM-lmH01-LY3fIFCq%|aVw*hE+z9ADs1-?)CLf4FLIe-t_1I62;y z$bZ-YSpr^8x+i5Tp0+=|{JOJTjj$U;H}4Gt0M~7IfEvHLoEwfmp7XSsxhGHZ!KUGd z9p#6mRY#Y7SwqzY6~S>k?ztnY;Ou>6HVud23dinhEWV(2%+VqT3nBc`qQS*FuVuZV z*~-9A<>aNtLn+HR4rMqJR*^58865@sipK=;&-Pa-JY z;BD9|0BqXU&nbhCn~N!$pKJ0b;ZGa^kYdgs1%gAMxx?YIXIOEk2QbjtFSwacyGO)Q 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--git a/distiller b/distiller index 23c185fa3..ea4436dba 160000 --- a/distiller +++ b/distiller @@ -1 +1 @@ -Subproject commit 23c185fa37fada2ed47b6aa81cd3189932a150f5 +Subproject commit ea4436dba4bc17a60216f27c8ded69b7faa5f501 From 89c97fb8a2dd6acd012c64ae91ad2a9674a6682a Mon Sep 17 00:00:00 2001 From: Robert Muchsel Date: Tue, 14 Nov 2023 15:27:20 -0600 Subject: [PATCH 02/27] Update copyright notices (#269) --- datasets/face_id/facenet_pytorch | 2 +- datasets/kws20.py | 11 ++------- losses/multiboxloss.py | 36 +++++++++++++++++++--------- nnplot.py | 26 +++++++++++++++++---- train.py | 16 ++++--------- utils/augmentation_utils.py | 40 +++++++++++++++++++++----------- 6 files changed, 80 insertions(+), 51 deletions(-) diff --git a/datasets/face_id/facenet_pytorch b/datasets/face_id/facenet_pytorch index c72fae839..14b312d4e 160000 --- a/datasets/face_id/facenet_pytorch +++ b/datasets/face_id/facenet_pytorch @@ -1 +1 @@ -Subproject commit c72fae8398860d76a30f911cf944a9683bd14b21 +Subproject commit 14b312d4ef4b1baf90438f89d49d3942e3c6df7f diff --git a/datasets/kws20.py b/datasets/kws20.py index 4215fac94..e63cc1c24 100644 --- a/datasets/kws20.py +++ b/datasets/kws20.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/losses/multiboxloss.py b/losses/multiboxloss.py index 5fba90a93..5b3e17e7f 100644 --- a/losses/multiboxloss.py +++ b/losses/multiboxloss.py @@ -1,18 +1,32 @@ -################################################################################################### # -# Copyright (C) 2022-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# Multi Box Loss code is from GitHub repo: -# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection: # MIT License +# # Copyright (c) 2019 Sagar Vinodababu -# Codes slightly with few minor modifications +# Portions Copyright (C) 2022-2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# Multi Box Loss code source: +# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection +# """ - Multi-box Loss for Object Detection Models +Multi-box Loss for Object Detection Models """ import torch from torch import nn diff --git a/nnplot.py b/nnplot.py index ca8ca040c..709849c37 100644 --- a/nnplot.py +++ b/nnplot.py @@ -1,11 +1,27 @@ -################################################################################################### # -# Copyright (C) 2018-2023 Maxim Integrated Products, Inc. All Rights Reserved. +# MIT License # -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# Copyright (c) 2018 Dabi Ahn +# Portions Copyright (C) 2018-2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of +# this software and associated documentation files (the "Software"), to deal in +# the Software without restriction, including without limitation the rights to +# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +# of the Software, and to permit persons to whom the Software is furnished to do +# so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. # -################################################################################################### """ Graphical output routines """ diff --git a/train.py b/train.py index 26742cabf..4b18be85f 100644 --- a/train.py +++ b/train.py @@ -1,16 +1,7 @@ #!/usr/bin/env python3 -################################################################################################### # -# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# pyright: reportMissingModuleSource=false, reportGeneralTypeIssues=false -# pyright: reportOptionalSubscript=false -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -24,7 +15,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # - +# pyright: reportMissingModuleSource=false, reportGeneralTypeIssues=false +# pyright: reportOptionalSubscript=false """This is an example application for compressing image classification models. The application borrows its main flow code from torchvision's ImageNet classification diff --git a/utils/augmentation_utils.py b/utils/augmentation_utils.py index 6635cadfd..60df39f6e 100644 --- a/utils/augmentation_utils.py +++ b/utils/augmentation_utils.py @@ -1,19 +1,33 @@ -################################################################################################### # -# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -################################################################################################### - -# -# GitHub repo for the below helper methods: -# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection: # MIT License +# # Copyright (c) 2019 Sagar Vinodababu - -# Some augmentation functions below have been adapted from -# From https://github.com/amdegroot/ssd.pytorch/blob/master/utils/augmentations.py +# Copyright (c) 2017 Max deGroot, Ellis Brown +# Portions Copyright (C) 2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# Helper methods originate from: +# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection +# Some augmentation functions have been adapted from: +# https://github.com/amdegroot/ssd.pytorch/blob/master/utils/augmentations.py +# """ Some utility functions for Augmentation Tasks """ import random From b8bed2be513607427c487a6247dcd2963975b524 Mon Sep 17 00:00:00 2001 From: Robert Muchsel Date: Wed, 15 Nov 2023 15:53:08 -0600 Subject: [PATCH 03/27] Remove regressions; update file headers (#270) --- .github/workflows/test.yml | 140 -------------------- attic/range_linear_ai84.py | 1 + datasets/cifar.py | 11 +- datasets/cifar100.py | 11 +- datasets/imagenet.py | 11 +- datasets/mixedkws.py | 11 +- datasets/mnist.py | 11 +- datasets/msnoise.py | 11 +- docs/Regression.md | 37 ------ models/ai85net-tinierssd-face.py | 33 +++-- models/ai85net-tinierssd.py | 33 +++-- parsecmd.py | 11 +- regression/create_eval_script.py | 65 ---------- regression/create_onnx_script.py | 127 ------------------ regression/create_test_script.py | 110 ---------------- regression/eval_pass_fail.py | 46 ------- regression/last_dev.py | 160 ----------------------- regression/log_comparison.py | 215 ------------------------------- regression/pass_fail.py | 107 --------------- regression/paths.yaml | 33 ----- regression/test_config.yaml | 99 -------------- utils/kd_relationbased.py | 12 +- utils/object_detection_utils.py | 35 +++-- 23 files changed, 87 insertions(+), 1243 deletions(-) delete mode 100644 .github/workflows/test.yml delete mode 100644 docs/Regression.md delete mode 100644 regression/create_eval_script.py delete mode 100644 regression/create_onnx_script.py delete mode 100644 regression/create_test_script.py delete mode 100644 regression/eval_pass_fail.py delete mode 100644 regression/last_dev.py delete mode 100644 regression/log_comparison.py delete mode 100644 regression/pass_fail.py delete mode 100644 regression/paths.yaml delete mode 100644 regression/test_config.yaml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index e1201c36c..000000000 --- a/.github/workflows/test.yml +++ /dev/null @@ -1,140 +0,0 @@ ---- -name: auto-testing -on: - pull_request: - branches: - - develop - -jobs: - eval: - runs-on: self-hosted - timeout-minutes: 345600 - steps: - - name: Checkout code - uses: actions/checkout@v2 - - name: Checkout PR - run: | - fork_repo=$(jq -r .pull_request.head.repo.clone_url $GITHUB_EVENT_PATH) - git clone $fork_repo --recursive - - name: Checkout synthesis - uses: actions/checkout@v2 - with: - repository: MaximIntegratedAI/ai8x-synthesis - ref: develop - path: ai8x-synthesis - - name: Setup Pyenv and Install Dependencies - uses: gabrielfalcao/pyenv-action@v13 - with: - default: 3.8.11 - - name: Create Venv - run: | - pyenv local 3.8.11 - python -m venv venv --prompt ai8x-training - - name: Activate Venv - run: source venv/bin/activate - - name: Install Dependencies - run: | - pip3 install -U pip wheel setuptools - pip3 install -r requirements-cu11.txt - - name: Create Evaluation Scripts - run: python ./regression/create_eval_script.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml - - name: Run Evaluation Scripts - run: bash ./scripts/evaluation_file.sh - - name: Save Evaluation Log Files - run: cp -r /home/test/actions-runner/_work/ai8x-training/ai8x-training/logs/ /home/test/max7800x/evaluation_logs/ - - name: Evaluation Results - run: python ./regression/eval_pass_fail.py --testpaths ./regression/paths.yaml - - build: - runs-on: self-hosted - needs: [eval] - timeout-minutes: 345600 - steps: - - name: Checkout last-dev - uses: actions/checkout@v2 - with: - repository: MaximIntegratedAI/ai8x-training - ref: develop - submodules: recursive - - name: Setup Pyenv and Install Dependencies - uses: gabrielfalcao/pyenv-action@v13 - with: - default: 3.8.11 - - name: Create Venv - run: | - pyenv local 3.8.11 - python -m venv venv --prompt ai8x-training - - name: Activate Venv - run: source venv/bin/activate - - name: Install Dependencies - run: | - pip3 install -U pip wheel setuptools - pip3 install -r requirements-cu11.txt - - name: Last Develop Check - run: python ./regression/last_dev.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml - - new-code: - runs-on: self-hosted - needs: [build] - timeout-minutes: 345600 - steps: - - name: Checkout code - uses: actions/checkout@v2 - - name: Checkout PR - run: | - fork_repo=$(jq -r .pull_request.head.repo.clone_url $GITHUB_EVENT_PATH) - git clone $fork_repo --recursive - - name: Setup Pyenv and Install Dependencies - uses: gabrielfalcao/pyenv-action@v13 - with: - default: 3.8.11 - - name: Create Venv - run: | - pyenv local 3.8.11 - python -m venv venv --prompt ai8x-training - - name: Activate Venv - run: source venv/bin/activate - - name: Install Dependencies - run: | - pip3 install -U pip wheel setuptools - pip3 install -r requirements-cu11.txt - - name: Create Test Script - run: python ./regression/create_test_script.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml - - name: Run Training Scripts - run: bash /home/test/actions-runner/_work/ai8x-training/ai8x-training/scripts/output_file.sh - - name: Save Log Files - run: cp -r /home/test/actions-runner/_work/ai8x-training/ai8x-training/logs/ /home/test/max7800x/test_logs/$(date +%Y-%m-%d_%H-%M-%S) - - name: Save Test Scripts - run: cp -r /home/test/actions-runner/_work/ai8x-training/ai8x-training/scripts/output_file.sh /home/test/max7800x/test_scripts/ - - name: Create and run ONNX script - run: python ./regression/create_onnx_script.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml - - test-results: - runs-on: self-hosted - needs: [new-code] - timeout-minutes: 345600 - steps: - - uses: actions/checkout@v2 - name: Checkout Test Code - with: - repository: MaximIntegratedAI/ai8x-training - ref: develop - submodules: recursive - - name: Setup Pyenv and Install Dependencies - uses: gabrielfalcao/pyenv-action@v13 - with: - default: 3.8.11 - - name: Create Venv - run: | - pyenv local 3.8.11 - python -m venv venv --prompt ai8x-training - - name: Activate Venv - run: source venv/bin/activate - - name: Install Dependencies - run: | - pip3 install -U pip wheel setuptools - pip3 install -r requirements-cu11.txt - - name: Log Diff - run: python ./regression/log_comparison.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml - - name: Test Results - run: python ./regression/pass_fail.py --testconf ./regression/test_config.yaml --testpaths ./regression/paths.yaml diff --git a/attic/range_linear_ai84.py b/attic/range_linear_ai84.py index d83c3a870..4d8755933 100644 --- a/attic/range_linear_ai84.py +++ b/attic/range_linear_ai84.py @@ -1,5 +1,6 @@ # # Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/cifar.py b/datasets/cifar.py index 0663a2395..88469a2d9 100644 --- a/datasets/cifar.py +++ b/datasets/cifar.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2018-2020 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/cifar100.py b/datasets/cifar100.py index aaef46b39..c41c60700 100644 --- a/datasets/cifar100.py +++ b/datasets/cifar100.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2018-2020 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/imagenet.py b/datasets/imagenet.py index 36b69bcc4..a1185f4e1 100644 --- a/datasets/imagenet.py +++ b/datasets/imagenet.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2018-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/mixedkws.py b/datasets/mixedkws.py index 0fc5f1f3c..5585729f3 100644 --- a/datasets/mixedkws.py +++ b/datasets/mixedkws.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2021-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/mnist.py b/datasets/mnist.py index ff598beda..0f5b17ad2 100644 --- a/datasets/mnist.py +++ b/datasets/mnist.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2018-2020 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/datasets/msnoise.py b/datasets/msnoise.py index 25630bf6b..9bfeb38fb 100644 --- a/datasets/msnoise.py +++ b/datasets/msnoise.py @@ -1,13 +1,6 @@ -################################################################################################### # -# Copyright (C) 2021-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/docs/Regression.md b/docs/Regression.md deleted file mode 100644 index cd298576a..000000000 --- a/docs/Regression.md +++ /dev/null @@ -1,37 +0,0 @@ -# Regression Test - -The regression test for the `ai8x-training` repository is tested when there is a pull request for the `develop` branch of `MaximIntegratedAI/ai8x-training` by triggering `test.yaml` GitHub actions. - -## Last Tested Code - -`last_dev.py` generates the log files for the last tested code. These log files are used for comparing the newly pushed code to check if there are any significant changes in the trained values. Tracking is done by checking the hash of the commit. - -## Creating Test Scripts - -The sample training scripts are under the `scripts` path. In order to create training scripts for regression tests, these scripts are rewritten by changing their epoch numbers by running `regression/create_test_script.py`. The aim of changing the epoch number is to keep the duration of the test under control. This epoch number is defined in `regression/test_config.yaml` for each model/dataset combination. Since the sizes of the models and the datasets vary, different epoch numbers can be defined for each of them in order to create a healthy test. If a new training script is added, the epoch number and threshold values must be defined in the `regression/test_config.yaml` file for the relevant model. - -## Comparing Log Files - -After running test scripts for newly pushed code, the log files are saved and compared to the last tested code’s log files by running `regression/log_comparison.py`, and the results are saved. - -## Pass-Fail Decision - -In the comparison, the test success criterion is that the difference does not exceed the threshold values defined in `regression/test_config.yaml` as a percentage. If all the training scripts pass the test, `pass_fail.py` completes with success. Otherwise, it fails and exits. - -## ONNX Export - -Scripts for ONNX export are created and run by running `create_onnx_scripts.py` by configuring `Onnx_Status: True` in `regression/test_config.yaml`. If it is set to `False`, ONNX export will be skipped. - -## Configuration - -In `regression/test_config.yaml`, the `Onnx_Status` and `Qat_Test` settings should be defined to `True` when ONNX export or QAT tests by using `policies/qat_policy.yaml` are desired. When `Qat_Test` is set to `False`, QAT will be done according to the main training script. All threshold values and test epoch numbers for each model/dataset combination are also configured in this file. In order to set up the test on a new system, `regression/paths.yaml` needs to be configured accordingly. - -## Setting Up Regression Test - -### GitHub Actions - -GitHub Actions is a continuous integration (CI) and continuous deployment (CD) platform provided by GitHub. It allows developers to automate various tasks, workflows, and processes directly within their GitHub repositories. A GitHub Workflow is an automated process defined using a YAML file that helps automate various tasks in a GitHub repository. - -In this project, with GitHub Actions, there is a 'test.yml' workflow that is triggered when a pull request is opened for the 'develop' branch of the 'MaximIntegratedAI/ai8x-training' repository. This workflow contains and runs the jobs and steps required for the regression test. Also, a self hosted GitHub Runner is used to run regression test actions in this workflow. In order to install GitHub Runner, go to Settings -> Actions -> Runners -> New self-hosted runner on GitHub. To learn more about GitHub Actions, see [GitHub Actions](https://docs.github.com/en/actions/quickstart). - -After installing and configuring a GitHub Runner in your local environment, configure it to start as a service during system startup in order to ensure that the self-hosted runner runs continuously and automatically. You can find more information about systemd services at [Systemd Services](https://linuxhandbook.com/create-systemd-services/). diff --git a/models/ai85net-tinierssd-face.py b/models/ai85net-tinierssd-face.py index 10402780c..37ebeec75 100644 --- a/models/ai85net-tinierssd-face.py +++ b/models/ai85net-tinierssd-face.py @@ -1,15 +1,30 @@ -################################################################################################### # -# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -# detect_objects function is adopted from: GitHub repository: -# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection: # MIT License +# # Copyright (c) 2019 Sagar Vinodababu -################################################################################################### +# Portions Copyright (C) 2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of +# this software and associated documentation files (the "Software"), to deal in +# the Software without restriction, including without limitation the rights to +# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +# of the Software, and to permit persons to whom the Software is furnished to do +# so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# detect_objects function is adopted from +# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection +# """ Tiny SSD (Single Shot Detector) Variant Model for Face Detection """ diff --git a/models/ai85net-tinierssd.py b/models/ai85net-tinierssd.py index d593ddfc8..bd6ce2681 100644 --- a/models/ai85net-tinierssd.py +++ b/models/ai85net-tinierssd.py @@ -1,15 +1,30 @@ -################################################################################################### # -# Copyright (C) 2022-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -# detect_objects function is adopted from: GitHub repository: -# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection: # MIT License +# # Copyright (c) 2019 Sagar Vinodababu -################################################################################################### +# Portions Copyright (C) 2022-2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of +# this software and associated documentation files (the "Software"), to deal in +# the Software without restriction, including without limitation the rights to +# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +# of the Software, and to permit persons to whom the Software is furnished to do +# so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# detect_objects function is adopted from +# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection +# """ Tiny SSD (Single Shot Detector) Variant Model """ diff --git a/parsecmd.py b/parsecmd.py index 1920a49a0..6f703b36a 100644 --- a/parsecmd.py +++ b/parsecmd.py @@ -1,15 +1,6 @@ -################################################################################################### -# -# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation # # Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/regression/create_eval_script.py b/regression/create_eval_script.py deleted file mode 100644 index 7ff2ebd2d..000000000 --- a/regression/create_eval_script.py +++ /dev/null @@ -1,65 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Create training bash scripts for test -""" -import argparse -import os - -import yaml - - -def joining(lst): - """ - Join list based on the ' ' delimiter - """ - join_str = ' '.join(lst) - return join_str - - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -# Folder containing the files to be concatenated -script_path = pathconfig["script_path"] - -# Output file name and path -output_file_path = pathconfig["output_file_path_evaluation"] - -# Loop through all files in the folder -with open(output_file_path, "w", encoding='utf-8') as evaluate_file: - for filename in os.listdir(script_path): - # Check if the file is a text file - if filename.startswith("evaluate"): - # Open the file and read its contents - with open(os.path.join(script_path, filename), encoding='utf-8') as input_file: - contents = input_file.read() - temp = contents.split() - temp.insert(1, "\n") - - i = temp.index("--exp-load-weights-from") - temp[i+1] = temp[i+1][1:] - - temp.insert(-1, "--name " + filename[9:-3]) - temp.insert(-1, "--data /data_ssd") - - temp.append(" \n") - contents = joining(temp) - evaluate_file.write(contents) diff --git a/regression/create_onnx_script.py b/regression/create_onnx_script.py deleted file mode 100644 index 9bb96e66b..000000000 --- a/regression/create_onnx_script.py +++ /dev/null @@ -1,127 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary and confidential to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Create onnx bash scripts for test -""" -import argparse -import datetime -import os -import subprocess -import sys - -import yaml - - -def joining(lst): - """ - Join list based on the ' ' delimiter - """ - joined_str = ' '.join(lst) - return joined_str - - -def time_stamp(): - """ - Take time stamp as string - """ - time = str(datetime.datetime.now()) - time = time.replace(' ', '.') - time = time.replace(':', '.') - return time - - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -if not config["Onnx_Status"]: - sys.exit(1) - -folder_path = pathconfig["folder_path"] -output_file_path = pathconfig["output_file_path_onnx"] -train_path = pathconfig["train_path"] - -logs_list = os.path.join(folder_path, sorted(os.listdir(folder_path))[-1]) - -models = [] -datasets = [] -devices = [] -model_paths = [] -bias = [] -tar_names = [] - - -with open(output_file_path, "w", encoding='utf-8') as onnx_scripts: - with open(train_path, "r", encoding='utf-8') as input_file: - contents = input_file.read() - lines = contents.split("#!/bin/sh ") - lines = lines[1:] - contents_t = contents.split() - - j = [i+1 for i in range(len(contents_t)) if contents_t[i] == '--model'] - for index in j: - models.append(contents_t[index]) - - j = [i+1 for i in range(len(contents_t)) if contents_t[i] == '--dataset'] - for index in j: - datasets.append(contents_t[index]) - - j = [i+1 for i in range(len(contents_t)) if contents_t[i] == '--device'] - for index in j: - devices.append(contents_t[index]) - - for i, line in enumerate(lines): - if "--use-bias" in line: - bias.append("--use-bias") - else: - bias.append("") - - for file_p in sorted(os.listdir(logs_list)): - temp_path = os.path.join(logs_list, file_p) - for temp_file in sorted(os.listdir(temp_path)): - if temp_file.endswith("_checkpoint.pth.tar"): - temp = os.path.join(temp_path, temp_file) - model_paths.append(temp) - tar_names.append(temp_file) - - for i, (model, dataset, bias_value, device_name) in enumerate( - zip(models, datasets, bias, devices) - ): - for tar in model_paths: - element = tar.split('-') - modelsearch = element[-4][3:] - datasearch = element[-3].split('_')[0] - if datasearch == dataset.split('_')[0] and modelsearch == model: - tar_path = tar - timestamp = time_stamp() - temp = ( - f"python train.py " - f"--model {model} " - f"--dataset {dataset} " - f"--evaluate " - f"--exp-load-weights-from {tar_path} " - f"--device {device_name} " - f"--summary onnx " - f"--summary-filename {model}_{dataset}_{timestamp}_onnx " - f"{bias_value}\n" - ) - onnx_scripts.write(temp) -cmd_command = "bash " + output_file_path - -subprocess.run(cmd_command, shell=True, check=True) diff --git a/regression/create_test_script.py b/regression/create_test_script.py deleted file mode 100644 index 7c3f4a5a0..000000000 --- a/regression/create_test_script.py +++ /dev/null @@ -1,110 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary and confidential to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Create training bash scripts for test -""" -import argparse -import os - -import yaml - - -def joining(lst): - """ - Join list based on the ' ' delimiter - """ - join_str = ' '.join(lst) - return join_str - - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -# Folder containing the files to be concatenated -script_path = pathconfig["script_path"] -# Output file name and path -output_file_path = pathconfig["output_file_path"] - -# global log_file_names -log_file_names = [] - -# Loop through all files in the folder -with open(output_file_path, "w", encoding='utf-8') as output_file: - for filename in os.listdir(script_path): - # Check if the file is a text file - if filename.startswith("train"): - # Open the file and read its contents - with open(os.path.join(script_path, filename), encoding='utf-8') as input_file: - contents = input_file.read() - - temp = contents.split() - temp.insert(1, "\n") - i = temp.index('--epochs') - j = temp.index('--model') - k = temp.index('--dataset') - - if config["Qat_Test"]: - if '--qat-policy' in temp: - x = temp.index('--qat-policy') - temp[x+1] = "policies/qat_policy.yaml" - else: - temp.insert(-1, ' --qat-policy policies/qat_policy.yaml') - - log_model = temp[j+1] - log_data = temp[k+1] - - if log_model == "ai87imageneteffnetv2": - num = temp.index("--batch-size") - temp[num+1] = "128" - - log_name = temp[j+1] + '-' + temp[k+1] - log_file_names.append(filename[:-3]) - - if log_data == "FaceID": - continue - - if log_data == "VGGFace2_FaceDetection": - continue - - try: - temp[i+1] = str(config[log_data][log_model]["epoch"]) - except KeyError: - print(f"\033[93m\u26A0\033[0m Warning: {temp[j+1]} model is" + - " missing information in test configuration files.") - continue - - if '--deterministic' not in temp: - temp.insert(-1, '--deterministic') - - temp.insert(-1, '--name ' + log_name) - - try: - path_data = config[log_data]["datapath"] - temp[i+1] = str(config[log_data][log_model]["epoch"]) - except KeyError: - print(f"\033[93m\u26A0\033[0m Warning: {temp[j+1]} model is" + - " missing information in test configuration files.") - continue - - temp.insert(-1, '--data ' + path_data) - temp.append("\n") - - contents = joining(temp) - output_file.write(contents) diff --git a/regression/eval_pass_fail.py b/regression/eval_pass_fail.py deleted file mode 100644 index 0de504ea7..000000000 --- a/regression/eval_pass_fail.py +++ /dev/null @@ -1,46 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Check the test results -""" -import argparse -import os - -import yaml - -parser = argparse.ArgumentParser() -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -test_path = args.testpaths - -# Open the YAML file -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -eval_path = pathconfig["eval_path"] -eval_file = os.listdir(eval_path)[-1] -directory_path = os.path.join(eval_path, eval_file) -passed = [] -failed = [] - -for filename in sorted(os.listdir(directory_path)): - path = os.path.join(directory_path, filename) - file_path = os.path.join(path, os.listdir(path)[0]) - with open(file_path, 'r', encoding='utf-8') as file: - content = file.read() - if "Loss" in content: - pass_file = filename.split("___")[0] - passed.append(f"\033[32m\u2714\033[0m Evaluation test passed for {pass_file}.") - else: - fail_file = filename.split("___")[0] - failed.append(f"\033[31m\u2718\033[0m Evaluation test failed for {fail_file}.") - -for filename in failed: - print(filename) -for filename in passed: - print(filename) diff --git a/regression/last_dev.py b/regression/last_dev.py deleted file mode 100644 index fa8565663..000000000 --- a/regression/last_dev.py +++ /dev/null @@ -1,160 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary and confidential to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Create the last developed code logs for base testing source -""" -import argparse -import datetime -import os -import subprocess - -import requests -import yaml - - -def joining(lst): - """ - Join based on the ' ' delimiter - """ - join_str = ' '.join(lst) - return join_str - - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -# Folder containing the files to be concatenated -script_path = pathconfig["script_path_dev"] -# Output file name and path -output_file_path = pathconfig["output_file_path_dev"] - -# global log_file_names -log_file_names = [] - - -def dev_scripts(script_pth, output_file_pth): - """ - Create training scripts for the last developed code - """ - with open(output_file_pth, "w", encoding='utf-8') as output_file: - for filename in os.listdir(script_pth): - # Check if the file is a text file - if filename.startswith("train"): - # Open the file and read its contents - with open(os.path.join(script_path, filename), encoding='utf-8') as input_file: - contents = input_file.read() - - temp = contents.split() - temp.insert(1, "\n") - i = temp.index('--epochs') - j = temp.index('--model') - k = temp.index('--dataset') - - if config["Qat_Test"]: - if '--qat-policy' in temp: - x = temp.index('--qat-policy') - temp[x+1] = "policies/qat_policy.yaml" - else: - temp.insert(-1, ' --qat-policy policies/qat_policy.yaml') - - log_model = temp[j+1] - log_data = temp[k+1] - - if log_model == "ai87imageneteffnetv2": - num = temp.index("--batch-size") - temp[num+1] = "128" - - log_name = temp[j+1] + '-' + temp[k+1] - log_file_names.append(filename[:-3]) - - if log_data == "FaceID": - continue - if log_data == "VGGFace2_FaceDetection": - continue - if log_data == "ai85tinierssdface": - continue - - try: - temp[i+1] = str(config[log_data][log_model]["epoch"]) - except KeyError: - print(f"\033[93m\u26A0\033[0m Warning: {temp[j+1]} model is" + - " missing information in test configuration files.") - continue - - if '--deterministic' not in temp: - temp.insert(-1, '--deterministic') - - temp.insert(-1, '--name ' + log_name) - - try: - path_data = config[log_data]["datapath"] - temp[i+1] = str(config[log_data][log_model]["epoch"]) - except KeyError: - print(f"\033[93m\u26A0\033[0m Warning: {temp[j+1]} model is" + - " missing information in test configuration files.") - continue - - temp.insert(-1, '--data ' + path_data) - temp.append("\n") - contents = joining(temp) - output_file.write(contents) - - -def dev_checkout(): - """ - Checkout the last developed code - """ - repository = 'MaximIntegratedAI/ai8x-training' - branch = 'develop' - - # Send a GET request to the GitHub API to retrieve the commit data - url = f'https://api.github.com/repos/{repository}/commits?sha={branch}' - response = requests.get(url) - - if response.status_code == 200: - commit_hash = response.json()[0]['sha'] - print(f"The hash of the last commit in the '{branch}' branch is: {commit_hash}") - else: - print(f"Failed to retrieve commit information. Status code: {response.status_code}") - - commit_num_path = pathconfig['commit_num_path'] - - try: - with open(commit_num_path, "r", encoding='utf-8') as file: - saved_commit_hash = file.read().strip() - except FileNotFoundError: - saved_commit_hash = "" - - if commit_hash != saved_commit_hash: - with open(commit_num_path, "w", encoding='utf-8') as file: - file.write(commit_hash) - dev_scripts(script_path, output_file_path) - cmd_command = "bash " + output_file_path - subprocess.run(cmd_command, shell=True, check=True) - - source_path = pathconfig["source_path"] - destination_path = os.path.join( - pathconfig["destination_path"], - datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") - ) - subprocess.run(['/bin/mv', source_path, destination_path], check=True) - - -dev_checkout() diff --git a/regression/log_comparison.py b/regression/log_comparison.py deleted file mode 100644 index 5e3e70f9a..000000000 --- a/regression/log_comparison.py +++ /dev/null @@ -1,215 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary and confidential to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Compare log files of the pulled code and the last developed -""" -import argparse -import datetime -import os -import sys - -import yaml -from tabulate import tabulate - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - - -def compare_logs(old_log, new_log, output_name, output_pth): - """ - Take diff top1 of log files of the pulled code and the last developed - """ - header = ["Epoch number", "Top1 Diff(%)", "Top5 Diff(%)"] - header_map = ["Epoch number", "mAP Diff(%)"] - - word = 'Best' - word2 = 'Top1' - word3 = 'mAP' - ex_list = [False] - - with open(new_log, 'r', encoding='utf-8') as f2: - file2_content = f2.read() - log_name = new_log.split('/')[-1].split('___')[0] - - if word2 not in file2_content and word3 not in file2_content: - print(f"\033[31m\u2718\033[0m {log_name} does not have any trained results." - " There is an error in training.") - ex_list.append(True) - - if all(ex_list): - print("\033[31m Cancelling github actions.") - sys.exit(1) - - with open(old_log, 'r', encoding='utf-8') as f1, open(new_log, 'r', encoding='utf-8') as f2: - file1_content = f1.readlines() - file2_content = f2.readlines() - - log1_list = [] - log2_list = [] - mAP_list1 = [] - mAP_list2 = [] - - word = 'Best' - word2 = 'Top1' - word3 = 'mAP' - map_value = False - - for line in file1_content: - if word in line and word2 in line: - lst = line.split() - log1_list.append(lst[5:]) - map_value = False - elif word in line and word3 in line: - lst = line.split() - mAP_list1.append(lst[5:7]) - map_value = True - - for line in file2_content: - if word in line and word2 in line: - lst = line.split() - log2_list.append(lst[5:]) - map_value = False - elif word in line and word3 in line: - lst = line.split() - mAP_list2.append(lst[5:7]) - map_value = True - - epoch_num_top = min(len(log1_list), len(log2_list)) - epoch_num_map = min(len(mAP_list1), len(mAP_list2)) - - log1_list = log1_list[:epoch_num_top] - log2_list = log2_list[:epoch_num_top] - mAP_list1 = mAP_list1[:epoch_num_map] - mAP_list2 = mAP_list2[:epoch_num_map] - - top1 = [] - map_list = [] - - if not map_value: - i = 0 - for (list1, list2) in zip(log1_list, log2_list): - if float(list1[1]) == 0: - print("Top1 value of " + output_name + " is 0.00.") - list1[1] = 0.000001 - i = i+1 - if '[Top1:' in list2: - top1_diff = ((float(list2[1])-float(list1[1]))/float(list1[1]))*100 - top1.append([i]) - top1[i-1].append(top1_diff) - - if 'Top5:' in list2: - top5_diff = ((float(list2[3])-float(list1[3]))/float(list1[1]))*100 - top1[i-1].append(top5_diff) - - output_path_2 = os.path.join(output_pth, (output_name + '.txt')) - with open(output_path_2, "w", encoding='utf-8') as output_file: - output_file.write(tabulate(top1, headers=header)) - - if map_value: - i = 0 - for (map1, map2) in zip(mAP_list1, mAP_list2): - if float(map1[1]) == 0: - print(f"Map value of {output_name} is 0.00 at epoch {i}.") - map1[1] = 0.000001 - i = i+1 - if '[mAP:' in map2: - map_diff = ((float(map2[1])-float(map1[1]))/float(map1[1]))*100 - map_list.append([i]) - map_list[i-1].append(map_diff) - - output_path_2 = os.path.join(output_pth, (output_name + '.txt')) - with open(output_path_2, "w", encoding='utf-8') as output_file: - output_file.write(tabulate(map_list, headers=header_map)) - return map_value - - -def log_path_list(path): - """ - Create log names - """ - lst = [] - for file in sorted(os.listdir(path)): - lst.append(file.split("___")[0]) - return lst - - -log_new = pathconfig["log_new"] -log_old = pathconfig["log_old"] -script_path = pathconfig["script_path_log"] - -time = str(datetime.datetime.now()) -time = time.replace(' ', '.') -time = time.replace(':', '.') -output_path = pathconfig["output_path"] + '/' + str(time) - -os.mkdir(output_path) - -loglist = sorted(os.listdir(log_new)) -loglist_old = sorted(os.listdir(log_old)) -old_logs_path = log_old + loglist_old[-1] -new_logs_path = log_new + loglist[-1] - -new_log_list = log_path_list(new_logs_path) -old_log_list = log_path_list(old_logs_path) - -with open(script_path, 'r', encoding='utf-8') as f: - scripts_t = f.read() - scripts = scripts_t.split(' ') -name_indices = [i+1 for i, x in enumerate(scripts) if x == "--name"] -values = [scripts[j] for j in name_indices] - -ex_list2 = [False] -for log in values: - if log not in new_log_list: - print(f"\033[31m\u2718\033[0m {log} does not have any trained log file." - " There is an error in training.") - ex_list2.append(True) - -if all(ex_list2): - print("\033[31m Cancelling github actions.") - sys.exit(1) - -not_found_model = [] -map_value_list = {} - -for files_new in sorted(os.listdir(new_logs_path)): - files_new_temp = files_new.split("___")[0] - if files_new_temp not in old_log_list: - not_found_model.append(files_new_temp + " not found in last developed log files.") - for files_old in sorted(os.listdir(old_logs_path)): - files_old_temp = files_old.split("___")[0] - if files_old_temp == files_new_temp: - - old_path = os.path.join(old_logs_path, files_old) - new_path = os.path.join(new_logs_path, files_new) - - old_files = sorted(os.listdir(old_path)) - new_files = sorted(os.listdir(new_path)) - - old_log_file = [file for file in old_files if file.endswith(".log")][0] - new_log_file = [file for file in new_files if file.endswith(".log")][0] - - old_path_log = os.path.join(old_path, old_log_file) - new_path_log = os.path.join(new_path, new_log_file) - - map_value_list[files_new_temp] = compare_logs( - old_path_log, new_path_log, files_new, output_path - ) - break diff --git a/regression/pass_fail.py b/regression/pass_fail.py deleted file mode 100644 index cdb93a779..000000000 --- a/regression/pass_fail.py +++ /dev/null @@ -1,107 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# This software is proprietary and confidential to Analog Devices, Inc. and its licensors. -# -################################################################################################### -""" -Check the test results -""" -import argparse -import os -import sys - -import yaml -from log_comparison import map_value_list, not_found_model - -parser = argparse.ArgumentParser() -parser.add_argument('--testconf', help='Enter the config file for the test', required=True) -parser.add_argument('--testpaths', help='Enter the paths for the test', required=True) -args = parser.parse_args() -yaml_path = args.testconf -test_path = args.testpaths - -# Open the YAML file -with open(yaml_path, 'r', encoding='utf-8') as yaml_file: - # Load the YAML content into a Python dictionary - config = yaml.safe_load(yaml_file) - -with open(test_path, 'r', encoding='utf-8') as path_file: - # Load the YAML content into a Python dictionary - pathconfig = yaml.safe_load(path_file) - -log_path = pathconfig["log_path"] -log_path = os.path.join(log_path, sorted(os.listdir(log_path))[-1]) - - -def check_top_value(diff_file, threshold, map_value): - """ - Compare Top1 value with threshold - """ - if not map_value: - with open(diff_file, 'r', encoding='utf-8') as f: - model_name = diff_file.split('/')[-1].split('___')[0] - # Read all lines in the diff_file - lines = f.readlines() - # Extract the last line and convert it to a float - top1 = lines[-1].split() - try: - epoch_num = int(top1[0]) - except ValueError: - print(f"\033[31m\u2718\033[0m Test failed for {model_name}: " - f"Cannot convert {top1[0]} to an epoch number.") - return False - top1_diff = float(top1[1]) - - if top1_diff < threshold: - print(f"\033[31m\u2718\033[0m Test failed for {model_name} since" - f" Top1 value changed {top1_diff} % at {epoch_num}th epoch.") - return False - print(f"\033[32m\u2714\033[0m Test passed for {model_name} since" - f" Top1 value changed {top1_diff} % at {epoch_num}th epoch.") - return True - - with open(diff_file, 'r', encoding='utf-8') as f: - model_name = diff_file.split('/')[-1].split('___')[0] - # Read all lines in the diff_file - lines = f.readlines() - # Extract the last line and convert it to a float - top1 = lines[-1].split() - try: - epoch_num = int(top1[0]) - except ValueError: - print(f"\033[31m\u2718\033[0m Test failed for {model_name}: " - f"Cannot convert {top1[0]} to an epoch number.") - return False - top1_diff = float(top1[1]) - # top5_diff = float(top1[2]) - - if top1_diff < threshold: - print(f"\033[31m\u2718\033[0m Test failed for {model_name} since" - f" mAP value changed {top1_diff} % at {epoch_num}th epoch.") - return False - print(f"\033[32m\u2714\033[0m Test passed for {model_name} since" - f" mAP value changed {top1_diff} % at {epoch_num}th epoch.") - return True - - -passing = [] -for item in not_found_model: - print("\033[93m\u26A0\033[0m " + "Warning: " + item) - -for logs in sorted(os.listdir(log_path)): - log_name = (logs.split("___"))[0] - log_model = log_name.split("-")[0] - log_data = log_name.split("-")[1] - - if log_data in config and log_model in config[log_data]: - threshold_temp = float(config[f'{log_data}'][f'{log_model}']['threshold']) - else: - threshold_temp = 0 - logs = os.path.join(log_path, str(logs)) - map_val = map_value_list[log_name] - passing.append(check_top_value(logs, threshold_temp, map_val)) - -if not all(passing): - print("\033[31mOne or more tests did not pass. Cancelling github actions.") - sys.exit(1) diff --git a/regression/paths.yaml b/regression/paths.yaml deleted file mode 100644 index 4b51d0980..000000000 --- a/regression/paths.yaml +++ /dev/null @@ -1,33 +0,0 @@ ---- -# Create_onnx_script.py - -folder_path: /home/test/max7800x/test_logs/ -output_file_path_onnx: ./scripts/onnx_scripts.sh -train_path: /home/test/max7800x/test_scripts/output_file.sh - -# create_test_script.py - -script_path: ./scripts -output_file_path: ./scripts/output_file.sh -output_file_path_evaluation: ./scripts/evaluation_file.sh - -# last_dev.py - -script_path_dev: /home/test/max7800x/last_developed/last_dev_source/scripts -output_file_path_dev: /home/test/max7800x/last_developed/dev_scripts/last_dev_train.sh -source_path: /home/test/actions-runner/_work/ai8x-training/ai8x-training/logs/ -destination_path: /home/test/max7800x/last_developed/dev_logs/ -local_path: /home/test/max7800x/last_developed/last_dev_source/ -commit_num_path: /home/test/max7800x/last_developed/commit_number.txt - -# log_comparison.py - -log_new: /home/test/max7800x/test_logs/ -log_old: /home/test/max7800x/last_developed/dev_logs/ -script_path_log: /home/test/max7800x/test_scripts/output_file.sh -output_path: /home/test/max7800x/log_diff/ - -# pass_fail.py - -log_path: /home/test/max7800x/log_diff -eval_path: /home/test/max7800x/evaluation_logs/ diff --git a/regression/test_config.yaml b/regression/test_config.yaml deleted file mode 100644 index 856b584f3..000000000 --- a/regression/test_config.yaml +++ /dev/null @@ -1,99 +0,0 @@ ---- -Onnx_Status: true -Qat_Test: false -AISegment_352: - datapath: /data_ssd - ai85unetlarge: - threshold: -5 - epoch: 15 -CamVid_s352_c3: - datapath: /data_ssd - ai85unetlarge: - threshold: -5 - epoch: 15 -cats_vs_dogs: - datapath: /data_ssd - ai85cdnet: - threshold: -5 - epoch: 15 -CIFAR10: - datapath: "/data_ssd" - ai85nascifarnet: - threshold: -5 - epoch: 15 - ai85net6: - threshold: -5 - epoch: 15 - ai85squeezenet: - threshold: -5 - epoch: 15 -CIFAR100: - datapath: "/data_ssd" - ai85simplenet: - threshold: -5 - epoch: 15 - ai85nascifarnet: - threshold: -5 - epoch: 15 - ai87effnetv2: - threshold: -5 - epoch: 15 - ai87netmobilenetv2cifar100_m0_5: - threshold: -5 - epoch: 15 - ai87netmobilenetv2cifar100_m0_75: - threshold: -5 - epoch: 15 - ai85ressimplenet: - threshold: -5 - epoch: 15 - ai85simplenetwide2x: - threshold: -5 - epoch: 15 -ImageNet: - datapath: "/data_ssd" - ai87imageneteffnetv2: - threshold: -5 - epoch: 15 -ImageNet_Bayer: - datapath: "/data_ssd" - bayer2rgbnet: - threshold: -5 - epoch: 15 -KWS_20: - datapath: "/data_ssd" - ai85kws20net: - threshold: -5 - epoch: 15 - ai85kws20netv2: - threshold: -5 - epoch: 15 - ai85kws20netv3: - threshold: -5 - epoch: 15 - ai87kws20netv3: - threshold: -5 - epoch: 15 -MNIST: - datapath: "/data_ssd" - ai85netextrasmall: - threshold: -5 - epoch: 15 - ai85net5: - threshold: -500 - epoch: 15 -SVHN_74: - datapath: "/data_ssd" - ai85tinierssd: - threshold: -500 - epoch: 15 -PascalVOC_2007_2012_256_320_augmented: - datapath: "/data_ssd" - ai87fpndetector: - threshold: -500 - epoch: 15 -Kinetics400: - datapath: "/data_ssd" - ai85actiontcn: - threshold: -500 - epoch: 15 diff --git a/utils/kd_relationbased.py b/utils/kd_relationbased.py index 7ade8c293..5610f8448 100644 --- a/utils/kd_relationbased.py +++ b/utils/kd_relationbased.py @@ -1,15 +1,6 @@ -################################################################################################### -# -# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved. -# -# Analog Devices, Inc. Default Copyright Notice: -# https://www.analog.com/en/about-adi/legal-and-risk-oversight/intellectual-property/copyright-notice.html -# -################################################################################################### -# -# Portions Copyright (c) 2018 Intel Corporation # # Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2023 Analog Devices, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -23,7 +14,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # - """ Relation based Knowledge Distillation Policy""" from collections import namedtuple diff --git a/utils/object_detection_utils.py b/utils/object_detection_utils.py index ff2cc2a73..9f42c2855 100644 --- a/utils/object_detection_utils.py +++ b/utils/object_detection_utils.py @@ -1,17 +1,30 @@ -################################################################################################### # -# Copyright (C) 2022-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -################################################################################################### -# -# GitHub repo for the following helper methods: -# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection: # MIT License +# # Copyright (c) 2019 Sagar Vinodababu - -# Code slightly modified +# Portions Copyright (C) 2022-2023 Maxim Integrated Products, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of +# this software and associated documentation files (the "Software"), to deal in +# the Software without restriction, including without limitation the rights to +# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +# of the Software, and to permit persons to whom the Software is furnished to do +# so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# GitHub repo for the following helper methods +# https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection +# """ Utility functions for Object Detection Tasks """ import torch From 74bb06032d81b6b06dfd2b8010ce581a22891c0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Thu, 30 Nov 2023 17:17:19 +0300 Subject: [PATCH 04/27] Update optimizers after QAT (#272) --- .github/linters/.python-lint | 2 +- .pylintrc | 2 +- train.py | 8 ++++++++ 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/.github/linters/.python-lint b/.github/linters/.python-lint index dfd5982f9..892053922 100644 --- a/.github/linters/.python-lint +++ b/.github/linters/.python-lint @@ -9,7 +9,7 @@ max-line-length = 99 max-locals=100 max-statements=300 min-public-methods=1 -max-branches=100 +max-branches=120 max-module-lines=5000 max-args=20 max-returns=10 diff --git a/.pylintrc b/.pylintrc index 7f3d5a3e7..22fe62009 100644 --- a/.pylintrc +++ b/.pylintrc @@ -9,7 +9,7 @@ max-line-length = 99 max-locals=100 max-statements=300 min-public-methods=1 -max-branches=100 +max-branches=120 max-module-lines=5000 max-args=20 max-returns=10 diff --git a/train.py b/train.py index 4b18be85f..e88bc1f6b 100644 --- a/train.py +++ b/train.py @@ -527,6 +527,14 @@ def main(): # Update the optimizer to reflect fused batchnorm layers optimizer = ai8x.update_optimizer(model, optimizer) + # Update the compression scheduler to reflect the updated optimizer + for ep, _ in enumerate(compression_scheduler.policies): + for pol in compression_scheduler.policies[ep]: + for attr_key in dir(pol): + attr = getattr(pol, attr_key) + if hasattr(attr, 'optimizer'): + attr.optimizer = optimizer + # Switch model from unquantized to quantized for QAT ai8x.initiate_qat(model, qat_policy) From e6ab78a6b0499cdd345c0ef50d7fccd54bb55823 Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Fri, 1 Dec 2023 10:12:07 -0600 Subject: [PATCH 05/27] Remove unused files; simplify requirements*.txt; add additional PR actions; update license (#271) --- .github/workflows/linter.yml | 12 + LICENSE.md | 33 - LICENSE.txt | 201 +++++ attic/evaluate_cifar10_bias.sh | 2 - attic/evaluate_fashionmnist.sh | 2 - attic/go_prune.sh | 2 - attic/go_quantized.sh | 2 - attic/inspect_ckpt.py | 80 -- attic/logging.conf | 54 -- attic/post_train.yaml | 13 - attic/post_train_ai84.yaml | 24 - attic/prune.yaml | 33 - attic/quant_train.yaml | 34 - attic/quant_train_ai84.yaml | 36 - attic/quantize_cifar10.sh | 2 - attic/quantize_fashionmnist.sh | 2 - attic/quantize_mnist.sh | 2 - attic/range_linear_ai84.py | 1303 -------------------------------- attic/train_afsk.sh | 2 - attic/train_cifar10_bias.sh | 2 - devices.py | 2 +- requirements-cu11.txt | 21 +- requirements-win-cu11.txt | 21 +- train.py | 2 +- 24 files changed, 217 insertions(+), 1670 deletions(-) delete mode 100644 LICENSE.md create mode 100644 LICENSE.txt delete mode 100755 attic/evaluate_cifar10_bias.sh delete mode 100755 attic/evaluate_fashionmnist.sh delete mode 100755 attic/go_prune.sh delete mode 100755 attic/go_quantized.sh delete mode 100755 attic/inspect_ckpt.py delete mode 100755 attic/logging.conf delete mode 100644 attic/post_train.yaml delete mode 100644 attic/post_train_ai84.yaml delete mode 100644 attic/prune.yaml delete mode 100644 attic/quant_train.yaml delete mode 100644 attic/quant_train_ai84.yaml delete mode 100755 attic/quantize_cifar10.sh delete mode 100755 attic/quantize_fashionmnist.sh delete mode 100755 attic/quantize_mnist.sh delete mode 100644 attic/range_linear_ai84.py delete mode 100755 attic/train_afsk.sh delete mode 100755 attic/train_cifar10_bias.sh diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml index c775a91b8..ef0e38c3f 100644 --- a/.github/workflows/linter.yml +++ b/.github/workflows/linter.yml @@ -19,6 +19,18 @@ jobs: fetch-depth: 0 submodules: true + - name: Check source code files for Unicode and CRLF + run: | + set -e + find . -name 'distiller' -prune -o -type f -size +0c -regex '.