* Created cybersecurity notebook and downloaded data with wget

* Reorganized the cybersecurity notebook

* Read the entire dataset as a pytorch tensor and trained a simple MLP on the UNSW_NB15 dataset

* small changes to markdown

* Training and testing have the same integer encoding

* Added debugger tool

* added loss visualization

* aded 1hot encoder and separated dataloader

Added 1-hot encoder with scikit learn.
Seperated the dataloader into a python file.

* changes to get 99.998% accuracy

* changed loss plot

* accuracy at 70% after 50 epochs

However, loss is not ok

* got 75% accuracy

* see loss

* added scheduled statistics

* added iterator over all possible parameters

* updates on automation

debugging model error

* Delete cybersecurity-checkpoint.ipynb

* Delete cybersecurity_2-checkpoint.ipynb

* Delete exemplofixe-checkpoint.ipynb

* Delete UNSW_NB15_testing-set.csv

* Delete UNSW_NB15_train.csv

* Delete UNSW_NB15_training-set.csv

* Delete UNSW_NB15_val.csv

* general cleanup

* general cleanup

* updates with 75.7238% accuracy

* added quantization of the dataset

* debugging the quantization of the dataset

* added plots for the debugging

* debugging the quantization. Show the differences

* update debugging

* updates on debugging

* updates on debugging quantization

* updates on debug, uint32 df added

* updates on debugging

* added 2 pictures for the debugging

* Added quantization of the dataset

* aded results for training the model with the quantized dataset

* added quantization of the dataset

* added new notebook with model definition with brevitas

* cleaning up documents

* modified the model definition

* Changed the loss function

* Added debug with pdb

* Successfully created neural network with Brevitas

* Correctly quantize the dataset

* Added export of onnx model

* Added FINN validation of the Brevitas model

* improved dataloader

* general cleanup

* General Cleanup

* General Cleanup

* Completed verifying the FINN model against Brevitas

* Added new layer to MLP - Debugging

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 inputs

* verified that model with new layer and input shifted to accept {-1,1}, outputs the same in Brevitas (input is in {-1,+1}) and in FINN (input is in {-1,+1})  for the 82332 inputs

* General cleanup and added text

* General cleanup: improved text

* General cleanup: fixed text typos

* General cleanup: Added text

* Delete cybersecurity.ipynb

* Delete dataloader.py

* Rename cybersecurity_Brevitas_1bit.ipynb to 1-cybersecurity-Brevitas-1bit.ipynb

* Rename cybersecurity_Brevitas_Verification.ipynb to 2-cybersecurity-finn-verification.ipynb

* Added the last notebook

* Added last notebook describing the finn build

* Added changed parameters to see differences

* Added changed parameters and added text

* Fixed typo

* [Notebooks] reorganize into folders, add README for cybsec

* [Notebook] add license header and refs to cybsec dataset quantizer

* [Notebooks] rename cybsec notebook files

* [Notebook] first pass thru cybsec part 1

* [Notebook] refactor more of cybsec part 1

* [Notebook] add option to use pretrained weights

* fixed outline and typo

* [Notebook] update cybsec notebook #2 and gitignore

* [Notebook] start refactoring cybsec part 3

* [ConvertToHLS] allow out_scale=2 for bipolar MT

* [Build] add alternative set of steps for estimation only

* [Transform] attempt to handle padding for IODMAs

* [Transform] explicitly ignore IODMA nodes for InsertDWC

* [Notebook] full pass over cybsec notebook 3

* [Util] move vivado utils into finn-base

* fixed outline

* [HLSCustomOp] better err msg on ipgen failure

Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>

* [Util] Add mobilenet to test.py in util

* [Test] Add first draft of brevitas export unit test into test suite

* [Test] Shorten import of mobilenet and add example picture

* [Docker] Temporarily set brevitas commit version to auphelia brevitas fork

* [Test] Add transformation and execution function to mobilnet test

* [Docker] Change brevitas repo back to Xilinx repo

* [Test] Add transformations and execution to mobilenet test

* [Docker] Update brevitas commit version

* [Test] Insert Topk into mobilenet test

* [Test] Use Top5 to verify mobilenet functionality of execution in FINN

* [CustomOp] Remove rounding in QuantAvgPool2d

* [Test] Add first streamlining transformations to mobilenet test

* [Notebook] Add end2end notebook for mobilenet-v1

* [Test] Add transformation to streamline mobilenet-v1

* [Test & Notebook] Update streamlining and lowering of mobilenet-v1

* [Test] Add test setup for move flatten transformation

* [Test] Add tidy up trafo mobilenet test

* [Streamline] Add reorder fct to move flatten past matmul, mul or add

* [Test] Update test to check functional verification after MoveFlatten trafo

* [Test] Add new trafos to mobilenet test

* [Streamline] Add drafts for move transformations to reorder trafos

* [Test] Delete obsolete test and update mobilenet test

* [Streamline] Fix missing return in MoveTransposePastScalarMul

* [Test] Update mobilenet test with new transformations

* [Test] Add mul value to make model outputs comparable (mobilenet-v1)

