Move STE/LSQ activation quantizers from QAT to trainable infrastructure for PyTorch #1422

	name: Coverage Test
	on:
	workflow_dispatch: # Allow manual triggers
	schedule:
	- cron: 0 0 * * *
	pull_request:
	branches:
	- main

	jobs:
	build:
	runs-on: ubuntu-latest
	timeout-minutes: 80
	env:
	COVERAGE_THRESHOLD: 98
	steps:
	- uses: actions/checkout@v2
	- name: Install Python 3
	uses: actions/setup-python@v1
	with:
	python-version: '3.10'
	- name: Install dependencies
	run: \|
	python -m pip install --upgrade pip
	pip install -r requirements.txt
	pip install coverage
	- name: Prepare TF env
	run: pip install tensorflow==2.13.*
	- name: Run tensorflow testsuite
	run: coverage run --parallel-mode -m --omit "__init__.py" --include "model_compression_toolkit//.py" unittest tests/test_suite.py -v
	- name: Prepare Torch env
	run: pip uninstall tensorflow -y && pip install torch==2.0.* torchvision onnx onnxruntime onnxruntime-extensions
	- name: Run torch testsuite
	run: coverage run --parallel-mode -m --omit "__init__.py" --include "model_compression_toolkit//.py" unittest tests/test_suite.py -v
	- name: Combine Multiple Coverage Files
	run: coverage combine
	- name: Run Coverage HTML
	run: coverage html -i --directory ./coverage_report_html
	- name: Archive code coverage results
	uses: actions/upload-artifact@v4
	with:
	name: code-coverage-report
	path: \|
	coverage_report_html
	- name: Coverage Report
	run: coverage report -i --skip-covered --sort cover --fail-under $COVERAGE_THRESHOLD

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