*\.\(py\|yaml\|yml\|txt\|h\|c\|sh\)' -print0 | xargs -0 file | grep -P '^(?!.*ASCII)|CRLF' || exit 0 + exit 1 + + - name: Check shell scripts for x-bit + run: | + set -e + find . -name 'distiller' -prune -o -type f -name \*.sh \! -perm -1 -print0 | xargs -0 grep . || exit 0 + exit 1 + - name: Lint code uses: super-linter/super-linter/slim@v5 env: diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index e205efc25..000000000 --- a/LICENSE.md +++ /dev/null @@ -1,33 +0,0 @@ -# LICENSE - -Copyright (C) 2019-2023 Maxim Integrated Products, Inc., All rights Reserved. - -This software is protected by copyright laws of the United States and -of foreign countries. This material may also be protected by patent laws -and technology transfer regulations of the United States and of foreign -countries. This software is furnished under a license agreement and/or a -nondisclosure agreement and may only be used or reproduced in accordance -with the terms of those agreements. Dissemination of this information to -any party or parties not specified in the license agreement and/or -nondisclosure agreement is expressly prohibited. - -The above copyright notice and this permission notice shall be included -in all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS -OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. -IN NO EVENT SHALL MAXIM INTEGRATED BE LIABLE FOR ANY CLAIM, DAMAGES -OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, -ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR -OTHER DEALINGS IN THE SOFTWARE. - -Except as contained in this notice, the name of Maxim Integrated -Products, Inc. shall not be used except as stated in the Maxim Integrated -Products, Inc. Branding Policy. - -The mere transfer of this software does not imply any licenses -of trade secrets, proprietary technology, copyrights, patents, -trademarks, maskwork rights, or any other form of intellectual -property whatsoever. Maxim Integrated Products, Inc. retains all -ownership rights. diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + 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. diff --git a/attic/evaluate_cifar10_bias.sh b/attic/evaluate_cifar10_bias.sh deleted file mode 100755 index 525517bcc..000000000 --- a/attic/evaluate_cifar10_bias.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --model ai84net5 --dataset CIFAR10 --confusion --evaluate --exp-load-weights-from ../ai8x-synthesis/trained/ai84-cifar10-bias.pth.tar -8 --use-bias "$@" diff --git a/attic/evaluate_fashionmnist.sh b/attic/evaluate_fashionmnist.sh deleted file mode 100755 index 52817f5b4..000000000 --- a/attic/evaluate_fashionmnist.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --model ai84net5 --dataset FashionMNIST --confusion --evaluate --exp-load-weights-from ../ai8x-synthesis/trained/ai84-fashionmnist.pth.tar -8 "$@" diff --git a/attic/go_prune.sh b/attic/go_prune.sh deleted file mode 100755 index ce51cee9b..000000000 --- a/attic/go_prune.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --epochs 300 --deterministic --compress prune.yaml --model ai84net5 --dataset FashionMNIST --confusion --resume-from logs/FashionMNIST/checkpoint.pth.tar "$@" diff --git a/attic/go_quantized.sh b/attic/go_quantized.sh deleted file mode 100755 index 917ba6095..000000000 --- a/attic/go_quantized.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --epochs 200 --deterministic --compress quant_train_ai84.yaml --model ai84net5 --dataset FashionMNIST --confusion "$@" diff --git a/attic/inspect_ckpt.py b/attic/inspect_ckpt.py deleted file mode 100755 index 7acab898b..000000000 --- a/attic/inspect_ckpt.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright (c) 2018 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. -# - -"""A small utility to inspect the contents of checkpoint files. - -Sometimes it is useful to look at the contents of a checkpoint file, and this utility is meant to help -with this. -By default this utility will print just the names and types of the keys it finds in the checkpoint -file. If the key type is simple (i.e. integer, float, or string), then the value is printed as well. - -You can also print the model keys (i.e. the names of the parameters tensors in the model), and the -weight tensor masks in the schedule). - -$ python3 inspect_ckpt.py checkpoint.pth.tar --model --schedule -""" -import argparse - -import torch - -import distiller -from distiller.apputils.checkpoint import get_contents_table -from tabulate import tabulate - - -def inspect_checkpoint(chkpt_file, args): - print("Inspecting checkpoint file: ", chkpt_file) - checkpoint = torch.load(chkpt_file, map_location='cpu') - - print(get_contents_table(checkpoint)) - - if 'extras' in checkpoint and checkpoint['extras']: - print("\nContents of Checkpoint['extras']:") - print(get_contents_table(checkpoint['extras'])) - - if args.model and "state_dict" in checkpoint: - print("\nModel keys (state_dict):\n{}".format(", ".join(list(checkpoint["state_dict"].keys())))) - if args.dump: - for i, k in enumerate(checkpoint["state_dict"].keys()): - print(k, "------------------------------------------") - print(checkpoint['state_dict'][k]) - - if args.schedule and "compression_sched" in checkpoint: - compression_sched = checkpoint["compression_sched"] - print("\nSchedule keys (compression_sched):\n{}\n".format("\n\t".join(list(compression_sched.keys())))) - sched_keys = [[k, type(compression_sched[k]).__name__] for k in compression_sched.keys()] - print(tabulate(sched_keys, headers=["Key", "Type"], tablefmt="fancy_grid")) - if "masks_dict" in checkpoint["compression_sched"]: - print("compression_sched[\"masks_dict\"] keys:\n{}".format(", ".join( - list(compression_sched["masks_dict"].keys())))) - - if args.thinning and "thinning_recipes" in checkpoint: - for recipe in checkpoint["thinning_recipes"]: - print(recipe) - - -if __name__ == '__main__': - torch.set_printoptions(threshold=10000000, linewidth=190) - - parser = argparse.ArgumentParser(description='Distiller checkpoint inspection') - parser.add_argument('chkpt_file', help='path to the checkpoint file') - parser.add_argument('-m', '--model', action='store_true', help='print the model keys') - parser.add_argument('-d', '--dump', action='store_true', help='dump values') - parser.add_argument('-s', '--schedule', action='store_true', help='print the schedule keys') - parser.add_argument('-t', '--thinning', action='store_true', help='print the thinning keys') - args = parser.parse_args() - inspect_checkpoint(args.chkpt_file, args) diff --git a/attic/logging.conf b/attic/logging.conf deleted file mode 100755 index 429419b05..000000000 --- a/attic/logging.conf +++ /dev/null @@ -1,54 +0,0 @@ -[formatters] -keys: simple, time_simple - -[handlers] -keys: console, file - -[loggers] -keys: root, app_cfg, distiller.thinning, apputils.model_summaries - -[formatter_simple] -format: %(message)s - -[formatter_time_simple] -format: %(asctime)s - %(message)s - -[handler_console] -class: StreamHandler -propagate: 0 -args: [] -formatter: simple - -[handler_file] -class: FileHandler -mode: 'w' -args=('%(logfilename)s', 'w') -formatter: time_simple - -[logger_root] -level: INFO -propagate: 1 -handlers: console, file - -[logger_app_cfg] -# Use this logger to log the application configuration and execution environment -level: DEBUG -qualname: app_cfg -propagate: 0 -handlers: file - -# Example of adding a module-specific logger -# Do not forget to add distiller.thinning to the list of keys in section [loggers] -[logger_distiller.thinning] -level: INFO -qualname: distiller.thinning -propagate: 0 -handlers: console, file - -# Example of adding a module-specific logger -# Do not forget to add apputils.model_summaries to the list of keys in section [loggers] -[logger_apputils.model_summaries] -level: INFO -qualname: apputils.model_summaries -propagate: 0 -handlers: console, file diff --git a/attic/post_train.yaml b/attic/post_train.yaml deleted file mode 100644 index ae2d5d6ef..000000000 --- a/attic/post_train.yaml +++ /dev/null @@ -1,13 +0,0 @@ ---- -quantizers: - post_train_quantizer: - class: PostTrainLinearQuantizer - bits_activations: 8 - bits_parameters: 8 - bits_accum: 32 - # SYMMETRIC, ASYMMETRIC_UNSIGNED, ASYMMETRIC_SIGNED - mode: SYMMETRIC - per_channel_wts: false - # NONE, AVG, N_STD - clip_acts: AVG - no_clip_layers: fc diff --git a/attic/post_train_ai84.yaml b/attic/post_train_ai84.yaml deleted file mode 100644 index 65c89f394..000000000 --- a/attic/post_train_ai84.yaml +++ /dev/null @@ -1,24 +0,0 @@ ---- -quantizers: - post_train_quantizer: - class: PostTrainLinearQuantizerAI84 - bits_activations: 8 - bits_parameters: 5 - bits_accum: 32 - # int8: true - scale_approx_mult_bits: 8 - # SYMMETRIC, ASYMMETRIC_UNSIGNED, ASYMMETRIC_SIGNED - mode: SYMMETRIC - per_channel_wts: false - # NONE, AVG, N_STD - # clip_acts: N_STD - # clip_n_stds: 3 - clip_acts: AVG - no_clip_layers: fc - global_scale: false - # global_sat_scale: 25.0 - overrides: - fc: - bits_weights: 8 - bits_bias: 8 - scale_approx_mult_bits: null diff --git a/attic/prune.yaml b/attic/prune.yaml deleted file mode 100644 index 91c081ab3..000000000 --- a/attic/prune.yaml +++ /dev/null @@ -1,33 +0,0 @@ ---- -version: 1 -pruners: - channel_pruner: - class: 'L1RankedStructureParameterPruner' - group_type: Channels - desired_sparsity: 0.35 - weights: [ - layer1.0.conv1.weight, - layer1.0.conv2.weight] - -extensions: - net_thinner: - class: 'FilterRemover' - thinning_func_str: remove_channels - arch: 'ai84net5' - dataset: 'FashionMNIST' - -lr_schedulers: - exp_finetuning_lr: - class: ExponentialLR - gamma: 0.95 - -policies: - - pruner: - instance_name: channel_pruner - epochs: [201] - - - lr_scheduler: - instance_name: exp_finetuning_lr - starting_epoch: 210 - ending_epoch: 300 - frequency: 1 diff --git a/attic/quant_train.yaml b/attic/quant_train.yaml deleted file mode 100644 index 8706373b6..000000000 --- a/attic/quant_train.yaml +++ /dev/null @@ -1,34 +0,0 @@ ---- -quantizers: - linear_quantizer: - class: QuantAwareTrainRangeLinearQuantizer - bits_weights: 8 - # Decay value for exponential moving average tracking of activation ranges - ema_decay: 0.999 - bits_activations: 8 - # bits_parameters: 8 - # bits_accum: 32 - # SYMMETRIC, ASYMMETRIC_UNSIGNED, ASYMMETRIC_SIGNED - mode: SYMMETRIC - per_channel_wts: false - # NONE, AVG, N_STD - # clip_acts: AVG - # no_clip_layers: fc - -lr_schedulers: - training_lr: - class: MultiStepLR - milestones: [100, 140, 170] - gamma: 0.1 - -policies: - - lr_scheduler: - instance_name: training_lr - starting_epoch: 0 - ending_epoch: 200 - frequency: 1 - - quantizer: - instance_name: linear_quantizer - starting_epoch: 0 - ending_epoch: 300 - frequency: 1 diff --git a/attic/quant_train_ai84.yaml b/attic/quant_train_ai84.yaml deleted file mode 100644 index 6bdd84ee0..000000000 --- a/attic/quant_train_ai84.yaml +++ /dev/null @@ -1,36 +0,0 @@ ---- -quantizers: - linear_quantizer: - class: QuantAwareTrainRangeLinearQuantizerAI84 - bits_weights: 8 - # Decay value for exponential moving average tracking of activation ranges - ema_decay: 0.999 - bits_activations: 8 - # bits_parameters: 8 - # bits_accum: 32 - # SYMMETRIC, ASYMMETRIC_UNSIGNED, ASYMMETRIC_SIGNED - mode: SYMMETRIC - per_channel_wts: false - # int8: true - scale_approx_mult_bits: 2 - # NONE, AVG, N_STD - # clip_acts: AVG - # no_clip_layers: fc - -lr_schedulers: - training_lr: - class: MultiStepLR - milestones: [100, 140, 170] - gamma: 0.1 - -policies: - - lr_scheduler: - instance_name: training_lr - starting_epoch: 0 - ending_epoch: 200 - frequency: 1 - - quantizer: - instance_name: linear_quantizer - starting_epoch: 0 - ending_epoch: 300 - frequency: 1 diff --git a/attic/quantize_cifar10.sh b/attic/quantize_cifar10.sh deleted file mode 100755 index e1f4d72d5..000000000 --- a/attic/quantize_cifar10.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --model ai84net5 --dataset CIFAR10 --confusion --evaluate --qe --qe-config-file post_train_ai84.yaml --resume-from logs/CIFAR10/checkpoint.pth.tar -1 "$@" diff --git a/attic/quantize_fashionmnist.sh b/attic/quantize_fashionmnist.sh deleted file mode 100755 index 43b3ddae8..000000000 --- a/attic/quantize_fashionmnist.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --model ai84net5 --dataset FashionMNIST --confusion --evaluate --qe --qe-config-file post_train_ai84.yaml --resume-from logs/FashionMNIST/checkpoint.pth.tar -1 "$@" diff --git a/attic/quantize_mnist.sh b/attic/quantize_mnist.sh deleted file mode 100755 index df24f09c2..000000000 --- a/attic/quantize_mnist.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --model ai84net5 --dataset MNIST --confusion --evaluate --qe --qe-config-file post_train_ai84.yaml --resume-from logs/MNIST/checkpoint.pth.tar -1 "$@" diff --git a/attic/range_linear_ai84.py b/attic/range_linear_ai84.py deleted file mode 100644 index 4d8755933..000000000 --- a/attic/range_linear_ai84.py +++ /dev/null @@ -1,1303 +0,0 @@ -# -# Copyright (c) 2018 Intel Corporation -# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. -# -# 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 torch.nn as nn -import argparse -from enum import Enum -from collections import OrderedDict -from functools import reduce, partial -import logging -import os -import distiller -import distiller.utils -from distiller.quantization.quantizer import Quantizer -from distiller.quantization.q_utils import get_tensor_max_abs, get_tensor_avg_max_abs, \ - get_tensor_mean_n_stds_max_abs, get_tensor_min_max, get_tensor_avg_min_max, \ - get_tensor_mean_n_stds_min_max, _prep_saturation_val_tensor, get_quantized_range, \ - linear_quantize_clamp, linear_dequantize, clamp, torch, LinearQuantizeSTE, \ - approx_scale_as_mult_and_shift -from distiller.utils import filter_kwargs -import distiller.modules -import yaml - - -msglogger = logging.getLogger() - - -def pow2_round(val, nbits): - # return 2 ** torch.log2(val.clamp(min=1)).round().clamp(max=nbits) - return val.clamp(min=1, max=2**nbits-1).round() - - -def linear_quantize_ai84(input, scale, zero_point, inplace=False): - if inplace: - input.mul_(scale).sub_(zero_point).round_() - return input - return torch.round(scale * input - zero_point) - - -def linear_quantize_clamp_ai84(input, scale, zero_point, clamp_min, clamp_max, inplace=False): - print(f'linear_quantize_clamp_ai84 scale={scale}, zp={zero_point}, min={clamp_min}, max={clamp_max}') - output = linear_quantize_ai84(input, scale, zero_point, inplace) - return clamp(output, clamp_min, clamp_max, inplace) - - -def symmetric_linear_quantization_params(num_bits, saturation_val): - is_scalar, sat_val = _prep_saturation_val_tensor(saturation_val) - - if any(sat_val < 0): - raise ValueError('Saturation value must be >= 0') - - # Leave one bit for sign - n = 2 ** (num_bits - 1) - 1 - - # If float values are all 0, we just want the quantized values to be 0 as well. So overriding the saturation - # value to 'n', so the scale becomes 1 - sat_val[sat_val == 0] = n - scale = n / sat_val - # scale = torch.round(n / sat_val) - # scale = 2 ** (torch.log2(sat_val).trunc().clamp(min=-1) + 1) - # scale = torch.ones_like(sat_val) - zero_point = torch.zeros_like(scale) - - if is_scalar: - # If input was scalar, return scalars - return scale.item(), zero_point.item() - return scale, zero_point - - -def asymmetric_linear_quantization_params(num_bits, saturation_min, saturation_max, - integral_zero_point=True, signed=False): - scalar_min, sat_min = _prep_saturation_val_tensor(saturation_min) - scalar_max, sat_max = _prep_saturation_val_tensor(saturation_max) - is_scalar = scalar_min and scalar_max - - if scalar_max and not scalar_min: - sat_max = sat_max.to(sat_min.device) - elif scalar_min and not scalar_max: - sat_min = sat_min.to(sat_max.device) - - if any(sat_min > sat_max): - raise ValueError('saturation_min must be smaller than saturation_max') - - n = 2 ** num_bits - 1 - - # Make sure 0 is in the range - sat_min = torch.min(sat_min, torch.zeros_like(sat_min)) - sat_max = torch.max(sat_max, torch.zeros_like(sat_max)) - - diff = sat_max - sat_min - # If float values are all 0, we just want the quantized values to be 0 as well. So overriding the saturation - # value to 'n', so the scale becomes 1 - diff[diff == 0] = n - - scale = torch.round(n / diff) - zero_point = scale * sat_min - if integral_zero_point: - zero_point = zero_point.round() - if signed: - zero_point += 2 ** (num_bits - 1) - if is_scalar: - return scale.item(), zero_point.item() - return scale, zero_point - - -def _enum_to_str(enum_val): - return str(enum_val).split('.')[1] - - -class LinearQuantAI84Mode(Enum): - SYMMETRIC = 1 - ASYMMETRIC_UNSIGNED = 2 - ASYMMETRIC_SIGNED = 3 - - -class ClipModeAI84(Enum): - NONE = 0 - AVG = 1 - N_STD = 2 - - -def quantize_clamp(input, clamp_min, clamp_max, inplace=False): - if inplace: - input.round_().clamp_(clamp_min, clamp_max) - return input - return torch.round(input).clamp(clamp_min, clamp_max) - - -def _verify_enum_value(val, enum_cls): - cls_name = enum_cls.__name__ - if isinstance(val, str): - try: - return enum_cls[val] - except KeyError: - raise ValueError("Input string '{0}' doesn't match any of the values of {1}: {2}" - .format(val, cls_name, [e.name for e in enum_cls])) - elif isinstance(val, enum_cls): - return val - else: - raise TypeError("Argument can be either a string or member of {0} (got {1})".format(cls_name, val)) - - -def verify_quant_mode(mode): - return _verify_enum_value(mode, LinearQuantAI84Mode) - - -def verify_clip_mode(mode): - return _verify_enum_value(mode, ClipModeAI84) - - -def _get_saturation_fn(quant_mode, clip_mode, num_stds): - if quant_mode == LinearQuantAI84Mode.SYMMETRIC: - fns = {ClipModeAI84.NONE: get_tensor_max_abs, - ClipModeAI84.AVG: get_tensor_avg_max_abs, - ClipModeAI84.N_STD: partial(get_tensor_mean_n_stds_max_abs, n_stds=num_stds)} - else: # Asymmetric mode - fns = {ClipModeAI84.NONE: get_tensor_min_max, - ClipModeAI84.AVG: get_tensor_avg_min_max, - ClipModeAI84.N_STD: partial(get_tensor_mean_n_stds_min_max, n_stds=num_stds)} - return fns[clip_mode] - - -def _get_quant_params_from_tensor(tensor, num_bits, mode, clip=ClipModeAI84.NONE, per_channel=False, num_stds=None, - scale_approx_mult_bits=None): - if per_channel and tensor.dim() not in [2, 4]: - raise ValueError('Per channel quantization possible only with 2D or 4D tensors (linear or conv layer weights)') - - if clip == ClipModeAI84.N_STD: - if per_channel: - raise ValueError('N_STD clipping not supported with per-channel quantization') - if num_stds is None: - raise ValueError('Clip mode set top N_STD but \'num_stds\' parameter not provided') - - dim = 0 if clip == ClipModeAI84.AVG or per_channel else None - sat_fn = _get_saturation_fn(mode, clip, num_stds) - if mode == LinearQuantAI84Mode.SYMMETRIC: - sat_val = sat_fn(tensor, dim) - scale, zp = symmetric_linear_quantization_params(num_bits, sat_val) - else: # Asymmetric mode - sat_min, sat_max = sat_fn(tensor, dim) - signed = mode == LinearQuantAI84Mode.ASYMMETRIC_SIGNED - scale, zp = asymmetric_linear_quantization_params(num_bits, sat_min, sat_max, signed=signed) - - if per_channel: - # Reshape scale and zero_points so they can be broadcast properly with the weight tensor - dims = [scale.shape[0]] + [1] * (tensor.dim() - 1) - scale = scale.view(dims) - zp = zp.view(dims) - - if scale_approx_mult_bits is not None: - # scale = approx_scale_as_mult_and_shift(scale, scale_approx_mult_bits) - scale = pow2_round(scale, scale_approx_mult_bits) - - return scale, zp - - -def _get_quant_params_from_model(sat_val, num_bits, mode, clip=ClipModeAI84.NONE, num_stds=None, - scale_approx_mult_bits=None): - if clip == ClipModeAI84.N_STD: - if num_stds is None: - raise ValueError('Clip mode set top N_STD but \'num_stds\' parameter not provided') - - if mode == LinearQuantAI84Mode.SYMMETRIC: - scale, zp = symmetric_linear_quantization_params(num_bits, sat_val) - else: - raise NotImplementedError('Only SYMMETRIC quantization mode is implemented') - - if scale_approx_mult_bits is not None: - # scale = approx_scale_as_mult_and_shift(scale, scale_approx_mult_bits) - scale = pow2_round(scale, scale_approx_mult_bits) - - return scale, zp - - -def _get_quant_params_from_stats_dict(stats, num_bits, mode, clip=ClipModeAI84.NONE, num_stds=None, - scale_approx_mult_bits=None): - if clip == ClipModeAI84.N_STD: - if num_stds is None: - raise ValueError('Clip mode set to N_STD but \'num_stds\' parameter not provided') - if num_stds <= 0: - raise ValueError('n_stds must be > 0, got {}'.format(num_stds)) - prefix = 'avg_' if clip == ClipModeAI84.AVG else '' - sat_min = torch.tensor(float(stats[prefix + 'min'])) - sat_max = torch.tensor(float(stats[prefix + 'max'])) - if clip == ClipModeAI84.N_STD: - mean = torch.tensor(float(stats['mean'])) - std = torch.tensor(float(stats['std'])) - sat_min = torch.max(sat_min, mean - num_stds * std) - sat_max = torch.min(sat_max, mean + num_stds * std) - if mode == LinearQuantAI84Mode.SYMMETRIC: - scale, zp = symmetric_linear_quantization_params(num_bits, torch.max(sat_min.abs_(), sat_max.abs_())) - else: - signed = mode == LinearQuantAI84Mode.ASYMMETRIC_SIGNED - scale, zp = asymmetric_linear_quantization_params(num_bits, sat_min, sat_max, signed=signed) - - if scale_approx_mult_bits is not None: - scale = approx_scale_as_mult_and_shift(scale, scale_approx_mult_bits) - - return scale, zp - - -############################################################################### -# Post Training -############################################################################### - - -def add_post_train_quant_ai84_args(argparser): - str_to_quant_mode_map = {'sym': LinearQuantAI84Mode.SYMMETRIC, - 'asym_s': LinearQuantAI84Mode.ASYMMETRIC_SIGNED, - 'asym_u': LinearQuantAI84Mode.ASYMMETRIC_UNSIGNED} - - str_to_clip_mode_map = {'none': ClipModeAI84.NONE, 'avg': ClipModeAI84.AVG, 'n_std': ClipModeAI84.N_STD} - - def from_dict(d, val_str): - try: - return d[val_str] - except KeyError: - raise argparse.ArgumentTypeError('Must be one of {0} (received {1})'.format(list(d.keys()), val_str)) - - linear_quant_mode_str = partial(from_dict, str_to_quant_mode_map) - clip_mode_str = partial(from_dict, str_to_clip_mode_map) - - group = argparser.add_argument_group('Arguments controlling quantization at evaluation time ' - '("post-training quantization")') - exc_group = group.add_mutually_exclusive_group() - exc_group.add_argument('--quantize-eval', '--qe', action='store_true', - help='Apply linear quantization to model before evaluation. Applicable only if ' - '--evaluate is also set') - exc_group.add_argument('--qe-calibration', type=distiller.utils.float_range_argparse_checker(exc_min=True), - metavar='PORTION_OF_TEST_SET', - help='Run the model in evaluation mode on the specified portion of the test dataset and ' - 'collect statistics. Ignores all other \'qe--*\' arguments') - group.add_argument('--qe-mode', '--qem', type=linear_quant_mode_str, default='sym', - help='Linear quantization mode. Choices: ' + ' | '.join(str_to_quant_mode_map.keys())) - group.add_argument('--qe-bits-acts', '--qeba', type=int, default=8, metavar='NUM_BITS', - help='Number of bits for quantization of activations') - group.add_argument('--qe-bits-wts', '--qebw', type=int, default=8, metavar='NUM_BITS', - help='Number of bits for quantization of weights') - group.add_argument('--qe-bits-accum', type=int, default=32, metavar='NUM_BITS', - help='Number of bits for quantization of the accumulator') - group.add_argument('--qe-clip-acts', '--qeca', type=clip_mode_str, default='none', - help='Activations clipping mode. Choices: ' + ' | '.join(str_to_clip_mode_map.keys())) - group.add_argument('--qe-clip-n-stds', type=float, - help='When qe-clip-acts is set to \'n_std\', this is the number of standard deviations to use') - group.add_argument('--qe-no-clip-layers', '--qencl', type=str, nargs='+', metavar='LAYER_NAME', default=[], - help='List of layer names for which not to clip activations. Applicable ' - 'only if --qe-clip-acts is not \'none\'') - group.add_argument('--qe-per-channel', '--qepc', action='store_true', - help='Enable per-channel quantization of weights (per output channel)') - group.add_argument('--qe-scale-approx-bits', '--qesab', type=int, metavar='NUM_BITS', - help='Enables scale factor approximation using integer multiply + bit shift, using ' - 'this number of bits the integer multiplier') - group.add_argument('--qe-stats-file', type=str, metavar='PATH', - help='Path to YAML file with calibration stats. If not given, dynamic quantization will ' - 'be run (Note that not all layer types are supported for dynamic quantization)') - group.add_argument('--qe-config-file', type=str, metavar='PATH', - help='Path to YAML file containing configuration for PostTrainLinearQuantizerAI84 (if present, ' - 'all other --qe* arguments are ignored)') - - -class RangeLinearQuantAI84Wrapper(nn.Module): - """ - Base class for module which wraps an existing module with linear range-base quantization functionality - - Args: - wrapped_module (torch.nn.Module): Module to be wrapped - num_bits_acts (int): Number of bits used for inputs and output quantization - num_bits_accum (int): Number of bits allocated for the accumulator of intermediate integer results - mode (LinearQuantAI84Mode): Quantization mode to use (symmetric / asymmetric-signed / unsigned) - clip_acts (ClipModeAI84): Activations clipping mode to use - activation_stats (dict): Dict containing activation stats, used for static calculation of quantization - parameters. Dict should be in the format exported by distiller.data_loggers.QuantCalibrationStatsCollector. - If None then parameters are calculated dynamically. - clip_n_stds (float): When clip_acts == ClipMode.N_STD, this is the number of standard deviations to use - scale_approx_mult_bits (int): If not None, scale factors will be approximated using an integer multiplication - followed by a bit-wise shift. This eliminates floating-point scale factors, replacing them with integer - calculations. - If None, scale factors will be kept in their original FP32 values. - """ - - def __init__(self, wrapped_module, num_bits_acts, num_bits_accum=32, mode=LinearQuantAI84Mode.SYMMETRIC, - clip_acts=ClipModeAI84.NONE, activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None): - super(RangeLinearQuantAI84Wrapper, self).__init__() - - self.wrapped_module = wrapped_module - self.num_bits_acts = num_bits_acts - self.num_bits_accum = num_bits_accum - self.mode = mode - self.clip_acts = clip_acts - self.clip_n_stds = clip_n_stds - self.scale_approx_mult_bits = scale_approx_mult_bits - - # Controls whether output is de-quantized at end of forward op. Meant as a debug / test flag only - # (note that if False, the quantized output will be returned, but without any quantization parameters, - # so other than inspecting the contents there's not much to do with it) - self._dequant_out = True - - signed = mode != LinearQuantAI84Mode.ASYMMETRIC_UNSIGNED - self.acts_min_q_val, self.acts_max_q_val = get_quantized_range(num_bits_acts, signed=signed) - # The accumulator is always signed - self.accum_min_q_val, self.accum_max_q_val = get_quantized_range(num_bits_accum, signed=True) - - if activation_stats: - self.preset_act_stats = True - self.num_inputs = 0 - for idx, stats in activation_stats['inputs'].items(): - self.num_inputs += 1 - scale, zp = _get_quant_params_from_stats_dict(stats, num_bits_acts, mode, clip_acts, clip_n_stds, - scale_approx_mult_bits) - prefix = 'in_{0}_'.format(idx) - self.register_buffer(prefix + 'scale', scale) - self.register_buffer(prefix + 'zero_point', zp) - - scale, zp = _get_quant_params_from_stats_dict(activation_stats['output'], num_bits_acts, mode, clip_acts, - clip_n_stds, scale_approx_mult_bits) - self.register_buffer('output_scale', scale) - self.register_buffer('output_zero_point', zp) - else: - self.preset_act_stats = False - - def inputs_scales(self): - for scale in self._inputs_qparam('scale'): - yield scale - - def inputs_zero_points(self): - for zp in self._inputs_qparam('zero_point'): - yield zp - - def _inputs_qparam(self, type_str): - if type_str not in ['scale', 'zero_point']: - raise ValueError('Unknown quantization parameter type') - if not self.preset_act_stats: - raise RuntimeError('Input quantization parameter iterators only available when activation stats were given') - for idx in range(self.num_inputs): - name = 'in_{0}_{1}'.format(idx, type_str) - yield getattr(self, name) - - def forward(self, *inputs): - if self.training: - raise RuntimeError(self.__class__.__name__ + " can only be used in eval mode") - device = inputs[0].device - for buffer_name, buffer in self._buffers.items(): - setattr(self, buffer_name, buffer.to(device)) - - in_scales, in_zero_points = self.get_inputs_quantization_params(*inputs) - - # Quantize inputs - inputs_q = [linear_quantize_clamp(input.data, scale, zp, - self.acts_min_q_val, self.acts_max_q_val, inplace=False) - for input, scale, zp in zip(inputs, in_scales, in_zero_points)] - - # Forward through wrapped module - accum = self.quantized_forward(*inputs_q) - - # Re-quantize accumulator to quantized output range - out_scale, out_zero_point = self.get_output_quantization_params(accum) - requant_scale, requant_zero_point = self.get_accum_to_output_re_quantization_params(out_scale, out_zero_point) - out_q = linear_quantize_clamp(accum.data, requant_scale, requant_zero_point, - self.acts_min_q_val, self.acts_max_q_val, inplace=True) - - if not self._dequant_out: - return torch.autograd.Variable(out_q) - - # De-quantize back to FP32 - out_f = linear_dequantize(out_q, out_scale, out_zero_point, inplace=True) - - return out_f - - def get_inputs_quantization_params(self, *inputs): - """ - Calculate input quantization parameters (scale and zero-point) - - Should be overridden by all subclasses - - :param inputs: Current input tensors passed to forward method - :return: Tuple of 2 lists - list of scales per input and list of zero-point per input - """ - raise NotImplementedError - - def quantized_forward(self, *inputs_q): - """ - Perform forward pass with quantized inputs and return quantized outputs - - :param inputs_q: Tensor (or list of tensors) with quantized input values - :return: Tensor with quantized output values - """ - raise NotImplementedError - - def get_output_quantization_params(self, accumulator): - """ - Calculate quantization parameters (scale and zero-point) for the output. - This is used for: - * Calculating the accumulator-to-output re-quantization parameters - (see get_accum_to_output_re_quantization_params) - * De-quantizing the output back to FP32 - - Should be overridden by all subclasses - - :param accumulator: Tensor with accumulator values - :return: Tuple of scale and zero-point - """ - raise NotImplementedError - - def get_accum_to_output_re_quantization_params(self, output_scale, output_zero_point): - """ - Calculate quantization parameters (scale and zero-point) for re-quantization, that is: - Converting the intermediate integer accumulator to the output range - - Should be overridden by all subclasses - - :param output_scale: Output scale factor - :param output_zero_point: Output zero-point - :return: Tuple of scale and zero-point - """ - raise NotImplementedError - - def extra_repr(self): - tmpstr = 'mode={0}, '.format(str(self.mode).split('.')[1]) - tmpstr += 'num_bits_acts={0}, num_bits_accum={1}, '.format(self.num_bits_acts, self.num_bits_accum) - tmpstr += 'clip_acts={0}, '.format(_enum_to_str(self.clip_acts)) - if self.clip_acts == ClipModeAI84.N_STD: - tmpstr += 'num_stds={} '.format(self.clip_n_stds) - tmpstr += 'scale_approx_mult_bits={}'.format(self.scale_approx_mult_bits) - tmpstr += '\npreset_activation_stats={0}'.format(self.preset_act_stats) - if self.preset_act_stats: - for idx, (in_scale, in_zp) in enumerate(zip(self.inputs_scales(), self.inputs_zero_points())): - tmpstr += '\nin_{i}_scale={sc}, in_{i}_zero_point={zp}'.format(i=idx, sc=in_scale.item(), - zp=in_zp.item()) - tmpstr += '\nout_scale={sc}, out_zero_point={zp}'.format(sc=self.output_scale.item(), - zp=self.output_zero_point.item()) - return tmpstr - - -class RangeLinearQuantAI84ParamLayerWrapper(RangeLinearQuantAI84Wrapper): - """ - Linear range-based quantization wrappers for layers with weights and bias (namely torch.nn.ConvNd and - torch.nn.Linear) - - Assume: - - x_q = round(scale_x * x_f) - zero_point_x - - Hence: - - x_f = 1/scale_x * x_q + zero_point_x - - (And the same for y_q, w_q and b_q) - - So, we get: (use "zp" as abbreviation for zero_point) - - y_f = x_f * w_f + b_f - - y_q = scale_y * y_f + zp_y = scale_y * (x_f * w_f + b_f) + zp_y = - - scale_y scale_x * scale_w - = ------------------- * ((x_q + zp_x) * (w_q + zp_w) + ------------------- * (b_q + zp_b)) + zp_y - scale_x * scale_w scale_b - - Args: - wrapped_module (torch.nn.Module): Module to be wrapped - num_bits_acts (int): Number of bits used for inputs and output quantization - num_bits_params (int): Number of bits used for parameters (weights and bias) quantization - num_bits_accum (int): Number of bits allocated for the accumulator of intermediate integer results - mode (LinearQuantAI84Mode): Quantization mode to use (symmetric / asymmetric-signed/unsigned) - clip_acts (ClipModeAI84): See RangeLinearQuantWrapper - per_channel_wts (bool): Enable quantization of weights using separate quantization parameters per - output channel - activation_stats (dict): See RangeLinearQuantWrapper - clip_n_stds (int): See RangeLinearQuantWrapper - scale_approx_mult_bits (int): See RangeLinearQuantWrapper - """ - def __init__(self, wrapped_module, num_bits_acts, num_bits_params, num_bits_accum=32, - mode=LinearQuantAI84Mode.SYMMETRIC, clip_acts=ClipModeAI84.NONE, per_channel_wts=False, activation_stats=None, - clip_n_stds=None, scale_approx_mult_bits=None, - global_scale=False, sat_val_scale=1.0, weight_stddev=None): - super(RangeLinearQuantAI84ParamLayerWrapper, self).__init__(wrapped_module, num_bits_acts, num_bits_accum, mode, - clip_acts, activation_stats, clip_n_stds, - scale_approx_mult_bits) - - if not isinstance(wrapped_module, (nn.Conv2d, nn.Linear)): - raise ValueError(self.__class__.__name__ + ' can wrap only Conv2D and Linear modules') - - self.num_bits_params = num_bits_params - self.per_channel_wts = per_channel_wts - - self.params_min_q_val, self.params_max_q_val = get_quantized_range( - num_bits_params, signed=mode != LinearQuantAI84Mode.ASYMMETRIC_UNSIGNED) - - # Quantize weights - overwrite FP32 weights - if not global_scale: - w_scale, w_zero_point = _get_quant_params_from_tensor(wrapped_module.weight, num_bits_params, self.mode, - per_channel=per_channel_wts) - scale = w_scale - else: - w_scale, w_zero_point = _get_quant_params_from_model(weight_stddev * sat_val_scale, - num_bits_params, self.mode) - # print('w_scale:', w_scale) - scale = w_scale - w_scale = torch.tensor(float(1.0)) - - self.register_buffer('w_scale', w_scale) - self.register_buffer('w_zero_point', w_zero_point) - linear_quantize_clamp_ai84(wrapped_module.weight.data, scale, self.w_zero_point, self.params_min_q_val, - self.params_max_q_val, inplace=True) - - device = self.w_scale.device - - if self.preset_act_stats: - self.in_0_scale = self.in_0_scale.to(device) - self.register_buffer('accum_scale', self.in_0_scale * self.w_scale) - if self.per_channel_wts: - self.accum_scale = self.accum_scale.squeeze(dim=-1) - else: - self.accum_scale = 1 - - # Quantize bias - self.has_bias = hasattr(wrapped_module, 'bias') and wrapped_module.bias is not None - if self.has_bias: - if self.preset_act_stats: - linear_quantize_clamp(wrapped_module.bias.data, self.accum_scale.squeeze(), 0, - self.accum_min_q_val, self.accum_max_q_val, inplace=True) - else: - if not global_scale: - b_scale, b_zero_point = _get_quant_params_from_tensor(wrapped_module.bias, num_bits_params, self.mode) - scale = b_scale - else: - b_scale, b_zero_point = _get_quant_params_from_model(weight_stddev * sat_val_scale, - num_bits_params, self.mode) - # print('b_scale:', b_scale) - scale = b_scale - b_scale = torch.tensor(float(1.0)) - - self.register_buffer('b_scale', b_scale) - self.register_buffer('b_zero_point', b_zero_point) - base_b_q = linear_quantize_clamp_ai84(wrapped_module.bias.data, scale, self.b_zero_point, - self.params_min_q_val, self.params_max_q_val) - # Dynamic ranges - save in auxiliary buffer, requantize each time based on dynamic input scale factor - self.register_buffer('base_b_q', base_b_q) - - def get_inputs_quantization_params(self, input): - if not self.preset_act_stats: - self.in_0_scale, self.in_0_zero_point = _get_quant_params_from_tensor( - input, self.num_bits_acts, self.mode, clip=self.clip_acts, - num_stds=self.clip_n_stds, scale_approx_mult_bits=self.scale_approx_mult_bits) - return [self.in_0_scale], [self.in_0_zero_point] - - def quantized_forward(self, input_q): - # See class documentation for quantized calculation details. - - if not self.preset_act_stats: - self.accum_scale = self.in_0_scale * self.w_scale - if self.per_channel_wts: - self.accum_scale = self.accum_scale.squeeze(dim=-1) - - if self.has_bias: - # Re-quantize bias to match x * w scale: b_q' = (in_scale * w_scale / b_scale) * (b_q + b_zero_point) - bias_requant_scale = self.accum_scale.squeeze() / self.b_scale - if self.scale_approx_mult_bits is not None: - bias_requant_scale = approx_scale_as_mult_and_shift(bias_requant_scale, self.scale_approx_mult_bits) - self.wrapped_module.bias.data = linear_quantize_clamp(self.base_b_q + self.b_zero_point, - bias_requant_scale, 0, - self.accum_min_q_val, self.accum_max_q_val) - - # Note the main terms within the summation is: - # (x_q + zp_x) * (w_q + zp_w) - # In a performance-optimized solution, we would expand the parentheses and perform the computation similar - # to what is described here: - # https://github.com/google/gemmlowp/blob/master/doc/low-precision.md#efficient-handling-of-offsets - # However, for now we're more concerned with simplicity rather than speed. So we'll just add the zero points - # to the input and weights and pass those to the wrapped model. Functionally, since at this point we're - # dealing solely with integer values, the results are the same either way. - - if self.mode != LinearQuantAI84Mode.SYMMETRIC: - input_q += self.in_0_zero_point - self.wrapped_module.weight.data += self.w_zero_point - - accum = self.wrapped_module.forward(input_q) - clamp(accum.data, self.accum_min_q_val, self.accum_max_q_val, inplace=True) - - if self.mode != LinearQuantAI84Mode.SYMMETRIC: - self.wrapped_module.weight.data -= self.w_zero_point - return accum - - def get_output_quantization_params(self, accumulator): - if self.preset_act_stats: - return self.output_scale, self.output_zero_point - - y_f = accumulator / self.accum_scale - return _get_quant_params_from_tensor(y_f, self.num_bits_acts, self.mode, clip=self.clip_acts, - num_stds=self.clip_n_stds, - scale_approx_mult_bits=self.scale_approx_mult_bits) - - def get_accum_to_output_re_quantization_params(self, output_scale, output_zero_point): - requant_scale = output_scale / self.accum_scale - if self.scale_approx_mult_bits is not None: - requant_scale = approx_scale_as_mult_and_shift(requant_scale, self.scale_approx_mult_bits) - return requant_scale, output_zero_point - - def extra_repr(self): - tmpstr = 'mode={0}, '.format(str(self.mode).split('.')[1]) - tmpstr += 'num_bits_acts={0}, num_bits_params={1}, num_bits_accum={2}, '.format(self.num_bits_acts, - self.num_bits_params, - self.num_bits_accum) - tmpstr += 'clip_acts={0}, '.format(_enum_to_str(self.clip_acts)) - if self.clip_acts == ClipModeAI84.N_STD: - tmpstr += 'num_stds={} '.format(self.clip_n_stds) - tmpstr += 'per_channel_wts={}, scale_approx_mult_bits={}'.format(self.per_channel_wts, - self.scale_approx_mult_bits) - tmpstr += '\npreset_activation_stats={0}'.format(self.preset_act_stats) - if self.per_channel_wts: - tmpstr += '\nw_scale=PerCh, w_zero_point=PerCh' - else: - tmpstr += '\nw_scale={0:.4f}, w_zero_point={1:.4f}'.format(self.w_scale.item(), self.w_zero_point.item()) - if self.preset_act_stats: - tmpstr += '\nin_scale={0:.4f}, in_zero_point={1:.4f}'.format(self.in_0_scale.item(), - self.in_0_zero_point.item()) - tmpstr += '\nout_scale={0:.4f}, out_zero_point={1:.4f}'.format(self.output_scale.item(), - self.output_zero_point.item()) - elif self.has_bias: - tmpstr += '\nbase_b_scale={0:.4f}, base_b_zero_point={1:.4f}'.format(self.b_scale.item(), - self.b_zero_point.item()) - return tmpstr - - -class NoStatsError(NotImplementedError): - pass - - -class RangeLinearQuantAI84ConcatWrapper(RangeLinearQuantAI84Wrapper): - def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantAI84Mode.SYMMETRIC, clip_acts=ClipModeAI84.NONE, - activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None): - if not isinstance(wrapped_module, distiller.modules.Concat): - raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.Concat modules') - - if not activation_stats: - raise NoStatsError(self.