* [Test&Notebook] Update mobilenet-v1 streamlining

* [Test] Add preprocessing as exportable pytorch module for mobilenet and merge models

* [Util] Add pytorch modules for imagenet normalize preprocessing

* [Util] Add functions to resize and centercrop a PIL image

* [Test] Refactor mobilenet test

* [Test] Set input finn dtype and fix bug with saving onnx checkpoints

* [Test] First draft of end2end test mobilenet (prepare model for flow)

* [Test] Add streamlining and lowering to end2end mobilenet test

* [Test] Add hls conversion and dataflow partitioning to mobilenet end2end test

* [Transform] ConvertToHLSLayers add support for QuantAvgPool2d with data layout NHWC

* [Test] Save golden output for end2end mobilenet

* [Test] Add folding and draft for verification to end2end mobilenet

* [Transform] Fix bug in insertion of pool batch node

* [Test] Add time measurement to end2end mobilenet

* [Test] Add ip gen and rtlsim to end2end mobilenet test

* [Transform] Add missing import HLS conversion

* [Test] Clean dataflow partition of mobilenet before saving

* [Docker] mount imgnet val if specified

* [Util] add some ImageNet val utils

* [Test] add validation test for MobileNet-v1

* [Util] support logging QuantTensors in forward hook

* [Test] add debug option for tensorwise comparison in validate_mobilenet

* [Test] Delete streamlining part from mobilenet export test

* [Test] pre-commit test_brevitas_mobilenet

* [Util] Set resample=0 in PIL resize function

* [Doc] document the imagenet val env.var

* [Test] mark mobilenet val test as xfail

* [Test] correct typo in MobileNet-v1 val test

* [Test] fix MobileNet-v1 validation test for multiple imgs

* [Util] fix get_val_images for ImageNet validation

* [Util] more ImageNet testing utils

* [Test] use new utils in MobileNet-v1 tests

* [Util] update ImageNet utils to use torchvision utils

* [Test] test preproc only in test_brevitas_mobilenet_preproc

* [Util] add option to control get_val_images order

* [Test] different classes for mobilenet comparison

* [StreamingFC] clip thresholds larger than acc

* [VVAU] add accumulator minimization and threshold clipping

* [HLSCustomOp] clip thresholds on both sides if needed

* [Transform] call acc minimization for VVAU too

* [Test] reorder tests for end2end mobilenet

* [Test] fixes to MobileNet validation after merge

* [Test] MobileNet-v1: temp fix for export + add fifo set and build

* [Transform] fix num inp vectors for InferLabelSelect

* [Test] MobileNet: bring back labelselect, use dataflow partition

* [Deps] update Brevitas to get mobilenet export fix

* [Test] bring back export for mobilenet-v1 end2end

* [Test] MobileNet-v1: add extra_fold, reorder tests

* [Test] MobileNet-v1: additional marks + bugfix

* [Test] MobileNet-v1: fix build dir

* [LabelSelect] fix cppsim bug

* [SetFIFODepths] allow overriding auto for large FIFOs

* [Test] MobileNet-v1: add more config options to mnv1 end2end test

* [Vitis] enable Vivado physopts with PERFORMANCE_BEST

* [Test] MobileNet-v1 edn2end: aim for higher perf

* [Build] add more build options + minor improvements

including Vitis build strategy, large FIFO mem mode + ability to
spec custom fifo depths

* [Docker] minor improvements in run-docker.sh

* [Docker] new attempt at handling XRT deps

* [Test] mark semi-failing MNv1 tests as xfail

* [Infra] fix entrypoint script working dirs

* [Build] allow specifying fxns as build steps

* [Build] print build log location

* [InsertFIFO] allow creating shallow FIFOs if desired

* [Build] create shallow FIFOs to use ApplyConfig, then remove as needed

* [Infra] use abspath for Dockerfile

* Revert "[Infra] use abspath for Dockerfile"

This reverts commit 010fb910b140e7539e1599862681a4d520171388.

* [Infra] better solution for run-docker.sh from outside

* [HLSCustomOp] add directory check after running IPGenBuilder

* [Build] rename to step_set_fifo_depths, fix non-auto depth case

* [Build] typo fix

Co-authored-by: auphelia <jakobapk@web.de>

* [Refactor] use getCustomOp instead of direct registry access

* [Refactor] move HLSCustomOp base to own file

* [Refactor] register all HLSCustomOps in new style

* [Refactor] use correct domain for custom ops acc. to new style

* [Deps] update finn-base to get new-style customop domains

* [Refactor] more domain fixes

* [Test] fix ipstitch expected io values in rtlsim

* [Deps] update finn-base and brevitas

* [Docs] link to CustomOp reorg PR

called prior to transformations now by default

[Notebook] Update advanced Jupyter notebooks and add section about parallelization to transformation pass notebook

[Notebook] Update basics folder by deleting obsolete notebooks and integrating new util functions for visualization