__class__.__name__ + - ' must get activation stats, dynamic quantization not supported') - - super(RangeLinearQuantAI84ConcatWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode, - clip_acts=clip_acts, activation_stats=activation_stats, - clip_n_stds=clip_n_stds, - scale_approx_mult_bits=scale_approx_mult_bits) - - if self.preset_act_stats: - # For concatenation to make sense, we need to match all the inputs' scales, so we - # set a re-scale factor based on the preset output scale - for idx, in_scale in enumerate(self.inputs_scales()): - requant_scale = self.output_scale / in_scale - if self.scale_approx_mult_bits is not None: - requant_scale = approx_scale_as_mult_and_shift(requant_scale, self.scale_approx_mult_bits) - self.register_buffer('in_{0}_requant_scale'.format(idx), requant_scale) - - def inputs_requant_scales(self): - if not self.preset_act_stats: - raise RuntimeError('Input quantization parameter iterators only available when activation stats were given') - for idx in range(self.num_inputs): - name = 'in_{0}_requant_scale'.format(idx) - yield getattr(self, name) - - def get_inputs_quantization_params(self, *inputs): - return self.inputs_scales(), self.inputs_zero_points() - - def quantized_forward(self, *inputs_q): - # Re-quantize all inputs based to the same range (the output range) - inputs_re_q = [linear_quantize_clamp(input_q + zp, requant_scale, self.output_zero_point, - self.acts_min_q_val, self.acts_max_q_val, inplace=False) - for input_q, requant_scale, zp in zip(inputs_q, self.inputs_requant_scales(), - self.inputs_zero_points())] - return self.wrapped_module(*inputs_re_q) - - def get_output_quantization_params(self, accumulator): - return self.output_scale, self.output_zero_point - - def get_accum_to_output_re_quantization_params(self, output_scale, output_zero_point): - # Nothing to do here, since we already re-quantized in quantized_forward prior to the actual concatenation - return 1., 0. - - -class RangeLinearQuantAI84EltwiseAddWrapper(RangeLinearQuantAI84Wrapper): - def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantAI84Mode.SYMMETRIC, clip_acts=ClipModeAI84.NONE, - activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None): - if not isinstance(wrapped_module, distiller.modules.EltwiseAdd): - raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.EltwiseAdd modules') - - if not activation_stats: - raise NoStatsError(self.__class__.__name__ + - ' must get activation stats, dynamic quantization not supported') - - super(RangeLinearQuantAI84EltwiseAddWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode, - clip_acts=clip_acts, activation_stats=activation_stats, - clip_n_stds=clip_n_stds, - scale_approx_mult_bits=scale_approx_mult_bits) - - if self.preset_act_stats: - # For addition to make sense, all input scales must match. So we set a re-scale factor according - # to the preset output scale - requant_scales = [self.output_scale / in_scale for in_scale in self.inputs_scales()] - if scale_approx_mult_bits is not None: - requant_scales = [approx_scale_as_mult_and_shift(requant_scale, scale_approx_mult_bits) - for requant_scale in requant_scales] - for idx, requant_scale in enumerate(requant_scales): - self.register_buffer('in_{0}_requant_scale'.format(idx), requant_scale) - - def inputs_requant_scales(self): - if not self.preset_act_stats: - raise RuntimeError('Input quantization parameter iterators only available when activation stats were given') - for idx in range(self.num_inputs): - name = 'in_{0}_requant_scale'.format(idx) - yield getattr(self, name) - - def get_inputs_quantization_params(self, *inputs): - return self.inputs_scales(), self.inputs_zero_points() - - def quantized_forward(self, *inputs_q): - # Re-scale inputs to the accumulator range - inputs_re_q = [linear_quantize_clamp(input_q + zp, requant_scale, 0, - self.accum_min_q_val, self.accum_max_q_val, inplace=False) - for input_q, requant_scale, zp in zip(inputs_q, self.inputs_requant_scales(), - self.inputs_zero_points())] - accum = self.wrapped_module(*inputs_re_q) - clamp(accum.data, self.accum_min_q_val, self.accum_max_q_val, inplace=True) - - return accum - - def get_output_quantization_params(self, accumulator): - return self.output_scale, self.output_zero_point - - def get_accum_to_output_re_quantization_params(self, output_scale, output_zero_point): - return 1., self.output_zero_point - - -class RangeLinearQuantAI84EltwiseMultWrapper(RangeLinearQuantAI84Wrapper): - def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantAI84Mode.SYMMETRIC, clip_acts=ClipModeAI84.NONE, - activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None): - if not isinstance(wrapped_module, distiller.modules.EltwiseMult): - raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.EltwiseMult modules') - - if not activation_stats: - raise NoStatsError(self.__class__.__name__ + - ' must get activation stats, dynamic quantization not supported') - - super(RangeLinearQuantAI84EltwiseMultWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode, - clip_acts=clip_acts, activation_stats=activation_stats, - clip_n_stds=clip_n_stds, - scale_approx_mult_bits=scale_approx_mult_bits) - - if self.preset_act_stats: - self.register_buffer('accum_scale', reduce(lambda x, y: x * y, self.inputs_scales())) - - def get_inputs_quantization_params(self, *inputs): - return self.inputs_scales(), self.inputs_zero_points() - - def quantized_forward(self, *inputs_q): - if self.mode != LinearQuantAI84Mode.SYMMETRIC: - for input_q, zp in zip(inputs_q, self.inputs_zero_points()): - input_q += zp - - accum = self.wrapped_module(*inputs_q) - clamp(accum.data, self.accum_min_q_val, self.accum_max_q_val, inplace=True) - - return accum - - def get_output_quantization_params(self, accumulator): - return self.output_scale, self.output_zero_point - - def get_accum_to_output_re_quantization_params(self, output_scale, output_zero_point): - requant_scale = output_scale / self.accum_scale - if self.scale_approx_mult_bits is not None: - requant_scale = approx_scale_as_mult_and_shift(requant_scale, self.scale_approx_mult_bits) - return requant_scale, output_zero_point - - -class Int8Wrapper(nn.Module): - """ - A wrapper that replaces a module with a int8 precision version. - - Args: - module (nn.Module): The module to be replaced. - convert_input (:obj:`bool`, optional): Specifies whether an input conversion - to int8 is required for forward. Default: True. - return_fp32 (:obj:`bool`, optional): Specifies whether the output needs - to be converted back to fp32. Default: True. - """ - def __init__(self, module: nn.Module, convert_input=True, return_fp32=True): - super(Int8Wrapper, self).__init__() - self.wrapped_module = module.type(torch.float8) - self.return_fp32 = return_fp32 - self.convert_input_int8 = convert_input - - def forward(self, *input): - if self.convert_input_int8: - input = distiller.convert_tensors_recursively_to(input, dtype=torch.float8) - - result = self.wrapped_module(*input) - if self.return_fp32: - return distiller.convert_tensors_recursively_to(result, dtype=torch.float32) - - return result - - -class RangeLinearAI84EmbeddingWrapper(nn.Module): - def __init__(self, wrapped_module, num_bits, mode=LinearQuantAI84Mode.SYMMETRIC, stats=None): - if not isinstance(wrapped_module, nn.Embedding): - raise ValueError(self.__class__.__name__ + ' can only wrap torch.nn.Embedding modules') - - super(RangeLinearAI84EmbeddingWrapper, self).__init__() - - self.min_q_val, self.max_q_val = get_quantized_range(num_bits, - signed=mode != LinearQuantAI84Mode.ASYMMETRIC_UNSIGNED) - - if stats is None: - w_scale, w_zero_point = _get_quant_params_from_tensor(wrapped_module.weight, num_bits, self.mode) - else: - w_scale, w_zero_point = _get_quant_params_from_stats_dict(stats['output'], num_bits, mode) - - device = wrapped_module.weight.device - - self.register_buffer('w_scale', w_scale.to(device)) - self.register_buffer('w_zero_point', w_zero_point.to(device)) - linear_quantize_clamp(wrapped_module.weight.data, self.w_scale, self.w_zero_point, self.min_q_val, - self.max_q_val, inplace=True) - - self.wrapped_module = wrapped_module - - def forward(self, input): - out_q = self.wrapped_module(input) - out_f = linear_dequantize(out_q, self.w_scale, self.w_zero_point, inplace=True) - return out_f - - -class PostTrainLinearQuantizerAI84(Quantizer): - """ - Applies range-based linear quantization to a model. - This quantizer is expected to be executed at evaluation only, on a pre-trained model - Currently, the following Modules are supported: torch.nn.Conv2d, torch.nn.Linear - - Args: - model (torch.nn.Module): Model to be quantized - bits_activations/parameters/accum (int): Number of bits to be used when quantizing each tensor type - overrides (:obj:`OrderedDict`, optional): Overrides the layers quantization settings. - mode (LinearQuantAI84Mode): Quantization mode to use (symmetric / asymmetric-signed / unsigned) - clip_acts (ClipModeAI84): Activations clipping mode to use - no_clip_layers (list): List of fully-qualified layer names for which activations clipping should not be done. - A common practice is to not clip the activations of the last layer before softmax. - Applicable only if clip_acts is True. - per_channel_wts (bool): Enable quantization of weights using separate quantization parameters per - output channel - model_activation_stats (str / dict / OrderedDict): Either a path to activation stats YAML file, or a dictionary - containing the stats. The stats are used for static calculation of quantization parameters. - The dict should be in the format exported by distiller.data_loggers.QuantCalibrationStatsCollector. - If None then parameters are calculated dynamically. - int8 (bool): Set to True to convert modules to int8 precision. - clip_n_stds (float): When clip_acts == ClipModeAI84.N_STD, this is the number of standard deviations to use - scale_approx_mult_bits (int): If not None, scale factors will be approximated using an integer multiplication - followed by a bit-wise shift. This eliminates floating-point scale factors, replacing them with integer - calculations. - If None, scale factors will be kept in their original FP32 values. - Note: - If int8 is set to True, all the layers (except those overridden in `overrides`) will be converted - to int8 precision, regardless of bits_activations/parameters/accum. - """ - def __init__(self, model, bits_activations=8, bits_parameters=8, bits_accum=32, - overrides=None, mode=LinearQuantAI84Mode.SYMMETRIC, clip_acts=ClipModeAI84.NONE, no_clip_layers=None, - per_channel_wts=False, model_activation_stats=None, int8=False, clip_n_stds=None, - scale_approx_mult_bits=None, global_scale=False, sat_val_scale=1.0): - super(PostTrainLinearQuantizerAI84, self).__init__(model, bits_activations=bits_activations, - bits_weights=bits_parameters, bits_bias=bits_accum, - overrides=overrides, train_with_fp_copy=False) - - mode = verify_quant_mode(mode) - clip_acts = verify_clip_mode(clip_acts) - if clip_acts == ClipModeAI84.N_STD and clip_n_stds is None: - raise ValueError('clip_n_stds must not be None when clip_acts set to N_STD') - - if model_activation_stats is not None: - if isinstance(model_activation_stats, str): - if not os.path.isfile(model_activation_stats): - raise ValueError("Model activation stats file not found at: " + model_activation_stats) - msglogger.info('Loading activation stats from: ' + model_activation_stats) - with open(model_activation_stats, 'r') as stream: - model_activation_stats = distiller.utils.yaml_ordered_load(stream) - elif not isinstance(model_activation_stats, (dict, OrderedDict)): - raise TypeError('model_activation_stats must either be a string, a dict / OrderedDict or None') - - # Get min/max weight/bias from model - with torch.no_grad(): - self.weight_min = torch.tensor(float('inf')) - self.weight_max = torch.tensor(float('-inf')) - self.weight_count = torch.tensor(0, dtype=torch.int) - self.weight_sum = torch.tensor(0.0) - self.weight_stddev = torch.tensor(0.0) - - def traverse_pass1(m): - """ - Traverse model to build weight stats - """ - if isinstance(m, nn.Conv2d): - device = m.weight.device - self.weight_min = torch.min(torch.min(m.weight), self.weight_min.to(device)) - self.weight_max = torch.max(torch.max(m.weight), self.weight_max.to(device)) - self.weight_count = self.weight_count.to(device) - self.weight_sum = self.weight_sum.to(device) - self.weight_count += len(m.weight.flatten()) - self.weight_sum += m.weight.flatten().sum() - if hasattr(m, 'bias') and m.bias is not None: - self.weight_min = torch.min(torch.min(m.bias), self.weight_min) - self.weight_max = torch.max(torch.max(m.bias), self.weight_max) - self.weight_count += len(m.bias.flatten()) - self.weight_sum += m.bias.flatten().sum() - - def traverse_pass2(m): - """ - Traverse model to build weight stats - """ - if isinstance(m, nn.Conv2d): - self.weight_stddev += ((m.weight.flatten() - self.weight_mean) ** 2).sum() - if hasattr(m, 'bias') and m.bias is not None: - self.weight_stddev += ((m.bias.flatten() - self.weight_mean) ** 2).sum() - - model.apply(traverse_pass1) - - self.weight_mean = self.weight_sum / self.weight_count - - model.apply(traverse_pass2) - - self.weight_stddev = torch.sqrt(self.weight_stddev / self.weight_count) - - print(f"Total weights: {self.weight_count} --> min: {self.weight_min}, max: {self.weight_max}, " - f"stddev: {self.weight_stddev}") - - self.model.quantizer_metadata = {'type': type(self), - 'params': {'bits_activations': bits_activations, - 'bits_parameters': bits_parameters, - 'bits_accum': bits_accum, - 'mode': str(mode).split('.')[1], - 'clip_acts': _enum_to_str(clip_acts), - 'clip_n_stds': clip_n_stds, - 'no_clip_layers': no_clip_layers, - 'per_channel_wts': per_channel_wts, - 'int8': int8, - 'scale_approx_mult_bits': scale_approx_mult_bits, - 'global_scale': global_scale, - 'sat_val_scale': sat_val_scale}} - - def replace_param_layer(module, name, qbits_map, per_channel_wts=per_channel_wts, - mode=mode, int8=int8, scale_approx_mult_bits=scale_approx_mult_bits, - global_scale=global_scale, - weight_min=self.weight_min, weight_max=self.weight_max, - weight_stddev=self.weight_stddev): - if int8: - return Int8Wrapper(module) - norm_name = distiller.utils.normalize_module_name(name) - clip = self.clip_acts if norm_name not in self.no_clip_layers else ClipModeAI84.NONE - return RangeLinearQuantAI84ParamLayerWrapper(module, qbits_map[name].acts, qbits_map[name].wts, - num_bits_accum=self.bits_accum, mode=mode, clip_acts=clip, - per_channel_wts=per_channel_wts, - activation_stats=self.model_activation_stats.get(norm_name, None), - clip_n_stds=clip_n_stds, - scale_approx_mult_bits=scale_approx_mult_bits, - global_scale=global_scale, - sat_val_scale=sat_val_scale, - weight_stddev=weight_stddev) - - def replace_non_param_layer(wrapper_type, module, name, qbits_map, int8=int8, - scale_approx_mult_bits=scale_approx_mult_bits): - if int8: - return Int8Wrapper(module) - norm_name = distiller.utils.normalize_module_name(name) - clip = self.clip_acts if norm_name not in self.no_clip_layers else ClipModeAI84.NONE - try: - return wrapper_type(module, qbits_map[name].acts, mode=mode, clip_acts=clip, - activation_stats=self.model_activation_stats.get(norm_name, None), - clip_n_stds=clip_n_stds, scale_approx_mult_bits=scale_approx_mult_bits) - except NoStatsError: - msglogger.warning('WARNING: {0} - quantization of {1} without stats not supported. ' - 'Keeping the original FP32 module'.format(name, module.__class__.__name__)) - return module - - def replace_embedding(module, name, qbits_map, int8=int8): - if int8: - return Int8Wrapper(module, convert_input=False) - norm_name = distiller.utils.normalize_module_name(name) - return RangeLinearAI84EmbeddingWrapper(module, qbits_map[name].wts, mode=mode, - stats=self.model_activation_stats.get(norm_name, None)) - - self.clip_acts = clip_acts - self.no_clip_layers = no_clip_layers or [] - self.clip_n_stds = clip_n_stds - self.model_activation_stats = model_activation_stats or {} - self.bits_accum = bits_accum - self.mode = mode - # self.model = model - - self.replacement_factory[nn.Conv2d] = replace_param_layer - self.replacement_factory[nn.Linear] = replace_param_layer - - self.replacement_factory[distiller.modules.Concat] = partial( - replace_non_param_layer, RangeLinearQuantAI84ConcatWrapper) - self.replacement_factory[distiller.modules.EltwiseAdd] = partial( - replace_non_param_layer, RangeLinearQuantAI84EltwiseAddWrapper) - self.replacement_factory[distiller.modules.EltwiseMult] = partial( - replace_non_param_layer, RangeLinearQuantAI84EltwiseMultWrapper) - self.replacement_factory[nn.Embedding] = replace_embedding - - @classmethod - def from_args(cls, model, args): - """ - Returns an instance of PostTrainLinearQuantizerAI84 based on the set command-line arguments that are - given by add_post_train_quant_args() - """ - if args.qe_config_file: - return config_component_from_file_by_class(model, args.qe_config_file, - 'PostTrainLinearQuantizerAI84') - else: - return cls(model, - bits_activations=args.qe_bits_acts, - bits_parameters=args.qe_bits_wts, - bits_accum=args.qe_bits_accum, - mode=args.qe_mode, - clip_acts=args.qe_clip_acts, - no_clip_layers=args.qe_no_clip_layers, - per_channel_wts=args.qe_per_channel, - model_activation_stats=args.qe_stats_file, - clip_n_stds=args.qe_clip_n_stds, - scale_approx_mult_bits=args.qe_scale_approx_bits) - - -############################################################################### -# Quantization-aware training -############################################################################### - - -def update_ema(biased_ema, value, decay, step): - biased_ema = biased_ema * decay + (1 - decay) * value - unbiased_ema = biased_ema / (1 - decay ** step) # Bias correction - return biased_ema, unbiased_ema - - -def inputs_quantize_wrapped_forward(self, input): - input = self.inputs_quant(input) - return self.original_forward(input) - - -class FakeLinearQuantizationAI84(nn.Module): - def __init__(self, num_bits=8, mode=LinearQuantAI84Mode.SYMMETRIC, ema_decay=0.999, dequantize=True, inplace=False): - super(FakeLinearQuantizationAI84, self).__init__() - - self.num_bits = num_bits - self.mode = mode - self.dequantize = dequantize - self.inplace = inplace - - # We track activations ranges with exponential moving average, as proposed by Jacob et al., 2017 - # https://arxiv.org/abs/1712.05877 - # We perform bias correction on the EMA, so we keep both unbiased and biased values and the iterations count - # For a simple discussion of this see here: - # https://www.coursera.org/lecture/deep-neural-network/bias-correction-in-exponentially-weighted-averages-XjuhD - self.register_buffer('ema_decay', torch.tensor(ema_decay)) - self.register_buffer('tracked_min_biased', torch.zeros(1)) - self.register_buffer('tracked_min', torch.zeros(1)) - self.register_buffer('tracked_max_biased', torch.zeros(1)) - self.register_buffer('tracked_max', torch.zeros(1)) - self.register_buffer('iter_count', torch.zeros(1)) - self.register_buffer('scale', torch.ones(1)) - self.register_buffer('zero_point', torch.zeros(1)) - - def forward(self, input): - # We update the tracked stats only in training - # - # Due to the way DataParallel works, we perform all updates in-place so the "main" device retains - # its updates. (see https://pytorch.org/docs/stable/nn.html#dataparallel) - # However, as it is now, the in-place update of iter_count causes an error when doing - # back-prop with multiple GPUs, claiming a variable required for gradient calculation has been modified - # in-place. Not clear why, since it's not used in any calculations that keep a gradient. - # It works fine with a single GPU. TODO: Debug... - if self.training: - with torch.no_grad(): - current_min, current_max = get_tensor_min_max(input) - self.iter_count += 1 - self.tracked_min_biased.data, self.tracked_min.data = update_ema(self.tracked_min_biased.data, - current_min, self.ema_decay, - self.iter_count) - self.tracked_max_biased.data, self.tracked_max.data = update_ema(self.tracked_max_biased.data, - current_max, self.ema_decay, - self.iter_count) - - if self.mode == LinearQuantAI84Mode.SYMMETRIC: - max_abs = max(abs(self.tracked_min), abs(self.tracked_max)) - actual_min, actual_max = -max_abs, max_abs - if self.training: - self.scale.data, self.zero_point.data = symmetric_linear_quantization_params(self.num_bits, max_abs) - else: - actual_min, actual_max = self.tracked_min, self.tracked_max - signed = self.mode == LinearQuantAI84Mode.ASYMMETRIC_SIGNED - if self.training: - self.scale.data, self.zero_point.data = asymmetric_linear_quantization_params(self.num_bits, - self.tracked_min, - self.tracked_max, - signed=signed) - - input = clamp(input, actual_min.item(), actual_max.item(), False) - input = LinearQuantizeSTE.apply(input, self.scale, self.zero_point, self.dequantize, False) - - return input - - def extra_repr(self): - mode_str = str(self.mode).split('.')[1] - return 'mode={0}, num_bits={1}, ema_decay={2:.4f})'.format(mode_str, self.num_bits, self.ema_decay) - - -class FakeQuantizationAI84Wrapper(nn.Module): - def __init__(self, wrapped_module, num_bits, quant_mode, ema_decay): - super(FakeQuantizationAI84Wrapper, self).__init__() - self.wrapped_module = wrapped_module - self.fake_q = FakeLinearQuantizationAI84(num_bits, quant_mode, ema_decay, dequantize=True, - inplace=getattr(wrapped_module, 'inplace', False)) - - def forward(self, *input): - res = self.wrapped_module(*input) - res = self.fake_q(res) - return res - - -class QuantAwareTrainRangeLinearQuantizerAI84(Quantizer): - def __init__(self, model, optimizer=None, bits_activations=32, bits_weights=32, bits_bias=32, - overrides=None, mode=LinearQuantAI84Mode.SYMMETRIC, ema_decay=0.999, per_channel_wts=False, - quantize_inputs=True, num_bits_inputs=None): - super(QuantAwareTrainRangeLinearQuantizerAI84, self).__init__(model, optimizer=optimizer, - bits_activations=bits_activations, - bits_weights=bits_weights, - bits_bias=bits_bias, - overrides=overrides, - train_with_fp_copy=True) - - if isinstance(model, nn.DataParallel) and len(model.device_ids) > 1: - raise RuntimeError('QuantAwareTrainRangeLinearQuantizerAI84 currently does not support running with ' - 'multiple GPUs') - - mode = verify_quant_mode(mode) - - self.model.quantizer_metadata['params']['mode'] = str(mode).split('.')[1] - self.model.quantizer_metadata['params']['ema_decay'] = ema_decay - self.model.quantizer_metadata['params']['per_channel_wts'] = per_channel_wts - self.model.quantizer_metadata['params']['quantize_inputs'] = quantize_inputs - - # Keeping some parameters for input quantization - self.quantize_inputs = quantize_inputs - if num_bits_inputs is not None: - self.num_bits_inputs = num_bits_inputs - else: - self.num_bits_inputs = bits_activations - self.mode = mode - self.decay = ema_decay - self.per_channel_wts = per_channel_wts - - def linear_quantize_param(param_fp, param_meta): - m = param_meta.module - # We don't quantize the learned weights of embedding layers per-channel, because they're used - # as inputs in subsequent layers and we don't support per-channel activations quantization yet - perch = not isinstance(m, nn.Embedding) and per_channel_wts and param_fp.dim() in [2, 4] - - with torch.no_grad(): - scale, zero_point = _get_quant_params_from_tensor(param_fp, param_meta.num_bits, mode, - per_channel=perch) - setattr(m, param_meta.q_attr_name + '_scale', scale) - setattr(m, param_meta.q_attr_name + '_zero_point', zero_point) - out = LinearQuantizeSTE.apply(param_fp, scale, zero_point, True, False) - return out - - def activation_replace_fn(module, name, qbits_map): - bits_acts = qbits_map[name].acts - if bits_acts is None: - return module - return FakeQuantizationAI84Wrapper(module, bits_acts, mode, ema_decay) - - self.param_quantization_fn = linear_quantize_param - - self.activation_replace_fn = activation_replace_fn - self.replacement_factory[nn.ReLU] = self.activation_replace_fn - - def _prepare_model_impl(self): - super(QuantAwareTrainRangeLinearQuantizerAI84, self)._prepare_model_impl() - - if self.quantize_inputs: - if isinstance(self.model, nn.DataParallel): - m = self.model.module - else: - m = self.model - - m.inputs_quant = FakeLinearQuantizationAI84(self.num_bits_inputs, self.mode, self.decay, - dequantize=True, inplace=False) - m.__class__.original_forward = m.__class__.forward - m.__class__.forward = inputs_quantize_wrapped_forward - - # Prepare scale and zero point buffers in modules where parameters are being quantized - # We're calculating "dummy" scale and zero point just to get their dimensions - for ptq in self.params_to_quantize: - m = ptq.module - param_fp = getattr(m, ptq.fp_attr_name) - perch = not isinstance(m, nn.Embedding) and self.per_channel_wts and param_fp.dim() in [2, 4] - with torch.no_grad(): - scale, zero_point = _get_quant_params_from_tensor(param_fp, ptq.num_bits, self.mode, - per_channel=perch) - m.register_buffer(ptq.q_attr_name + '_scale', torch.ones_like(scale)) - m.register_buffer(ptq.q_attr_name + '_zero_point', torch.zeros_like(zero_point)) - - -def config_component_from_file_by_class(model, filename, class_name, **extra_args): - with open(filename, 'r') as stream: - msglogger.info('Reading configuration from: %s', filename) - try: - config_dict = distiller.utils.yaml_ordered_load(stream) - config_dict.pop('policies', None) - for section_name, components in config_dict.items(): - for component_name, user_args in components.items(): - if user_args['class'] == class_name: - msglogger.info( - 'Found component of class {0}: Name: {1} ; Section: {2}'.format(class_name, component_name, - section_name)) - user_args.update(extra_args) - return build_component(model, component_name, user_args) - raise ValueError( - 'Component of class {0} does not exist in configuration file {1}'.format(class_name, filename)) - except yaml.YAMLError: - print("\nFATAL parsing error while parsing the configuration file %s" % filename) - raise - - -def build_component(model, name, user_args, **extra_args): - # Instantiate component using the 'class' argument - class_name = user_args.pop('class') - try: - class_ = globals()[class_name] - except KeyError as ex: - raise ValueError("Class named '{0}' does not exist".format(class_name)) from ex - - # First we check that the user defined dict itself does not contain invalid args - valid_args, invalid_args = filter_kwargs(user_args, class_.__init__) - if invalid_args: - raise ValueError( - '{0} does not accept the following arguments: {1}'.format(class_name, list(invalid_args.keys()))) - - # Now we add some "hard-coded" args, which some classes may accept and some may not - # So then we filter again, this time ignoring any invalid args - valid_args.update(extra_args) - valid_args['model'] = model - valid_args['name'] = name - final_valid_args, _ = filter_kwargs(valid_args, class_.__init__) - instance = class_(**final_valid_args) - return instance diff --git a/attic/train_afsk.sh b/attic/train_afsk.sh deleted file mode 100755 index 087e31037..000000000 --- a/attic/train_afsk.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -python3 train.py --epochs 100 --deterministic --compress schedule-afsk.yaml --model ai85afsknet --dataset AFSK --confusion --device MAX78000 --embedding "$@" diff --git a/attic/train_cifar10_bias.sh b/attic/train_cifar10_bias.sh deleted file mode 100755 index 7d903bf79..000000000 --- a/attic/train_cifar10_bias.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python3 train.py --epochs 200 --deterministic --compress schedule.yaml --model ai84net5 --dataset CIFAR10 --confusion --use-bias --param-hist "$@" diff --git a/devices.py b/devices.py index ab72514a7..8693ea430 100644 --- a/devices.py +++ b/devices.py @@ -1,5 +1,5 @@ ################################################################################################### -# Copyright (C) Maxim Integrated Products, Inc. All Rights Reserved. +# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html diff --git a/requirements-cu11.txt b/requirements-cu11.txt index d7790e764..90d038a5c 100644 --- a/requirements-cu11.txt +++ b/requirements-cu11.txt @@ -1,22 +1,3 @@ -numpy>=1.22,<1.23 -PyYAML>=5.1.1 -scipy>=1.3.0 -librosa>=0.7.2 -Pillow>=7 -shap>=0.34.0 -tk>=0.1.0 https://download.pytorch.org/whl/cu111/torch-1.8.1%2Bcu111-cp38-cp38-linux_x86_64.whl -torchaudio==0.8.1 https://download.pytorch.org/whl/cu111/torchvision-0.9.1%2Bcu111-cp38-cp38-linux_x86_64.whl -tensorboard>=2.9.0,<2.10.0 -protobuf>=3.20.1,<4.0 -numba<0.50.0 -opencv-python>=4.4.0 -h5py>=3.7.0 -torchmetrics==0.6.0 -pycocotools==2.0.6 -albumentations>=1.3.0 -pytube>=12.1.3 -pyffmpeg==2.0 -GitPython>=3.1.18 --e distiller +-r requirements.txt diff --git a/requirements-win-cu11.txt b/requirements-win-cu11.txt index 4f3e8b48b..716c30e0b 100644 --- a/requirements-win-cu11.txt +++ b/requirements-win-cu11.txt @@ -1,22 +1,3 @@ -numpy>=1.22,<1.23 -PyYAML>=5.1.1 -scipy>=1.3.0 -librosa>=0.7.2 -Pillow>=7 -shap>=0.34.0 -tk>=0.1.0 https://download.pytorch.org/whl/cu111/torch-1.8.1%2Bcu111-cp38-cp38-win_amd64.whl -torchaudio==0.8.1 https://download.pytorch.org/whl/cu111/torchvision-0.9.1%2Bcu111-cp38-cp38-win_amd64.whl -tensorboard>=2.9.0,<2.10.0 -protobuf>=3.20.1,<4.0 -numba<0.50.0 -opencv-python>=4.4.0 -h5py>=3.7.0 -torchmetrics==0.6.0 -pycocotools==2.0.6 -albumentations>=1.3.0 -pytube>=12.1.3 -pyffmpeg==2.0 -GitPython>=3.1.18 --e distiller +-r requirements.txt diff --git a/train.py b/train.py index e88bc1f6b..724705888 100644 --- a/train.py +++ b/train.py @@ -17,7 +17,7 @@ # # pyright: reportMissingModuleSource=false, reportGeneralTypeIssues=false # pyright: reportOptionalSubscript=false -"""This is an example application for compressing image classification models. +"""This is the example training application for MAX7800x. The application borrows its main flow code from torchvision's ImageNet classification training sample application (https://github.com/pytorch/examples/tree/master/imagenet). From a0a12acb3af97832569a2ffea315541738010fbe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Tue, 5 Dec 2023 01:12:02 +0300 Subject: [PATCH 06/27] Initiate_qat override support for submodule layers (#274) --- ai8x.py | 45 +++++++++++++++++++++------------------------ 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/ai8x.py b/ai8x.py index fbe44ad75..282035a4f 100644 --- a/ai8x.py +++ b/ai8x.py @@ -1754,30 +1754,27 @@ def initiate_qat(m, qat_policy): """ Modify model `m` to start quantization aware training. """ - def _initiate_qat(m): - for attr_str in dir(m): - target_attr = getattr(m, attr_str) - if isinstance(target_attr, QuantizationAwareModule): - if 'shift_quantile' in qat_policy: - target_attr.init_module(qat_policy['weight_bits'], - qat_policy['weight_bits'], - True, qat_policy['shift_quantile']) - else: - target_attr.init_module(qat_policy['weight_bits'], - qat_policy['weight_bits'], True, 1.0) - if 'overrides' in qat_policy: - if attr_str in qat_policy['overrides']: - weight_field = qat_policy['overrides'][attr_str]['weight_bits'] - if 'shift_quantile' in qat_policy: - target_attr.init_module(weight_field, weight_field, - True, qat_policy['shift_quantile']) - else: - target_attr.init_module(weight_field, - weight_field, True, 1.0) - - setattr(m, attr_str, target_attr) - - m.apply(_initiate_qat) + if isinstance(m, nn.DataParallel): + m = m.module + + for name, module in m.named_modules(): + if isinstance(module, QuantizationAwareModule) and hasattr(module, 'weight_bits'): + if 'shift_quantile' in qat_policy: + module.init_module(qat_policy['weight_bits'], + qat_policy['weight_bits'], + True, qat_policy['shift_quantile']) + else: + module.init_module(qat_policy['weight_bits'], + qat_policy['weight_bits'], True, 1.0) + if 'overrides' in qat_policy: + if name in qat_policy['overrides']: + weight_field = qat_policy['overrides'][name]['weight_bits'] + if 'shift_quantile' in qat_policy: + module.init_module(weight_field, weight_field, + True, qat_policy['shift_quantile']) + else: + module.init_module(weight_field, + weight_field, True, 1.0) def update_model(m): From 0c35aff2c044569d171e1702f2ee646d0cdf72d6 Mon Sep 17 00:00:00 2001 From: asyatrhl <123384000+asyatrhl@users.noreply.github.com> Date: Wed, 13 Dec 2023 00:48:26 +0300 Subject: [PATCH 07/27] Update evaluation scripts (#277) --- scripts/evaluate_cifar100_effnet2.sh | 2 +- scripts/evaluate_cifar100_mobilenet_v2_0.5.sh | 2 +- scripts/evaluate_cifar100_mobilenet_v2_0.75.sh | 2 +- scripts/evaluate_cifar10_1x1.sh | 2 -- scripts/evaluate_imagenet_effnet2.sh | 2 +- 5 files changed, 4 insertions(+), 6 deletions(-) delete mode 100755 scripts/evaluate_cifar10_1x1.sh diff --git a/scripts/evaluate_cifar100_effnet2.sh b/scripts/evaluate_cifar100_effnet2.sh index 1f7cdeac7..1eb9f36f3 100755 --- a/scripts/evaluate_cifar100_effnet2.sh +++ b/scripts/evaluate_cifar100_effnet2.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87effnetv2 --dataset CIFAR100 --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-effnet2-qat8-q.pth.tar -8 --use-bias "$@" +python train.py --model ai87effnetv2 --dataset CIFAR100 --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-effnet2-qat8-q.pth.tar -8 --use-bias "$@" diff --git a/scripts/evaluate_cifar100_mobilenet_v2_0.5.sh b/scripts/evaluate_cifar100_mobilenet_v2_0.5.sh index 6d6e20f25..87c4a529c 100755 --- a/scripts/evaluate_cifar100_mobilenet_v2_0.5.sh +++ b/scripts/evaluate_cifar100_mobilenet_v2_0.5.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87netmobilenetv2cifar100_m0_5 --dataset CIFAR100 --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-mobilenet-v2-0.5-qat8-q.pth.tar -8 --use-bias "$@" +python train.py --model ai87netmobilenetv2cifar100_m0_5 --dataset CIFAR100 --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-mobilenet-v2-0.5-qat8-q.pth.tar -8 --use-bias "$@" diff --git a/scripts/evaluate_cifar100_mobilenet_v2_0.75.sh b/scripts/evaluate_cifar100_mobilenet_v2_0.75.sh index b1d3ec88f..54c94a5fc 100755 --- a/scripts/evaluate_cifar100_mobilenet_v2_0.75.sh +++ b/scripts/evaluate_cifar100_mobilenet_v2_0.75.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87netmobilenetv2cifar100_m0_75 --dataset CIFAR100 --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-mobilenet-v2-0.75-qat8-q.pth.tar -8 --use-bias "$@" +python train.py --model ai87netmobilenetv2cifar100_m0_75 --dataset CIFAR100 --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-cifar100-mobilenet-v2-0.75-qat8-q.pth.tar -8 --use-bias "$@" diff --git a/scripts/evaluate_cifar10_1x1.sh b/scripts/evaluate_cifar10_1x1.sh deleted file mode 100755 index 46f27ae79..000000000 --- a/scripts/evaluate_cifar10_1x1.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python train.py --model ai85net6 --dataset CIFAR10 --confusion --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-cifar10-1x1.pth.tar -8 "$@" diff --git a/scripts/evaluate_imagenet_effnet2.sh b/scripts/evaluate_imagenet_effnet2.sh index aa6161718..4eec90836 100755 --- a/scripts/evaluate_imagenet_effnet2.sh +++ b/scripts/evaluate_imagenet_effnet2.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87imageneteffnetv2 --dataset ImageNet --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-imagenet-effnet2-q.pth.tar -8 --use-bias "$@" +python train.py --model ai87imageneteffnetv2 --dataset ImageNet --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-imagenet-effnet2-q.pth.tar -8 --use-bias --qat-policy policies/qat_policy_imagenet.yaml "$@" From 750a052d92673d7d21202c1bd6231302cea7fc1c Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Fri, 15 Dec 2023 13:12:45 -0600 Subject: [PATCH 08/27] Add NOTICE; multi-platform .yamllint (#279) --- .yamllint | 3 + NOTICE.txt | 2486 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 2489 insertions(+) create mode 100644 NOTICE.txt diff --git a/.yamllint b/.yamllint index f893d85a1..3d75f8a4b 100644 --- a/.yamllint +++ b/.yamllint @@ -2,3 +2,6 @@ extends: default rules: line-length: {max: 1024} + new-lines: + type: platform + new-line-at-end-of-file: enable diff --git a/NOTICE.txt b/NOTICE.txt new file mode 100644 index 000000000..cdcb89c82 --- /dev/null +++ b/NOTICE.txt @@ -0,0 +1,2486 @@ +OSPO_MAX78000_78002_Neural_Net_ML_ai8x-training develop +Copyright 2023 Analog Devices, Inc. + +The main license for this software is identified in the LICENSE file. + +This product includes third party IP identified below: + +Components: + +amdegroot/ssd.pytorch 20180330-snapshot-8dd38657 : MIT License +andabi/deep-voice-conversion 983c275d669e8f6c935b08582063f8aaaecff48e : MIT License +facenet-pytorch 2.4.1 : (MIT License AND Apache License 2.0) +jquery/jquery 2.1.1 : MIT License +lunr.js 2.1.6 : MIT License +NervanaSystems/distiller 94af2955f99de8222bd83c1fc46f4000b3ecb130 : (MATPLOTLIB License 1.3.0 or Later AND MIT License AND BSD 2-clause "Simplified" License AND MATPLOTLIB License Prior 1.3.0 AND Apache License 2.0 AND BSD 3-clause "New" or "Revised" License AND GNU Lesser General Public License v3.0 or later AND Common Public License 1.0 AND BSD 3-Clause (Numpy) AND BSD 4-clause "Original" or "Old" License AND GNU General Public License v3.0 or later) +sgrvinod/a-PyTorch-Tutorial-to-Object-Detection 20200808-snapshot-43fd8be9 : MIT License + +Licenses: + +-------------------------------------------------------------------------------- +amdegroot/ssd.pytorch 20180330-snapshot-8dd38657 +License: MIT License +Origin: https://github.com/amdegroot/ssd.pytorch + + + +License: MIT License +SPDX-License-Identifier: MIT +The MIT License +=============== + +Copyright (c) 2017 Max deGroot, Ellis Brown + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in the +Software without restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN +AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + +-------------------------------------------------------------------------------- +andabi/deep-voice-conversion 983c275d669e8f6c935b08582063f8aaaecff48e +License: MIT License +Origin: https://github.com/andabi/deep-voice-conversion + + + +License: MIT License +SPDX-License-Identifier: MIT +The MIT License +=============== + +Copyright (c) 2018 Dabi Ahn + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in the +Software without restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Subject to the terms and conditions of this +License, each Contributor hereby grants to You a perpetual, worldwide, +non-exclusive, no-charge, royalty-free, irrevocable copyright license to +reproduce, prepare Derivative Works of, publicly display, publicly perform, +sublicense, and distribute the Work and such Derivative Works in Source or Object +form. + +3. Grant of Patent License. Subject to the terms and conditions of this License, +each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, +no-charge, royalty-free, irrevocable (except as stated in this section) patent +license to make, have made, use, offer to sell, sell, import, and otherwise +transfer the Work, where such license applies only to those patent claims +licensable by such Contributor that are necessarily infringed by their +Contribution(s) alone or by combination of their Contribution(s) with the Work to +which such Contribution(s) was submitted. If You institute patent litigation +against any entity (including a cross-claim or counterclaim in a lawsuit) +alleging that the Work or a Contribution incorporated within the Work constitutes +direct or contributory patent infringement, then any patent licenses granted to +You under this License for that Work shall terminate as of the date such +litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the Work or +Derivative Works thereof in any medium, with or without modifications, and in +Source or Object form, provided that You meet the following conditions: + + a. You must give any other recipients of the Work or Derivative Works a copy of + this License; and + + b. You must cause any modified files to carry prominent notices stating that + You changed the files; and + + c. You must retain, in the Source form of any Derivative Works that You + distribute, all copyright, patent, trademark, and attribution notices from + the Source form of the Work, excluding those notices that do not pertain to + any part of the Derivative Works; and + + d. If the Work includes a "NOTICE" text file as part of its distribution, then + any Derivative Works that You distribute must include a readable copy of the + attribution notices contained within such NOTICE file, excluding those + notices that do not pertain to any part of the Derivative Works, in at least + one of the following places: within a NOTICE text file distributed as part of + the Derivative Works; within the Source form or documentation, if provided + along with the Derivative Works; or, within a display generated by the + Derivative Works, if and wherever such third-party notices normally appear. + The contents of the NOTICE file are for informational purposes only and do + not modify the License. You may add Your own attribution notices within + Derivative Works that You distribute, alongside or as an addendum to the + NOTICE text from the Work, provided that such additional attribution notices + cannot be construed as modifying the License. + +You may add Your own copyright statement to Your modifications and may provide +additional or different license terms and conditions for use, reproduction, or +distribution of Your modifications, or for any such Derivative Works as a whole, +provided Your use, reproduction, and distribution of the Work otherwise complies +with the conditions stated in this License. + +5. Submission of Contributions. Unless You explicitly state otherwise, any +Contribution intentionally submitted for inclusion in the Work by You to the +Licensor shall be under the terms and conditions of this License, without any +additional terms or conditions. Notwithstanding the above, nothing herein shall +supersede or modify the terms of any separate license agreement you may have +executed with Licensor regarding such Contributions. + +6. Trademarks. This License does not grant permission to use the trade names, +trademarks, service marks, or product names of the Licensor, except as required +for reasonable and customary use in describing the origin of the Work and +reproducing the content of the NOTICE file. + +7. Disclaimer of Warranty. Unless required by applicable law or agreed to in +writing, Licensor provides the Work (and each Contributor provides its +Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, +either express or implied, including, without limitation, any warranties or +conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A +PARTICULAR PURPOSE. You are solely responsible for determining the +appropriateness of using or redistributing the Work and assume any risks +associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, whether in +tort (including negligence), contract, or otherwise, unless required by +applicable law (such as deliberate and grossly negligent acts) or agreed to in +writing, shall any Contributor be liable to You for damages, including any +direct, indirect, special, incidental, or consequential damages of any character +arising as a result of this License or out of the use or inability to use the +Work (including but not limited to damages for loss of goodwill, work stoppage, +computer failure or malfunction, or any and all other commercial damages or +losses), even if such Contributor has been advised of the possibility of such +damages. + +9. Accepting Warranty or Additional Liability. While redistributing the Work or +Derivative Works thereof, You may choose to offer, and charge a fee for, +acceptance of support, warranty, indemnity, or other liability obligations and/or +rights consistent with this License. However, in accepting such obligations, You +may act only on Your own behalf and on Your sole responsibility, not on behalf of +any other Contributor, and only if You agree to indemnify, defend, and hold each +Contributor harmless for any liability incurred by, or claims asserted against, +such Contributor by reason of your accepting any such warranty or additional +liability. + +END OF TERMS AND CONDITIONS + +APPENDIX: How to apply the Apache License to your work + +To apply the Apache License to your work, attach the following boilerplate +notice, with the fields enclosed by brackets "[]" replaced with your own +identifying information. (Don't include the brackets!) The text should be +enclosed in the appropriate comment syntax for the file format. We also recommend +that a file or class name and description of purpose be included on the same +"printed page" as the copyright notice for easier identification within +third-party archives. + + Copyright [yyyy] [name of copyright owner] 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. + + +-------------------------------------------------------------------------------- +jquery/jquery 2.1.1 +License: MIT License +Origin: http://jquery.com/ + + + +License: MIT License +SPDX-License-Identifier: MIT +Copyright 2014 jQuery Foundation and other contributors +http://jquery.com/ + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE + + +-------------------------------------------------------------------------------- +lunr.js 2.1.6 +License: MIT License +Origin: https://www.npmjs.org/package/lunr#2.1.6 + + + +License: MIT License +SPDX-License-Identifier: MIT +Copyright (C) 2018 Oliver Nightingale + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE + + +-------------------------------------------------------------------------------- +NervanaSystems/distiller 94af2955f99de8222bd83c1fc46f4000b3ecb130 +License: (MATPLOTLIB License 1.3.0 or Later AND MIT License AND BSD 2-clause "Simplified" License AND MATPLOTLIB License Prior 1.3.0 AND Apache License 2.0 AND BSD 3-clause "New" or "Revised" License AND GNU Lesser General Public License v3.0 or later AND Common Public License 1.0 AND BSD 3-Clause (Numpy) AND BSD 4-clause "Original" or "Old" License AND GNU General Public License v3.0 or later) +Origin: https://github.com/IntelLabs/distiller + +The source code for this component has been modified by Analog Devices. + + + +License: MATPLOTLIB License 1.3.0 or Later +License agreement for matplotlib versions 1.3.0 and later +========================================================= + +========================================================= + + 1. This LICENSE AGREEMENT is between the Matplotlib Development Team ("MDT"), + and the Individual or Organization ("Licensee") accessing and otherwise using + matplotlib software in source or binary form and its associated + documentation. + + + 2. Subject to the terms and conditions of this License Agreement, MDT hereby + grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, + analyze, test, perform and/or display publicly, prepare derivative works, + distribute, and otherwise use matplotlib alone or in any derivative version, + provided, however, that MDT's License Agreement and MDT's notice of + copyright, i.e., "Copyright (c) 2012- Matplotlib Development Team; All Rights + Reserved" are retained in matplotlib alone or in any derivative version + prepared by Licensee. + + + 3. In the event Licensee prepares a derivative work that is based on or + incorporates matplotlib or any part thereof, and wants to make the derivative + work available to others as provided herein, then Licensee hereby agrees to + include in any such work a brief summary of the changes made to matplotlib. + + + 4. MDT is making matplotlib available to Licensee on an "AS IS" basis. MDT + MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF + EXAMPLE, BUT NOT LIMITATION, MDT MAKES NO AND DISCLAIMS ANY REPRESENTATION OR + WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE + USE OF MATPLOTLIB WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. + + + 5. MDT SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB FOR ANY + INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF + MODIFYING, DISTRIBUTING, OR OTHERWISE USING MATPLOTLIB , OR ANY DERIVATIVE + THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + + + 6. 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By copying, installing or otherwise using matplotlib , Licensee agrees to be + bound by the terms and conditions of this License Agreement. + +License: MIT License +SPDX-License-Identifier: MIT +The MIT License +=============== + +Copyright (c) 2018 MIT_Han_Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in the +Software without restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Subject to the terms and conditions of this License Agreement, JDH hereby + grants Licensee a nonexclusive, royalty-free, world-wide license to + reproduce, analyze, test, perform and/or display publicly, prepare derivative + works, distribute, and otherwise use matplotlib alone or in any derivative + version, provided, however, that JDH's License Agreement and JDH's notice of + copyright, i.e., "Copyright (c) 2002-2005 John D. Hunter; All Rights + Reserved" are retained in matplotlib alone or in any derivative version + prepared by Licensee. + + 3. In the event Licensee prepares a derivative work that is based on or + incorporates matplotlib or any part thereof, and wants to make the derivative + work available to others as provided herein, then Licensee hereby agrees to + include in any such work a brief summary of the changes made to matplotlib. + + 4. JDH is making matplotlib available to Licensee on an "AS IS" basis. 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This License Agreement does not grant permission to use JDH + trademarks or trade name in a trademark sense to endorse or promote products + or services of Licensee, or any third party. + + 8. By copying, installing or otherwise using matplotlib, Licensee agrees to be + bound by the terms and conditions of this License Agreement. + +License: Apache License 2.0 +SPDX-License-Identifier: Apache-2.0 +Apache License +Version 2.0, January 2004 +========================= + + +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. Definitions. + +"License" shall mean the terms and conditions for use, reproduction, and +distribution as defined by Sections 1 through 9 of this document. + +"Licensor" shall mean the copyright owner or entity authorized by the copyright +owner that is granting the License. + +"Legal Entity" shall mean the union of the acting entity and all other entities +that control, are controlled by, or are under common control with that entity. +For the purposes of this definition, "control" means (i) the power, direct or +indirect, to cause the direction or management of such entity, whether by +contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the +outstanding shares, or (iii) beneficial ownership of such entity. + +"You" (or "Your") shall mean an individual or Legal Entity exercising permissions +granted by this License. + +"Source" form shall mean the preferred form for making modifications, including +but not limited to software source code, documentation source, and configuration +files. + +"Object" form shall mean any form resulting from mechanical transformation or +translation of a Source form, including but not limited to compiled object code, +generated documentation, and conversions to other media types. + +"Work" shall mean the work of authorship, whether in Source or Object form, made +available under the License, as indicated by a copyright notice that is included +in or attached to the work (an example is provided in the Appendix below). + +"Derivative Works" shall mean any work, whether in Source or Object form, that is +based on (or derived from) the Work and for which the editorial revisions, +annotations, elaborations, or other modifications represent, as a whole, an +original work of authorship. For the purposes of this License, Derivative Works +shall not include works that remain separable from, or merely link (or bind by +name) to the interfaces of, the Work and Derivative Works thereof. + +"Contribution" shall mean any work of authorship, including the original version +of the Work and any modifications or additions to that Work or Derivative Works +thereof, that is intentionally submitted to Licensor for inclusion in the Work by +the copyright owner or by an individual or Legal Entity authorized to submit on +behalf of the copyright owner. For the purposes of this definition, "submitted" +means any form of electronic, verbal, or written communication sent to the +Licensor or its representatives, including but not limited to communication on +electronic mailing lists, source code control systems, and issue tracking systems +that are managed by, or on behalf of, the Licensor for the purpose of discussing +and improving the Work, but excluding communication that is conspicuously marked +or otherwise designated in writing by the copyright owner as "Not a +Contribution." + +"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of +whom a Contribution has been received by Licensor and subsequently incorporated +within the Work. + +2. Grant of Copyright License. Subject to the terms and conditions of this +License, each Contributor hereby grants to You a perpetual, worldwide, +non-exclusive, no-charge, royalty-free, irrevocable copyright license to +reproduce, prepare Derivative Works of, publicly display, publicly perform, +sublicense, and distribute the Work and such Derivative Works in Source or Object +form. + +3. Grant of Patent License. Subject to the terms and conditions of this License, +each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, +no-charge, royalty-free, irrevocable (except as stated in this section) patent +license to make, have made, use, offer to sell, sell, import, and otherwise +transfer the Work, where such license applies only to those patent claims +licensable by such Contributor that are necessarily infringed by their +Contribution(s) alone or by combination of their Contribution(s) with the Work to +which such Contribution(s) was submitted. If You institute patent litigation +against any entity (including a cross-claim or counterclaim in a lawsuit) +alleging that the Work or a Contribution incorporated within the Work constitutes +direct or contributory patent infringement, then any patent licenses granted to +You under this License for that Work shall terminate as of the date such +litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the Work or +Derivative Works thereof in any medium, with or without modifications, and in +Source or Object form, provided that You meet the following conditions: + + a. You must give any other recipients of the Work or Derivative Works a copy of + this License; and + + b. You must cause any modified files to carry prominent notices stating that + You changed the files; and + + c. You must retain, in the Source form of any Derivative Works that You + distribute, all copyright, patent, trademark, and attribution notices from + the Source form of the Work, excluding those notices that do not pertain to + any part of the Derivative Works; and + + d. If the Work includes a "NOTICE" text file as part of its distribution, then + any Derivative Works that You distribute must include a readable copy of the + attribution notices contained within such NOTICE file, excluding those + notices that do not pertain to any part of the Derivative Works, in at least + one of the following places: within a NOTICE text file distributed as part of + the Derivative Works; within the Source form or documentation, if provided + along with the Derivative Works; or, within a display generated by the + Derivative Works, if and wherever such third-party notices normally appear. + The contents of the NOTICE file are for informational purposes only and do + not modify the License. You may add Your own attribution notices within + Derivative Works that You distribute, alongside or as an addendum to the + NOTICE text from the Work, provided that such additional attribution notices + cannot be construed as modifying the License. + +You may add Your own copyright statement to Your modifications and may provide +additional or different license terms and conditions for use, reproduction, or +distribution of Your modifications, or for any such Derivative Works as a whole, +provided Your use, reproduction, and distribution of the Work otherwise complies +with the conditions stated in this License. + +5. Submission of Contributions. Unless You explicitly state otherwise, any +Contribution intentionally submitted for inclusion in the Work by You to the +Licensor shall be under the terms and conditions of this License, without any +additional terms or conditions. 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You are solely responsible for determining the +appropriateness of using or redistributing the Work and assume any risks +associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, whether in +tort (including negligence), contract, or otherwise, unless required by +applicable law (such as deliberate and grossly negligent acts) or agreed to in +writing, shall any Contributor be liable to You for damages, including any +direct, indirect, special, incidental, or consequential damages of any character +arising as a result of this License or out of the use or inability to use the +Work (including but not limited to damages for loss of goodwill, work stoppage, +computer failure or malfunction, or any and all other commercial damages or +losses), even if such Contributor has been advised of the possibility of such +damages. + +9. Accepting Warranty or Additional Liability. While redistributing the Work or +Derivative Works thereof, You may choose to offer, and charge a fee for, +acceptance of support, warranty, indemnity, or other liability obligations and/or +rights consistent with this License. However, in accepting such obligations, You +may act only on Your own behalf and on Your sole responsibility, not on behalf of +any other Contributor, and only if You agree to indemnify, defend, and hold each +Contributor harmless for any liability incurred by, or claims asserted against, +such Contributor by reason of your accepting any such warranty or additional +liability. + +END OF TERMS AND CONDITIONS + +APPENDIX: How to apply the Apache License to your work + +To apply the Apache License to your work, attach the following boilerplate +notice, with the fields enclosed by brackets "[]" replaced with your own +identifying information. (Don't include the brackets!) The text should be +enclosed in the appropriate comment syntax for the file format. We also recommend +that a file or class name and description of purpose be included on the same +"printed page" as the copyright notice for easier identification within +third-party archives. + + Copyright [yyyy] [name of copyright owner] 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. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + Copyright (C) + + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate parts +of the General Public License. Of course, your program's commands might be +different; for a GUI interface, you would use an "about box". + +You should also get your employer (if you work as a programmer) or school, if +any, to sign a "copyright disclaimer" for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider +it more useful to permit linking proprietary applications with the library. If +this is what you want to do, use the GNU Lesser General Public License instead of +this License. But first, please read +. + + +-------------------------------------------------------------------------------- +sgrvinod/a-PyTorch-Tutorial-to-Object-Detection 20200808-snapshot-43fd8be9 +License: MIT License +Origin: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection + +The source code for this component has been modified by Analog Devices. + + + +License: MIT License +SPDX-License-Identifier: MIT +The MIT License +=============== + +Copyright (c) 2019 Sagar Vinodababu + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in the +Software without restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN +AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + From 2cab47488c4fda55be2711b6865009978f72a2d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Mon, 8 Jan 2024 21:08:45 +0300 Subject: [PATCH 09/27] Add improved FaceID model and new datasets (#276) --- .github/linters/.python-lint | 2 +- .gitmodules | 3 - .pylintrc | 2 +- README.md | 4 +- README.pdf | Bin 6777350 -> 6778163 bytes ai8x_blocks.py | 76 ++- datasets/face_id/README.md | 41 -- datasets/face_id/facenet_pytorch | 1 - datasets/face_id/gen_vggface2_embeddings.py | 171 ------- .../face_id/gen_youtubefaces_embeddings.py | 216 -------- datasets/face_id/merge_vggface2_dataset.py | 104 ---- .../face_id/merge_youtubefaces_dataset.py | 99 ---- datasets/faceid.py | 77 --- datasets/vggface2.py | 468 +++++++++++++++--- datasets/vggface2_facedet.py | 214 -------- datasets/youtube_faces.py | 91 ---- distiller | 2 +- docs/FacialRecognitionSystem.md | 102 ++++ docs/RelationBasedKD.png | Bin 0 -> 71570 bytes docs/SubCenterArcFaceLoss.png | Bin 0 -> 105524 bytes docs/dimensionreductionlayers.png | Bin 0 -> 63516 bytes docs/facialrecognition.png | Bin 0 -> 65955 bytes models/ai85net-faceid_112.py | 141 ++++++ models/ai87net-mobilefacenet_112.py | 140 ++++++ models/model_irse_DRL.py | 436 ++++++++++++++++ parsecmd.py | 18 + policies/qat_policy_faceid_112.yaml | 17 + policies/qat_policy_mobilefacenet_112.yaml | 3 + policies/schedule-faceid_112.yaml | 13 + policies/schedule-mobilefacenet_112.yaml | 13 + requirements.txt | 9 +- sample.py | 3 + scripts/evaluate_faceid.sh | 2 - scripts/evaluate_faceid_112.sh | 2 + scripts/evaluate_mobilefacenet_112.sh | 2 + scripts/train_facedet_tinierssd.sh | 2 +- scripts/train_faceid.sh | 2 - scripts/train_faceid_112.sh | 3 + scripts/train_mobilefacenet_112.sh | 3 + train.py | 109 +++- train_all_models.sh | 7 +- utils/kd_relationbased.py | 5 +- 42 files changed, 1497 insertions(+), 1106 deletions(-) delete mode 100644 datasets/face_id/README.md delete mode 160000 datasets/face_id/facenet_pytorch delete mode 100755 datasets/face_id/gen_vggface2_embeddings.py delete mode 100755 datasets/face_id/gen_youtubefaces_embeddings.py delete mode 100755 datasets/face_id/merge_vggface2_dataset.py delete mode 100755 datasets/face_id/merge_youtubefaces_dataset.py delete mode 100644 datasets/faceid.py delete mode 100644 datasets/vggface2_facedet.py delete mode 100644 datasets/youtube_faces.py create mode 100644 docs/FacialRecognitionSystem.md create mode 100644 docs/RelationBasedKD.png create mode 100644 docs/SubCenterArcFaceLoss.png create mode 100644 docs/dimensionreductionlayers.png create mode 100644 docs/facialrecognition.png create mode 100644 models/ai85net-faceid_112.py create mode 100644 models/ai87net-mobilefacenet_112.py create mode 100644 models/model_irse_DRL.py create mode 100644 policies/qat_policy_faceid_112.yaml create mode 100644 policies/qat_policy_mobilefacenet_112.yaml create mode 100644 policies/schedule-faceid_112.yaml create mode 100644 policies/schedule-mobilefacenet_112.yaml delete mode 100755 scripts/evaluate_faceid.sh create mode 100755 scripts/evaluate_faceid_112.sh create mode 100755 scripts/evaluate_mobilefacenet_112.sh delete mode 100755 scripts/train_faceid.sh create mode 100755 scripts/train_faceid_112.sh create mode 100755 scripts/train_mobilefacenet_112.sh diff --git a/.github/linters/.python-lint b/.github/linters/.python-lint index 892053922..2c02be588 100644 --- a/.github/linters/.python-lint +++ b/.github/linters/.python-lint @@ -7,7 +7,7 @@ ignored-classes = ModelProto max-line-length = 99 [DESIGN] max-locals=100 -max-statements=300 +max-statements=350 min-public-methods=1 max-branches=120 max-module-lines=5000 diff --git a/.gitmodules b/.gitmodules index d37537691..ecdf70507 100644 --- a/.gitmodules +++ b/.gitmodules @@ -2,6 +2,3 @@ path = distiller url = https://github.com/MaximIntegratedAI/distiller.git branch = pytorch-1.8 -[submodule "datasets/face_id/facenet_pytorch"] - path = datasets/face_id/facenet_pytorch - url = https://github.com/MaximIntegratedAI/facenet-pytorch.git diff --git a/.pylintrc b/.pylintrc index 22fe62009..26cbe5d6a 100644 --- a/.pylintrc +++ b/.pylintrc @@ -7,7 +7,7 @@ ignored-classes = ModelProto max-line-length = 99 [DESIGN] max-locals=100 -max-statements=300 +max-statements=350 min-public-methods=1 max-branches=120 max-module-lines=5000 diff --git a/README.md b/README.md index 8dbe7cf2a..82d1d6205 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -November 13, 2023 +December 22, 2023 ADI’s MAX78000/MAX78002 project is comprised of five repositories: @@ -3340,6 +3340,8 @@ Additional information about the evaluation kits, and the software development k [AHB Addresses for MAX78000 and MAX78002](docs/AHBAddresses.md) +[Facial Recognition System](https://github.com/MaximIntegratedAI/ai8x-training/blob/develop/docs/FacialRecognitionSystem.md) + --- diff --git a/README.pdf b/README.pdf index 944c6fe876c39b8ffaf14e52374c2569c0f83a1d..022f64b95635e4216ba6b51cc8541c28ade20cf7 100644 GIT binary patch delta 147767 zcmZsibzD?k_x7c`8>AcQ9=cmP1!RVlZj>B41f&@nLcK+ zX|1u^ADC|lUZLex0qK?NU~=Bl&D)!2R}g$#o_FjHqZSzB~ldLG0o zT<>F$r`;vQ(?YL7H3&q+Q8_96%p_aC*i@fJ(Lx!P3UC)RgRQ#?9Pl39}Cvb4ESJC%$5gYyUm9?1_QKm8IzIOtCzzV>7+ z-EQ;xdbpV*`n#q6r01fNeDGR?D@S5LI)5-)<1+XX-jlWse5rK?93h@3G?!S7bx_GuYF}+x``S!bc zA9R-)I+~je6~?80bo)AYsJ;SjLr}D7Bm_GqOCvz+6(>K^qi1JH(+;oKTtfpa7P$hT zEuw2C?M>M3Qbqnk7A|^Mo}z;Oan4)ZSflec&k;TbdUMov%^0<^cYXAu;*nhu`LtOx zc-VaE*8Mzgr6^O!?zy}2+aFTTghvrNz$XfC!RzYO?P$}0wyR!CBd4`g|-rQ?T5W84g$ zv-3DZFP2ftj#!KgGwL3-b007~D(EO9Ov6=2c7FDQpI0Fte}m98Q*dUaU)g1)lKoSypw`L!6j=&d#GwPTDYI3>i9du{(^4 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All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +FaceID networks for MAX78000 + +""" +import torch.nn.functional as F +from torch import nn + +import ai8x +import ai8x_blocks + + +class AI85FaceIDNet_112(nn.Module): + """ + FaceID Network for MAX78000 with 112x112 input + """ + + def __init__( # pylint: disable=too-many-arguments + self, + pre_layer_stride, + bottleneck_settings, + last_layer_width, + emb_dimensionality, + num_classes=None, # pylint: disable=unused-argument + avg_pool_size=(7, 7), + num_channels=3, + dimensions=(112, 112), # pylint: disable=unused-argument + bias=False, + depthwise_bias=False, + reduced_depthwise_bias=False, + **kwargs + ): + super().__init__() + # bias = False due to streaming + self.pre_stage = ai8x.FusedConv2dReLU(num_channels, bottleneck_settings[0][1], 3, + padding=1, stride=pre_layer_stride, + bias=False, **kwargs) + # bias = False due to streaming + self.pre_stage_2 = ai8x.FusedMaxPoolConv2dReLU(bottleneck_settings[0][1], + bottleneck_settings[0][1], 3, padding=1, + stride=1, pool_size=2, pool_stride=2, + bias=False, **kwargs) + self.feature_stage = nn.ModuleList([]) + for setting in bottleneck_settings: + self._create_bottleneck_stage(setting, bias, depthwise_bias, + reduced_depthwise_bias, **kwargs) + + self.post_stage = ai8x.FusedConv2dReLU(bottleneck_settings[-1][2], last_layer_width, 1, + padding=0, stride=1, bias=False, **kwargs) + + self.pre_avg = ai8x.Conv2d(last_layer_width, last_layer_width, 3, padding=1, stride=1, + bias=False, **kwargs) + self.avg_pool = ai8x.AvgPool2d(avg_pool_size, stride=1) + self.linear = ai8x.Linear(last_layer_width, emb_dimensionality, bias=bias, **kwargs) + + def _create_bottleneck_stage(self, setting, bias, depthwise_bias, + reduced_depthwise_bias, **kwargs): + """Function to create bottlencek stage. Setting format is: + [num_repeat, in_channels, out_channels, stride, expansion_factor] + """ + stage = [] + + if setting[0] > 0: + stage.append(ai8x_blocks.ConvResidualBottleneck(in_channels=setting[1], + out_channels=setting[2], + stride=setting[3], + expansion_factor=setting[4], bias=bias, + depthwise_bias=depthwise_bias, + **kwargs)) + + for i in range(1, setting[0]): + if reduced_depthwise_bias: + stage.append(ai8x_blocks.ConvResidualBottleneck(in_channels=setting[2], + out_channels=setting[2], + stride=1, + expansion_factor=setting[4], + bias=bias, + depthwise_bias=(i % 2 == 0) and + depthwise_bias, **kwargs)) + else: + stage.append(ai8x_blocks.ConvResidualBottleneck(in_channels=setting[2], + out_channels=setting[2], + stride=1, + expansion_factor=setting[4], + bias=bias, + depthwise_bias=depthwise_bias, + **kwargs)) + + self.feature_stage.append(nn.Sequential(*stage)) + + def forward(self, x): # pylint: disable=arguments-differ + """Forward prop""" + if x.shape[1] == 6: + x = x[:, 0:3, :, :] + x = self.pre_stage(x) + x = self.pre_stage_2(x) + for stage in self.feature_stage: + x = stage(x) + x = self.post_stage(x) + x = self.pre_avg(x) + x = self.avg_pool(x) + x = x.view(x.size(0), -1) + x = self.linear(x) + x = F.normalize(x, p=2, dim=1) + return x + + +def ai85faceidnet_112(pretrained=False, **kwargs): + """ + Constructs a FaceIDNet_112 model. + """ + assert not pretrained + # settings for bottleneck stages in format + # [num_repeat, in_channels, out_channels, stride, expansion_factor] + bottleneck_settings = [ + [1, 32, 48, 2, 2], + [1, 48, 64, 2, 4], + [1, 64, 64, 1, 2], + [1, 64, 96, 2, 4], + [1, 96, 128, 1, 2] + ] + + return AI85FaceIDNet_112(pre_layer_stride=1, bottleneck_settings=bottleneck_settings, + last_layer_width=128, emb_dimensionality=64, avg_pool_size=(7, 7), + depthwise_bias=True, reduced_depthwise_bias=True, **kwargs) + + +models = [ + { + 'name': 'ai85faceidnet_112', + 'min_input': 1, + 'dim': 3, + } +] diff --git a/models/ai87net-mobilefacenet_112.py b/models/ai87net-mobilefacenet_112.py new file mode 100644 index 000000000..c039d1e3e --- /dev/null +++ b/models/ai87net-mobilefacenet_112.py @@ -0,0 +1,140 @@ +################################################################################################### +# +# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +MobileFaceNet [1] network implementation for MAX78002. + +[1] Chen, Sheng, et al. "Mobilefacenets: Efficient cnns for accurate real-time face verification +on mobile devices." Biometric Recognition: 13th Chinese Conference, CCBR 2018, Urumqi, China, +August 11-12, 2018, Proceedings 13. Springer International Publishing, 2018. +""" +import torch.nn.functional as F +from torch import nn + +import ai8x +import ai8x_blocks + + +class AI87MobileFaceNet(nn.Module): + """ + MobileFaceNet for MAX78002 + """ + def __init__( # pylint: disable=too-many-arguments + self, + pre_layer_stride, + bottleneck_settings, + last_layer_width, + emb_dimensionality, + num_classes=None, # pylint: disable=unused-argument + avg_pool_size=(7, 7), + num_channels=3, + dimensions=(112, 112), # pylint: disable=unused-argument + bias=False, + depthwise_bias=False, + reduced_depthwise_bias=False, + **kwargs + ): + super().__init__() + + # bias = False due to streaming + self.pre_stage = ai8x.FusedConv2dReLU(num_channels, bottleneck_settings[0][1], 3, + padding=1, stride=pre_layer_stride, + bias=False, **kwargs) + + self.dwise = ai8x.FusedMaxPoolDepthwiseConv2dReLU(64, 64, 3, padding=1, stride=1, + pool_size=2, pool_stride=2, + bias=depthwise_bias, **kwargs) + self.feature_stage = nn.ModuleList([]) + for setting in bottleneck_settings: + self._create_bottleneck_stage(setting, bias, depthwise_bias, + reduced_depthwise_bias, **kwargs) + + self.post_stage = ai8x.FusedConv2dReLU(bottleneck_settings[-1][2], last_layer_width, 1, + padding=0, stride=1, bias=False, **kwargs) + self.classifier = ai8x.FusedAvgPoolConv2d(last_layer_width, emb_dimensionality, + 1, padding=0, stride=1, pool_size=avg_pool_size, + pool_stride=1, bias=False, wide=False, + **kwargs) + + def _create_bottleneck_stage(self, setting, bias, depthwise_bias, + reduced_depthwise_bias, **kwargs): + """Function to create bottlencek stage. Setting format is: + [num_repeat, in_channels, out_channels, stride, expansion_factor] + """ + stage = [] + + if setting[0] > 0: + stage.append(ai8x_blocks.ResidualBottleneck(in_channels=setting[1], + out_channels=setting[2], + stride=setting[3], + expansion_factor=setting[4], + bias=bias, depthwise_bias=depthwise_bias, + **kwargs)) + + for i in range(1, setting[0]): + if reduced_depthwise_bias: + stage.append(ai8x_blocks.ResidualBottleneck(in_channels=setting[2], + out_channels=setting[2], + stride=1, + expansion_factor=setting[4], + bias=bias, + depthwise_bias=(i % 2 == 0) and + depthwise_bias, **kwargs)) + else: + stage.append(ai8x_blocks.ResidualBottleneck(in_channels=setting[2], + out_channels=setting[2], + stride=1, + expansion_factor=setting[4], + bias=bias, + depthwise_bias=depthwise_bias, + **kwargs)) + + self.feature_stage.append(nn.Sequential(*stage)) + + def forward(self, x): # pylint: disable=arguments-differ + """Forward prop""" + if x.shape[1] == 6: + x = x[:, 0:3, :, :] + x = self.pre_stage(x) + x = self.dwise(x) + for stage in self.feature_stage: + x = stage(x) + x = self.post_stage(x) + x = self.classifier(x) + x = F.normalize(x, p=2, dim=1) + x = x.squeeze() + return x + + +def ai87netmobilefacenet_112(pretrained=False, **kwargs): + """ + Constructs a MobileFaceNet model. + """ + assert not pretrained + # settings for bottleneck stages in format + # [num_repeat, in_channels, out_channels, stride, expansion_factor] + bottleneck_settings = [ + [5, 64, 64, 2, 2], + [1, 64, 128, 2, 4], + [6, 128, 128, 1, 2], + [1, 128, 128, 2, 4], + [2, 128, 128, 1, 2] + ] + + return AI87MobileFaceNet(pre_layer_stride=1, bottleneck_settings=bottleneck_settings, + last_layer_width=128, emb_dimensionality=64, avg_pool_size=(7, 7), + depthwise_bias=True, reduced_depthwise_bias=True, **kwargs) + + +models = [ + { + 'name': 'ai87netmobilefacenet_112', + 'min_input': 1, + 'dim': 3, + }, +] diff --git a/models/model_irse_DRL.py b/models/model_irse_DRL.py new file mode 100644 index 000000000..bd1050e79 --- /dev/null +++ b/models/model_irse_DRL.py @@ -0,0 +1,436 @@ +################################################################################################### +# +# MIT License + +# Copyright (c) 2019 Jian Zhao + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +################################################################################################### +# +# Portions Copyright (C) 2023 Maxim Integrated Products, Inc. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +FaceID Teacher Model to be used for Knowledge Distillation +""" +from collections import namedtuple + +import torch +import torch.nn.functional as F +import torchvision.transforms.functional as FT +from torch import nn + + +class DRL(nn.Module): + """ + Dimensionality reduction layers + Expects unnormalized 512 embeddings from the Teacher Model + """ + def __init__( + self, + dimensionality, + bias=True, + ): + super().__init__() + self.conv1 = nn.Conv1d(512, 512, 1, padding=0, bias=bias) + self.BN1 = nn.BatchNorm1d(512) + self.PRelu1 = nn.PReLU(512) + self.conv2 = nn.Conv1d(512, dimensionality, 1, padding=0, bias=bias) + self.BN2 = nn.BatchNorm1d(dimensionality) + + def forward(self, x): # pylint: disable=arguments-differ + """Forward prop""" + x = torch.unsqueeze(x, 2) + x = self.conv1(x) + x = self.BN1(x) + x = self.PRelu1(x) + x = self.conv2(x) + x = self.BN2(x) + x = torch.squeeze(x, 2) + return x + + +class Ensemble(nn.Module): + """ + Ensemble of Teacher and DRL + """ + def __init__(self, resnet, drl): + super().__init__() + self.resnet = resnet + self.DRL = drl + self.Teacher_mode = False + + def forward(self, x): + """Forward prop""" + if x.shape[1] == 6: + if not self.Teacher_mode: + self.Teacher_mode = True + x = x[:, 3:, :, :] + x_flip = FT.hflip(x) + x = torch.cat((x, x_flip), 0) + x = self.resnet(x) + x = self.DRL(x) + if self.Teacher_mode: + x = x[:x.shape[0]//2] + x[x.shape[0]//2:] # Flip fusion + x = F.normalize(x, p=2, dim=1) + return x + + +class Flatten(nn.Module): + """Flattens the input""" + def forward(self, x): + """Forward prop""" + return x.view(x.size(0), -1) + + +def l2_norm(x, axis=1): + """l2 norm""" + norm = torch.norm(x, 2, axis, True) + output = torch.div(x, norm) + return output + + +class SEModule(nn.Module): + """ + SEModule + """ + def __init__(self, channels, reduction): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc1 = nn.Conv2d( + channels, channels // reduction, kernel_size=1, padding=0, bias=False) + + nn.init.xavier_uniform_(self.fc1.weight.data) + + self.relu = nn.ReLU(inplace=True) + self.fc2 = nn.Conv2d( + channels // reduction, channels, kernel_size=1, padding=0, bias=False) + + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + """Forward prop""" + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + + return module_input * x + + +class bottleneck_IR(nn.Module): + """ + IR bottleneck module + """ + def __init__(self, in_channel, depth, stride): + super().__init__() + if in_channel == depth: + self.shortcut_layer = nn.MaxPool2d(1, stride) + else: + self.shortcut_layer = nn.Sequential( + nn.Conv2d(in_channel, depth, (1, 1), stride, bias=False), nn.BatchNorm2d(depth)) + self.res_layer = nn.Sequential( + nn.BatchNorm2d(in_channel), + nn.Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), nn.PReLU(depth), + nn.Conv2d(depth, depth, (3, 3), stride, 1, bias=False), nn.BatchNorm2d(depth)) + + def forward(self, x): + """Forward prop""" + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + + return res + shortcut + + +class bottleneck_IR_SE(nn.Module): + """ + IR bottleneck module with SE + """ + def __init__(self, in_channel, depth, stride): + super().__init__() + if in_channel == depth: + self.shortcut_layer = nn.MaxPool2d(1, stride) + else: + self.shortcut_layer = nn.Sequential( + nn.Conv2d(in_channel, depth, (1, 1), stride, bias=False), + nn.BatchNorm2d(depth)) + self.res_layer = nn.Sequential( + nn.BatchNorm2d(in_channel), + nn.Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + nn.PReLU(depth), + nn.Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + nn.BatchNorm2d(depth), + SEModule(depth, 16) + ) + + def forward(self, x): + """Forward prop""" + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + + return res + shortcut + + +class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): + '''A named tuple describing a ResNet block.''' + + +def get_block(in_channel, depth, num_units, stride=2): + """Creates a bottleneck block.""" + return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) + for i in range(num_units - 1)] + + +def get_blocks(num_layers): + """Creates the block architecture for the given model.""" + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3) + ] + + return blocks + + +class Backbone(nn.Module): + """ + Constructs a backbone with the given parameters. + """ + def __init__(self, input_size, num_layers, mode='ir'): + super().__init__() + assert input_size[0] in [112, 224], "input_size should be [112, 112] or [224, 224]" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = nn.Sequential(nn.Conv2d(3, 64, (3, 3), 1, 1, bias=False), + nn.BatchNorm2d(64), + nn.PReLU(64)) + if input_size[0] == 112: + # Dropout is set to 0, due to the train.py structure + self.output_layer = nn.Sequential(nn.BatchNorm2d(512), + nn.Dropout(p=0), + Flatten(), + nn.Linear(512 * 7 * 7, 512), + nn.BatchNorm1d(512)) + else: + self.output_layer = nn.Sequential(nn.BatchNorm2d(512), + nn.Dropout(p=0), + Flatten(), + nn.Linear(512 * 14 * 14, 512), + nn.BatchNorm1d(512)) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append( + unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = nn.Sequential(*modules) + + self._initialize_weights() + + def forward(self, x): + """Forward prop""" + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return x + + def _initialize_weights(self): + """Initializes the weights.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_uniform_(m.weight.data) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm1d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight.data) + if m.bias is not None: + m.bias.data.zero_() + + +def ir_50(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir-50 model. + """ + model = Backbone(input_size, 50, 'ir') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + ensemble = Ensemble(model, drl) + + return ensemble + + +def ir_101(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir-101 model. + """ + model = Backbone(input_size, 100, 'ir') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + ensemble = Ensemble(model, drl) + + return ensemble + + +def ir_152(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir-152 model. + """ + model = Backbone(input_size, 152, 'ir') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + + ensemble = Ensemble(model, drl) + + return ensemble + + +def ir_se_50(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir_se-50 model. + """ + model = Backbone(input_size, 50, 'ir_se') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + ensemble = Ensemble(model, drl) + + return ensemble + + +def ir_se_101(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir_se-101 model. + """ + model = Backbone(input_size, 100, 'ir_se') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + ensemble = Ensemble(model, drl) + + return ensemble + + +def ir_se_152(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """Constructs a ir_se-152 model. + """ + model = Backbone(input_size, 152, 'ir_se') + if backbone_checkpoint is not None: + model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + for param in model.parameters(): + param.requires_grad = False + drl = DRL(dimensionality) + ensemble = Ensemble(model, drl) + + return ensemble + + +models = [ + { + 'name': 'ir_50', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + { + 'name': 'ir_101', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + { + 'name': 'ir_152', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + { + 'name': 'ir_se_50', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + { + 'name': 'ir_se_101', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + { + 'name': 'ir_se_152', + 'min_input': 1, + 'dim': 2, + 'dr': True, + }, + +] diff --git a/parsecmd.py b/parsecmd.py index 6f703b36a..6f0690dbb 100644 --- a/parsecmd.py +++ b/parsecmd.py @@ -67,6 +67,17 @@ def get_parser(model_names, dataset_names): parser.add_argument('--avg-pool-rounding', action='store_true', default=False, help='when simulating, use "round()" in AvgPool operations ' '(default: use "floor()")') + parser.add_argument('--dr', type=int, default=None, + help='Embedding dimensionality for dimensionality' + 'reduction (default: None)') + parser.add_argument('--scaf-margin', default=28.6, + type=float, help='Margin hyperparameter' + 'for Sub-center ArcFace Loss') + parser.add_argument('--scaf-scale', default=64, + type=int, help='Scale hyperparameter for Sub-center ArcFace Loss') + parser.add_argument('--backbone-checkpoint', type=str, default=None, metavar='PATH', + help='path to checkpoint from which to load' + 'backbone weights (default: None)') parser.add_argument('--copy-output-folder', type=str, default=None, metavar='PATH', help='Path to copy output folder (default: None)') parser.add_argument('--kd-relationbased', action='store_true', default=False, @@ -95,6 +106,10 @@ def get_parser(model_names, dataset_names): help='optimizer for training (default: SGD)') optimizer_args.add_argument('--lr', '--learning-rate', type=float, metavar='LR', help='initial learning rate') + optimizer_args.add_argument('--scaf-lr', default=1e-4, + type=float, metavar='SCAF_LR', + help='initial learning rate for Sub-center' + 'ArcFace Loss optimizer') optimizer_args.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') optimizer_args.add_argument('--weight-decay', '--wd', default=1e-4, type=float, @@ -126,6 +141,9 @@ def get_parser(model_names, dataset_names): help='save as CSVs with the given prefix during evaluation') mgroup.add_argument('--save-sample', dest='generate_sample', type=int, help='save the sample at given index as NumPy sample data') + parser.add_argument('--slice-sample', action='store_true', default=False, + help='for models that require RGB input, when the sample from the dataset ' + 'has additional channels, slice the sample into 3 channels') parser.add_argument('--shap', default=0, type=int, help='select # of images from the test set and plot SHAP after evaluation') parser.add_argument('--activation-stats', '--act-stats', nargs='+', metavar='PHASE', diff --git a/policies/qat_policy_faceid_112.yaml b/policies/qat_policy_faceid_112.yaml new file mode 100644 index 000000000..a40fdeaea --- /dev/null +++ b/policies/qat_policy_faceid_112.yaml @@ -0,0 +1,17 @@ +--- +start_epoch: 25 +weight_bits: 4 +shift_quantile: 0.6 +overrides: + pre_stage: + weight_bits: 8 + pre_stage_2: + weight_bits: 8 + feature_stage.1.0.conv2: + weight_bits: 2 + feature_stage.2.0.conv2: + weight_bits: 2 + feature_stage.4.0.conv2: + weight_bits: 2 + linear: + weight_bits: 8 diff --git a/policies/qat_policy_mobilefacenet_112.yaml b/policies/qat_policy_mobilefacenet_112.yaml new file mode 100644 index 000000000..f8a4e6247 --- /dev/null +++ b/policies/qat_policy_mobilefacenet_112.yaml @@ -0,0 +1,3 @@ +--- +start_epoch: 25 +weight_bits: 8 diff --git a/policies/schedule-faceid_112.yaml b/policies/schedule-faceid_112.yaml new file mode 100644 index 000000000..6d36bb38e --- /dev/null +++ b/policies/schedule-faceid_112.yaml @@ -0,0 +1,13 @@ +--- +lr_schedulers: + training_lr: + class: MultiStepLR + milestones: [10, 15, 20, 25, 40, 50, 60] + gamma: 0.5 + +policies: + - lr_scheduler: + instance_name: training_lr + starting_epoch: 0 + ending_epoch: 80 + frequency: 1 diff --git a/policies/schedule-mobilefacenet_112.yaml b/policies/schedule-mobilefacenet_112.yaml new file mode 100644 index 000000000..5959971ff --- /dev/null +++ b/policies/schedule-mobilefacenet_112.yaml @@ -0,0 +1,13 @@ +--- +lr_schedulers: + training_lr: + class: MultiStepLR + milestones: [10, 15, 20, 25, 26, 28, 30, 32, 34] + gamma: 0.5 + +policies: + - lr_scheduler: + instance_name: training_lr + starting_epoch: 0 + ending_epoch: 35 + frequency: 1 diff --git a/requirements.txt b/requirements.txt index bb7a713bb..88eaf2b3a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,9 +14,16 @@ numba<0.50.0 opencv-python>=4.4.0 h5py>=3.7.0 torchmetrics==0.6.0 -pycocotools==2.0.6 +pycocotools==2.0.7 albumentations>=1.3.0 pytube>=12.1.3 pyffmpeg==2.0 GitPython>=3.1.18 +pytorch-metric-learning==2.3.0 +faiss-cpu==1.7.4 +hawk-eyes==2.1.2 +onnx==1.15.0 +onnxruntime-gpu==1.10.0 +imutils==0.5.4 +kornia==0.6.8 -e distiller diff --git a/sample.py b/sample.py index 21e0cc63e..c1616e497 100644 --- a/sample.py +++ b/sample.py @@ -19,6 +19,7 @@ def generate( outputs, # pylint: disable=unused-argument dataset_name, search=False, # pylint: disable=unused-argument + slice_sample=False, ): """ Save the element `index` from the `inputs` batch to a file named "sample_`dataset_name`.npy". @@ -33,6 +34,8 @@ def generate( print(f'==> Saving sample at index {index} to {sample_name}.npy') x = inputs[index].cpu().numpy().astype('int64') + if slice_sample: + x = x[0:3, :, :] x = np.clip(x, -128, 127) np.save(sample_name, x, allow_pickle=False, fix_imports=False) diff --git a/scripts/evaluate_faceid.sh b/scripts/evaluate_faceid.sh deleted file mode 100755 index c12272e99..000000000 --- a/scripts/evaluate_faceid.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python train.py --model ai85faceidnet --dataset FaceID --regression --evaluate --exp-load-weights-from ../ai8x-synthesis/trained/ai85-faceid-qat8-q.pth.tar -8 --device MAX78000 "$@" diff --git a/scripts/evaluate_faceid_112.sh b/scripts/evaluate_faceid_112.sh new file mode 100755 index 000000000..d2634a5a8 --- /dev/null +++ b/scripts/evaluate_faceid_112.sh @@ -0,0 +1,2 @@ +#!/bin/sh +python train.py --model ai85faceidnet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-faceid_112-qat-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" \ No newline at end of file diff --git a/scripts/evaluate_mobilefacenet_112.sh b/scripts/evaluate_mobilefacenet_112.sh new file mode 100755 index 000000000..8b7399506 --- /dev/null +++ b/scripts/evaluate_mobilefacenet_112.sh @@ -0,0 +1,2 @@ +#!/bin/sh +python train.py --model ai87netmobilefacenet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-mobilefacenet_112_qat_best-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" \ No newline at end of file diff --git a/scripts/train_facedet_tinierssd.sh b/scripts/train_facedet_tinierssd.sh index 94ebd2549..d3c67f68f 100755 --- a/scripts/train_facedet_tinierssd.sh +++ b/scripts/train_facedet_tinierssd.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --deterministic --print-freq 1 --epochs 3 --optimizer Adam --lr 1e-3 --wd 5e-4 --model ai85tinierssdface --use-bias --momentum 0.9 --dataset VGGFace2_FaceDetection --device MAX78000 --obj-detection --obj-detection-params parameters/obj_detection_params_facedet.yaml --batch-size 100 --qat-policy policies/qat_policy_facedet.yaml --validation-split 0.1 "$@" +python train.py --deterministic --print-freq 100 --epochs 3 --optimizer Adam --lr 1e-3 --wd 5e-4 --model ai85tinierssdface --use-bias --momentum 0.9 --dataset VGGFace2_FaceDetection --device MAX78000 --obj-detection --obj-detection-params parameters/obj_detection_params_facedet.yaml --batch-size 100 --qat-policy policies/qat_policy_facedet.yaml --validation-split 0.1 "$@" diff --git a/scripts/train_faceid.sh b/scripts/train_faceid.sh deleted file mode 100755 index 1465c8b5d..000000000 --- a/scripts/train_faceid.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python train.py --epochs 100 --optimizer Adam --lr 0.001 --wd 0 --deterministic --compress policies/schedule-faceid.yaml --model ai85faceidnet --dataset FaceID --batch-size 100 --device MAX78000 --regression --print-freq 250 "$@" diff --git a/scripts/train_faceid_112.sh b/scripts/train_faceid_112.sh new file mode 100755 index 000000000..594ff32d3 --- /dev/null +++ b/scripts/train_faceid_112.sh @@ -0,0 +1,3 @@ +#!/bin/sh +python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" +python train.py --epochs 80 --optimizer Adam --lr 0.001 --compress policies/schedule-faceid_112.yaml --kd-student-wt 0 --kd-distill-wt 1 --qat-policy policies/qat_policy_faceid_112.yaml --model ai85faceidnet_112 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --wd 0 --deterministic --workers 8 --use-bias --dataset VGGFace2_FaceID --batch-size 256 --device MAX78000 --print-freq 100 --validation-split 0 "$@" diff --git a/scripts/train_mobilefacenet_112.sh b/scripts/train_mobilefacenet_112.sh new file mode 100755 index 000000000..47a48c942 --- /dev/null +++ b/scripts/train_mobilefacenet_112.sh @@ -0,0 +1,3 @@ +#!/bin/sh +python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" +python train.py --epochs 35 --optimizer Adam --lr 0.001 --compress policies/schedule-mobilefacenet_112.yaml --kd-student-wt 0 --kd-distill-wt 1 --qat-policy policies/qat_policy_mobilefacenet_112.yaml --model ai87netmobilefacenet_112 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --wd 0 --deterministic --workers 8 --use-bias --dataset VGGFace2_FaceID --batch-size 100 --device MAX78002 --validation-split 0 --print-freq 100 "$@" diff --git a/train.py b/train.py index 724705888..27eeb55d8 100644 --- a/train.py +++ b/train.py @@ -95,6 +95,11 @@ RecordsActivationStatsCollector, SummaryActivationStatsCollector, collectors_context) from distiller.quantization.range_linear import PostTrainLinearQuantizer +from pytorch_metric_learning import losses as pml_losses +from pytorch_metric_learning import testers +from pytorch_metric_learning.distances import CosineSimilarity +from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator +from pytorch_metric_learning.utils.inference import CustomKNN from torchmetrics.detection.map import MAP as MeanAveragePrecision # pylint: enable=no-name-in-module @@ -333,6 +338,7 @@ def main(): # We can optionally resume from a checkpoint optimizer = None + loss_optimizer = None if args.resumed_checkpoint_path: update_old_model_params(args.resumed_checkpoint_path, model) if qat_policy is not None: @@ -387,6 +393,28 @@ def main(): alpha=obj_detection_params['multi_box_loss']['alpha'], neg_pos_ratio=obj_detection_params['multi_box_loss'] ['neg_pos_ratio'], device=args.device).to(args.device) + + elif args.dr: + + criterion = pml_losses.SubCenterArcFaceLoss(num_classes=args.num_classes, + embedding_size=args.dr, + margin=args.scaf_margin, + scale=args.scaf_scale) + if args.resumed_checkpoint_path: + checkpoint = torch.load(args.resumed_checkpoint_path, + map_location=lambda storage, loc: storage) + criterion.W = checkpoint['extras']['loss_weights'] + criterion = criterion.to(args.device) + + loss_optimizer = torch.optim.Adam(criterion.parameters(), lr=args.scaf_lr) + if args.resumed_checkpoint_path: + loss_optimizer.load_state_dict(checkpoint['extras']['loss_optimizer_state_dict']) + + distance_fn = CosineSimilarity() + custom_knn = CustomKNN(distance_fn, batch_size=args.batch_size) + accuracy_calculator = AccuracyCalculator(knn_func=custom_knn, + include=("precision_at_1",), k=1) + else: if not args.regression: if 'weight' in selected_source: @@ -430,22 +458,14 @@ def main(): args.sensitivity_range[2]) return sensitivity_analysis(model, criterion, test_loader, pylogger, args, sensitivities) - if args.evaluate: - msglogger.info('Dataset sizes:\n\ttest=%d', len(test_loader.sampler)) - return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, - args, compression_scheduler) - - assert train_loader and val_loader - msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d', - len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler)) - if args.compress: # The main use-case for this sample application is CNN compression. Compression # requires a compression schedule configuration file in YAML. compression_scheduler = distiller.file_config(model, optimizer, args.compress, compression_scheduler, (start_epoch-1) - if args.resumed_checkpoint_path else None) + if args.resumed_checkpoint_path + else None, loss_optimizer) elif compression_scheduler is None: compression_scheduler = distiller.CompressionScheduler(model) @@ -475,7 +495,8 @@ def main(): dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt, args.kd_teacher_wt) if args.kd_relationbased: - args.kd_policy = kd_relationbased.RelationBasedKDPolicy(model, teacher, dlw) + args.kd_policy = kd_relationbased.RelationBasedKDPolicy(model, teacher, + dlw, args.act_mode_8bit) else: args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw) @@ -514,6 +535,15 @@ def main(): args.epochs) create_nas_kd_policy(model, compression_scheduler, start_epoch, kd_end_epoch, args) + if args.evaluate: + msglogger.info('Dataset sizes:\n\ttest=%d', len(test_loader.sampler)) + return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, + args, compression_scheduler) + + assert train_loader and val_loader + msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d', + len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler)) + vloss = 10**6 for epoch in range(start_epoch, ending_epoch): # pylint: disable=unsubscriptable-object @@ -557,7 +587,8 @@ def main(): # Train for one epoch with collectors_context(activations_collectors["train"]) as collectors: train(train_loader, model, criterion, optimizer, epoch, compression_scheduler, - loggers=all_loggers, args=args) + loggers=all_loggers, args=args, loss_optimizer=loss_optimizer) + # distiller.log_weights_sparsity(model, epoch, loggers=all_loggers) distiller.log_activation_statistics(epoch, "train", loggers=all_tbloggers, collector=collectors["sparsity"]) @@ -584,8 +615,11 @@ def main(): checkpoint_name = f'nas_stg{stage}_lev{level}' with collectors_context(activations_collectors["valid"]) as collectors: - top1, top5, vloss, mAP = validate(val_loader, model, criterion, [pylogger], - args, epoch, tflogger) + if not args.dr: + top1, top5, vloss, mAP = validate(val_loader, model, criterion, [pylogger], + args, epoch, tflogger) + else: + top1, top5, vloss, mAP = scaf_test(val_loader, model, accuracy_calculator) distiller.log_activation_statistics(epoch, "valid", loggers=all_tbloggers, collector=collectors["sparsity"]) save_collectors_data(collectors, msglogger.logdir) @@ -596,7 +630,7 @@ def main(): if not args.regression: stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), ('Top1', top1)])) - if args.num_classes > 5: + if args.num_classes > 5 and not args.dr: stats[1]['Top5'] = top5 else: stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), @@ -621,6 +655,9 @@ def main(): is_best = False checkpoint_extras = {'current_top1': top1, 'current_mAP': mAP} + if args.dr: + checkpoint_extras['loss_weights'] = criterion.W + checkpoint_extras['loss_optimizer_state_dict'] = loss_optimizer.state_dict() apputils.save_checkpoint(epoch, args.cnn, model, optimizer=optimizer, scheduler=compression_scheduler, extras=checkpoint_extras, @@ -631,7 +668,8 @@ def main(): compression_scheduler.on_epoch_end(epoch, optimizer) # Finally run results on the test set - test(test_loader, model, criterion, [pylogger], activations_collectors, args=args) + if not args.dr: + test(test_loader, model, criterion, [pylogger], activations_collectors, args=args) if args.copy_output_folder: msglogger.info('Copying output folder to: %s', args.copy_output_folder) @@ -664,6 +702,10 @@ def create_model(supported_models, dimensions, args, mode='default'): if not Model: raise RuntimeError("Model " + args.kd_teacher + " not found\n") + if args.dr: + if 'dr' not in module or not module['dr']: + raise ValueError("Dimensionality reduction is not supported for this model") + # Set model parameters if args.act_mode_8bit: weight_bits = 8 @@ -684,6 +726,12 @@ def create_model(supported_models, dimensions, args, mode='default'): model_args["bias_bits"] = bias_bits model_args["quantize_activation"] = quantize_activation + if args.dr: + model_args["dimensionality"] = args.dr + + if args.backbone_checkpoint: + model_args["backbone_checkpoint"] = args.backbone_checkpoint + if args.obj_detection: model_args["device"] = args.device @@ -729,7 +777,7 @@ def create_nas_kd_policy(model, compression_scheduler, epoch, next_state_start_e def train(train_loader, model, criterion, optimizer, epoch, - compression_scheduler, loggers, args): + compression_scheduler, loggers, args, loss_optimizer=None): """Training loop for one epoch.""" losses = OrderedDict([(OVERALL_LOSS_KEY, tnt.AverageValueMeter()), (OBJECTIVE_LOSS_KEY, tnt.AverageValueMeter())]) @@ -823,7 +871,7 @@ def train(train_loader, model, criterion, optimizer, epoch, loss = criterion(output, target) # TODO Early exit mechanism for Object Detection case is NOT implemented yet - if not args.obj_detection and not args.kd_relationbased: + if not args.obj_detection and not args.dr and not args.kd_relationbased: if not args.earlyexit_lossweights: # Measure accuracy if the conditions are set. For `Last Batch` only accuracy # calculation last two batches are used as the last batch might include just a few @@ -868,11 +916,16 @@ def train(train_loader, model, criterion, optimizer, epoch, # Compute the gradient and do SGD step optimizer.zero_grad() + if args.dr: + loss_optimizer.zero_grad() + loss.backward() if compression_scheduler: compression_scheduler.before_parameter_optimization(epoch, train_step, steps_per_epoch, optimizer) optimizer.step() + if args.dr: + loss_optimizer.step() if compression_scheduler: compression_scheduler.on_minibatch_end(epoch, train_step, steps_per_epoch, optimizer) @@ -946,6 +999,23 @@ def update_bn_stats(train_loader, model, args): _ = model(inputs) +def get_all_embeddings(dataset, model): + """Get all embeddings from the test set""" + tester = testers.BaseTester() + return tester.get_all_embeddings(dataset, model) + + +def scaf_test(val_loader, model, accuracy_calculator): + """Perform test for SCAF""" + test_embeddings, test_labels = get_all_embeddings(val_loader.dataset, model) + test_labels = test_labels.squeeze(1) + accuracies = accuracy_calculator.get_accuracy( + test_embeddings, test_labels, None, None, True + ) + msglogger.info('Test set accuracy (Precision@1) = %f', accuracies['precision_at_1']) + return accuracies["precision_at_1"], 0, 0, 0 + + def validate(val_loader, model, criterion, loggers, args, epoch=-1, tflogger=None): """Model validation""" if epoch > -1: @@ -1192,7 +1262,8 @@ def save_tensor(t, f, regression=True): target /= 128. if args.generate_sample is not None and args.act_mode_8bit and not sample_saved: - sample.generate(args.generate_sample, inputs, target, output, args.dataset, False) + sample.generate(args.generate_sample, inputs, target, output, + args.dataset, False, args.slice_sample) sample_saved = True if args.csv_prefix is not None: diff --git a/train_all_models.sh b/train_all_models.sh index 5b23dcac5..1baac4a05 100755 --- a/train_all_models.sh +++ b/train_all_models.sh @@ -33,8 +33,11 @@ echo "-----------------------------" echo "Training kws20_v3 model" scripts/train_kws20_v3.sh "$@" echo "-----------------------------" -echo "Training faceid model" -scripts/train_faceid.sh "$@" +echo "Training faceid_112 model" +scripts/train_faceid_112.sh "$@" +echo "-----------------------------" +echo "Training mobilefacenet_112 model" +scripts/train_mobilefacenet_112.sh "$@" echo "-----------------------------" echo "Training unet model" scripts/train_camvid_unet.sh "$@" diff --git a/utils/kd_relationbased.py b/utils/kd_relationbased.py index 5610f8448..ed6c721b3 100644 --- a/utils/kd_relationbased.py +++ b/utils/kd_relationbased.py @@ -33,7 +33,7 @@ class RelationBasedKDPolicy(ScheduledTrainingPolicy): the distiller's ScheduledTrainingPolicy class. """ def __init__(self, student_model, teacher_model, - loss_weights=DistillationLossWeights(0.5, 0.5, 0)): + loss_weights=DistillationLossWeights(0.5, 0.5, 0), act_mode_8bit=False): super().__init__() self.student = student_model @@ -43,6 +43,7 @@ def __init__(self, student_model, teacher_model, self.loss_wts = loss_weights self.distillation_loss = nn.MSELoss() self.overall_loss = None + self.act_mode_8bit = act_mode_8bit # Active is always true, because test will be based on the overall loss and it will be # realized outside of the epoch loop @@ -65,6 +66,8 @@ def forward(self, *inputs): self.teacher_output = self.teacher(*inputs) out = self.student(*inputs) + if self.act_mode_8bit: + out /= 128. self.student_output = out.clone() return out From 377e7395202f1e0ec6556539c6dc672af18211fc Mon Sep 17 00:00:00 2001 From: asyatrhl <123384000+asyatrhl@users.noreply.github.com> Date: Tue, 9 Jan 2024 18:53:28 +0300 Subject: [PATCH 10/27] Add regression test workflow trigger (#278) --- .github/workflows/test.yml | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 000000000..560ebe5ab --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,36 @@ +--- +name: Trigger External Workflow + +on: + pull_request_target: + types: [opened, edited, synchronize] + +jobs: + trigger_dispatch: + runs-on: ubuntu-latest + steps: + - name: Check repository + run: | + echo "IS_SPECIFIC_REPOSITORY=${{ github.repository == 'MaximIntegratedAI/ai8x-training' }}" >> $GITHUB_ENV + echo "IS_DEVELOP_BRANCH=${{ github.ref == 'refs/heads/develop' }}" >> $GITHUB_ENV + + - name: Set up environment + if: env.IS_SPECIFIC_REPOSITORY == 'true' && env.IS_DEVELOP_BRANCH == 'true' + run: | + PR_Branch=${{ github.event.pull_request.head.ref }} + Repository=${{ github.repository }} + PR_Number=${{ github.event.pull_request.number }} + PR_Sha=${{ github.event.pull_request.head.sha }} + echo "PR_Branch: $PR_Branch" + echo "Repository: $Repository" + echo "PR_Number: $PR_Number" + echo "PR_Sha: $PR_Sha" + + - name: Dispatch event + if: env.IS_SPECIFIC_REPOSITORY == 'true' && env.IS_DEVELOP_BRANCH == 'true' + run: | + curl -X POST \ + -H "Authorization: Bearer ${{ secrets.REGRESSION_TEST }}" \ + -H "Accept: application/vnd.github.v3+json" \ + "https://api.github.com/repos/MaximIntegratedAI/ai8x-regression/dispatches" \ + -d '{"event_type": "repo-pull-request", "client_payload": {"PR_Branch": "${{ github.event.pull_request.head.ref }}", "Repository": "${{ github.repository }}","PR_Number": "${{ github.event.pull_request.number }}","PR_Sha": "${{ github.event.pull_request.head.sha }}" }' From 14412c39606f226fe6fd09ec8a415527ecd15260 Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Tue, 9 Jan 2024 10:12:12 -0600 Subject: [PATCH 11/27] Minor cleanup (#282) --- docs/FacialRecognitionSystem.md | 204 +++++++++--------- .../{model_irse_DRL.py => model_irse_drl.py} | 16 +- requirements.txt | 40 ++-- train.py | 7 +- 4 files changed, 130 insertions(+), 137 deletions(-) rename models/{model_irse_DRL.py => model_irse_drl.py} (97%) diff --git a/docs/FacialRecognitionSystem.md b/docs/FacialRecognitionSystem.md index a9346cb10..92cb03eff 100644 --- a/docs/FacialRecognitionSystem.md +++ b/docs/FacialRecognitionSystem.md @@ -1,102 +1,102 @@ -# Facial Recognition System - -This document aims to explain facial recognition applications for MAX7800x series microcontrollers. Facial recognition task consists from three main parts, face detection, face identification and dot product. - - - The face detection model detects faces in the captured image and extracts a rectangular sub-image containing only one face. - - The face Identification model identifies a person from their facial images by generating the embedding for a given face image. - - The dot product layer outputs the dot product representing the similarity between the embedding from the given image and embeddings in the database. - -Figure 1 depicts the facial recognition system sequential diagram. - - - -​Figure 1. MAX7800x facial recognition system - -## Dataset - -The first step will be the dataset preparation. The dataset is VGGFace-2 [1]. -Please review the term and conditions at [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/). Then, download the dataset and extract raw images to the train and test subfolders. - -Expected folder structure: - - - root_dir - - VGGFace-2 - - train - - test - -FaceID and Face Detection tasks share the same ground truth pickle, and it will be automatically generated when one of these tasks started. - -## Face Detection - -To be able to localize faces in a facial recognition system, a face detection algorithm is generally used in facial recognition systems. Face detection is an object detection problem that has various solutions in the literature. In this work, a face detection algorithm that will run on MAX7800x series microcontrollers with a real-time performance was targeted. - -For digit detection problem, previously, a Tiny SSD[2] based MAX7800x object detection algorithm was developed , named Tinier SSD. The face detection model is a modified version of the digit detection model. The modification was realized to reduce the number of parameters and enable larger input size. - -To train the facedetection model, "scripts/train_facedet_tinierssd.sh" script can be used. - -## FaceID - -To train a FaceID model for MAX7800x microcontrollers, there are multiple steps. As the MAX7800x FaceID models will be trained in a knowledge distillation fashion, the first step will be downloading a backbone checkpoint for the teacher model. - -The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_DRL.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. - -There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named faceid_112, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named mobilefacenet_112. To train the FaceID models, "scripts/train_faceid_112.sh" and "scripts/train_mobilefacenet_112.sh" scripts can be used, respectivey. Training scripts will realize Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. - -### Dimensionality Reduction on the Teacher Model - -Reducing embedding dimensionality can greatly reduce the post-processing operations and memory usage for the facial recognition system. To achieve this, the teacher backbone will be frozen and two additional Conv1d layers will be added to the teacher models. These additions are called dimension reduction layers. For the example in the repository, the length of the embeddings produced by the teacher model is 512 and the optimum length for the student model is found to be 64. Still, other choices like 32, 128 or 256 can be examined for different application areas. A summary of the dimensionality reduction is shown in Figure 2, and dimension reduction layers' details are represented in Table 1. - - - - -​Figure 2. Dimensionality Reduction - - - -Table 1. Dimension Reduction Layers - -| Layer1 | Layer2 | -|--------------------------------------| ------------------------------------| -| Conv1d(In=512ch, Out=512Ch, Kernel=1)| Conv1d(In=512ch, Out=64Ch, Kernel=1)| -| BatchNorm1d(512) | BatchNorm1d(64) | -| PReLU(512) | | - - - -To train dimensionality reduction layers Sub-Center ArcFace loss is used. The SubCenterArcFace Loss was presented in the [4], summary of the training framework can be seen in Figure 3. The loss function uses cosine similarity as the distance metric, and in the framework embedding network is trained as a part of the classification problem. The Normalized Sub-Centers(also known as the prototypes) must be learned from zero as no model is available to extract embeddings at the beginning. - - - -​Figure 3. Sub-Center ArcFace Loss[4] - -### Relation-Based Knowledge Distillation - -The knowledge distillation choice for the FaceID models was a relation-based one. The distillation loss was calculated as the MSE between teacher model and student model. - -To train the student FaceID models, no student loss was used, so student weight was set to 0. - -From Figure 4, a visual can be seen for the relation-based knowledge distillation. - - - -​Figure 4. Relation-Based Knowledge Distillation[5] - - - -## Dot Product Layer - -The dot product layer weights will be populated with the embeddings that are generated by MAX7800x FaceID models. Outputs of the FaceID models are normalized at both inference and recording. Therefore, the result of the dot product layer equals cosine similarity. Using the cosine similarity as a distance metric, the image is identified as either one of the known subjects or 'Unknown' depending on the embedding distances. To record new people in the database, there are two options. The first one is using the Python scripts that are available on the SDK demos. The second option is to use "record on hardware" mode which does not require any external connection. The second option is not available for all platforms, so please check SDK demo ReadMEs to see if it is supported. - - - -## References - -[1] [Cao, Qiong, et al. "Vggface2: A dataset for recognising faces across pose and age." 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). IEEE, 2018.](https://arxiv.org/abs/1710.08092) - -[2] [A. Womg, M. J. Shafiee, F. Li and B. Chwyl, "Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection," 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada, 2018, pp. 95-101, doi: 10.1109/CRV.2018.00023.](https://ieeexplore.ieee.org/document/8575741) - -[3] [face.evoLVe, High-Performance Face Recognition Library on PaddlePaddle & PyTorch](https://github.com/ZhaoJ9014/face.evoLVe) - -[4] [Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.](https://arxiv.org/abs/1801.07698) - -[5] [Gou, Jianping, et al. "Knowledge distillation: A survey." International Journal of Computer Vision 129 (2021): 1789-1819.](https://arxiv.org/abs/2006.05525) +# Facial Recognition System + +This document aims to explain facial recognition applications for MAX7800x series microcontrollers. Facial recognition task consists from three main parts, face detection, face identification and dot product. + + - The face detection model detects faces in the captured image and extracts a rectangular sub-image containing only one face. + - The face Identification model identifies a person from their facial images by generating the embedding for a given face image. + - The dot product layer outputs the dot product representing the similarity between the embedding from the given image and embeddings in the database. + +Figure 1 depicts the facial recognition system sequential diagram. + + + +​Figure 1. MAX7800x facial recognition system + +## Dataset + +The first step will be the dataset preparation. The dataset is VGGFace-2 [1]. +Please review the term and conditions at [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/). Then, download the dataset and extract raw images to the train and test subfolders. + +Expected folder structure: + + - root_dir + - VGGFace-2 + - train + - test + +FaceID and Face Detection tasks share the same ground truth pickle, and it will be automatically generated when one of these tasks started. + +## Face Detection + +To be able to localize faces in a facial recognition system, a face detection algorithm is generally used in facial recognition systems. Face detection is an object detection problem that has various solutions in the literature. In this work, a face detection algorithm that will run on MAX7800x series microcontrollers with a real-time performance was targeted. + +For digit detection problem, previously, a Tiny SSD[2] based MAX7800x object detection algorithm was developed , named Tinier SSD. The face detection model is a modified version of the digit detection model. The modification was realized to reduce the number of parameters and enable larger input size. + +To train the facedetection model, "scripts/train_facedet_tinierssd.sh" script can be used. + +## FaceID + +To train a FaceID model for MAX7800x microcontrollers, there are multiple steps. As the MAX7800x FaceID models will be trained in a knowledge distillation fashion, the first step will be downloading a backbone checkpoint for the teacher model. + +The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_drl.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. + +There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named faceid_112, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named mobilefacenet_112. To train the FaceID models, "scripts/train_faceid_112.sh" and "scripts/train_mobilefacenet_112.sh" scripts can be used, respectivey. Training scripts will realize Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. + +### Dimensionality Reduction on the Teacher Model + +Reducing embedding dimensionality can greatly reduce the post-processing operations and memory usage for the facial recognition system. To achieve this, the teacher backbone will be frozen and two additional Conv1d layers will be added to the teacher models. These additions are called dimension reduction layers. For the example in the repository, the length of the embeddings produced by the teacher model is 512 and the optimum length for the student model is found to be 64. Still, other choices like 32, 128 or 256 can be examined for different application areas. A summary of the dimensionality reduction is shown in Figure 2, and dimension reduction layers' details are represented in Table 1. + + + + +​Figure 2. Dimensionality Reduction + + + +Table 1. Dimension Reduction Layers + +| Layer1 | Layer2 | +|--------------------------------------| ------------------------------------| +| Conv1d(In=512ch, Out=512Ch, Kernel=1)| Conv1d(In=512ch, Out=64Ch, Kernel=1)| +| BatchNorm1d(512) | BatchNorm1d(64) | +| PReLU(512) | | + + + +To train dimensionality reduction layers Sub-Center ArcFace loss is used. The SubCenterArcFace Loss was presented in the [4], summary of the training framework can be seen in Figure 3. The loss function uses cosine similarity as the distance metric, and in the framework embedding network is trained as a part of the classification problem. The Normalized Sub-Centers(also known as the prototypes) must be learned from zero as no model is available to extract embeddings at the beginning. + + + +​Figure 3. Sub-Center ArcFace Loss[4] + +### Relation-Based Knowledge Distillation + +The knowledge distillation choice for the FaceID models was a relation-based one. The distillation loss was calculated as the MSE between teacher model and student model. + +To train the student FaceID models, no student loss was used, so student weight was set to 0. + +From Figure 4, a visual can be seen for the relation-based knowledge distillation. + + + +​Figure 4. Relation-Based Knowledge Distillation[5] + + + +## Dot Product Layer + +The dot product layer weights will be populated with the embeddings that are generated by MAX7800x FaceID models. Outputs of the FaceID models are normalized at both inference and recording. Therefore, the result of the dot product layer equals cosine similarity. Using the cosine similarity as a distance metric, the image is identified as either one of the known subjects or 'Unknown' depending on the embedding distances. To record new people in the database, there are two options. The first one is using the Python scripts that are available on the SDK demos. The second option is to use "record on hardware" mode which does not require any external connection. The second option is not available for all platforms, so please check SDK demo ReadMEs to see if it is supported. + + + +## References + +[1] [Cao, Qiong, et al. "Vggface2: A dataset for recognising faces across pose and age." 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). IEEE, 2018.](https://arxiv.org/abs/1710.08092) + +[2] [A. Womg, M. J. Shafiee, F. Li and B. Chwyl, "Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection," 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada, 2018, pp. 95-101, doi: 10.1109/CRV.2018.00023.](https://ieeexplore.ieee.org/document/8575741) + +[3] [face.evoLVe, High-Performance Face Recognition Library on PaddlePaddle & PyTorch](https://github.com/ZhaoJ9014/face.evoLVe) + +[4] [Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.](https://arxiv.org/abs/1801.07698) + +[5] [Gou, Jianping, et al. "Knowledge distillation: A survey." International Journal of Computer Vision 129 (2021): 1789-1819.](https://arxiv.org/abs/2006.05525) diff --git a/models/model_irse_DRL.py b/models/model_irse_drl.py similarity index 97% rename from models/model_irse_DRL.py rename to models/model_irse_drl.py index bd1050e79..eba2b0795 100644 --- a/models/model_irse_DRL.py +++ b/models/model_irse_drl.py @@ -1,19 +1,20 @@ ################################################################################################### # # MIT License - +# # Copyright (c) 2019 Jian Zhao - +# Portions Copyright (C) 2023-2024 Maxim Integrated Products, Inc. +# # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: - +# # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. - +# # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE @@ -23,13 +24,6 @@ # SOFTWARE. # ################################################################################################### -# -# Portions Copyright (C) 2023 Maxim Integrated Products, Inc. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### """ FaceID Teacher Model to be used for Knowledge Distillation """ diff --git a/requirements.txt b/requirements.txt index 88eaf2b3a..6ae276138 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,29 +1,29 @@ -numpy>=1.22,<1.23 -PyYAML>=5.1.1 -scipy>=1.3.0 -librosa>=0.7.2 -Pillow>=7 -shap>=0.34.0 -tk>=0.1.0 torch==1.8.1 torchaudio==0.8.1 torchvision==0.9.1 -tensorboard>=2.9.0,<2.10.0 -protobuf>=3.20.1,<4.0 -numba<0.50.0 -opencv-python>=4.4.0 -h5py>=3.7.0 -torchmetrics==0.6.0 -pycocotools==2.0.7 -albumentations>=1.3.0 -pytube>=12.1.3 -pyffmpeg==2.0 GitPython>=3.1.18 -pytorch-metric-learning==2.3.0 +Pillow>=7 +PyYAML>=5.1.1 +albumentations>=1.3.0 faiss-cpu==1.7.4 +h5py>=3.7.0 hawk-eyes==2.1.2 -onnx==1.15.0 -onnxruntime-gpu==1.10.0 imutils==0.5.4 kornia==0.6.8 +librosa>=0.7.2 +numba<0.50.0 +numpy>=1.22,<1.23 +onnx==1.15.0 +onnxruntime-gpu==1.10.0 +opencv-python>=4.4.0 +protobuf>=3.20.1,<4.0 +pycocotools==2.0.7 +pyffmpeg==2.0 +pytorch-metric-learning==2.3.0 +pytube>=12.1.3 +scipy>=1.3.0 +shap>=0.34.0 +tensorboard>=2.9.0,<2.10.0 +tk>=0.1.0 +torchmetrics==0.6.0 -e distiller diff --git a/train.py b/train.py index 27eeb55d8..c05e90814 100644 --- a/train.py +++ b/train.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # # Copyright (c) 2018 Intel Corporation -# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. +# Portions Copyright (C) 2019-2024 Maxim Integrated Products, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -702,9 +702,8 @@ def create_model(supported_models, dimensions, args, mode='default'): if not Model: raise RuntimeError("Model " + args.kd_teacher + " not found\n") - if args.dr: - if 'dr' not in module or not module['dr']: - raise ValueError("Dimensionality reduction is not supported for this model") + if args.dr and ('dr' not in module or not module['dr']): + raise ValueError("Dimensionality reduction is not supported for this model") # Set model parameters if args.act_mode_8bit: From da4387e73a993c8038a4014df5263392dee369ad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Thu, 11 Jan 2024 00:48:10 +0300 Subject: [PATCH 12/27] Improve compatibility and documentation (#283) --- README.md | 8 +++++++- README.pdf | Bin 6778163 -> 6782989 bytes requirements.txt | 4 ++-- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 82d1d6205..8a3161e66 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -December 22, 2023 +January 10, 2024 ADI’s MAX78000/MAX78002 project is comprised of five repositories: @@ -1534,6 +1534,11 @@ The following table describes the most important command line arguments for `tra | `--nas` | Enable network architecture search | | | `--nas-policy` | Define NAS policy in YAML file | `--nas-policy nas/nas_policy.yaml` | | `--regression` | Select regression instead of classification (changes Loss function, and log output) | | +| `--dr` | Set target embedding dimensionality for dimensionality reduction |`--dr 64` | +| `--scaf-lr` | Initial learning rate for sub-center ArcFace loss optimizer | | +| `--scaf-scale` |Scale hyperparameter for sub-center ArcFace loss | | +| `--scaf-margin` |Margin hyperparameter for sub-center ArcFace loss | | +| `--backbone-checkpoint` |Path to checkpoint from which to load backbone weights | | | *Display and statistics* | | | | `--enable-tensorboard` | Enable logging to TensorBoard (default: disabled) | | | `--confusion` | Display the confusion matrix | | @@ -1552,6 +1557,7 @@ The following table describes the most important command line arguments for `tra | `--summary onnx_simplified` | Export trained model to simplified [ONNX](https://onnx.ai/) file (default name: model.onnx) | | | `--summary-filename` | Change the file name for the exported model | `--summary-filename mnist.onnx` | | `--save-sample` | Save data[index] from the test set to a NumPy pickle for use as sample data | `--save-sample 10` | +| `--slice-sample` | For models that require RGB input, when the sample from the dataset has additional channels, slice the sample into 3 channels | | #### ONNX Model Export diff --git a/README.pdf b/README.pdf index 022f64b95635e4216ba6b51cc8541c28ade20cf7..fbea4318bbddeb178d1059a4775aec76e00b22a9 100644 GIT binary patch delta 391316 zcmY(~V|3=s+W_#|#@6=Mwrv|*V{5y2ZQHhO+uYiAx8}Be@8|#R{qQ?E$t2fY=Omdk zGnq7P_u`nHefZ`V(vD#|X{ zf!GG~hNf`bz*TTWgWzyoL$) z(+q-~C^x%SCx^WbUAi`3&1c>~I^T##XvI&w`5(7xsT#5#q3->AUqf``3%%8212UIJ zuT(JDUCe?#Pv6jI<9?{llt;4UA$ZM~=ZN&mF=?Yx; ziO7t*4%55>#qq09c^-3-X}D3C-x+!T*a2<_(XUGT$e(JhzKG~{O-WIFXZ_=EM`J^` zBC5B6l!DwdKi&x>&p1av4_@3?PqV5xHH{F@n*W;1)lT->jg3$ 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zcWqzfEh0tg1a_t^*3LTh6q;zC2yKb&oWI*saqN8Em<7}xV=)o41*Y@C^^oF{=Qf5_ z4HX9{TMc(r>L>ZwqQt;NiD-M~?!&5UWpKqj=;%zc`XJ=~P?ViGSg(w|O2qEUeF_d_ zw@<<)+>_0i2UhYz`UsA zL}b!_fHPY_r|5usW-On7Rz?zMDlYCjjI4!M)6x(&(PpgJNtNzNW5P^&CdBS&3Awo1 zx;##)w(N;uOFhXQTj1%2_Wn?Cfa(O*7?^ZVaA_)0vFA*Ioy8j{maqnFMMS3uVW(Wxyjs`1DfF2k z6;dk$-iv;5mcdy1Pq=C?Ayq_m#mMs4&7rMU1{^EvS=j=i%91(Yp%Mdu$*#n1N}!55 zuI0ZJ4LFbmZxxHO`y%7GS9Rb>^N>~ntTT|np?DvXG8j#T6$dB+LWtlw*t&$L>>%2z zgaj}p_yo4eHOq^NG?rmBj+Ul+lnoYhWVJdBff-XOxvnSQjWQ_*82Y=F$6aRvwbiMn zaIIhzOVI!wYD|Q4`Jg~dZeti0{Ab6NQ!b(@9F2XCaf#IIFw|mO9%l>;O{-vp*X;Pg z(WL;#S3VRDMJGfPQ_^&SUk5LFB`qe*N_~?qP_(cX992_dfi|FW$a> zkB~Z+Wd83@-}|L|AG!PjVIH?%xcvO9+=$mc|KR$S>&LHNUSE9v;_@{rhu%K_#Q*=v pz0clz{`n_<^Nm-&{Ki+l{MzjkWXC>w0)ga-XP^D}i=TMn{{Z`-h426X diff --git a/requirements.txt b/requirements.txt index 6ae276138..a0e0ac082 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,14 +7,14 @@ PyYAML>=5.1.1 albumentations>=1.3.0 faiss-cpu==1.7.4 h5py>=3.7.0 -hawk-eyes==2.1.2 +hawk-eyes==2.1.0 imutils==0.5.4 kornia==0.6.8 librosa>=0.7.2 numba<0.50.0 numpy>=1.22,<1.23 onnx==1.15.0 -onnxruntime-gpu==1.10.0 +onnxruntime==1.7.0 opencv-python>=4.4.0 protobuf>=3.20.1,<4.0 pycocotools==2.0.7 From bbcc2aea169b1de9e8c01016d7805818bf6a2327 Mon Sep 17 00:00:00 2001 From: asyatrhl <123384000+asyatrhl@users.noreply.github.com> Date: Mon, 22 Jan 2024 18:07:46 +0300 Subject: [PATCH 13/27] Update ai85net-bayer2rgbnet.py --- models/ai85net-bayer2rgbnet.py | 1 + 1 file changed, 1 insertion(+) diff --git a/models/ai85net-bayer2rgbnet.py b/models/ai85net-bayer2rgbnet.py index 9ed812b3e..4d9f08ba1 100644 --- a/models/ai85net-bayer2rgbnet.py +++ b/models/ai85net-bayer2rgbnet.py @@ -6,6 +6,7 @@ ################################################################################################### """ Bayer to Rgb network for AI85 +PR for test """ from torch import nn From ff5ad098d5a50165b2f4e55c91f82a2d1fc612d5 Mon Sep 17 00:00:00 2001 From: asyatrhl <123384000+asyatrhl@users.noreply.github.com> Date: Tue, 23 Jan 2024 23:07:31 +0300 Subject: [PATCH 14/27] Update regression test hook (#285) --- .github/workflows/test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 560ebe5ab..b71b9b5a3 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -18,7 +18,7 @@ jobs: if: env.IS_SPECIFIC_REPOSITORY == 'true' && env.IS_DEVELOP_BRANCH == 'true' run: | PR_Branch=${{ github.event.pull_request.head.ref }} - Repository=${{ github.repository }} + Repository=${{ github.event.repository.full_name }} PR_Number=${{ github.event.pull_request.number }} PR_Sha=${{ github.event.pull_request.head.sha }} echo "PR_Branch: $PR_Branch" @@ -33,4 +33,4 @@ jobs: -H "Authorization: Bearer ${{ secrets.REGRESSION_TEST }}" \ -H "Accept: application/vnd.github.v3+json" \ "https://api.github.com/repos/MaximIntegratedAI/ai8x-regression/dispatches" \ - -d '{"event_type": "repo-pull-request", "client_payload": {"PR_Branch": "${{ github.event.pull_request.head.ref }}", "Repository": "${{ github.repository }}","PR_Number": "${{ github.event.pull_request.number }}","PR_Sha": "${{ github.event.pull_request.head.sha }}" }' + -d '{"event_type": "repo-pull-request", "client_payload": {"PR_Branch": "${{ github.event.pull_request.head.ref }}", "Repository": "${{ github.event.repository.full_name }}","PR_Number": "${{ github.event.pull_request.number }}","PR_Sha": "${{ github.event.pull_request.head.sha }}" }' From e28e64249ff924ed5ceb9283d9382f35e14cedb9 Mon Sep 17 00:00:00 2001 From: asyatrhl <123384000+asyatrhl@users.noreply.github.com> Date: Tue, 23 Jan 2024 23:29:56 +0300 Subject: [PATCH 15/27] Update regression test hook (#286) --- .github/workflows/test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b71b9b5a3..7c165b208 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -18,7 +18,7 @@ jobs: if: env.IS_SPECIFIC_REPOSITORY == 'true' && env.IS_DEVELOP_BRANCH == 'true' run: | PR_Branch=${{ github.event.pull_request.head.ref }} - Repository=${{ github.event.repository.full_name }} + Repository=${{ github.event.pull_request.head.repo.full_name }} PR_Number=${{ github.event.pull_request.number }} PR_Sha=${{ github.event.pull_request.head.sha }} echo "PR_Branch: $PR_Branch" @@ -33,4 +33,4 @@ jobs: -H "Authorization: Bearer ${{ secrets.REGRESSION_TEST }}" \ -H "Accept: application/vnd.github.v3+json" \ "https://api.github.com/repos/MaximIntegratedAI/ai8x-regression/dispatches" \ - -d '{"event_type": "repo-pull-request", "client_payload": {"PR_Branch": "${{ github.event.pull_request.head.ref }}", "Repository": "${{ github.event.repository.full_name }}","PR_Number": "${{ github.event.pull_request.number }}","PR_Sha": "${{ github.event.pull_request.head.sha }}" }' + -d '{"event_type": "repo-pull-request", "client_payload": {"PR_Branch": "${{ github.event.pull_request.head.ref }}", "Repository": "${{ github.event.pull_request.head.repo.full_name }}","PR_Number": "${{ github.event.pull_request.number }}","PR_Sha": "${{ github.event.pull_request.head.sha }}" }' From 3a4a661c958c50a9d448c8de4e97296b3bb164f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alican=20G=C3=B6k?= Date: Fri, 2 Feb 2024 00:43:13 +0300 Subject: [PATCH 16/27] Add NAS KWS model, Dynamic Augmentation and Automated Evaluation Notebook (#280) Co-authored-by: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Co-authored-by: Merve Eyuboglu <141146522+EyubogluMerve@users.noreply.github.com> --- datasets/kws20.py | 261 +++++--- datasets/msnoise.py | 226 +++---- datasets/signalmixer.py | 136 +++++ models/ai85net-kws20-nas.py | 107 ++++ notebooks/Bayer2RGB_Evaluation.ipynb | 6 +- notebooks/KWS_Noise_Evaluation.ipynb | 868 +++++++++++++++++++++++++++ scripts/evaluate_kws20_nas.sh | 2 + scripts/train_kws20_nas.sh | 2 + train_all_models.sh | 3 + 9 files changed, 1384 insertions(+), 227 deletions(-) create mode 100644 datasets/signalmixer.py create mode 100644 models/ai85net-kws20-nas.py create mode 100644 notebooks/KWS_Noise_Evaluation.ipynb create mode 100755 scripts/evaluate_kws20_nas.sh create mode 100755 scripts/train_kws20_nas.sh diff --git a/datasets/kws20.py b/datasets/kws20.py index e63cc1c24..245450ede 100644 --- a/datasets/kws20.py +++ b/datasets/kws20.py @@ -1,6 +1,7 @@ # # Copyright (c) 2018 Intel Corporation # Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. +# Portions Copyright (C) 2023-2024 Analog Devices, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -44,14 +45,14 @@ class KWS: Dataset, 1D folded. Args: - root (string): Root directory of dataset where ``KWS/processed/dataset.pt`` - exist. + root (string): Root directory of dataset where ``KWS/processed/dataset.pt`` exist. classes(array): List of keywords to be used. d_type(string): Option for the created dataset. ``train`` or ``test``. - n_augment(int, optional): Number of augmented samples added to the dataset from - each sample by random modifications, i.e. stretching, shifting and random noise. - transform (callable, optional): A function/transform that takes in an PIL image - and returns a transformed version. + transform (callable, optional): A function/transform that takes in a signal between [0, 1] + and returns a transformed version, suitable for ai8x training / evaluation. + quantization_scheme (dict, optional): Dictionary containing quantization scheme parameters. + If not provided, default values are used. + augmentation (dict, optional): Dictionary containing augmentation parameters. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. @@ -86,15 +87,15 @@ def __init__(self, root, classes, d_type, t_type, transform=None, quantization_s self.__parse_augmentation(augmentation) if not self.save_unquantized: - self.data_file = 'dataset2.pt' + self.data_file = 'dataset.pt' else: self.data_file = 'unquantized.pt' if download: self.__download() - self.data, self.targets, self.data_type = torch.load(os.path.join( - self.processed_folder, self.data_file)) + self.data, self.targets, self.data_type, self.shift_limits = \ + torch.load(os.path.join(self.processed_folder, self.data_file)) print(f'\nProcessing {self.d_type}...') self.__filter_dtype() @@ -156,10 +157,6 @@ def __parse_augmentation(self, augmentation): print('No key `shift` in input augmentation dictionary! ' 'Using defaults: [Min:-0.1, Max: 0.1]') self.augmentation['shift'] = {'min': -0.1, 'max': 0.1} - if 'stretch' not in augmentation: - print('No key `stretch` in input augmentation dictionary! ' - 'Using defaults: [Min: 0.8, Max: 1.3]') - self.augmentation['stretch'] = {'min': 0.8, 'max': 1.3} def __download(self): @@ -363,23 +360,27 @@ def __filter_dtype(self): # take a copy of the original data and targets temporarily for validation set self.data_original = self.data.clone() self.targets_original = self.targets.clone() + self.data_type_original = self.data_type.clone() self.data = self.data[idx_to_select, :] self.targets = self.targets[idx_to_select, :] + self.data_type = self.data_type[idx_to_select, :] # append validation set to the training set if validation examples are explicitly included if self.d_type == 'train': - idx_to_select = (self.data_type == 2)[:, -1] + idx_to_select = (self.data_type_original == 2)[:, -1] if idx_to_select.sum() > 0: # if validation examples exist self.data = torch.cat((self.data, self.data_original[idx_to_select, :]), dim=0) self.targets = \ torch.cat((self.targets, self.targets_original[idx_to_select, :]), dim=0) + self.data_type = \ + torch.cat((self.data_type, self.data_type_original[idx_to_select, :]), dim=0) # indicate the list of validation indices to be used by distiller's dataloader self.valid_indices = range(set_size, set_size + idx_to_select.sum()) print(f'validation set: {idx_to_select.sum()} elements') del self.data_original del self.targets_original - del self.data_type + del self.data_type_original def __filter_classes(self): initial_new_class_label = len(self.class_dict) @@ -401,28 +402,63 @@ def __filter_classes(self): def __len__(self): return len(self.data) + def __reshape_audio(self, audio, row_len=128): + # add overlap if necessary later on + return torch.transpose(audio.reshape((-1, row_len)), 1, 0) + + def shift_and_noise_augment(self, audio, shift_limits): + """Augments audio by adding random shift and noise. + """ + random_noise_var_coeff = np.random.uniform(self.augmentation['noise_var']['min'], + self.augmentation['noise_var']['max']) + random_shift_sample = np.random.randint(shift_limits[0], shift_limits[1]) + + aug_audio = self.shift(audio, random_shift_sample) + aug_audio = self.add_quantized_white_noise(aug_audio, random_noise_var_coeff) + + return aug_audio + def __getitem__(self, index): - inp, target = self.data[index].type(torch.FloatTensor), int(self.targets[index]) + inp, target = self.data[index], int(self.targets[index]) + data_type, shift_limits = self.data_type[index], self.shift_limits[index] + + # apply dynamic shift and noise augmentation to training examples + if data_type == 0: + inp = self.shift_and_noise_augment(inp, shift_limits) + + # reshape to 2D + inp = self.__reshape_audio(inp) + + inp = inp.type(torch.FloatTensor) + if not self.save_unquantized: inp /= 256 if self.transform is not None: inp = self.transform(inp) + return inp, target @staticmethod def add_white_noise(audio, noise_var_coeff): - """Adds zero mean Gaussian noise to image with specified variance. + """Adds zero mean Gaussian noise to the audio with specified variance. """ coeff = noise_var_coeff * np.mean(np.abs(audio)) noisy_audio = audio + coeff * np.random.randn(len(audio)) return noisy_audio @staticmethod - def shift(audio, shift_sec, fs): + def add_quantized_white_noise(audio, noise_var_coeff): + """Adds zero mean Gaussian noise to the audio with specified variance. + """ + coeff = noise_var_coeff * torch.mean(torch.abs(audio.type(torch.float)-128)) + noise = (coeff * torch.randn(len(audio))).type(torch.int16) + return (audio + noise).clip(0, 255).type(torch.uint8) + + @staticmethod + def shift(audio, shift_sample): """Shifts audio. """ - shift_count = int(shift_sec * fs) - return np.roll(audio, shift_count) + return torch.roll(audio, shift_sample) @staticmethod def stretch(audio, rate=1): @@ -437,36 +473,6 @@ def stretch(audio, rate=1): return audio2 - def augment(self, audio, fs, verbose=False): - """Augments audio by adding random noise, shift and stretch ratio. - """ - random_noise_var_coeff = np.random.uniform(self.augmentation['noise_var']['min'], - self.augmentation['noise_var']['max']) - random_shift_time = np.random.uniform(self.augmentation['shift']['min'], - self.augmentation['shift']['max']) - random_stretch_coeff = np.random.uniform(self.augmentation['stretch']['min'], - self.augmentation['stretch']['max']) - - sox_effects = [["speed", str(random_stretch_coeff)], ["rate", str(fs)]] - aug_audio, _ = torchaudio.sox_effects.apply_effects_tensor( - torch.unsqueeze(torch.from_numpy(audio).float(), dim=0), fs, sox_effects) - aug_audio = aug_audio.numpy().squeeze() - aug_audio = self.shift(aug_audio, random_shift_time, fs) - aug_audio = self.add_white_noise(aug_audio, random_noise_var_coeff) - - if verbose: - print(f'random_noise_var_coeff: {random_noise_var_coeff:.2f}\nrandom_shift_time: \ - {random_shift_time:.2f}\nrandom_stretch_coeff: {random_stretch_coeff:.2f}') - return aug_audio - - def augment_multiple(self, audio, fs, n_augment, verbose=False): - """Calls `augment` function for n_augment times for given audio data. - Finally the original audio is added to have (n_augment+1) audio data. - """ - aug_audio = [self.augment(audio, fs, verbose=verbose) for i in range(n_augment)] - aug_audio.insert(0, audio) - return aug_audio - @staticmethod def compand(data, mu=255): """Compand the signal level to warp from Laplacian distribution to uniform distribution""" @@ -498,9 +504,59 @@ def quantize_audio(data, num_bits=8, compand=False, mu=255): q_data = np.clip(q_data, 0, max_val) return np.uint8(q_data) - def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): - print('Generating dataset from raw data samples for the first time. ') - print('This process will take significant time (~60 minutes)...') + def get_audio_endpoints(self, audio, fs): + """Future: May implement a method to detect the beginning & end of voice activity in audio. + Currently, it returns end points compatible with augmentation['shift'] values + """ + return int(-self.augmentation['shift']['min'] * fs), \ + int(len(audio) - self.augmentation['shift']['max'] * fs) + + def speed_augment(self, audio, fs, sample_no=0): + """Augments audio by randomly changing the speed of the audio. + The generated coefficient follows 0.9, 1.1, 0.95, 1.05... pattern + """ + speed_multiplier = 1.0 + 0.2 * (sample_no % 2 - 0.5) / (1 + sample_no // 2) + + sox_effects = [["speed", str(speed_multiplier)], ["rate", str(fs)]] + aug_audio, _ = torchaudio.sox_effects.apply_effects_tensor( + torch.unsqueeze(torch.from_numpy(audio).float(), dim=0), fs, sox_effects) + aug_audio = aug_audio.numpy().squeeze() + + return aug_audio, speed_multiplier + + def speed_augment_multiple(self, audio, fs, exp_len, n_augment): + """Calls `speed_augment` function for n_augment times for given audio data. + Finally the original audio is added to have (n_augment+1) audio data. + """ + aug_audio = [None] * (n_augment + 1) + aug_speed = np.ones((n_augment + 1,)) + shift_limits = np.zeros((n_augment + 1, 2)) + voice_begin_idx, voice_end_idx = self.get_audio_endpoints(audio, fs) + aug_audio[0] = audio + for i in range(n_augment): + aug_audio[i+1], aug_speed[i+1] = self.speed_augment(audio, fs, sample_no=i) + for i in range(n_augment + 1): + if len(aug_audio[i]) < exp_len: + aug_audio[i] = np.pad(aug_audio[i], (0, exp_len - len(aug_audio[i])), 'constant') + aug_begin_idx = voice_begin_idx * aug_speed[i] + aug_end_idx = voice_end_idx * aug_speed[i] + if aug_end_idx - aug_begin_idx <= exp_len: + # voice activity duration is shorter than the expected length + segment_begin = max(aug_end_idx, exp_len) - exp_len + segment_end = max(aug_end_idx, exp_len) + aug_audio[i] = aug_audio[i][segment_begin:segment_end] + shift_limits[i, 0] = -aug_begin_idx + (max(aug_end_idx, exp_len) - exp_len) + shift_limits[i, 1] = max(aug_end_idx, exp_len) - aug_end_idx + else: + # voice activity duraction is longer than the expected length + midpoint = (aug_begin_idx + aug_end_idx) // 2 + aug_audio[i] = aug_audio[i][midpoint - exp_len // 2: midpoint + exp_len // 2] + shift_limits[i, :] = [0, 0] + return aug_audio, shift_limits + + def __gen_datasets(self, exp_len=16384): + print('Generating dataset from raw data samples for the first time.') + print('This process may take a few minutes.') with warnings.catch_warnings(): warnings.simplefilter('error') @@ -508,12 +564,6 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): labels = [d for d in lst if os.path.isdir(os.path.join(self.raw_folder, d)) and d[0].isalpha()] - # PARAMETERS - overlap = int(np.ceil(row_len * overlap_ratio)) - num_rows = int(np.ceil(exp_len / (row_len - overlap))) - data_len = int((num_rows * row_len - (num_rows - 1) * overlap)) - print(f'data_len: {data_len}') - # show the size of dataset for each keyword print('------------- Label Size ---------------') for i, label in enumerate(labels): @@ -534,27 +584,36 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): for i, label in enumerate(labels): print(f'Processing the label: {label}. {i + 1} of {len(labels)}') record_list = sorted(os.listdir(os.path.join(self.raw_folder, label))) + record_len = len(record_list) + + # get the number testing samples for the class + test_count_class = 0 + for r, record_name in enumerate(record_list): + local_filename = os.path.join(label, record_name) + if local_filename in testing_set: + test_count_class += 1 + + # no augmentation for testing set, subtract them accordingly + number_of_total_samples = record_len * (self.augmentation['aug_num'] + 1) - \ + test_count_class * self.augmentation['aug_num'] - # dimension: row_length x number_of_rows if not self.save_unquantized: - data_in = np.empty(((self.augmentation['aug_num'] + 1) * len(record_list), - row_len, num_rows), dtype=np.uint8) + data_in = np.empty((number_of_total_samples, exp_len), dtype=np.uint8) else: - data_in = np.empty(((self.augmentation['aug_num'] + 1) * len(record_list), - row_len, num_rows), dtype=np.float32) - data_type = np.empty(((self.augmentation['aug_num'] + 1) * len(record_list), 1), - dtype=np.uint8) - # create data classes - data_class = np.full(((self.augmentation['aug_num'] + 1) * len(record_list), 1), i, - dtype=np.uint8) + data_in = np.empty((number_of_total_samples, exp_len), dtype=np.float32) + + data_type = np.empty((number_of_total_samples, 1), dtype=np.uint8) + data_shift_limits = np.empty((number_of_total_samples, 2), dtype=np.int16) + data_class = np.full((number_of_total_samples, 1), i, dtype=np.uint8) time_s = time.time() + sample_index = 0 for r, record_name in enumerate(record_list): local_filename = os.path.join(label, record_name) if r % 1000 == 0: - print(f'\t{r + 1} of {len(record_list)}') + print(f'\t{r + 1} of {record_len}') if local_filename in testing_set: d_typ = np.uint8(1) # test @@ -568,29 +627,28 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): record_pth = os.path.join(self.raw_folder, label, record_name) record, fs = librosa.load(record_pth, offset=0, sr=None) - audio_seq_list = self.augment_multiple(record, fs, - self.augmentation['aug_num']) - for n_a, audio_seq in enumerate(audio_seq_list): - # store set type: train, test or validate - data_type[(self.augmentation['aug_num'] + 1) * r + n_a, 0] = d_typ - - # Write audio 128x128=16384 samples without overlap - for n_r in range(num_rows): - start_idx = n_r * (row_len - overlap) - end_idx = start_idx + row_len - audio_chunk = audio_seq[start_idx:end_idx] - # pad zero if the length of the chunk is smaller than row_len - audio_chunk = np.pad(audio_chunk, [0, row_len - audio_chunk.size]) - # store input data after quantization - data_idx = (self.augmentation['aug_num'] + 1) * r + n_a - if not self.save_unquantized: - data_in[data_idx, :, n_r] = \ - KWS.quantize_audio(audio_chunk, - num_bits=self.quantization['bits'], - compand=self.quantization['compand'], - mu=self.quantization['mu']) - else: - data_in[data_idx, :, n_r] = audio_chunk + + # normalize dynamic range to [-1, +1] + record = record / np.max(np.abs(record)) + + if d_typ != 1: # training and validation examples get speed augmentation + no_augmentations = self.augmentation['aug_num'] + else: # test examples don't get speed augmentation + no_augmentations = 0 + + # apply speed augmentations and calculate shift limits + audio_seq_list, shift_limits = \ + self.speed_augment_multiple(record, fs, exp_len, no_augmentations) + + for local_id, audio_seq in enumerate(audio_seq_list): + data_in[sample_index] = \ + KWS.quantize_audio(audio_seq, + num_bits=self.quantization['bits'], + compand=self.quantization['compand'], + mu=self.quantization['mu']) + data_shift_limits[sample_index] = shift_limits[local_id] + data_type[sample_index] = d_typ + sample_index += 1 dur = time.time() - time_s print(f'Finished in {dur:.3f} seconds.') @@ -600,19 +658,30 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): data_in_all = data_in.copy() data_class_all = data_class.copy() data_type_all = data_type.copy() + data_shift_limits_all = data_shift_limits.copy() else: data_in_all = np.concatenate((data_in_all, data_in), axis=0) data_class_all = np.concatenate((data_class_all, data_class), axis=0) data_type_all = np.concatenate((data_type_all, data_type), axis=0) + data_shift_limits_all = \ + np.concatenate((data_shift_limits_all, data_shift_limits), axis=0) dur = time.time() - time_s print(f'Data concatenation finished in {dur:.3f} seconds.') data_in_all = torch.from_numpy(data_in_all) data_class_all = torch.from_numpy(data_class_all) data_type_all = torch.from_numpy(data_type_all) + data_shift_limits_all = torch.from_numpy(data_shift_limits_all) + + # apply static shift & noise augmentation for validation examples + for sample_index in range(data_in_all.shape[0]): + if data_type_all[sample_index] == 2: + data_in_all[sample_index] = \ + self.shift_and_noise_augment(data_in_all[sample_index], + data_shift_limits_all[sample_index]) - mfcc_dataset = (data_in_all, data_class_all, data_type_all) - torch.save(mfcc_dataset, os.path.join(self.processed_folder, self.data_file)) + raw_dataset = (data_in_all, data_class_all, data_type_all, data_shift_limits_all) + torch.save(raw_dataset, os.path.join(self.processed_folder, self.data_file)) print('Dataset created.') print(f'Training: {train_count}, Validation: {valid_count}, Test: {test_count}') @@ -658,7 +727,7 @@ def KWS_get_datasets(data, load_train=True, load_test=True, num_classes=6): else: raise ValueError(f'Unsupported num_classes {num_classes}') - augmentation = {'aug_num': 2, 'shift': {'min': -0.15, 'max': 0.15}, + augmentation = {'aug_num': 2, 'shift': {'min': -0.1, 'max': 0.1}, 'noise_var': {'min': 0, 'max': 1.0}} quantization_scheme = {'compand': False, 'mu': 10} diff --git a/datasets/msnoise.py b/datasets/msnoise.py index 9bfeb38fb..e2eaee42e 100644 --- a/datasets/msnoise.py +++ b/datasets/msnoise.py @@ -1,6 +1,7 @@ # # Copyright (c) 2018 Intel Corporation # Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. +# Portions Copyright (C) 2023-2024 Analog Devices, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -44,6 +45,7 @@ class MSnoise: exist. classes(array): List of keywords to be used. d_type(string): Option for the created dataset. ``train`` or ``test``. + dataset_len(int): Dataset length to be returned. remove_unknowns (bool, optional): If true, unchosen classes are not gathered as the unknown class. transform (callable, optional): A function/transform that takes in an PIL image @@ -62,17 +64,23 @@ class MSnoise: 'CopyMachine': 6, 'Field': 7, 'Hallway': 8, 'Kitchen': 9, 'LivingRoom': 10, 'Metro': 11, 'Munching': 12, 'NeighborSpeaking': 13, 'Office': 14, 'Park': 15, 'Restaurant': 16, 'ShuttingDoor': 17, - 'Square': 18, 'SqueakyChair': 19, 'Station': 20, 'Traffic': 21, - 'Typing': 22, 'VacuumCleaner': 23, 'WasherDryer': 24, 'Washing': 25} + 'Square': 18, 'SqueakyChair': 19, 'Station': 20, 'TradeShow': 21, 'Traffic': 22, + 'Typing': 23, 'VacuumCleaner': 24, 'WasherDryer': 25, 'Washing': 26} - def __init__(self, root, classes, d_type, remove_unknowns=False, - transform=None, quantize=False, download=False): + def __init__(self, root, classes, d_type, dataset_len, exp_len=16384, fs=16000, + noise_time_step=0.25, remove_unknowns=False, transform=None, + quantize=False, download=False): self.root = root self.classes = classes self.d_type = d_type self.remove_unknowns = remove_unknowns self.transform = transform + self.dataset_len = dataset_len + self.exp_len = exp_len + self.fs = fs + self.noise_time_step = noise_time_step + self.noise_train_folder = os.path.join(self.raw_folder, 'noise_train') self.noise_test_folder = os.path.join(self.raw_folder, 'noise_test') self.url_train = \ @@ -81,16 +89,13 @@ def __init__(self, root, classes, d_type, remove_unknowns=False, 'https://api.github.com/repos/microsoft/MS-SNSD/contents/noise_test?ref=master' self.quantize = quantize - if self.quantize: - self.data_file = 'dataset_quantized.pt' - else: - self.data_file = 'dataset_unquantized.pt' - if download: self.__download() - self.data, self.targets, self.data_type = torch.load(os.path.join( - self.processed_folder, self.data_file)) + self.data, self.targets, self.data_type, self.rms_val = self.__gen_datasets() + + # rms values for each sample to be returned + self.rms = np.zeros(self.dataset_len) self.__filter_dtype() self.__filter_classes() @@ -101,24 +106,16 @@ def raw_folder(self): """ return os.path.join(self.root, self.__class__.__name__, 'raw') - @property - def processed_folder(self): - """Folder for the processed data. - """ - return os.path.join(self.root, self.__class__.__name__, 'processed') - def __download(self): - if self.__check_exists(): + if os.path.exists(self.raw_folder): return self.__makedir_exist_ok(self.noise_train_folder) self.__makedir_exist_ok(self.noise_test_folder) - self.__makedir_exist_ok(self.processed_folder) self.__download_raw(self.url_train) self.__download_raw(self.url_test) - self.__gen_datasets() def __download_raw(self, api_url): opener = urllib.request.build_opener() @@ -145,9 +142,6 @@ def __download_raw(self, api_url): print('Interrupted while downloading!') sys.exit() - def __check_exists(self): - return os.path.exists(os.path.join(self.processed_folder, self.data_file)) - def __makedir_exist_ok(self, dirpath): try: os.makedirs(dirpath) @@ -158,21 +152,25 @@ def __makedir_exist_ok(self, dirpath): raise def __filter_dtype(self): + if self.d_type == 'train': - idx_to_select = (self.data_type == 0)[:, -1] + bool_list = [i == 0 for i in self.data_type] + idx_to_select = [i for i, x in enumerate(bool_list) if x] elif self.d_type == 'test': - idx_to_select = (self.data_type == 1)[:, -1] + bool_list = [i == 1 for i in self.data_type] + idx_to_select = [i for i, x in enumerate(bool_list) if x] else: print(f'Unknown data type: {self.d_type}') return - print(self.data.shape) - self.data = self.data[idx_to_select, :] - self.targets = self.targets[idx_to_select, :] + self.data = [self.data[i] for i in idx_to_select] + self.targets = [self.targets[i] for i in idx_to_select] + self.rms_val = [self.rms_val[i] for i in idx_to_select] del self.data_type def __filter_classes(self): print('\n') + self.targets = np.array(self.targets) initial_new_class_label = len(self.class_dict) new_class_label = initial_new_class_label for c in self.classes: @@ -181,21 +179,22 @@ def __filter_classes(self): return # else: print(f'Class {c}, {self.class_dict[c]}') - num_elems = (self.targets == self.class_dict[c]).cpu().sum() - print(f'Number of elements in class {c}: {num_elems}') - self.targets[(self.targets == self.class_dict[c])] = new_class_label + bool_list = [self.class_dict[c] == i for i in self.targets] + idx = [i for i, x in enumerate(bool_list) if x] + self.targets[idx] = new_class_label + print(f'{c}: {new_class_label - initial_new_class_label}') new_class_label += 1 - num_elems = (self.targets < initial_new_class_label).cpu().sum() - print(f'Number of elements in class unknown: {num_elems}') self.targets[(self.targets < initial_new_class_label)] = new_class_label if self.remove_unknowns: - idx_to_remove = (self.targets == new_class_label)[:, -1] - idx_to_keep = torch.logical_not(idx_to_remove) - self.data = self.data[idx_to_keep, :] - self.targets = self.targets[idx_to_keep, :] - self.targets -= initial_new_class_label - print(np.unique(self.targets.data.cpu())) + bool_list = [i != new_class_label for i in self.targets] + idx_to_keep = [i for i, x in enumerate(bool_list) if x] + + self.data = [self.data[i] for i in idx_to_keep] + self.targets = [self.targets[i] for i in idx] + self.rms_val = [self.rms_val[i] for i in idx] + + self.targets = [target - initial_new_class_label for target in self.targets] print('\n') @staticmethod @@ -210,20 +209,36 @@ def quantize_audio(data, num_bits=8): return np.uint8(q_data) def __len__(self): - return len(self.data) + return self.dataset_len def __getitem__(self, index): - inp, target = self.data[index].type(torch.FloatTensor), int(self.targets[index]) + + rec_num = len(self.data) + + rnd_num = np.random.randint(0, rec_num) + self.rms[index] = self.rms_val[rnd_num] + + rec_len = len(self.data[rnd_num]) + + max_start_idx = rec_len - self.exp_len + start_idx = np.random.randint(0, max_start_idx) + end_idx = start_idx + self.exp_len + + inp = self.__reshape_audio(self.data[rnd_num][start_idx:end_idx]) + target = int(self.targets[rnd_num]) + if self.quantize: inp /= 256 if self.transform is not None: inp = self.transform(inp) return inp, target - def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0, - noise_time_step=0.25, train_ratio=0.6): - print('Generating dataset from raw data samples for the first time. ') - print('Warning: This process could take 5-10 minutes!') + def __reshape_audio(self, audio, row_len=128): + + return torch.transpose(torch.tensor(audio.reshape((-1, row_len))), 1, 0) + + def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0): + with warnings.catch_warnings(): warnings.simplefilter('error') @@ -253,76 +268,33 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0, # Folders train_test_folders = [self.noise_train_folder, self.noise_test_folder] - # Determining the array sizes - num_seqs = 0 - for label in labels: - train_count = 0 - test_count = 0 + data_in = [] + data_type = [] + data_class = [] + rms_val = [] + + for i, label in enumerate(labels): for folder in train_test_folders: for record_name in os.listdir(folder): if record_name.split('_')[0] in label: record_path = os.path.join(folder, record_name) - record, fs = librosa.load(record_path, offset=0, sr=None) - rec_len = np.size(record) - max_start_time = ((rec_len / fs - 1) - - (rec_len / fs % noise_time_step)) - num_seqs += int(max_start_time / noise_time_step + 1) - print(f'Num sequences: {num_seqs}') - - # Creating the empty arrays - if self.quantize: - data_in = np.zeros((num_seqs, row_len, num_rows), dtype=np.uint8) - else: - data_in = np.zeros((num_seqs, row_len, num_rows), dtype=np.float32) - data_type = np.zeros((num_seqs, 1), dtype=np.uint8) - data_class = np.zeros((num_seqs, 1), dtype=np.uint8) + record, _ = librosa.load(record_path, offset=0, sr=None) - data_idx = 0 - for i, label in enumerate(labels): - print(f'Processing label:{label}') - train_count = 0 - test_count = 0 - for folder in train_test_folders: - for record_name in sorted(os.listdir(folder)): - if record_name.split('_')[0] in label: - if hash(record_name) % 10 < 10*train_ratio: - d_type = np.uint8(0) # train+val - train_count += 1 + if self.quantize: + data_in.append(self.quantize_audio(record)) else: - d_type = np.uint8(1) # test - test_count += 1 - record_path = os.path.join(folder, record_name) - record, fs = librosa.load(record_path, offset=0, sr=None) - rec_len = np.size(record) - max_start_time = \ - (rec_len / fs - 1) - (rec_len / fs % noise_time_step) - for start_time in np.arange(0, - int((max_start_time+noise_time_step)*fs), - int(noise_time_step*fs)): - end_time = start_time + fs - audio_seq = record[start_time:end_time] - data_type[data_idx, 0] = d_type - data_class[data_idx, 0] = i - for n_r in range(num_rows): - start_idx = n_r*(row_len - overlap) - end_idx = start_idx + row_len - audio_chunk = audio_seq[start_idx:end_idx] - audio_chunk = \ - np.pad(audio_chunk, [0, row_len-audio_chunk.size]) - if self.quantize: - data_in[data_idx, :, n_r] = \ - self.quantize_audio(audio_chunk) - else: - data_in[data_idx, :, n_r] = audio_chunk - data_idx += 1 - - data_in = torch.from_numpy(data_in) - data_class = torch.from_numpy(data_class) - data_type = torch.from_numpy(data_type) - - noise_dataset = (data_in, data_class, data_type) - torch.save(noise_dataset, os.path.join(self.processed_folder, self.data_file)) - print('Dataset created!') + data_in.append(record) + + if folder == self.noise_train_folder: + data_type.append(0) # train + val + elif folder == self.noise_test_folder: + data_type.append(1) # test + + data_class.append(i) + rms_val.append(np.mean(record**2)**0.5) + + noise_dataset = (data_in, data_class, data_type, rms_val) + return noise_dataset def MSnoise_get_datasets(data, load_train=True, load_test=True): @@ -338,9 +310,11 @@ def MSnoise_get_datasets(data, load_train=True, load_test=True): classes = ['AirConditioner', 'AirportAnnouncements', 'Babble', 'Bus', 'CafeTeria', 'Car', - 'CopyMachine', 'Metro', - 'Office', 'Restaurant', 'ShuttingDoor', - 'Traffic', 'Typing', 'VacuumCleaner', 'Washing'] + 'CopyMachine', 'Field', 'Hallway', 'Kitchen', + 'LivingRoom', 'Metro', 'Munching', 'NeighborSpeaking', + 'Office', 'Park', 'Restaurant', 'ShuttingDoor', + 'Square', 'SqueakyChair', 'Station', 'Traffic', + 'Typing', 'VacuumCleaner', 'WasherDryer', 'Washing', 'TradeShow'] remove_unknowns = True transform = transforms.Compose([ @@ -349,14 +323,14 @@ def MSnoise_get_datasets(data, load_train=True, load_test=True): quantize = True if load_train: - train_dataset = MSnoise(root=data_dir, classes=classes, d_type='train', + train_dataset = MSnoise(root=data_dir, classes=classes, d_type='train', dataset_len=11005, remove_unknowns=remove_unknowns, transform=transform, quantize=quantize, download=True) else: train_dataset = None if load_test: - test_dataset = MSnoise(root=data_dir, classes=classes, d_type='test', + test_dataset = MSnoise(root=data_dir, classes=classes, d_type='test', dataset_len=11005, remove_unknowns=remove_unknowns, transform=transform, quantize=quantize, download=True) @@ -378,33 +352,27 @@ def MSnoise_get_unquantized_datasets(data, load_train=True, load_test=True): """ (data_dir, args) = data - # classes = ['AirConditioner', 'AirportAnnouncements', - # 'Babble', 'Bus', 'CafeTeria', 'Car', - # 'CopyMachine', 'Field', 'Hallway', 'Kitchen', - # 'LivingRoom', 'Metro', 'Munching', 'NeighborSpeaking', - # 'Office', 'Park', 'Restaurant', 'ShuttingDoor', - # 'Square', 'SqueakyChair', 'Station', 'Traffic', - # 'Typing', 'VacuumCleaner', 'WasherDryer', 'Washing'] - - classes = ['AirConditioner', - 'CafeTeria', 'Car', - 'CopyMachine', - 'Office', 'Restaurant', - 'Typing', 'VacuumCleaner', 'WasherDryer'] + classes = ['AirConditioner', 'AirportAnnouncements', + 'Babble', 'Bus', 'CafeTeria', 'Car', + 'CopyMachine', 'Field', 'Hallway', 'Kitchen', + 'LivingRoom', 'Metro', 'Munching', 'NeighborSpeaking', + 'Office', 'Park', 'Restaurant', 'ShuttingDoor', + 'Square', 'SqueakyChair', 'Station', 'Traffic', + 'Typing', 'VacuumCleaner', 'WasherDryer', 'Washing', 'TradeShow'] remove_unknowns = True transform = None quantize = False if load_train: - train_dataset = MSnoise(root=data_dir, classes=classes, d_type='train', + train_dataset = MSnoise(root=data_dir, classes=classes, d_type='train', dataset_len=11005, remove_unknowns=remove_unknowns, transform=transform, quantize=quantize, download=True) else: train_dataset = None if load_test: - test_dataset = MSnoise(root=data_dir, classes=classes, d_type='test', + test_dataset = MSnoise(root=data_dir, classes=classes, d_type='test', dataset_len=11005, remove_unknowns=remove_unknowns, transform=transform, quantize=quantize, download=True) diff --git a/datasets/signalmixer.py b/datasets/signalmixer.py new file mode 100644 index 000000000..68845f1ae --- /dev/null +++ b/datasets/signalmixer.py @@ -0,0 +1,136 @@ +# +# Copyright (c) 2018 Intel Corporation +# Portions Copyright (C) 2019-2023 Maxim Integrated Products, Inc. +# Portions Copyright (C) 2023-2024 Analog Devices, Inc. +# +# 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. +# +""" +Classes and functions used to create noisy keyword spotting dataset. +""" +import numpy as np +import torch + + +class signalmixer: + """ + Signal mixer dataloader to create datasets with specified + length using a noise dataset and a speech dataset and a specified SNR level. + + Args: + signal_dataset(object): KWS dataset object. + snr(int): SNR level to be created in the mixed dataset. + noise_kind(string): Noise kind that will be applied to the speech dataset. + noise_dataset(object, optional): MSnoise dataset object. + """ + + def __init__(self, signal_dataset, snr, noise_kind, noise_dataset=None): + + self.signal_data = signal_dataset.data + self.signal_targets = signal_dataset.targets + + if noise_kind != 'WhiteNoise': + self.noise_data = noise_dataset.data + self.noise_targets = noise_dataset.targets + + # using getitem to reach the noise test data + self.noise_dataset_float = next(iter(torch.utils.data.DataLoader( + noise_dataset, batch_size=noise_dataset.dataset_len)))[0] + + self.noise_rms = noise_dataset.rms + + self.snr = snr + self.noise_kind = noise_kind + + # using getitem to reach the speech test data + self.test_dataset_float = next(iter(torch.utils.data.DataLoader( + signal_dataset, batch_size=signal_dataset.data.shape[0])))[0] + + if noise_kind == 'WhiteNoise': + self.mixed_signal = self.white_noise_mixer() + else: + self.mixed_signal = self.snr_mixer() + + def __getitem__(self, index): + + inp = self.mixed_signal[index].type(torch.FloatTensor) + target = int(self.signal_targets[index]) + return inp, target + + def __len__(self): + return len(self.mixed_signal) + + def snr_mixer(self): + ''' creates mixed signal dataset using the SNR level and the noise dataset + ''' + clean = self.test_dataset_float + noise = self.noise_dataset_float + + idx = np.random.randint(0, noise.shape[0], clean.shape[0]) + noise = noise[idx] + rms_noise = self.noise_rms[idx] + + snr = self.snr + + rmsclean = torch.sqrt(torch.mean(clean.reshape( + clean.shape[0], -1)**2, 1, keepdims=True)).unsqueeze(1) + scalarclean = 1 / rmsclean + clean = clean * scalarclean + + scalarnoise = 1 / rms_noise.reshape(-1, 1, 1) + noise = noise * scalarnoise + + cleanfactor = 10**(snr/20) + noisyspeech = cleanfactor * clean + noise + noisyspeech = noisyspeech / (torch.tensor(scalarnoise) + cleanfactor * scalarclean) + + # 16384 --> (noisyspeech[0].shape[0])*(noisyspeech[0].shape[1]) + speech_shape = noisyspeech[0].shape[0]*noisyspeech[0].shape[1] + max_mixed = torch.max(abs(noisyspeech.reshape( + noisyspeech.shape[0], speech_shape)), 1, keepdims=True).values + + noisyspeech = noisyspeech * (1 / max_mixed).unsqueeze(1) + return noisyspeech + + def white_noise_mixer(self): + + '''creates mixed signal dataset using the SNR level and white noise + ''' + clean = self.test_dataset_float + snr = self.snr + + mean = 0 + std = 1 + noise = np.random.normal(mean, std, clean.shape) + noise = torch.tensor(noise, dtype=torch.float32) + + rmsclean = (torch.mean(clean.reshape( + clean.shape[0], -1)**2, 1, keepdims=True)**0.5).unsqueeze(1) + scalarclean = 1 / rmsclean + clean = clean * scalarclean + + rmsnoise = (torch.mean(noise.reshape( + noise.shape[0], -1)**2, 1, keepdims=True)**0.5).unsqueeze(1) + scalarnoise = 1 / rmsnoise + noise = noise * scalarnoise + + cleanfactor = 10**(snr/20) + noisyspeech = cleanfactor * clean + noise + noisyspeech = noisyspeech / (scalarnoise + cleanfactor * scalarclean) + + # scaling to ~[-1,1] + max_mixed = torch.max(abs(noisyspeech.reshape( + noisyspeech.shape[0], 16384)), 1, keepdims=True).values + noisyspeech = noisyspeech * (1 / max_mixed).unsqueeze(1) + + return noisyspeech diff --git a/models/ai85net-kws20-nas.py b/models/ai85net-kws20-nas.py new file mode 100644 index 000000000..fa6d108aa --- /dev/null +++ b/models/ai85net-kws20-nas.py @@ -0,0 +1,107 @@ +################################################################################################### +# +# Copyright (C) 2023-2024 Analog Devices, Inc. All Rights Reserved. +# +# Analog Devices, Inc. Default Copyright Notice: +# https://www.analog.com/en/about-adi/legal-and-risk-oversight/intellectual-property/copyright-notice.html +# +################################################################################################### +# +# Copyright (C) 2021-2023 Maxim Integrated Products, Inc. All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +Keyword spotting network for AI85 +""" +from torch import nn + +import ai8x + + +class AI85KWS20NetNAS(nn.Module): + """ + KWS20 NAS Audio net, found via Neural Architecture Search + It significantly outperforms earlier networks (v1, v2, v3), though with a higher + parameter count and slightly increased latency. + """ + + # num_classes = n keywords + 1 unknown + def __init__( + self, + num_classes=21, + num_channels=128, + dimensions=(128, 1), # pylint: disable=unused-argument + bias=True, + **kwargs + ): + super().__init__() + self.conv1_1 = ai8x.FusedConv1dBNReLU(num_channels, 128, 1, stride=1, padding=0, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv1_2 = ai8x.FusedConv1dBNReLU(128, 64, 3, stride=1, padding=1, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv1_3 = ai8x.FusedConv1dBNReLU(64, 128, 3, stride=1, padding=1, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv2_1 = ai8x.FusedMaxPoolConv1dBNReLU(128, 128, 3, stride=1, padding=1, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv2_2 = ai8x.FusedConv1dBNReLU(128, 64, 1, stride=1, padding=0, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv2_3 = ai8x.FusedConv1dBNReLU(64, 128, 1, stride=1, padding=0, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv3_1 = ai8x.FusedMaxPoolConv1dBNReLU(128, 128, 3, stride=1, padding=1, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv3_2 = ai8x.FusedConv1dBNReLU(128, 64, 5, stride=1, padding=2, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv4_1 = ai8x.FusedMaxPoolConv1dBNReLU(64, 128, 5, stride=1, padding=2, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv4_2 = ai8x.FusedConv1dBNReLU(128, 128, 1, stride=1, padding=0, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv5_1 = ai8x.FusedMaxPoolConv1dBNReLU(128, 128, 5, stride=1, padding=2, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv5_2 = ai8x.FusedConv1dBNReLU(128, 64, 3, stride=1, padding=1, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv6_1 = ai8x.FusedMaxPoolConv1dBNReLU(64, 64, 5, stride=1, padding=2, + bias=bias, batchnorm="NoAffine", **kwargs) + self.conv6_2 = ai8x.FusedConv1dBNReLU(64, 128, 1, stride=1, padding=0, + bias=bias, batchnorm="NoAffine", **kwargs) + self.fc = ai8x.Linear(512, num_classes, bias=bias, wide=True, **kwargs) + + def forward(self, x): # pylint: disable=arguments-differ + """Forward prop""" + # Run CNN + x = self.conv1_1(x) + x = self.conv1_2(x) + x = self.conv1_3(x) + x = self.conv2_1(x) + x = self.conv2_2(x) + x = self.conv2_3(x) + x = self.conv3_1(x) + x = self.conv3_2(x) + x = self.conv4_1(x) + x = self.conv4_2(x) + x = self.conv5_1(x) + x = self.conv5_2(x) + x = self.conv6_1(x) + x = self.conv6_2(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + return x + + +def ai85kws20netnas(pretrained=False, **kwargs): + """ + Constructs a AI85KWS20NetNAS model. + """ + assert not pretrained + return AI85KWS20NetNAS(**kwargs) + + +models = [ + { + 'name': 'ai85kws20netnas', + 'min_input': 1, + 'dim': 1, + }, +] diff --git a/notebooks/Bayer2RGB_Evaluation.ipynb b/notebooks/Bayer2RGB_Evaluation.ipynb index 24059aec0..c793e444f 100644 --- a/notebooks/Bayer2RGB_Evaluation.ipynb +++ b/notebooks/Bayer2RGB_Evaluation.ipynb @@ -22,8 +22,10 @@ "source": [ "###################################################################################################\n", "#\n", - "# Copyright © 2023 Analog Devices, Inc. All Rights Reserved.\n", - "# This software is proprietary and confidential to Analog Devices, Inc. and its licensors.\n", + "# Copyright (C) 2023-2024 Analog Devices, Inc. All Rights Reserved.\n", + "#\n", + "# Analog Devices, Inc. Default Copyright Notice:\n", + "# https://www.analog.com/en/about-adi/legal-and-risk-oversight/intellectual-property/copyright-notice.html\n", "#\n", "###################################################################################################import cv2\n", "import importlib\n", diff --git a/notebooks/KWS_Noise_Evaluation.ipynb b/notebooks/KWS_Noise_Evaluation.ipynb new file mode 100644 index 000000000..934c52387 --- /dev/null +++ b/notebooks/KWS_Noise_Evaluation.ipynb @@ -0,0 +1,868 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Automated Evaluation for KWS Under Different Noise Scenarios\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this notebook, you can test previously trained KWS models under various noise types at desired SNR levels. You'll need:\n", + "- the trained model's checkpoint file\n", + "- a list specifying the types of noise\n", + "- a list specifying the SNR levels.\n", + "\n", + "This notebook uses the signalmixer data loader to mix the KWS and specified noise data, and creates a mixed dataset using the specified SNR level. The notebook performs the evaluation on these mixed datasets and creates comparison plots for different types of models." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###################################################################################################\n", + "#\n", + "# Copyright (C) 2023-2024 Analog Devices, Inc. All Rights Reserved.\n", + "#\n", + "# Analog Devices, Inc. Default Copyright Notice:\n", + "# https://www.analog.com/en/about-adi/legal-and-risk-oversight/intellectual-property/copyright-notice.html\n", + "#\n", + "###################################################################################################\n", + "#\n", + "# Copyright (C) 2022-2023 Maxim Integrated Products, Inc. All Rights Reserved.\n", + "#\n", + "# Maxim Integrated Products, Inc. Default Copyright Notice:\n", + "# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html\n", + "#\n", + "###################################################################################################\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "import numpy as np\n", + "import torch\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import torchnet.meter as tnt\n", + "from collections import OrderedDict\n", + "import importlib\n", + "from torchvision import transforms\n", + "\n", + "sys.path.append(os.path.join(os.getcwd(), '..'))\n", + "sys.path.append(os.path.join(os.getcwd(), '..', 'models'))\n", + "sys.path.append(os.path.join(os.getcwd(), '..', 'datasets'))\n", + "\n", + "from types import SimpleNamespace\n", + "\n", + "import ai8x\n", + "\n", + "import msnoise\n", + "from signalmixer import signalmixer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Initialize and load the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, set the checkpoint path and the corresponding model name. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "trained_checkpoint_path = os.path.join('..', '..', 'ai8x-synthesis', 'trained', 'ai85-kws20_nas-qat8.pth.tar')\n", + "mod = importlib.import_module(\"ai85net-kws20-nas\")\n", + "model_file = \"ai85net-kws20-nas\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working with device: cuda\n", + "Configuring device: MAX78000, simulate=False.\n" + ] + } + ], + "source": [ + "dataset = importlib.import_module(\"kws20\")\n", + "\n", + "classes = ['up', 'down', 'left', 'right', 'stop', 'go', 'yes', 'no', 'on', 'off', 'one',\n", + " 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'zero', 'unknown']\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(\"Working with device:\", device)\n", + "\n", + "ai8x.set_device(device=85, simulate=False, round_avg=False)\n", + "qat_policy = {'start_epoch': 10, 'weight_bits': 8, 'bias_bits': 8}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = mod.AI85KWS20NetNAS(num_classes=len(classes), num_channels=128, dimensions=(128, 1), bias=True, \n", + " quantize_activation=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint = torch.load(trained_checkpoint_path)\n", + "\n", + "state_dict = checkpoint['state_dict']\n", + "new_state_dict = OrderedDict()\n", + "for k, v in state_dict.items():\n", + " if k.startswith('module.'):\n", + " k = k[7:]\n", + " new_state_dict[k] = v\n", + "checkpoint['state_dict'] = new_state_dict\n", + "\n", + "ai8x.fuse_bn_layers(model)\n", + "ai8x.initiate_qat(model, qat_policy)\n", + "model.load_state_dict(checkpoint['state_dict'], strict=False)\n", + "\n", + "ai8x.update_model(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Set up the test set and evaluation parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, load the KWS test set, specify the noise list and the SNR level list. \n", + "\n", + "Note: Noise types should be picked from the available classes within the MSnoise dataset (`datasets/msnoise.py`)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Processing test...\n", + "test set: 11005 elements\n", + "Class up (# 31): 425 elements\n", + "Class down (# 5): 406 elements\n", + "Class left (# 15): 412 elements\n", + "Class right (# 23): 396 elements\n", + "Class stop (# 27): 411 elements\n", + "Class go (# 11): 402 elements\n", + "Class yes (# 34): 419 elements\n", + "Class no (# 19): 405 elements\n", + "Class on (# 21): 396 elements\n", + "Class off (# 20): 402 elements\n", + "Class one (# 22): 399 elements\n", + "Class two (# 30): 424 elements\n", + "Class three (# 28): 405 elements\n", + "Class four (# 10): 400 elements\n", + "Class five (# 7): 445 elements\n", + "Class six (# 26): 394 elements\n", + "Class seven (# 24): 406 elements\n", + "Class eight (# 6): 408 elements\n", + "Class nine (# 18): 408 elements\n", + "Class zero (# 35): 418 elements\n", + "Class UNKNOWN: 2824 elements\n" + ] + } + ], + "source": [ + "sn = SimpleNamespace()\n", + "sn.truncate_testset = False\n", + "sn.act_mode_8bit = False\n", + "\n", + "data_path = '/data'\n", + "\n", + "_, test_dataset = dataset.KWS_20_get_datasets( (data_path, sn), load_train=False, load_test=True)\n", + "\n", + "originals = list(range(0, len(test_dataset), 3))\n", + "test_dataset.data = test_dataset.data[originals]\n", + "test_dataset.targets = test_dataset.targets[originals]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "noise_list = ['AirConditioner',\n", + " 'AirportAnnouncements',\n", + " 'Babble',\n", + " 'CopyMachine',\n", + " 'Munching',\n", + " 'NeighborSpeaking',\n", + " 'ShuttingDoor',\n", + " 'Typing',\n", + " 'VacuumCleaner',\n", + " 'TradeShow',\n", + " 'WhiteNoise']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "snr_list = [-5, 0, 5, 10, 15, 20]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Define functions to evaluate the model under different SNR levels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `evaluate`, `snr_testing` and `benchmark` functions, you can evaluate the trained model under different SNR levels.\n", + "\n", + "- The `evaluate` function evaluates the model under the specified SNR level and noise type.\n", + "- The `snr_testing` function executes a loop over the SNR list. It calls the `evaluate` function for each SNR level, for the specified noise type.\n", + "- The `benchmark` function executes a loop over the list of noise types. It calls the `snr_testing` function for each type of noise." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate(db = None, noise = False, noise_type = None):\n", + "\n", + " if (noise == True) and (noise_type == None):\n", + " print('Noise type is not specified. Noise will not be applied.')\n", + " noise = False\n", + "\n", + " model.eval()\n", + " model.to(device) \n", + " classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, min(len(classes), 5)))\n", + "\n", + " transform = transforms.Compose([\n", + " ai8x.normalize(args=sn)\n", + " ])\n", + " \n", + " if noise:\n", + " if (noise_type == 'WhiteNoise'):\n", + "\n", + " mixed_signals = signalmixer(test_dataset, snr = db, noise_type = noise_type, noise_dataset = None)\n", + " mixed_signals_loader = torch.utils.data.DataLoader(mixed_signals, batch_size = 256)\n", + "\n", + " else:\n", + " noise_dataset = msnoise.MSnoise(root = data_path, classes = [noise_type], d_type = 'test', dataset_len = 11005, remove_unknowns=True,\n", + " transform=None, quantize=False, download=False)\n", + "\n", + " mixed_signals = signalmixer(test_dataset, snr = db, noise_type = noise_type, noise_dataset = noise_dataset)\n", + " mixed_signals_loader = torch.utils.data.DataLoader(mixed_signals, batch_size = 256)\n", + " else:\n", + " mixed_signals_loader = torch.utils.data.DataLoader(test_dataset, batch_size = 256)\n", + " \n", + " with torch.no_grad():\n", + " for batch_idx, (inputs, targets) in enumerate(mixed_signals_loader):\n", + " inputs = inputs.to(device)\n", + " targets = targets.to(device)\n", + " outputs = model(inputs)\n", + " classerr.add(outputs, targets)\n", + "\n", + " print(\"Batch: [\",batch_idx*256 ,\"/\", len(test_dataset),\"]\")\n", + " acc = classerr.value()[0]\n", + " print(\"Accuracy: \", acc)\n", + " \n", + " print(\"Total Accuracy: \", acc)\n", + " return acc" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def snr_testing(snr_list = None, noise_type = None, noise = False):\n", + "\n", + " # raw test set evaluation\n", + " if noise == False:\n", + " db = None\n", + " noise_type = None\n", + " accuracies = np.zeros(1)\n", + " accuracies[0] = evaluate(db, noise, noise_type)\n", + "\n", + " # noisy test set evaluation\n", + " else:\n", + " accuracies = np.zeros(len(snr_list))\n", + "\n", + " for idx, db in enumerate(snr_list):\n", + " print(\"Evaluating SNR levels of\", db)\n", + " accuracies[idx] = evaluate(db, noise, noise_type)\n", + "\n", + " return accuracies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark(noise_list = None, snr_list = None):\n", + "\n", + " if noise_list == None:\n", + " print('Noise type is not specified. Noise will not be applied.')\n", + " noise = False\n", + " snr_list = None\n", + " else:\n", + " noise = True\n", + " if snr_list == None:\n", + " print('Using default values for SNR levels: [-5, 20] dB.')\n", + " snr_list = range(-5, 20)\n", + "\n", + " if noise:\n", + "\n", + " accuracies = np.zeros((len(noise_list) + 1, len(snr_list)))\n", + "\n", + " for idx, n in enumerate(noise_list):\n", + " print(f'{n} Noise Evaluation')\n", + " accuracies[idx] = snr_testing(snr_list, noise_type = n, noise = noise)\n", + "\n", + " accuracies[-1] = snr_testing(noise = False)\n", + "\n", + " return accuracies " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "accuracies_nas = benchmark(noise_list = noise_list, snr_list = snr_list)\n", + "accuracies = [accuracies_nas]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional Step: Adding different models for comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- v3 Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trained_checkpoint_path = os.path.join('..', '..', 'ai8x-synthesis', 'trained', 'ai85-kws20_v3-qat8.pth.tar') \n", + "mod = importlib.import_module(\"ai85net-kws20-v3\")\n", + "model_file = \"ai85net-kws20-v3\"\n", + "\n", + "checkpoint = torch.load(trained_checkpoint_path)\n", + "\n", + "model = mod.AI85KWS20Netv3(num_classes=len(classes), num_channels=128, dimensions=(128, 1), bias=False, \n", + " quantize_activation=False)\n", + "\n", + "state_dict = checkpoint['state_dict']\n", + "new_state_dict = OrderedDict()\n", + "for k, v in state_dict.items():\n", + " if k.startswith('module.'):\n", + " k = k[7:]\n", + " new_state_dict[k] = v\n", + "checkpoint['state_dict'] = new_state_dict\n", + "\n", + "ai8x.fuse_bn_layers(model)\n", + "ai8x.initiate_qat(model, qat_policy)\n", + "model.load_state_dict(checkpoint['state_dict'], strict=False)\n", + "\n", + "ai8x.update_model(model)\n", + "\n", + "accuracies_v3 = benchmark(noise_list = noise_list, snr_list = snr_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- v2 Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trained_checkpoint_path = os.path.join('..', '..', 'ai8x-synthesis', 'trained', 'ai85-kws20_v2-qat8.pth.tar')\n", + "mod = importlib.import_module(\"ai85net-kws20-v2\")\n", + "model_file = \"ai85net-kws20-v2\"\n", + "\n", + "checkpoint = torch.load(trained_checkpoint_path)\n", + "\n", + "model = mod.AI85KWS20Netv2(num_classes=len(classes), num_channels=128, dimensions=(128, 1), bias=False, \n", + " quantize_activation=False)\n", + "\n", + "state_dict = checkpoint['state_dict']\n", + "new_state_dict = OrderedDict()\n", + "for k, v in state_dict.items():\n", + " if k.startswith('module.'):\n", + " k = k[7:]\n", + " new_state_dict[k] = v\n", + "checkpoint['state_dict'] = new_state_dict\n", + "\n", + "ai8x.fuse_bn_layers(model)\n", + "ai8x.initiate_qat(model, qat_policy)\n", + "model.load_state_dict(checkpoint['state_dict'], strict=False)\n", + "\n", + "ai8x.update_model(model)\n", + "\n", + "accuracies_v2 = benchmark(noise_list = noise_list, snr_list = snr_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "accuracies = [accuracies_nas, accuracies_v2, accuracies_v3]\n", + "model_files = [\"ai85net-kws20-nas\", \"ai85net-kws20-v2\", \"ai85net-kws20-v3\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Comparing noise types (for specified models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can create data frames to examine the results of evaluation. Using the data frame, you can create plots to compare noise types for each model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "accs = []\n", + "for model_name, model_acc in enumerate(accuracies):\n", + " csv = {}\n", + "\n", + " for idx, i in enumerate(noise_list):\n", + " csv[i] = model_acc[idx]\n", + " \n", + " csv_list = []\n", + " csv_list.append(['raw', 'None', model_acc[-1][0]])\n", + "\n", + " for i in csv.keys():\n", + " for idx, j in enumerate(csv[i]):\n", + " csv_list.append([i, snr_list[idx], j])\n", + "\n", + " df = pd.DataFrame(csv_list, columns = ['Type', 'SNR (dB)', f'{model_files[model_name]}'])\n", + " \n", + " accs.append(df)\n", + "\n", + "for i in accs:\n", + " df[i.columns[-1]] = i[i.columns[-1]]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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TypeSNR (dB)ai85net-kws20-v3ai85net-kws20-nasai85net-kws20-v2
0rawNone89.86099894.14009391.550831
1AirConditioner-534.72335839.76560433.878441
2AirConditioner056.14608966.77568859.798310
3AirConditioner571.95421181.79340477.541564
4AirConditioner1081.33006389.56118885.799945
..................
62WhiteNoise071.49086984.65521980.703189
63WhiteNoise582.12046990.02453087.135459
64WhiteNoise1086.94467292.50477089.533933
65WhiteNoise1588.77078293.62224091.142001
66WhiteNoise2088.68901693.97656091.196511
\n", + "

67 rows × 5 columns

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" + ], + "text/plain": [ + " Type SNR (dB) ai85net-kws20-v3 ai85net-kws20-nas \\\n", + "0 raw None 89.860998 94.140093 \n", + "1 AirConditioner -5 34.723358 39.765604 \n", + "2 AirConditioner 0 56.146089 66.775688 \n", + "3 AirConditioner 5 71.954211 81.793404 \n", + "4 AirConditioner 10 81.330063 89.561188 \n", + ".. ... ... ... ... \n", + "62 WhiteNoise 0 71.490869 84.655219 \n", + "63 WhiteNoise 5 82.120469 90.024530 \n", + "64 WhiteNoise 10 86.944672 92.504770 \n", + "65 WhiteNoise 15 88.770782 93.622240 \n", + "66 WhiteNoise 20 88.689016 93.976560 \n", + "\n", + " ai85net-kws20-v2 \n", + "0 91.550831 \n", + "1 33.878441 \n", + "2 59.798310 \n", + "3 77.541564 \n", + "4 85.799945 \n", + ".. ... \n", + "62 80.703189 \n", + "63 87.135459 \n", + "64 89.533933 \n", + "65 91.142001 \n", + "66 91.196511 \n", + "\n", + "[67 rows x 5 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_values(model_num):\n", + "\n", + " accuracies_values = {}\n", + "\n", + " for noise in noise_list:\n", + " acc_list = []\n", + " for idx, i in enumerate(df['Type'].values[1:]): \n", + " if i == noise:\n", + " acc_list.append(df[model_num][1:][idx+1])\n", + " accuracies_values[noise] = acc_list\n", + "\n", + " return accuracies_values" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for model_num in df.columns[2:]:\n", + " accuracies_values = plot_values(model_num)\n", + " plt.figure()\n", + " plt.grid(visible=True, which='major', color='#300000', linestyle='-')\n", + " plt.minorticks_on()\n", + " plt.grid(visible=True, which='minor', color='#900000', linestyle=':')\n", + " for noise in noise_list:\n", + " plt.title(f'Accuracy Test on model {model_num}')\n", + " plt.xlabel('SNR (dB)')\n", + " plt.ylabel('Accuracy (%)')\n", + " plt.plot(snr_list, accuracies_values[noise])\n", + "\n", + " plt.legend(noise_list, bbox_to_anchor=(1.05, 0.75),\n", + " loc='upper left', borderaxespad=0.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5: Comparing different models (for specified noise types)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also see the comparison between each model for each noise type separately." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "acc_list = []\n", + "for model_num in df.columns[2:]:\n", + " acc_list.append(plot_values(model_num))\n", + " \n", + "for noise in noise_list:\n", + " plt.figure()\n", + " plt.grid(visible=True, which='major', color='#300000', linestyle='-')\n", + " plt.minorticks_on()\n", + " plt.grid(visible=True, which='minor', color='#900000', linestyle=':')\n", + " plt.title(f'{noise} - Accuracy Test')\n", + " plt.xlabel('SNR (dB)')\n", + " plt.ylabel('Accuracy (%)')\n", + " for model in acc_list:\n", + " plt.plot(snr_list , model[noise])\n", + " plt.legend(df.columns[2:], bbox_to_anchor=(1.05, 0.75),\n", + " loc='upper left', borderaxespad=0.)\n", + " \n", + " # you can remove the break to see all noise type comparisons\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional Step: Export to CSV" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also export the evaluation data to CSV files using the data frames calculated above. If the current model file is already in the CSV file, accuracies will not be added to the CSV file. Otherwise, the current accuracy results will be added to the CSV file as a new column.\n", + "\n", + "Note: Please make sure that you test the same noise types for the same SNR levels for all of the models." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.exists('KWS_snr_test.csv'):\n", + " current = pd.read_csv('KWS_snr_test.csv', decimal=',', sep=';')\n", + " if model_file in current.columns:\n", + " print(f'This model file ({model_file}) already exists!')\n", + " else:\n", + " current[model_file] = list(df[model_file].values)\n", + " current.to_csv('KWS_snr_test.csv', sep=';', decimal=',', index=False)\n", + "\n", + "else:\n", + " df.to_csv('KWS_snr_test.csv', sep=';', decimal=',', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/evaluate_kws20_nas.sh b/scripts/evaluate_kws20_nas.sh new file mode 100755 index 000000000..d615245fe --- /dev/null +++ b/scripts/evaluate_kws20_nas.sh @@ -0,0 +1,2 @@ +#!/bin/sh +python train.py --model ai85kws20netnas --use-bias --dataset KWS_20 --confusion --evaluate --exp-load-weights-from ../ai8x-synthesis/trained/ai85-kws20_nas-qat8-q.pth.tar -8 --device MAX78000 "$@" diff --git a/scripts/train_kws20_nas.sh b/scripts/train_kws20_nas.sh new file mode 100755 index 000000000..fe68123ce --- /dev/null +++ b/scripts/train_kws20_nas.sh @@ -0,0 +1,2 @@ +#!/bin/sh +python train.py --epochs 200 --optimizer Adam --lr 0.001 --wd 0 --deterministic --qat-policy policies/qat_policy_late_kws20.yaml --compress policies/schedule_kws20.yaml --model ai85kws20netnas --use-bias --dataset KWS_20 --confusion --device MAX78000 "$@" diff --git a/train_all_models.sh b/train_all_models.sh index 1baac4a05..cfb4e804f 100755 --- a/train_all_models.sh +++ b/train_all_models.sh @@ -56,3 +56,6 @@ scripts/train_facedet_tinierssd.sh "$@" echo "-----------------------------" echo "Training Bayer2RGB debayerization model" scripts/train_bayer2rgb_imagenet.sh "$@" +echo "-----------------------------" +echo "Training kws20_nas model" +scripts/train_kws20_nas.sh "$@" From 591cb941f6e8c647dad8996684ab73758421825c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Fri, 2 Feb 2024 18:38:05 +0300 Subject: [PATCH 17/27] Face Detection Library Replacement (#287) --- .gitignore | 1 + datasets/vggface2.py | 26 ++++---- docs/FacialRecognitionSystem.md | 2 +- models/model_irse_drl.py | 101 ++++++++++++++++---------------- requirements.txt | 4 +- 5 files changed, 68 insertions(+), 66 deletions(-) diff --git a/.gitignore b/.gitignore index c3e722ab8..d2fccf405 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ /logs/ /ninja-python-distributions /pip-selfcheck.json +/pretrained/ /sensitivity.csv /sensitivity.png /tensorflow/ diff --git a/datasets/vggface2.py b/datasets/vggface2.py index 24e95624a..a36d258c1 100644 --- a/datasets/vggface2.py +++ b/datasets/vggface2.py @@ -1,6 +1,6 @@ ################################################################################################### # -# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. +# Copyright (C) 2019-2024 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html @@ -23,9 +23,8 @@ from torchvision import transforms import cv2 +import face_detection import kornia.geometry.transform as GT -import onnxruntime -from hawk_eyes.face import RetinaFace from PIL import Image from skimage import transform as trans from tqdm import tqdm @@ -100,19 +99,26 @@ def __extract_gt(self): """ Extracts the ground truth from the dataset """ - onnxruntime.set_default_logger_severity(3) # suppress onnxruntime warnings - retina = RetinaFace(model_name='retina_l', conf=0.5) + detector = face_detection.build_detector("RetinaNetResNet50", confidence_threshold=.5, + nms_iou_threshold=.4) img_paths = list(glob.glob(os.path.join(self.d_path + '/**/', '*.jpg'), recursive=True)) nf_number = 0 - n_words = 0 + words_count = 0 pickle_dict = {key: [] for key in ["boxes", "landmarks", "img_list", "lbl_list"]} pickle_dict["word2index"] = {} for jpg in tqdm(img_paths): boxes = [] image = cv2.imread(jpg) - bboxes, lndmrks = retina.detect(image) - if len(bboxes) == 0: + + img_max = max(image.shape[0], image.shape[1]) + if img_max > 1320: + continue + bboxes, lndmrks = detector.batched_detect_with_landmarks(np.expand_dims(image, 0)) + bboxes = bboxes[0] + lndmrks = lndmrks[0] + + if (bboxes.shape[0] == 0) or (lndmrks.shape[0] == 0): nf_number += 1 continue @@ -126,8 +132,8 @@ def __extract_gt(self): lbl = os.path.relpath(dir_name, self.d_path) if lbl not in pickle_dict["word2index"]: - pickle_dict["word2index"][lbl] = n_words - n_words += 1 + pickle_dict["word2index"][lbl] = words_count + words_count += 1 pickle_dict["lbl_list"].append(lbl) pickle_dict["boxes"].append(boxes) diff --git a/docs/FacialRecognitionSystem.md b/docs/FacialRecognitionSystem.md index 92cb03eff..4534e5eca 100644 --- a/docs/FacialRecognitionSystem.md +++ b/docs/FacialRecognitionSystem.md @@ -38,7 +38,7 @@ To train the facedetection model, "scripts/train_facedet_tinierssd.sh" script ca To train a FaceID model for MAX7800x microcontrollers, there are multiple steps. As the MAX7800x FaceID models will be trained in a knowledge distillation fashion, the first step will be downloading a backbone checkpoint for the teacher model. -The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_drl.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. +The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_drl.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. Then, the checkpoint should be placed in a folder (e.g. "pretrained") in the root directory of the repository. By using the "--backbone-checkpoint" argument, the path to the checkpoint should be given to the training script. There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named faceid_112, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named mobilefacenet_112. To train the FaceID models, "scripts/train_faceid_112.sh" and "scripts/train_mobilefacenet_112.sh" scripts can be used, respectivey. Training scripts will realize Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. diff --git a/models/model_irse_drl.py b/models/model_irse_drl.py index eba2b0795..dc2e1aa60 100644 --- a/models/model_irse_drl.py +++ b/models/model_irse_drl.py @@ -26,7 +26,9 @@ ################################################################################################### """ FaceID Teacher Model to be used for Knowledge Distillation +See https://github.com/MaximIntegratedAI/ai8x-training/blob/develop/docs/FacialRecognitionSystem.md """ +import sys from collections import namedtuple import torch @@ -292,14 +294,24 @@ def _initialize_weights(self): m.bias.data.zero_() -def ir_50(input_size=(112, 112), # pylint: disable=unused-argument - dimensionality=64, - backbone_checkpoint=None, **kwargs): - """Constructs a ir-50 model. +def create_model(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, + model_name="ir", model_size=152, **kwargs): """ - model = Backbone(input_size, 50, 'ir') + Model + DRL constructor + """ + model = Backbone(input_size, model_size, model_name) if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) + try: + model.load_state_dict(torch.load(backbone_checkpoint, + map_location=torch.device('cpu'))) + except FileNotFoundError: + print('Backbone checkpoint was not found. Please follow the ' + 'instructions in the docs/FacialRecognitionSystem.md file, ' + 'FaceID section to download the backbone checkpoint.', + file=sys.stderr) + sys.exit() for param in model.parameters(): param.requires_grad = False drl = DRL(dimensionality) @@ -308,85 +320,70 @@ def ir_50(input_size=(112, 112), # pylint: disable=unused-argument return ensemble +def ir_50(input_size=(112, 112), # pylint: disable=unused-argument + dimensionality=64, + backbone_checkpoint=None, **kwargs): + """ + Constructs a ir-50 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir", 50) + + return model + + def ir_101(input_size=(112, 112), # pylint: disable=unused-argument dimensionality=64, backbone_checkpoint=None, **kwargs): - """Constructs a ir-101 model. """ - model = Backbone(input_size, 100, 'ir') - if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) - for param in model.parameters(): - param.requires_grad = False - drl = DRL(dimensionality) - ensemble = Ensemble(model, drl) + Constructs a ir-101 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir", 100) - return ensemble + return model def ir_152(input_size=(112, 112), # pylint: disable=unused-argument dimensionality=64, backbone_checkpoint=None, **kwargs): - """Constructs a ir-152 model. """ - model = Backbone(input_size, 152, 'ir') - if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) - for param in model.parameters(): - param.requires_grad = False - drl = DRL(dimensionality) - - ensemble = Ensemble(model, drl) + Constructs a ir-152 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir", 152) - return ensemble + return model def ir_se_50(input_size=(112, 112), # pylint: disable=unused-argument dimensionality=64, backbone_checkpoint=None, **kwargs): - """Constructs a ir_se-50 model. """ - model = Backbone(input_size, 50, 'ir_se') - if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) - for param in model.parameters(): - param.requires_grad = False - drl = DRL(dimensionality) - ensemble = Ensemble(model, drl) + Constructs a ir_se-50 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir_se", 50) - return ensemble + return model def ir_se_101(input_size=(112, 112), # pylint: disable=unused-argument dimensionality=64, backbone_checkpoint=None, **kwargs): - """Constructs a ir_se-101 model. """ - model = Backbone(input_size, 100, 'ir_se') - if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) - for param in model.parameters(): - param.requires_grad = False - drl = DRL(dimensionality) - ensemble = Ensemble(model, drl) + Constructs a ir_se-101 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir_se", 100) - return ensemble + return model def ir_se_152(input_size=(112, 112), # pylint: disable=unused-argument dimensionality=64, backbone_checkpoint=None, **kwargs): - """Constructs a ir_se-152 model. """ - model = Backbone(input_size, 152, 'ir_se') - if backbone_checkpoint is not None: - model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) - for param in model.parameters(): - param.requires_grad = False - drl = DRL(dimensionality) - ensemble = Ensemble(model, drl) + Constructs a ir_se-152 model. + """ + model = create_model(input_size, dimensionality, backbone_checkpoint, "ir_se", 152) - return ensemble + return model models = [ diff --git a/requirements.txt b/requirements.txt index a0e0ac082..aa5726440 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,15 +6,13 @@ Pillow>=7 PyYAML>=5.1.1 albumentations>=1.3.0 faiss-cpu==1.7.4 +face-detection==0.2.2 h5py>=3.7.0 -hawk-eyes==2.1.0 imutils==0.5.4 kornia==0.6.8 librosa>=0.7.2 numba<0.50.0 numpy>=1.22,<1.23 -onnx==1.15.0 -onnxruntime==1.7.0 opencv-python>=4.4.0 protobuf>=3.20.1,<4.0 pycocotools==2.0.7 From 4f664d7bd4775eafa367930e6d517c67a2a4a2ed Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Fri, 2 Feb 2024 15:31:43 -0600 Subject: [PATCH 18/27] Improve FaceID documentation and error checking (#288) --- .gitignore | 3 +- docs/FacialRecognitionSystem.md | 42 +++++++++++++++------------ models/model_irse_drl.py | 8 ++--- scripts/evaluate_faceid_112.sh | 2 +- scripts/evaluate_mobilefacenet_112.sh | 2 +- scripts/train_faceid_112.sh | 2 +- scripts/train_mobilefacenet_112.sh | 2 +- 7 files changed, 33 insertions(+), 28 deletions(-) diff --git a/.gitignore b/.gitignore index d2fccf405..061b59e12 100644 --- a/.gitignore +++ b/.gitignore @@ -2,9 +2,10 @@ *.mem *.prof /.mypy_cache/ +/.venv/ /.vscode/ **/build/ -/data/ +/data /latest_log_dir /latest_log_file /logs/ diff --git a/docs/FacialRecognitionSystem.md b/docs/FacialRecognitionSystem.md index 4534e5eca..b754ae7a1 100644 --- a/docs/FacialRecognitionSystem.md +++ b/docs/FacialRecognitionSystem.md @@ -1,16 +1,16 @@ # Facial Recognition System -This document aims to explain facial recognition applications for MAX7800x series microcontrollers. Facial recognition task consists from three main parts, face detection, face identification and dot product. +This document aims to explain facial recognition applications for MAX7800x series microcontrollers. The facial recognition task consists of three main parts: face detection, face identification and dot product: - - The face detection model detects faces in the captured image and extracts a rectangular sub-image containing only one face. - - The face Identification model identifies a person from their facial images by generating the embedding for a given face image. - - The dot product layer outputs the dot product representing the similarity between the embedding from the given image and embeddings in the database. +- The face detection model detects faces in the captured image and extracts a rectangular sub-image containing only one face. +- The face Identification model identifies a person from their facial images by generating the embedding for a given face image. +- The dot product layer outputs the dot product representing the similarity between the embedding from the given image and embeddings in the database. Figure 1 depicts the facial recognition system sequential diagram. -​Figure 1. MAX7800x facial recognition system +Figure 1. MAX7800x facial recognition system ## Dataset @@ -28,28 +28,32 @@ FaceID and Face Detection tasks share the same ground truth pickle, and it will ## Face Detection -To be able to localize faces in a facial recognition system, a face detection algorithm is generally used in facial recognition systems. Face detection is an object detection problem that has various solutions in the literature. In this work, a face detection algorithm that will run on MAX7800x series microcontrollers with a real-time performance was targeted. +To be able to localize faces in a facial recognition system, a face detection algorithm is generally used in facial recognition systems. Face detection is an object detection problem that has various solutions in the literature. In this work, a face detection algorithm that will run on MAX7800x series microcontrollers with real-time performance was targeted. -For digit detection problem, previously, a Tiny SSD[2] based MAX7800x object detection algorithm was developed , named Tinier SSD. The face detection model is a modified version of the digit detection model. The modification was realized to reduce the number of parameters and enable larger input size. +For the digit detection problem, previously, a TinySSD[2] based MAX7800x object detection algorithm was developed, named TinierSSD. The face detection model is a modified version of the digit detection model. The modification reduces the number of parameters and enables larger input sizes. -To train the facedetection model, "scripts/train_facedet_tinierssd.sh" script can be used. +To train the face detection model, `scripts/train_facedet_tinierssd.sh` can be used. ## FaceID To train a FaceID model for MAX7800x microcontrollers, there are multiple steps. As the MAX7800x FaceID models will be trained in a knowledge distillation fashion, the first step will be downloading a backbone checkpoint for the teacher model. -The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_drl.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. Then, the checkpoint should be placed in a folder (e.g. "pretrained") in the root directory of the repository. By using the "--backbone-checkpoint" argument, the path to the checkpoint should be given to the training script. +The suggested teacher model is IR-152, but the other teacher models defined in `model_irse_drl.py` may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. -There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named faceid_112, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named mobilefacenet_112. To train the FaceID models, "scripts/train_faceid_112.sh" and "scripts/train_mobilefacenet_112.sh" scripts can be used, respectivey. Training scripts will realize Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. +By default, both `scripts/train_faceid_112.sh` and `scripts/train_mobilefacenet_112.sh` use `Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth` which needs to be placed in the folder `pretrained` in the root directory of the repository. This checkpoint can be found via the [Model Zoo](https://github.com/ZhaoJ9014/face.evoLVe?tab=readme-ov-file#Model-Zoo) section of the face.evoLVe repository under “IR-152”. + +There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named `faceid_112`, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named `mobilefacenet_112`. To train the FaceID models, `scripts/train_faceid_112.sh` and `scripts/train_mobilefacenet_112.sh` scripts can be used, respectively. By using the `--backbone-checkpoint` argument, the path to the checkpoint can be changed. + +The training scripts will run the Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. ### Dimensionality Reduction on the Teacher Model -Reducing embedding dimensionality can greatly reduce the post-processing operations and memory usage for the facial recognition system. To achieve this, the teacher backbone will be frozen and two additional Conv1d layers will be added to the teacher models. These additions are called dimension reduction layers. For the example in the repository, the length of the embeddings produced by the teacher model is 512 and the optimum length for the student model is found to be 64. Still, other choices like 32, 128 or 256 can be examined for different application areas. A summary of the dimensionality reduction is shown in Figure 2, and dimension reduction layers' details are represented in Table 1. +Reducing embedding dimensionality can greatly reduce the post-processing operations and memory usage for the facial recognition system. To achieve this, the teacher backbone will be frozen and two additional Conv1d layers will be added to the teacher models. These additions are called dimension reduction layers. For the example in the repository, the length of the embeddings produced by the teacher model is 512 and the optimum length for the student model is found to be 64. Still, other choices like 32, 128 or 256 can be examined for different application areas. A summary of the dimensionality reduction is shown in Figure 2, and dimension reduction layer details are shown in Table 1. -​Figure 2. Dimensionality Reduction +Figure 2. Dimensionality Reduction @@ -63,29 +67,29 @@ Table 1. Dimension Reduction Layers -To train dimensionality reduction layers Sub-Center ArcFace loss is used. The SubCenterArcFace Loss was presented in the [4], summary of the training framework can be seen in Figure 3. The loss function uses cosine similarity as the distance metric, and in the framework embedding network is trained as a part of the classification problem. The Normalized Sub-Centers(also known as the prototypes) must be learned from zero as no model is available to extract embeddings at the beginning. +To train dimensionality reduction layers, the Sub-Center ArcFace loss is used. The SubCenterArcFace Loss was presented in the [4], and a summary of the training framework can be seen in Figure 3. This loss function uses cosine similarity as the distance metric, and in the framework embedding network is trained as a part of the classification problem. The Normalized Sub-Centers (also known as the prototypes) must be learned from zero as no model is available to extract embeddings at the beginning. -​Figure 3. Sub-Center ArcFace Loss[4] +Figure 3. Sub-Center ArcFace Loss[4] ### Relation-Based Knowledge Distillation -The knowledge distillation choice for the FaceID models was a relation-based one. The distillation loss was calculated as the MSE between teacher model and student model. +The knowledge distillation choice for the FaceID models is relation-based. The distillation loss is calculated as the MSE between teacher model and student model. -To train the student FaceID models, no student loss was used, so student weight was set to 0. +To train the student FaceID models, no student loss is used, so the student weight is set to 0. -From Figure 4, a visual can be seen for the relation-based knowledge distillation. +Figure 4 visually represents the relation-based knowledge distillation. -​Figure 4. Relation-Based Knowledge Distillation[5] +Figure 4. Relation-Based Knowledge Distillation[5] ## Dot Product Layer -The dot product layer weights will be populated with the embeddings that are generated by MAX7800x FaceID models. Outputs of the FaceID models are normalized at both inference and recording. Therefore, the result of the dot product layer equals cosine similarity. Using the cosine similarity as a distance metric, the image is identified as either one of the known subjects or 'Unknown' depending on the embedding distances. To record new people in the database, there are two options. The first one is using the Python scripts that are available on the SDK demos. The second option is to use "record on hardware" mode which does not require any external connection. The second option is not available for all platforms, so please check SDK demo ReadMEs to see if it is supported. +The dot product layer weights will be populated with the embeddings that are generated by MAX7800x FaceID models. The outputs of the FaceID models are normalized at both inference and recording. Therefore, the result of the dot product layer equals cosine similarity. Using the cosine similarity as a distance metric, the image is identified as either one of the known subjects or “Unknown”, depending on the embedding distances. To record new people in the database, there are two options. The first one is using the Python scripts that are available with the SDK demos. The second option is to use a “record on hardware” mode which does not require any external connection. The second option is not available for all platforms, therefore please check the SDK demo README files. diff --git a/models/model_irse_drl.py b/models/model_irse_drl.py index dc2e1aa60..11780bf04 100644 --- a/models/model_irse_drl.py +++ b/models/model_irse_drl.py @@ -307,11 +307,11 @@ def create_model(input_size=(112, 112), # pylint: disable=unused-argument model.load_state_dict(torch.load(backbone_checkpoint, map_location=torch.device('cpu'))) except FileNotFoundError: - print('Backbone checkpoint was not found. Please follow the ' - 'instructions in the docs/FacialRecognitionSystem.md file, ' - 'FaceID section to download the backbone checkpoint.', + print(f'Backbone checkpoint {backbone_checkpoint} not found. Please follow the ' + 'instructions in docs/FacialRecognitionSystem.md, section ## FaceID, ' + 'to download the backbone checkpoint.', file=sys.stderr) - sys.exit() + sys.exit(1) for param in model.parameters(): param.requires_grad = False drl = DRL(dimensionality) diff --git a/scripts/evaluate_faceid_112.sh b/scripts/evaluate_faceid_112.sh index d2634a5a8..c3ff45b6a 100755 --- a/scripts/evaluate_faceid_112.sh +++ b/scripts/evaluate_faceid_112.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai85faceidnet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-faceid_112-qat-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" \ No newline at end of file +python train.py --model ai85faceidnet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78000 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-faceid_112-qat-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" diff --git a/scripts/evaluate_mobilefacenet_112.sh b/scripts/evaluate_mobilefacenet_112.sh index 8b7399506..d1fa87f71 100755 --- a/scripts/evaluate_mobilefacenet_112.sh +++ b/scripts/evaluate_mobilefacenet_112.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87netmobilefacenet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-mobilefacenet_112_qat_best-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" \ No newline at end of file +python train.py --model ai87netmobilefacenet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-mobilefacenet_112_qat_best-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" diff --git a/scripts/train_faceid_112.sh b/scripts/train_faceid_112.sh index 594ff32d3..ef54f1b7c 100755 --- a/scripts/train_faceid_112.sh +++ b/scripts/train_faceid_112.sh @@ -1,3 +1,3 @@ #!/bin/sh -python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" +python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" || exit 1 python train.py --epochs 80 --optimizer Adam --lr 0.001 --compress policies/schedule-faceid_112.yaml --kd-student-wt 0 --kd-distill-wt 1 --qat-policy policies/qat_policy_faceid_112.yaml --model ai85faceidnet_112 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --wd 0 --deterministic --workers 8 --use-bias --dataset VGGFace2_FaceID --batch-size 256 --device MAX78000 --print-freq 100 --validation-split 0 "$@" diff --git a/scripts/train_mobilefacenet_112.sh b/scripts/train_mobilefacenet_112.sh index 47a48c942..a00fe7c69 100755 --- a/scripts/train_mobilefacenet_112.sh +++ b/scripts/train_mobilefacenet_112.sh @@ -1,3 +1,3 @@ #!/bin/sh -python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" +python train.py --epochs 4 --optimizer Adam --lr 0.001 --scaf-lr 1e-2 --scaf-scale 32 --copy-output-folder pretrained/ir152_dim64 --wd 5e-4 --deterministic --workers 8 --qat-policy None --model ir_152 --dr 64 --backbone-checkpoint pretrained/Backbone_IR_152_Epoch_112_Batch_2547328_Time_2019-07-13-02-59_checkpoint.pth --use-bias --dataset VGGFace2_FaceID_dr --batch-size 64 --device MAX78000 --validation-split 0 --print-freq 250 "$@" || exit 1 python train.py --epochs 35 --optimizer Adam --lr 0.001 --compress policies/schedule-mobilefacenet_112.yaml --kd-student-wt 0 --kd-distill-wt 1 --qat-policy policies/qat_policy_mobilefacenet_112.yaml --model ai87netmobilefacenet_112 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --wd 0 --deterministic --workers 8 --use-bias --dataset VGGFace2_FaceID --batch-size 100 --device MAX78002 --validation-split 0 --print-freq 100 "$@" From 98ccf64b0e9533d56b6e768e1a50af5f388516c1 Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Mon, 26 Feb 2024 15:44:58 -0600 Subject: [PATCH 19/27] Update links to MSDK (#291) --- README.md | 94 +++++------------------------------------------------ README.pdf | Bin 6782989 -> 6738345 bytes 2 files changed, 8 insertions(+), 86 deletions(-) diff --git a/README.md b/README.md index 8a3161e66..96bd64593 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,19 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -January 10, 2024 +February 26, 2024 ADI’s MAX78000/MAX78002 project is comprised of five repositories: 1. **Start here**: **[Top Level Documentation](https://github.com/MaximIntegratedAI/MaximAI_Documentation)** 2. The software development kit (MSDK), which contains drivers and example programs ready to run on the evaluation kits (EVkit and Feather): - [Analog Devices MSDK](https://github.com/Analog-Devices-MSDK/msdk) + [Analog Devices MSDK](https://github.com/analogdevicesinc/msdk) 3. The training repository, which is used for deep learning *model development and training*: [ai8x-training](https://github.com/MaximIntegratedAI/ai8x-training) **(described in this document)** 4. The synthesis repository, which is used to *convert a trained model into C code* using the “izer” tool: [ai8x-synthesis](https://github.com/MaximIntegratedAI/ai8x-synthesis) **(described in this document)** 5. The reference design repository, which contains host applications and sample applications for reference designs such as [MAXREFDES178 (Cube Camera)](https://www.analog.com/en/design-center/reference-designs/maxrefdes178.html): - [refdes](https://github.com/Analog-Devices-MSDK/refdes) + [refdes](https://github.com/analogdevicesinc/MAX78xxx-RefDes) *Note: Examples for EVkits and Feather boards are part of the MSDK* _Open the `.md` version of this file in a markdown enabled viewer, for example Typora (). @@ -423,7 +423,7 @@ For minor updates, pull the latest code and install the updated wheels: ##### MSDK Updates -Please *also* update the MSDK or use the Maintenance Tool as documented in the [Analog Devices MSDK documentation](https://github.com/Analog-Devices-MSDK/msdk). The Maintenance Tool automatically updates the MSDK. +Please *also* update the MSDK or use the Maintenance Tool as documented in the [Analog Devices MSDK documentation](https://github.com/analogdevicesinc/msdk). The Maintenance Tool automatically updates the MSDK. ##### Python Version Updates @@ -551,96 +551,18 @@ There are two ways to install the MSDK. #### Method 1: MSDK Installer -The [Analog Devices MSDK](https://github.com/Analog-Devices-MSDK/msdk) for MAX78000/MAX7802 is available via the installer links below. These installers require a GUI on your system. +An automatic installer is available for the MSDK. Instructions for downloading, installing, and getting started with the MSDK’s supported development environments are found in the [**MSDK User Guide**](https://analogdevicesinc.github.io/msdk/USERGUIDE/). -1. Download the MSDK installer for your operating system from one of the links below. - * [Windows](https://www.analog.com/en/design-center/evaluation-hardware-and-software/software/software-download?swpart=SFW0010820A) - * [Ubuntu Linux](https://www.analog.com/en/design-center/evaluation-hardware-and-software/software/software-download?swpart=SFW0018720A) - * [macOS](https://www.analog.com/en/design-center/evaluation-hardware-and-software/software/software-download?swpart=SFW0018610A) - -2. Run the installer executable. Note: On Linux, this may require making the file executable with the following command: - - ```bash - $ chmod +x MaximMicrosSDK_linux.run - ``` - -3. Follow the instructions in the installer to the component selection. - -4. Select the components to install. At _minimum_, the following components must be selected. This will enable command-line development. - - * GNU RISC-V Embedded GCC - * GNU Tools for ARM Embedded Processors - * Open On-Chip Debugger - * MSYS2 (only on Windows) - * Microcontrollers - * MAX78000 Resources - * MAX78002 Resources - * Product Libs - * CMSIS Core Libraries - * Miscellaneous Drivers - * Peripheral Drivers - -5. (Optional) Select the “Eclipse” and/or “Visual Studio Code Support” components to add support for those IDEs. - -6. Continue through the instructions to complete the installation of the MSDK. - -7. (macOS only) Install OpenOCD dependencies using [Homebrew](https://brew.sh/) - - ```shell - $ brew install libusb-compat libftdi hidapi libusb - ``` - -8. (Linux and macOS only) Add the location of the toolchain binaries to the system `PATH`. - - On Linux and macOS, copy the following contents into `~/.profile`... - On macOS, _also_ copy the following contents into `~/.zprofile`... - ...and change `MAXIM_PATH` to the installation location of the MSDK. - - ```shell - # MSDK location - MAXIM_PATH=$HOME/MaximSDK # Change me! - export MAXIM_PATH - - # Arm GCC -- adjust version number - ARMGCC_DIR=$MAXIM_PATH/Tools/GNUTools/12.3 - echo $PATH | grep -q -s "$ARMGCC_DIR/bin" - if [ $? -eq 1 ] ; then - PATH=$PATH:"$ARMGCC_DIR/bin" - export PATH - export ARMGCC_DIR - fi - - # RISC-V GCC -- adjust version number - RISCVGCC_DIR=$MAXIM_PATH/Tools/xPack/riscv-none-embed-gcc/12.3.0-2 - echo $PATH | grep -q -s "$RISCVGCC_DIR/bin" - if [ $? -eq 1 ] ; then -     PATH=$PATH:"$RISCVGCC_DIR/bin" -     export PATH -     export RISCVGCC_DIR - fi - - # OpenOCD - OPENOCD_DIR=$MAXIM_PATH/Tools/OpenOCD - echo $PATH | grep -q -s "$OPENOCD_DIR" - if [ $? -eq 1 ] ; then -     PATH=$PATH:$OPENOCD_DIR -     export PATH -     export OPENOCD_DIR - fi - ``` - - On Windows, this step is not necessary. However, “MaximSDK/Tools/MSYS2/msys.bat” file _must_ be used to launch the MSYS2 terminal for command-line development. - -Once the tools above have been installed, continue with [Final Check](#final-check). +After installation and setup, continue with the [Final Check](#final-check). #### Method 2: Manual Installation -The MAX78000/MAX78002 MSDK is available as a git repository. The repository contains all of the MSDK's components _except_ the Arm GCC, RISC-V GCC, and Make. These must be downloaded and installed manually. +The MSDK is also available as a [git repository](https://github.com/analogdevicesinc/msdk), which can be used to obtain the latest development resources. The repository contains all of the MSDK’s components _except_ the Arm GCC, RISC-V GCC, and Make. These can be downloaded and installed manually. 1. Clone the MSDK repository (recommendation: change to the *ai8x-synthesis* folder first): ```shell - $ git clone https://github.com/Analog-Devices-MSDK/msdk.git sdk + $ git clone https://github.com/analogdevicesinc/msdk.git sdk ``` 2. 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z04XM9>v(`~e|OFwp_}n|bZa5ph8Rt6P7F-UQ=by?cF1kNy3lP(5WEcVf~uQgLJ39! zEJ=bOoh_3I>k`@gAxEX^ZTj|k5?oRqWWEvt9tqL^CP>$r7H+kqAoQBpG(v!bf_PTN zwWD5gY#MgIp>Ow7e}8iRn(*IvxKa2vEEd2*ai#D0JeV{wjOx`Mqbp1lP)@EXy>?Q( zc5=oM)Ca=+f)Morl59x&i=DzEznRRSwZIpX9EhTc$>6w)V?$=g5fNw|oQZ6P4T<#X zzh_?v3XV38k2FpF0nvEwIRxCH9s$3EvAI z`Hqk8{lrrb#vv`Wd)!OTT)5^x!ec=oLS6w?S&acv=L1HqoPgzbTNzi@scPdQ{tg@0 zM~}CNN-8qMjUnbDE&{K~*aXmq;Gmvos0h&QsGN1*e$3|hLTC{Bje_j`MgfbftS7B+ zAna|%i2GZna!x_Le48NDdW`7dH$WODLOgg*7OH?Blg9L4VaRJ#m)ttNuk474L-0uL zC9u#&6x8Z`iWsde?GTve{gpHb`GRLCG!)u--~ieL)!An;?Zm%oR<#I-Yv> z2m|EFXPwC@cFlpC5+&(@h2vh*)^%1ArZ>r9qemFvXttdUKrP+c=niSe{@Vl<3g>5x zv%*yyw9?3CUMoXCW=P0~tN0{Xz^r^=A{p&S2z!bkVb!g2g#cZGy#3QxBm6e+Cx8)z z2Q1vl#7dGU)4lbTXC6Tu)9EXpcjJw(IZZD4>7CQhJALIdUv+ZhX}-w7HE#d;^wTds z{iLIBUL0fNy#8c$-Qmgg*R8In03ac#-%Q1A>;u;yKly_Hf0NTMJ^kb-U+}H3x$f1k bdEKjzPc9(yY;^%|%?mEO?5m#hj0^rBlMN>B diff --git a/ai8x_blocks.py b/ai8x_blocks.py index e95603675..fc2b314c7 100644 --- a/ai8x_blocks.py +++ b/ai8x_blocks.py @@ -1,6 +1,6 @@ ################################################################################################### # -# Copyright (C) 2020-2022 Maxim Integrated Products, Inc. All Rights Reserved. +# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html @@ -116,6 +116,80 @@ def forward(self, x): # pylint: disable=arguments-differ return self.resid(y, x) +class ConvResidualBottleneck(nn.Module): + """ + AI8X module based on Residual Bottleneck Layer. + Depthwise convolution is replaced with standard convolution. + This module uses ReLU activation not ReLU6 as the original study suggests [1], + because of MAX7800X capabilities. + + Args: + in_channels: number of input channels + out_channels: number of output channels + expansion_factor: expansion_factor + stride: stirde size (default=1) + bias: determines if bias used at non-depthwise layers. + depthwise_bias: determines if bias used at depthwise layers. + + References: + [1] https://arxiv.org/pdf/1801.04381.pdf (MobileNetV2) + """ + def __init__(self, in_channels, out_channels, expansion_factor, stride=1, bias=False, + depthwise_bias=False, **kwargs): + super().__init__() + self.stride = stride + hidden_channels = int(round(in_channels * expansion_factor)) + if hidden_channels == in_channels: + self.conv1 = ai8x.Empty() + else: + self.conv1 = ai8x.FusedConv2dBNReLU(in_channels, hidden_channels, 1, padding=0, + bias=bias, **kwargs) + if stride == 1: + if depthwise_bias: + self.conv2 = ai8x.FusedConv2dBN(hidden_channels, out_channels, 3, + padding=1, stride=stride, + bias=depthwise_bias, **kwargs) + + else: + self.conv2 = ai8x.Conv2d(hidden_channels, out_channels, 3, + padding=1, stride=stride, + bias=depthwise_bias, **kwargs) + + else: + if depthwise_bias: + self.conv2 = ai8x.FusedMaxPoolConv2dBN(hidden_channels, + out_channels, 3, + padding=1, pool_size=stride, + pool_stride=stride, + bias=depthwise_bias, **kwargs) + + else: + self.conv2 = ai8x.FusedMaxPoolConv2d(hidden_channels, + out_channels, 3, + padding=1, pool_size=stride, + pool_stride=stride, + bias=depthwise_bias, **kwargs) + + if (stride == 1) and (in_channels == out_channels): + self.resid = ai8x.Add() + else: + self.resid = self.NoResidual() + + class NoResidual(nn.Module): + """ + Does nothing. + """ + def forward(self, *x): # pylint: disable=arguments-differ + """Forward prop""" + return x[0] + + def forward(self, x): # pylint: disable=arguments-differ + """Forward prop""" + y = self.conv1(x) + y = self.conv2(y) + return self.resid(y, x) + + class MBConvBlock(nn.Module): """Mobile Inverted Residual Bottleneck Block. diff --git a/datasets/face_id/README.md b/datasets/face_id/README.md deleted file mode 100644 index e14459abb..000000000 --- a/datasets/face_id/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# FaceID Data Generation - -This folder contains scripts to generate data to train and test models for FaceID model using the following datasets: - - VGGFace-2: A large-scale face recognition dataset. [https://www.robots.ox.ac.uk/~vgg/data/](https://www.robots.ox.ac.uk/~vgg/data/) - - YouTubeFaces: A database of face videos designed for studying the problem of unconstrained face recognition. [https://www.cs.tau.ac.il/~wolf/ytfaces/](https://www.cs.tau.ac.il/~wolf/ytfaces/) - -## Dataset Generation - -### VGGFace-2 -**Warning:** The original dataset is about 40GB and the following scripts generate a new dataset with size of 183 GB. Be sure there is enough space on your hard drive before starting the execution. - -\ -Follow these steps for both train and test sets. -1. Download train and test the *VGG Face 2 Dataset* from [https://www.robots.ox.ac.uk/~vgg/data/](https://www.robots.ox.ac.uk/~vgg/data/) and extract the .tar.gz files. into the same folder. -2. Run gen_vggface2_embeddings.py: - ``` - python gen_vggface2_embeddings.py -r -d --type - ``` -3. Run merge_vggface2_dataset.py - ``` - python merge_vggface2_dataset.py -p --type - ``` - -### YouTubeFaces - -**Warning:** The original dataset is about 29GB and the following scripts generate a new dataset with size of 15 GB. Be sure there is enough space on your hard drive before starting the execution. - -\ -Follow these steps. -1. Download the dataset from [here](http://www.cslab.openu.ac.il/download/) and extract the tar.gz files. into the same folder. -2. Run gen_youtubefaces_embeddings.py: - ``` - python gen_youtubefaces_embeddings.py -r -d --type test - ``` -3. Run merge_youtubefaces_dataset.py - ``` - python merge_youtubefaces_dataset.py -p --type test - ``` - -**Note:** The default paths for generated dataset is set to AI8X_TRAINING_HOME/data so the data loaders can load them with default parameters. If the destination folder is changed, the ---data option should be added to the model training script. diff --git a/datasets/face_id/facenet_pytorch b/datasets/face_id/facenet_pytorch deleted file mode 160000 index 14b312d4e..000000000 --- a/datasets/face_id/facenet_pytorch +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 14b312d4ef4b1baf90438f89d49d3942e3c6df7f diff --git a/datasets/face_id/gen_vggface2_embeddings.py b/datasets/face_id/gen_vggface2_embeddings.py deleted file mode 100755 index 95043a229..000000000 --- a/datasets/face_id/gen_vggface2_embeddings.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -################################################################################################### -# -# Copyright (C) 2020-2021 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -Script to generate dataset for FaceID training and validation from VGGFace-2 dataset. -""" - -import argparse -import json -import os - -import numpy as np -import torch - -import scipy.ndimage -from facenet_pytorch import MTCNN, InceptionResnetV1 # pylint: disable=no-name-in-module -from matplotlib.image import imread - - -def generate_image(img, box, count): # pylint: disable=too-many-locals - """ - Generates images in size 120x160x3 that includes the detected face in the image. - - img, box are the original image and box. - count is how many pics you wanna generate - - box format: x1, y1, x3, y3 - """ - box[0] = np.max((box[0], 0)) - box[1] = np.max((box[1], 0)) - box[2] = np.min((box[2], img.shape[1])) - box[3] = np.min((box[3], img.shape[0])) - - factor = 1 - height = img.shape[0] - width = img.shape[1] - new_img = img - new_box = box - while True: - if height < 160 or width < 120: - factor += 1 - new_img = scipy.ndimage.zoom(img, [factor, factor, 1], order=1) - new_box = box * factor - height = new_img.shape[0] - width = new_img.shape[1] - else: - break - new_box = np.round(new_box).astype(np.int) - new_box_height = new_box[3] - new_box[1] - new_box_width = new_box[2] - new_box[0] - - scale_list = np.concatenate((np.arange(0.9, 0, -0.1), np.arange(0.09, 0, -0.02))) - ind = 0 - while (new_box_height > 160 or new_box_width > 120): - if ind < scale_list.size: - new_img = scipy.ndimage.zoom(img, [scale_list[ind], scale_list[ind], 1], order=1) - new_box = box * scale_list[ind] - new_box = np.round(new_box).astype(np.int) - new_box_height = new_box[3] - new_box[1] - new_box_width = new_box[2] - new_box[0] - ind += 1 - else: - pass - - min_x = np.max((0, new_box[0] - (120 - new_box_width))) - min_y = np.max((0, new_box[1] - (160 - new_box_height))) - max_x = np.min((new_box[0], width-120)) - max_y = np.min((new_box[1], height-160)) - - start_x = np.random.choice(np.arange(min_x, max_x+1), count, replace=True) - start_y = np.random.choice(np.arange(min_y, max_y+1), count, replace=True) - img_arr = [] - box_arr = [] - for i in range(count): - img_arr.append(new_img[start_y[i]:start_y[i]+160, start_x[i]:start_x[i]+120]) - temp_box = new_box.copy() - temp_box[0] -= start_x[i] - temp_box[2] -= start_x[i] - temp_box[1] -= start_y[i] - temp_box[3] -= start_y[i] - box_arr.append(temp_box) - new_img = img_arr - new_box = box_arr - return new_img, new_box, img, box - - -def main(source_path, dest_path): # pylint: disable=too-many-locals - """ - Main function to iterate over the images in the raw data and generate data samples - to train/test FaceID model. - """ - - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - print(f'Running on device: {device}') - - mtcnn = MTCNN( - image_size=80, margin=0, min_face_size=20, - thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, - device=device - ) - - resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device) - - data_dir_list = os.listdir(source_path) - for i, folder in enumerate(data_dir_list): - if i % 10 == 0: - print(f'{i} of {len(data_dir_list)}') - folder_path = os.path.join(source_path, folder) - prcssd_folder_path = os.path.join(dest_path, folder) - if not os.path.exists(prcssd_folder_path): - os.makedirs(prcssd_folder_path) - else: - continue - embedding_dict = {} - for image in os.listdir(folder_path): - image_path = os.path.join(folder_path, image) - img = imread(image_path) - x_aligned, prob, box = mtcnn(img, return_prob=True) - if box is not None and prob > 0.9: - x_aligned = x_aligned[None, :] - x_aligned = x_aligned.to(device) - embeddings = resnet(x_aligned).detach().cpu() - embedding_list = embeddings.numpy().ravel().tolist() - img_arr, _, img, box = generate_image(img, box, 1) - for ind, new_img in enumerate(img_arr): - new_img_name = image+'_160_120_'+str(ind)+'.npy' - new_img_path = os.path.join(prcssd_folder_path, new_img_name) - np.save(new_img_path, new_img) - embedding_dict[new_img_name] = embedding_list - json_bin = json.dumps(embedding_dict) - with open( - os.path.join(prcssd_folder_path, 'embeddings.json'), - mode='w', - encoding='utf-8', - ) as out_file: - out_file.write(json_bin) - - -def parse_args(): - """Parses command line arguments""" - data_folder = os.path.abspath(__file__) - for _ in range(3): - data_folder = os.path.dirname(data_folder) - data_folder = os.path.join(data_folder, 'data') - - parser = argparse.ArgumentParser(description='Generate VGGFace-2 dataset to train/test \ - FaceID model.') - parser.add_argument('-r', '--raw', dest='raw_data_path', type=str, - default=os.path.join(data_folder, 'VGGFace-2', 'raw'), - help='Path to raw VGG-Face-2 dataset folder.') - parser.add_argument('-d', '--dest', dest='dest_data_path', type=str, - default=os.path.join(data_folder, 'VGGFace-2'), - help='Folder path to store processed data') - parser.add_argument('--type', dest='data_type', type=str, required=True, - help='Data type to generate (train/test)') - args = parser.parse_args() - - source_path = os.path.join(args.raw_data_path, args.data_type) - dest_path = os.path.join(args.dest_data_path, args.data_type, 'temp') - return source_path, dest_path - - -if __name__ == "__main__": - raw_data_path, dest_data_path = parse_args() - main(raw_data_path, dest_data_path) diff --git a/datasets/face_id/gen_youtubefaces_embeddings.py b/datasets/face_id/gen_youtubefaces_embeddings.py deleted file mode 100755 index 2d271c2c0..000000000 --- a/datasets/face_id/gen_youtubefaces_embeddings.py +++ /dev/null @@ -1,216 +0,0 @@ -#!/usr/bin/env python3 -################################################################################################### -# -# Copyright (C) 2020-2021 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -Script to generate dataset for FaceID training and validation from YouTubeFaces dataset. -""" - -import argparse -import json -import os - -import numpy as np -import torch - -import scipy.ndimage -from facenet_pytorch import MTCNN, InceptionResnetV1 # pylint: disable=no-name-in-module -from matplotlib.image import imread - - -def generate_image(img, box, count): # pylint: disable=too-many-locals - """ - Generates images in size 120x160x3 that includes the detected face in the image. - - img, box are the original image and box. - count is how many pics you wanna generate - - box format: x1, y1, x3, y3 - """ - box[0] = np.max((box[0], 0)) - box[1] = np.max((box[1], 0)) - box[2] = np.min((box[2], img.shape[1])) - box[3] = np.min((box[3], img.shape[0])) - - factor = 1 - height = img.shape[0] - width = img.shape[1] - new_img = img - new_box = box - while True: - if height < 160 or width < 120: - factor += 1 - new_img = scipy.ndimage.zoom(img, [factor, factor, 1], order=1) - new_box = box * factor - height = new_img.shape[0] - width = new_img.shape[1] - else: - break - new_box = np.round(new_box).astype(np.int) - new_box_height = new_box[3] - new_box[1] - new_box_width = new_box[2] - new_box[0] - - scale_list = np.arange(0.9, 0, -0.1) - ind = 0 - while (new_box_height > 160 or new_box_width > 120): - new_img = scipy.ndimage.zoom(img, [scale_list[ind], scale_list[ind], 1], order=1) - new_box = box * scale_list[ind] - new_box = np.round(new_box).astype(np.int) - new_box_height = new_box[3] - new_box[1] - new_box_width = new_box[2] - new_box[0] - ind += 1 - - min_x = np.max((0, new_box[0] - (120 - new_box_width))) - min_y = np.max((0, new_box[1] - (160 - new_box_height))) - max_x = np.min((new_box[0], width-120)) - max_y = np.min((new_box[1], height-160)) - - start_x = np.random.choice(np.arange(min_x, max_x+1), count, replace=True) - start_y = np.random.choice(np.arange(min_y, max_y+1), count, replace=True) - img_arr = [] - box_arr = [] - for i in range(count): - img_arr.append(new_img[start_y[i]:start_y[i]+160, start_x[i]:start_x[i]+120]) - temp_box = new_box.copy() - temp_box[0] -= start_x[i] - temp_box[2] -= start_x[i] - temp_box[1] -= start_y[i] - temp_box[3] -= start_y[i] - box_arr.append(temp_box) - new_img = img_arr - new_box = box_arr - return new_img, new_box, img, box - - -def main(source_path, dest_path): - """ - Main function to iterate over the images in the raw data and generate data samples - to train/test FaceID model. - """ - - # img_dir = os.path.join(raw_data_path, 'aligned_images_DB') - frame_dir = os.path.join(source_path, 'frame_images_DB') - - if not os.path.exists(dest_path): - os.makedirs(dest_path) - - # set parameters - num_imgs_per_face = 1 - target_im_shape = (160, 120) - - # set device - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - print(f'Running on device: {device}') - - # create models - mtcnn = MTCNN( - image_size=80, margin=0, min_face_size=20, - thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, - device=device - ) - resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device) - - # run models on the images - num_persons = 0 - num_faces = 0 - - embedding_dict = {} - subj_name_list = os.listdir(frame_dir) - - for f_n, face_file in enumerate(subj_name_list): - if (f_n % 100) == 0: - print(f'Subject {f_n} of {len(subj_name_list)}') - f_path = os.path.join(frame_dir, face_file) - if os.path.isfile(f_path): - if face_file.endswith('txt'): - with open(f_path, mode='r', encoding='utf-8') as file: - lines = file.readlines() - num_persons += 1 - for line in lines: - num_faces += 1 - img_name = line.split(',')[0] - subj_name, video_no, file_name = img_name.split('\\') - img_path = os.path.join(frame_dir, subj_name, video_no, file_name) - img = imread(img_path) - - x_aligned, _, _ = mtcnn(img, return_prob=True) - if x_aligned is not None: - aligned = x_aligned[None, :, :, :].to(device) - embedding = resnet(aligned).detach().cpu().numpy()[0] - - if subj_name not in embedding_dict: - embedding_dict[subj_name] = {} - subj_path = os.path.join(dest_path, subj_name) - if not os.path.exists(subj_path): - os.mkdir(subj_path) - if video_no not in embedding_dict[subj_name]: - embedding_dict[subj_name][video_no] = {} - video_path = os.path.join(dest_path, subj_name, video_no) - if not os.path.exists(video_path): - os.mkdir(video_path) - - embedding_dict[subj_name][video_no][file_name] = embedding.tolist() - x_aligned_int = x_aligned.cpu().numpy() - x_aligned_int -= np.min(x_aligned_int) - x_aligned_int /= np.max(x_aligned_int) - x_aligned_int = (255.0 * x_aligned_int).astype(np.uint8) - np.save(os.path.join(dest_path, subj_name, video_no, file_name), - x_aligned_int) - - rect = line.split(',')[2:6] - for i in range(4): - rect[i] = int(rect[i]) - - box = np.array([int(rect[0]) - int(rect[2])//2, - int(rect[1]) - int(rect[3])//2, - int(rect[0]) + int(rect[2])//2, - int(rect[1]) + int(rect[3])//2]) - - img_arr, _, img, box = generate_image(img, box, num_imgs_per_face) - for img_idx in range(num_imgs_per_face): - new_file_name = '_'.join([file_name, str(target_im_shape[0]), - str(target_im_shape[1]), str(img_idx)]) - cropped_im_path = os.path.join(dest_path, subj_name, video_no, - new_file_name) - np.save(cropped_im_path, img_arr[img_idx]) - - print(f'Number of People: {num_persons}') - print(f'Number of Faces: {num_faces}') - - # save embeddings to json file - with open(os.path.join(dest_path, 'embeddings.json'), mode='w', encoding='utf-8') as out_file: - json.dump(embedding_dict, out_file) - - -def parse_args(): - """Parses command line arguments""" - data_folder = os.path.abspath(__file__) - for _ in range(3): - data_folder = os.path.dirname(data_folder) - data_folder = os.path.join(data_folder, 'data') - - parser = argparse.ArgumentParser(description='Generate YouTubeFaces dataset to train/test \ - FaceID model.') - parser.add_argument('-r', '--raw', dest='raw_data_path', type=str, - default=os.path.join(data_folder, 'YouTubeFaces', 'raw'), - help='Path to raw YouTubeFaces dataset folder.') - parser.add_argument('-d', '--dest', dest='dest_data_path', type=str, - default=os.path.join(data_folder, 'YouTubeFaces'), - help='Folder path to store processed data') - parser.add_argument('--type', dest='data_type', type=str, required=True, - help='Data type to generate (train/test)') - args = parser.parse_args() - - source_path = args.raw_data_path - dest_path = os.path.join(args.dest_data_path, args.data_type, 'temp') - return source_path, dest_path - - -if __name__ == "__main__": - raw_data_path, dest_data_path = parse_args() - main(raw_data_path, dest_data_path) diff --git a/datasets/face_id/merge_vggface2_dataset.py b/datasets/face_id/merge_vggface2_dataset.py deleted file mode 100755 index 0db35b3a9..000000000 --- a/datasets/face_id/merge_vggface2_dataset.py +++ /dev/null @@ -1,104 +0,0 @@ -#!/usr/bin/env python3 -################################################################################################### -# -# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -Script to merge VGGFace-2 data samples into more compact file series to effectively use during -FaceID model training. -""" - -import argparse -import json -import os -import pickle - -import numpy as np - - -def save_dataset(data, merged_data_path, part_no): - """ - Function to save merged file. - """ - merged_file_path = os.path.join(merged_data_path, f'whole_set_{part_no:02d}.pkl') - with open(merged_file_path, 'wb') as handle: - pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL) - - -def main(data_path): # pylint: disable=too-many-locals - """ - Main function to iterate over the data samples to merge. - """ - img_size = (3, 160, 120) - - subj_list = sorted(os.listdir(os.path.join(data_path, 'temp'))) - part_no = 0 - dataset = {} - num_empty_subjs = 0 - - for i, subj in enumerate(subj_list): - if subj == 'merged': - print(f'Folder {subj} skipped') - continue - - if (i % 250) == 0: - print(f'{i} of {subj_list}') - if i > 0: - save_dataset(dataset, data_path, part_no) - dataset = {} - part_no += 1 - - if subj not in dataset: - dataset[subj] = {} - - subj_path = os.path.join(data_path, 'temp', subj) - if not os.path.isdir(subj_path): - continue - - if not os.listdir(subj_path): - print(f'Empty folder: {subj_path}') - num_empty_subjs += 1 - continue - - embedding_path = os.path.join(subj_path, 'embeddings.json') - with open(embedding_path, encoding='utf-8') as file: - embeddings = json.load(file) - - for img_name, emb in embeddings.items(): - img_path = os.path.join(subj_path, img_name) - img = np.load(img_path).transpose([2, 0, 1]) - - if img.shape == img_size: - if np.min(img) != np.max(img): - dataset[subj][img_name] = {'embedding': emb, 'img': img} - - if dataset: - save_dataset(dataset, data_path, part_no) - - -def parse_args(): - """Parses command line arguments""" - parser = argparse.ArgumentParser(description='Merge VGGFace-2 data samples to effectively use\ - during training/testing FaceID model.') - default_data_path = os.path.abspath(__file__) - for _ in range(3): - default_data_path = os.path.dirname(default_data_path) - default_data_path = os.path.join(default_data_path, 'data', 'VGGFace-2') - parser.add_argument('-p', '--data_path', dest='data_path', type=str, - default=default_data_path, - help='Folder path to processed data') - parser.add_argument('--type', dest='data_type', type=str, required=True, - help='Data type to generate (train/test)') - args = parser.parse_args() - - data_path = os.path.join(args.data_path, args.data_type) - return data_path - - -if __name__ == "__main__": - data_folder = parse_args() - main(data_folder) diff --git a/datasets/face_id/merge_youtubefaces_dataset.py b/datasets/face_id/merge_youtubefaces_dataset.py deleted file mode 100755 index a4eb1af5d..000000000 --- a/datasets/face_id/merge_youtubefaces_dataset.py +++ /dev/null @@ -1,99 +0,0 @@ -#!/usr/bin/env python3 -################################################################################################### -# -# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -Script to merge YouTubeFaces data samples into more compact file series to effectively use during -FaceID model training. -""" - -import argparse -import json -import os -import pickle - -import numpy as np - - -def save_dataset(data, merged_data_path, part_no): - """ - Function to save merged file. - """ - merged_file_path = os.path.join(merged_data_path, f'whole_set_{part_no:02d}.pkl') - with open(merged_file_path, 'wb') as handle: - pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL) - - -def main(data_path): # pylint: disable=too-many-locals - """ - Main function to iterate over the data samples to merge. - """ - img_size = (3, 160, 120) - - num_imgs_per_face = 1 - - dataset = {} - part_no = 0 - - embedding_path = os.path.join(data_path, 'temp', 'embeddings.json') - with open(embedding_path, encoding='utf-8') as file: - embeddings = json.load(file) - - for i, (subj, val) in enumerate(embeddings.items()): # pylint: disable=too-many-nested-blocks - if (i % 200) == 0: - print(f'{i} of {len(embeddings)}') - if i > 0: - save_dataset(dataset, data_path, part_no) - dataset = {} - part_no += 1 - - if subj not in dataset: - dataset[subj] = {} - for video_num, val2 in val.items(): - img_folder = os.path.join(data_path, 'temp', subj, str(video_num)) - - if video_num not in dataset[subj]: - dataset[subj][video_num] = {} - - for img_name, embedding in val2.items(): - for idx in range(num_imgs_per_face): - img_name = '_'.join([img_name, str(img_size[1]), str(img_size[2]), str(idx)]) - img_path = os.path.join(img_folder, '.'.join([img_name, 'npy'])) - img = np.load(img_path).transpose([2, 0, 1]) - - if img.shape == img_size: - if np.min(img) != np.max(img): - dataset[subj][video_num][img_name] = {'embedding': embedding, - 'img': img} - - if dataset: - save_dataset(dataset, data_path, part_no) - - -def parse_args(): - """Parses command line arguments""" - parser = argparse.ArgumentParser(description='Merge YouTubeFaces data samples to effectively\ - use during training/testing FaceID model.') - default_data_path = os.path.abspath(__file__) - for _ in range(3): - default_data_path = os.path.dirname(default_data_path) - default_data_path = os.path.join(default_data_path, 'data', 'YouTubeFaces') - parser.add_argument('-p', '--data_path', dest='data_path', type=str, - default=default_data_path, - help='Folder path to processed data') - parser.add_argument('--type', dest='data_type', type=str, required=True, - help='Data type to generate (train/test)') - args = parser.parse_args() - - data_path = os.path.join(args.data_path, args.data_type) - return data_path - - -if __name__ == "__main__": - data_folder = parse_args() - main(data_folder) diff --git a/datasets/faceid.py b/datasets/faceid.py deleted file mode 100644 index 3f0039750..000000000 --- a/datasets/faceid.py +++ /dev/null @@ -1,77 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -Classes and functions used to utilize the Face ID dataset. -""" -import os - -from torchvision import transforms - -import ai8x -from datasets.vggface2 import VGGFace2Dataset -from datasets.youtube_faces import YouTubeFacesDataset - - -def faceid_get_datasets(data, load_train=True, load_test=True): - """ - Load the faceID dataset - - The dataset is loaded from the archive file, so the file is required for this version. - - The dataset consists of actually 2 different datasets, VGGFace2 for training and YouTubeFaces - for the test. The reason of this is proof-of-concept models are obtained by this way and the - losses At YTFaces are tracked for the sake of benchmarking. - - The images are all 3-color 160x120 sized and consist the face image. - """ - (data_dir, args) = data - - # These are hard coded for now, need to come from above in future. - train_resample_subj = 1 - train_resample_img_per_subj = 6 - test_resample_subj = 1 - test_resample_img_per_subj = 2 - train_data_dir = os.path.join(data_dir, 'VGGFace-2') - test_data_dir = os.path.join(data_dir, 'YouTubeFaces') - - transform = transforms.Compose([ - ai8x.normalize(args=args) - ]) - - if load_train: - train_dataset = VGGFace2Dataset(root_dir=train_data_dir, d_type='train', - transform=transform, - resample_subj=train_resample_subj, - resample_img_per_subj=train_resample_img_per_subj) - else: - train_dataset = None - - if load_test: - test_dataset = YouTubeFacesDataset(root_dir=test_data_dir, d_type='test', - transform=transform, - resample_subj=test_resample_subj, - resample_img_per_subj=test_resample_img_per_subj) - - if args.truncate_testset: - test_dataset.data = test_dataset.data[:1] # type: ignore # .data exists - else: - test_dataset = None - - return train_dataset, test_dataset - - -datasets = [ - { - 'name': 'FaceID', - 'input': (3, 160, 120), - 'output': ('id'), - 'regression': True, - 'loader': faceid_get_datasets, - }, -] diff --git a/datasets/vggface2.py b/datasets/vggface2.py index 6f872f967..24e95624a 100644 --- a/datasets/vggface2.py +++ b/datasets/vggface2.py @@ -1,6 +1,6 @@ ################################################################################################### # -# Copyright (C) 2019-2022 Maxim Integrated Products, Inc. All Rights Reserved. +# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html @@ -10,80 +10,436 @@ VGGFace2: A Dataset for Recognising Faces across Pose and Age https://ieeexplore.ieee.org/abstract/document/8373813 """ + +import errno +import glob import os import pickle -import time import numpy as np import torch -from torch.utils import data +import torchvision.transforms.functional as FT +from torch.utils.data import Dataset +from torchvision import transforms + +import cv2 +import kornia.geometry.transform as GT +import onnxruntime +from hawk_eyes.face import RetinaFace +from PIL import Image +from skimage import transform as trans +from tqdm import tqdm + +import ai8x +from utils import augmentation_utils -class VGGFace2Dataset(data.Dataset): +class VGGFace2(Dataset): """ - VGGFace2: A Dataset for Recognising Faces across Pose and Age - https://ieeexplore.ieee.org/abstract/document/8373813 + VGGFace2 Dataset """ - def __init__( - self, - root_dir, - d_type, - transform=None, - resample_subj=1, - resample_img_per_subj=1, - ): - data_folder = os.path.join(root_dir, d_type) - assert os.path.isdir(data_folder), (f'No dataset at {data_folder}.' - ' Follow the steps at datasets/face_id/README.md') - - data_file_list = sorted([d for d in os.listdir(data_folder) if d.startswith('whole_set')]) - - self.sid_list = [] - self.embedding_list = [] - self.img_list = [] + def __init__(self, root_dir, d_type, mode, transform=None, + teacher_transform=None, img_size=(112, 112)): + + if d_type not in ('test', 'train'): + raise ValueError("d_type can only be set to 'test' or 'train'") + + if mode not in ('detection', 'identification', 'identification_dr'): + raise ValueError("mode can only be set to 'detection', 'identification'," + "or 'identification_dr'") + + self.root_dir = root_dir + self.d_type = d_type self.transform = transform + self.teacher_transform = teacher_transform + self.img_size = img_size + self.mode = mode + self.dataset_path = os.path.join(self.root_dir, "VGGFace-2") + self.__makedir_exist_ok(self.dataset_path) + self.count = 0 + self.tform = trans.SimilarityTransform() + self.src = np.array([ + [38.2946, 51.6963], + [73.5318, 51.5014], + [56.0252, 71.7366], + [41.5493, 92.3655], + [70.7299, 92.2041]], dtype=np.float32) + + self.__makedir_exist_ok(self.dataset_path) + self.__makedir_exist_ok(os.path.join(self.dataset_path, "processed")) + + if self.d_type in ('train', 'test'): + self.gt_path = os.path.join(self.dataset_path, "processed", + self.d_type+"_vggface2.pickle") + self.d_path = os.path.join(self.dataset_path, self.d_type) + if not os.path.exists(self.gt_path): + assert os.path.isdir(self.d_path), (f'No dataset at {self.d_path}.\n' + ' Please review the term and' + ' conditions at https://www.robots.ox.ac.uk/' + '~vgg/data/vgg_face2/ . Then, download the' + ' dataset and extract raw images to the' + ' train and test subfolders.\n' + ' Expected folder structure: \n' + ' - root_dir \n' + ' - VGGFace-2 \n' + ' - train \n' + ' - test \n') + + print("Extracting ground truth from the " + self.d_type + " set") + self.__extract_gt() + + f = open(self.gt_path, 'rb') + self.pickle_dict = pickle.load(f) + f.close() + + else: + print(f'Unknown data type: {self.d_type}') + return - subj_idx = 0 - n_elems = 0 + def __extract_gt(self): + """ + Extracts the ground truth from the dataset + """ + onnxruntime.set_default_logger_severity(3) # suppress onnxruntime warnings + retina = RetinaFace(model_name='retina_l', conf=0.5) + img_paths = list(glob.glob(os.path.join(self.d_path + '/**/', '*.jpg'), recursive=True)) + nf_number = 0 + n_words = 0 + pickle_dict = {key: [] for key in ["boxes", "landmarks", "img_list", "lbl_list"]} + pickle_dict["word2index"] = {} - t_start = time.time() - print('Data loading...') - for n_file, data_file in enumerate(data_file_list): - if ((n_file+1) % 5) == 0: - print(f'\t{n_file+1} of {len(data_file_list)}') - f_path = os.path.join(data_folder, data_file) + for jpg in tqdm(img_paths): + boxes = [] + image = cv2.imread(jpg) + bboxes, lndmrks = retina.detect(image) + if len(bboxes) == 0: + nf_number += 1 + continue - with open(f_path, 'rb') as f: - x = pickle.load(f) + for box in bboxes: + box = np.clip(box[:4], 0, None) + boxes.append(box) - for key in list(x)[::resample_subj]: - val = x[key] - for key2 in list(val)[::resample_img_per_subj]: - self.img_list.append(val[key2]['img']) - self.embedding_list.append(np.array(val[key2]['embedding']).astype(np.float32)) - self.sid_list.append(subj_idx) - n_elems += 1 - subj_idx += resample_subj + lndmrks = lndmrks[0] - t_end = time.time() - print(f'{n_elems} of data samples loaded in {t_end-t_start:.4f} seconds.') + dir_name = os.path.dirname(jpg) + lbl = os.path.relpath(dir_name, self.d_path) - def __normalize_data(self, data_item): - data_item = data_item.astype(np.float32) - data_item /= 256 - return data_item + if lbl not in pickle_dict["word2index"]: + pickle_dict["word2index"][lbl] = n_words + n_words += 1 + + pickle_dict["lbl_list"].append(lbl) + pickle_dict["boxes"].append(boxes) + pickle_dict["landmarks"].append(lndmrks) + pickle_dict["img_list"].append(os.path.relpath(jpg, self.dataset_path)) + if nf_number > 0: + print(f'Not found any faces in {nf_number} images ') + + with open(self.gt_path, 'wb') as f: + pickle.dump(pickle_dict, f) def __len__(self): - return len(self.img_list) + return len(self.pickle_dict["img_list"]) - 1 + + def __getitem__(self, index): + """ + Get the image and associated target according to the mode + """ + if index >= len(self): + raise IndexError + + if self.mode == 'detection': + return self.__getitem_detection(index) + + if self.mode == 'identification': + return self.__getitem_identification(index) + + if self.mode == 'identification_dr': + return self.__getitem_identification_dr(index) + + # Will never reached + return None + + def __getitem_detection(self, index): + """ + Get the image and associated target for face detection + """ + if torch.is_tensor(index): + index = index.tolist() + + img = Image.open(os.path.join(self.dataset_path, self.pickle_dict["img_list"][index])) + img = FT.to_tensor(img) + + boxes = self.pickle_dict["boxes"][index] + boxes = torch.as_tensor(boxes, dtype=torch.float32) + + img, boxes = augmentation_utils.resize(img, boxes, + dims=(self.img_size[0], self.img_size[1])) - def __getitem__(self, idx): - embedding = self.embedding_list[idx] - embedding = np.expand_dims(embedding, 1) - embedding = np.expand_dims(embedding, 2) - embedding *= 6.0 + labels = [1] * boxes.shape[0] - inp = torch.tensor(self.__normalize_data(self.img_list[idx]), dtype=torch.float) if self.transform is not None: - inp = self.transform(inp) + img = self.transform(img) + + boxes = boxes.clamp_(min=0, max=1) + labels = torch.as_tensor(labels, dtype=torch.int64) + + return img, (boxes, labels) + + def __getitem_identification(self, index): + """ + Get the image and associated target for face identification + """ + if torch.is_tensor(index): + index = index.tolist() + + lbl = self.pickle_dict["lbl_list"][index] + lbl_index = self.pickle_dict["word2index"][lbl] + lbl_index = torch.tensor(lbl_index, dtype=torch.long) + box = self.pickle_dict["boxes"][index][0] + img = Image.open(os.path.join(self.dataset_path, self.pickle_dict["img_list"][index])) + img_A = img.copy() + + # Apply transformation to the image that will be aligned + if self.teacher_transform is not None: + img_A = self.teacher_transform(img_A) + + # Apply transformation to the image that will be cropped + if self.transform is not None: + img = self.transform(img) + + # Use landmarks to estimate affine transformation + landmark = self.pickle_dict["landmarks"][index] + self.tform.estimate(landmark, self.src) + A = self.tform.params[0:2, :] + A = torch.as_tensor(A, dtype=torch.float32) + A = A.unsqueeze(0) + + # Apply affine transformation to obtain aligned image + img_A = GT.warp_affine(img_A.unsqueeze(0), A, (self.img_size[0], self.img_size[1])) + img_A = img_A.squeeze(0) + + # Convert bounding box to square + height = box[3] - box[1] + width = box[2] - box[0] + max_dim = max(height, width) + box[0] = np.clip(box[0] - (max_dim - width) / 2, 0, img.shape[2]) + box[1] = np.clip(box[1] - (max_dim - height) / 2, 0, img.shape[1]) + box[2] = np.clip(box[2] + (max_dim - width) / 2, 0, img.shape[2]) + box[3] = np.clip(box[3] + (max_dim - height) / 2, 0, img.shape[1]) + + # Crop image with the square bounding box + img_C = FT.crop(img=img, top=int(box[1]), left=int(box[0]), + height=int(box[3]-box[1]), width=int(box[2]-box[0])) + + # Check if the cropped image is square, if not, pad it + _, h, w = img_C.shape + if w != h: + max_dim = max(w, h) + h_padding = (max_dim - h) / 2 + w_padding = (max_dim - w) / 2 + l_pad = w_padding if w_padding % 1 == 0 else w_padding+0.5 + t_pad = h_padding if h_padding % 1 == 0 else h_padding+0.5 + r_pad = w_padding if w_padding % 1 == 0 else w_padding-0.5 + b_pad = h_padding if h_padding % 1 == 0 else h_padding-0.5 + padding = (int(l_pad), int(t_pad), int(r_pad), int(b_pad)) + img_C = FT.pad(img_C, padding, 0, 'constant') + + # Resize cropped image to the desired size + img_C = FT.resize(img_C, (self.img_size[0], self.img_size[1])) + + # Concatenate images + concat_img = torch.cat((img_C, img_A), 0) + + return concat_img, lbl_index + + def __getitem_identification_dr(self, index): + """ + Get the image and associated target for dimensionality reduction + """ + if torch.is_tensor(index): + index = index.tolist() + + lbl = self.pickle_dict["lbl_list"][index] + lbl_index = self.pickle_dict["word2index"][lbl] + lbl_index = torch.tensor(lbl_index, dtype=torch.long) + img = Image.open(os.path.join(self.dataset_path, self.pickle_dict["img_list"][index])) + + # Apply transformation to the image that will be aligned + if self.transform is not None: + img = self.transform(img) + + # Use landmarks to estimate affine transformation + landmark = self.pickle_dict["landmarks"][index] + self.tform.estimate(landmark, self.src) + A = self.tform.params[0:2, :] + A = torch.as_tensor(A, dtype=torch.float32) + A = A.unsqueeze(0) + + # Apply affine transformation to obtain aligned image + img = GT.warp_affine(img.unsqueeze(0), A, (self.img_size[0], self.img_size[1])) + img = img.squeeze(0) + + return img, lbl_index + + @staticmethod + def __makedir_exist_ok(dirpath): + """Make directory if not already exists + """ + try: + os.makedirs(dirpath) + except OSError as e: + if e.errno == errno.EEXIST: + pass + else: + raise + + @staticmethod + def collate_fn(batch): + """ + Since each image may have a different number of objects, we need a collate function + (to be passed to the DataLoader). + This describes how to combine these tensors of different sizes. We use lists. + :param batch: an iterable of N sets from __getitem__() + :return: a tensor of images, lists of varying-size tensors of bounding boxes and labels + """ + images = [] + boxes_and_labels = [] + + for b in batch: + images.append(b[0]) + boxes_and_labels.append(b[1]) + + images = torch.stack(images, dim=0) + return images, boxes_and_labels + + +def VGGFace2_FaceID_get_datasets(data, load_train=True, load_test=True, img_size=(112, 112)): + + """ Returns FaceID Dataset + """ + (data_dir, args) = data + + train_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.RandomHorizontalFlip(p=0.5), + transforms.ColorJitter(brightness=(0.6, 1.4), saturation=(0.6, 1.4), + contrast=(0.6, 1.4), hue=(-0.4, 0.4)), + transforms.RandomErasing(p=0.1), + ai8x.normalize(args=args)]) + + teacher_transform = transforms.Compose([ + transforms.ToTensor(), + ai8x.normalize(args=args)]) + + if load_train: + + train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='identification', + transform=train_transform, teacher_transform=teacher_transform, + img_size=img_size) + + print(f'Train dataset length: {len(train_dataset)}\n') + else: + train_dataset = None + + if load_test: + test_transform = transforms.Compose([transforms.ToTensor(), + ai8x.normalize(args=args)]) + + test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='identification', + transform=test_transform, teacher_transform=teacher_transform, + img_size=img_size) + + print(f'Test dataset length: {len(test_dataset)}\n') + else: + test_dataset = None + + return train_dataset, test_dataset + + +def VGGFace2_FaceID_dr_get_datasets(data, load_train=True, load_test=True, img_size=(112, 112)): + + """ Returns FaceID Dataset for dimensionality reduction + """ + (data_dir, args) = data + + train_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.RandomHorizontalFlip(p=0.5), + ai8x.normalize(args=args)]) + + if load_train: + + train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='identification_dr', + transform=train_transform, img_size=img_size) + + print(f'Train dataset length: {len(train_dataset)}\n') + else: + train_dataset = None + + if load_test: + test_transform = transforms.Compose([transforms.ToTensor(), + ai8x.normalize(args=args)]) + + test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='identification_dr', + transform=test_transform, img_size=img_size) + + print(f'Test dataset length: {len(test_dataset)}\n') + else: + test_dataset = None + + return train_dataset, test_dataset + + +def VGGFace2_Facedet_get_datasets(data, load_train=True, load_test=True, img_size=(224, 168)): + + """ Returns FaceDetection Dataset + """ + (data_dir, args) = data + + if load_train: + train_transform = transforms.Compose([ + ai8x.normalize(args=args)]) + + train_dataset = VGGFace2(root_dir=data_dir, d_type='train', mode='detection', + transform=train_transform, img_size=img_size) + + print(f'Train dataset length: {len(train_dataset)}\n') + else: + train_dataset = None + + if load_test: + test_transform = transforms.Compose([ai8x.normalize(args=args)]) + + test_dataset = VGGFace2(root_dir=data_dir, d_type='test', mode='detection', + transform=test_transform, img_size=img_size) + + print(f'Test dataset length: {len(test_dataset)}\n') + else: + test_dataset = None + + return train_dataset, test_dataset + - return inp, torch.tensor(embedding, dtype=torch.float) +datasets = [ + { + 'name': 'VGGFace2_FaceID', + 'input': (3, 112, 112), + 'output': ('id'), + 'loader': VGGFace2_FaceID_get_datasets, + }, + { + 'name': 'VGGFace2_FaceID_dr', + 'input': (3, 112, 112), + 'output': [*range(0, 8631, 1)], + 'loader': VGGFace2_FaceID_dr_get_datasets, + }, + { + 'name': 'VGGFace2_FaceDetection', + 'input': (3, 224, 168), + 'output': ([1]), + 'loader': VGGFace2_Facedet_get_datasets, + 'collate': VGGFace2.collate_fn + } +] diff --git a/datasets/vggface2_facedet.py b/datasets/vggface2_facedet.py deleted file mode 100644 index d63a4dc2b..000000000 --- a/datasets/vggface2_facedet.py +++ /dev/null @@ -1,214 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -VGGFace2: A Dataset for Recognising Faces across Pose and Age -https://ieeexplore.ieee.org/abstract/document/8373813 -""" - - -import errno -import glob -import os -import pickle - -import torch -from torch.utils.data import Dataset -from torchvision import transforms - -from PIL import Image -from tqdm import tqdm - -import ai8x -from datasets.face_id.facenet_pytorch import MTCNN - - -class VGGFace2_FaceDetectionDataset(Dataset): - """ - VGGFace2 Dataset for face detection - - MTCNN is used to extract the ground truth from the dataset as it provides - the ground truth for multiple faces in an image. - - GT Format: 0-3:Box Coordinates - - """ - def __init__(self, root_dir, d_type, transform=None, img_size=(224, 168)): - - if d_type not in ('test', 'train'): - raise ValueError("d_type can only be set to 'test' or 'train'") - - self.root_dir = root_dir - self.d_type = d_type - self.transform = transform - self.img_size = img_size - self.dataset_path = os.path.join(self.root_dir, "VGGFace-2") - self.__makedir_exist_ok(self.dataset_path) - self.__makedir_exist_ok(os.path.join(self.dataset_path, "processed")) - - if self.d_type in ('train', 'test'): - self.gt_path = os.path.join(self.dataset_path, "processed", self.d_type+"_gt.pickle") - self.d_path = os.path.join(self.dataset_path, self.d_type) - if not os.path.exists(self.gt_path): - assert os.path.isdir(self.d_path), (f'No dataset at {self.d_path}.\n' - ' Please review the term and' - ' conditions at https://www.robots.ox.ac.uk/' - '~vgg/data/vgg_face2/ . Then, download the' - ' dataset and extract raw images to the' - ' train and test subfolders.\n' - ' Expected folder structure: \n' - ' - root_dir \n' - ' - VGGFace-2 \n' - ' - train \n' - ' - test \n') - - print("Extracting ground truth from the " + self.d_type + " set") - self.__extract_gt() - - else: - print(f'Unknown data type: {self.d_type}') - return - - f = open(self.gt_path, 'rb') - self.pickle_dict = pickle.load(f) - f.close() - - def __extract_gt(self): - """ - Extracts the ground truth from the dataset - """ - mtcnn = MTCNN() - img_paths = list(glob.glob(os.path.join(self.d_path + '/**/', '*.jpg'), recursive=True)) - nf_number = 0 - pickle_dict = {key: [] for key in ["gt", "img_list"]} - - for jpg in tqdm(img_paths): - img = Image.open(jpg) - img = img.resize((self.img_size[1], self.img_size[0])) - # pylint: disable-next=unbalanced-tuple-unpacking - gt, _ = mtcnn.detect(img, landmarks=False) # type: ignore # returns tuple of 2 - - if gt is None or None in gt: - nf_number += 1 - continue - - pickle_dict["gt"].append(gt) - pickle_dict["img_list"].append(os.path.relpath(jpg, self.dataset_path)) - - if nf_number > 0: - print(f'Not found any faces in {nf_number} images ') - - with open(self.gt_path, 'wb') as f: - pickle.dump(pickle_dict, f) - - def __len__(self): - return len(self.pickle_dict["img_list"]) - 1 - - def __getitem__(self, index): - if index >= len(self): - raise IndexError - - if torch.is_tensor(index): - index = index.tolist() - - img = Image.open(os.path.join(self.dataset_path, self.pickle_dict["img_list"][index])) - - ground_truth = self.pickle_dict["gt"][index] - - lbls = [1] * ground_truth.shape[0] - if self.transform is not None: - img = self.transform(img) - for box in ground_truth: - box[0] = box[0] / self.img_size[1] - box[2] = box[2] / self.img_size[1] - box[1] = box[1] / self.img_size[0] - box[3] = box[3] / self.img_size[0] - - boxes = torch.as_tensor(ground_truth, dtype=torch.float32) - boxes = boxes.clamp_(min=0, max=1) - - labels = torch.as_tensor(lbls, dtype=torch.int64) - - return img, (boxes, labels) - - @staticmethod - def collate_fn(batch): - """ - Since each image may have a different number of objects, we need a collate function - (to be passed to the DataLoader). - This describes how to combine these tensors of different sizes. We use lists. - :param batch: an iterable of N sets from __getitem__() - :return: a tensor of images, lists of varying-size tensors of bounding boxes and labels - """ - images = [] - boxes_and_labels = [] - - for b in batch: - images.append(b[0]) - boxes_and_labels.append(b[1]) - - images = torch.stack(images, dim=0) - return images, boxes_and_labels - - @staticmethod - def __makedir_exist_ok(dirpath): - """Make directory if not already exists - """ - try: - os.makedirs(dirpath) - except OSError as e: - if e.errno == errno.EEXIST: - pass - else: - raise - - -def VGGFace2_Facedet_get_datasets(data, load_train=True, load_test=True, img_size=(224, 168)): - - """ Returns FaceDetection Dataset - """ - (data_dir, args) = data - - if load_train: - train_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Resize(img_size), - ai8x.normalize(args=args) - ]) - - train_dataset = VGGFace2_FaceDetectionDataset(root_dir=data_dir, d_type='train', - transform=train_transform, img_size=img_size) - - print(f'Train dataset length: {len(train_dataset)}\n') - else: - train_dataset = None - - if load_test: - test_transform = transforms.Compose([transforms.ToTensor(), - transforms.Resize(img_size), - ai8x.normalize(args=args)]) - - test_dataset = VGGFace2_FaceDetectionDataset(root_dir=data_dir, d_type='test', - transform=test_transform, img_size=img_size) - - print(f'Test dataset length: {len(test_dataset)}\n') - else: - test_dataset = None - - return train_dataset, test_dataset - - -datasets = [ - { - 'name': 'VGGFace2_FaceDetection', - 'input': (3, 224, 168), - 'output': ([1]), - 'loader': VGGFace2_Facedet_get_datasets, - 'collate': VGGFace2_FaceDetectionDataset.collate_fn - } -] diff --git a/datasets/youtube_faces.py b/datasets/youtube_faces.py deleted file mode 100644 index 6bf4bee58..000000000 --- a/datasets/youtube_faces.py +++ /dev/null @@ -1,91 +0,0 @@ -################################################################################################### -# -# Copyright (C) 2019-2022 Maxim Integrated Products, Inc. All Rights Reserved. -# -# Maxim Integrated Products, Inc. Default Copyright Notice: -# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html -# -################################################################################################### -""" -YouTube Faces Dataset -https://www.cs.tau.ac.il/~wolf/ytfaces/ -""" -import os -import pickle -import time - -import numpy as np -import torch -from torch.utils import data - - -class YouTubeFacesDataset(data.Dataset): - """ - YouTube Faces Dataset - https://www.cs.tau.ac.il/~wolf/ytfaces/ - """ - def __init__( - self, - root_dir, - d_type, - transform=None, - resample_subj=1, - resample_img_per_subj=1, - ): - data_folder = os.path.join(root_dir, d_type) - assert os.path.isdir(data_folder), (f'No dataset at {data_folder}.' - ' Follow the steps at datasets/face_id/README.md') - - data_file_list = sorted([d for d in os.listdir(data_folder) if d.startswith('whole_set')]) - - self.sid_list = [] - self.embedding_list = [] - self.img_list = [] - self.transform = transform - - subj_idx = 0 - n_elems = 0 - - t_start = time.time() - print('Data loading...') - for n_file, data_file in enumerate(data_file_list): - print(f'\t{n_file+1} of {len(data_file_list)}') - f_path = os.path.join(data_folder, data_file) - - with open(f_path, 'rb') as f: - x = pickle.load(f) - - for key in list(x)[::resample_subj]: - val = x[key] - for key2 in list(val)[::resample_img_per_subj]: - for key3 in list(val[key2]): - self.img_list.append(val[key2][key3]['img']) - self.embedding_list.append( - np.array(val[key2][key3]['embedding']).astype(np.float32) - ) - self.sid_list.append(subj_idx) - n_elems += 1 - subj_idx += resample_subj - - t_end = time.time() - print(f'{n_elems} of data samples loaded in {t_end-t_start:.4f} seconds.') - - def __normalize_data(self, data_item): - data_item = data_item.astype(np.float32) - data_item /= 256 - return data_item - - def __len__(self): - return len(self.img_list) - - def __getitem__(self, idx): - embedding = self.embedding_list[idx] - embedding = np.expand_dims(embedding, 1) - embedding = np.expand_dims(embedding, 2) - embedding *= 6.0 - - inp = torch.tensor(self.__normalize_data(self.img_list[idx]), dtype=torch.float) - if self.transform is not None: - inp = self.transform(inp) - - return inp, torch.tensor(embedding, dtype=torch.float) diff --git a/distiller b/distiller index ea4436dba..0477a66ef 160000 --- a/distiller +++ b/distiller @@ -1 +1 @@ -Subproject commit ea4436dba4bc17a60216f27c8ded69b7faa5f501 +Subproject commit 0477a66ef0ace09f5572f27c0178ea422ed9bf4e diff --git a/docs/FacialRecognitionSystem.md b/docs/FacialRecognitionSystem.md new file mode 100644 index 000000000..a9346cb10 --- /dev/null +++ b/docs/FacialRecognitionSystem.md @@ -0,0 +1,102 @@ +# Facial Recognition System + +This document aims to explain facial recognition applications for MAX7800x series microcontrollers. Facial recognition task consists from three main parts, face detection, face identification and dot product. + + - The face detection model detects faces in the captured image and extracts a rectangular sub-image containing only one face. + - The face Identification model identifies a person from their facial images by generating the embedding for a given face image. + - The dot product layer outputs the dot product representing the similarity between the embedding from the given image and embeddings in the database. + +Figure 1 depicts the facial recognition system sequential diagram. + + + +​Figure 1. MAX7800x facial recognition system + +## Dataset + +The first step will be the dataset preparation. The dataset is VGGFace-2 [1]. +Please review the term and conditions at [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/). Then, download the dataset and extract raw images to the train and test subfolders. + +Expected folder structure: + + - root_dir + - VGGFace-2 + - train + - test + +FaceID and Face Detection tasks share the same ground truth pickle, and it will be automatically generated when one of these tasks started. + +## Face Detection + +To be able to localize faces in a facial recognition system, a face detection algorithm is generally used in facial recognition systems. Face detection is an object detection problem that has various solutions in the literature. In this work, a face detection algorithm that will run on MAX7800x series microcontrollers with a real-time performance was targeted. + +For digit detection problem, previously, a Tiny SSD[2] based MAX7800x object detection algorithm was developed , named Tinier SSD. The face detection model is a modified version of the digit detection model. The modification was realized to reduce the number of parameters and enable larger input size. + +To train the facedetection model, "scripts/train_facedet_tinierssd.sh" script can be used. + +## FaceID + +To train a FaceID model for MAX7800x microcontrollers, there are multiple steps. As the MAX7800x FaceID models will be trained in a knowledge distillation fashion, the first step will be downloading a backbone checkpoint for the teacher model. + +The suggested teacher model is IR-152, but the other teacher models defined in "model_irse_DRL.py" may be used as well. Please review the terms and conditions at face.evoLVe[3] repository, and download the checkpoint according to your teacher model selection. + +There are two FaceID models, one for the MAX78000 and one for the MAX78002. The MAX78000 one is named faceid_112, and it is a relatively lightweight model. To enable more performance on MAX78002, a more complex model was developed, which is named mobilefacenet_112. To train the FaceID models, "scripts/train_faceid_112.sh" and "scripts/train_mobilefacenet_112.sh" scripts can be used, respectivey. Training scripts will realize Dimensionality Reduction and Relation Based-Knowledge Knowledge Distillation steps automatically. A summary of Dimensionality Reduction and Relation-Based Knowledge Distillation can be found in the following sub-sections. + +### Dimensionality Reduction on the Teacher Model + +Reducing embedding dimensionality can greatly reduce the post-processing operations and memory usage for the facial recognition system. To achieve this, the teacher backbone will be frozen and two additional Conv1d layers will be added to the teacher models. These additions are called dimension reduction layers. For the example in the repository, the length of the embeddings produced by the teacher model is 512 and the optimum length for the student model is found to be 64. Still, other choices like 32, 128 or 256 can be examined for different application areas. A summary of the dimensionality reduction is shown in Figure 2, and dimension reduction layers' details are represented in Table 1. + + + + +​Figure 2. Dimensionality Reduction + + + +Table 1. Dimension Reduction Layers + +| Layer1 | Layer2 | +|--------------------------------------| ------------------------------------| +| Conv1d(In=512ch, Out=512Ch, Kernel=1)| Conv1d(In=512ch, Out=64Ch, Kernel=1)| +| BatchNorm1d(512) | BatchNorm1d(64) | +| PReLU(512) | | + + + +To train dimensionality reduction layers Sub-Center ArcFace loss is used. The SubCenterArcFace Loss was presented in the [4], summary of the training framework can be seen in Figure 3. The loss function uses cosine similarity as the distance metric, and in the framework embedding network is trained as a part of the classification problem. The Normalized Sub-Centers(also known as the prototypes) must be learned from zero as no model is available to extract embeddings at the beginning. + + + +​Figure 3. Sub-Center ArcFace Loss[4] + +### Relation-Based Knowledge Distillation + +The knowledge distillation choice for the FaceID models was a relation-based one. The distillation loss was calculated as the MSE between teacher model and student model. + +To train the student FaceID models, no student loss was used, so student weight was set to 0. + +From Figure 4, a visual can be seen for the relation-based knowledge distillation. + + + +​Figure 4. Relation-Based Knowledge Distillation[5] + + + +## Dot Product Layer + +The dot product layer weights will be populated with the embeddings that are generated by MAX7800x FaceID models. Outputs of the FaceID models are normalized at both inference and recording. Therefore, the result of the dot product layer equals cosine similarity. Using the cosine similarity as a distance metric, the image is identified as either one of the known subjects or 'Unknown' depending on the embedding distances. To record new people in the database, there are two options. The first one is using the Python scripts that are available on the SDK demos. The second option is to use "record on hardware" mode which does not require any external connection. The second option is not available for all platforms, so please check SDK demo ReadMEs to see if it is supported. + + + +## References + +[1] [Cao, Qiong, et al. "Vggface2: A dataset for recognising faces across pose and age." 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). IEEE, 2018.](https://arxiv.org/abs/1710.08092) + +[2] [A. Womg, M. J. Shafiee, F. Li and B. Chwyl, "Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection," 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada, 2018, pp. 95-101, doi: 10.1109/CRV.2018.00023.](https://ieeexplore.ieee.org/document/8575741) + +[3] [face.evoLVe, High-Performance Face Recognition Library on PaddlePaddle & PyTorch](https://github.com/ZhaoJ9014/face.evoLVe) + +[4] [Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.](https://arxiv.org/abs/1801.07698) + +[5] [Gou, Jianping, et al. "Knowledge distillation: A survey." 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