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Port to v3 #44
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Port to v3 #44
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Perfect!
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now needs update
…o port-to-v3 # Conflicts: # ocrd_kraken/binarize.py
(after MacOS fails with `torch ... not supported on this platform` 🙄 )
CONFIGS = ['', 'pageparallel', 'metscache', 'pageparallel+metscache'] | ||
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@pytest.fixture(params=CONFIGS) | ||
def workspace(tmpdir, pytestconfig, request): | ||
def _make_workspace(workspace_path): | ||
initLogging() | ||
if pytestconfig.getoption('verbose') > 0: | ||
setOverrideLogLevel('DEBUG') | ||
with pushd_popd(tmpdir): | ||
directory = str(tmpdir) | ||
resolver = Resolver() | ||
workspace = resolver.workspace_from_url(workspace_path, dst_dir=directory, download=True) | ||
config.OCRD_MISSING_OUTPUT = "ABORT" | ||
if 'metscache' in request.param: | ||
config.OCRD_METS_CACHING = True | ||
print("enabled METS caching") | ||
if 'pageparallel' in request.param: | ||
config.OCRD_MAX_PARALLEL_PAGES = 4 | ||
print("enabled page-parallel processing") | ||
def _start_mets_server(*args, **kwargs): | ||
print("running with METS server") | ||
server = OcrdMetsServer(*args, **kwargs) | ||
server.startup() | ||
process = Process(target=_start_mets_server, | ||
kwargs={'workspace': workspace, 'url': 'mets.sock'}) | ||
process.start() | ||
sleep(1) | ||
workspace = Workspace(resolver, directory, mets_server_url='mets.sock') | ||
yield {'workspace': workspace, 'mets_server_url': 'mets.sock'} | ||
process.terminate() | ||
else: | ||
yield {'workspace': workspace} | ||
config.reset_defaults() | ||
return _make_workspace | ||
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@pytest.fixture | ||
def workspace_manifesto(workspace): | ||
yield from workspace(assets.path_to('communist_manifesto/data/mets.xml')) | ||
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@pytest.fixture | ||
def workspace_aufklaerung(workspace): | ||
yield from workspace(assets.path_to('kant_aufklaerung_1784/data/mets.xml')) |
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BTW, this could be a template for all processor tests. Testing w/o METS Server and w/o is important IMO.
We can easily add more configuration scenarios there.
def test_recognize(workspace_aufklaerung): | ||
# some models (like default en) require binarized images | ||
run_processor(KrakenBinarize, | ||
input_file_grp="OCR-D-GT-PAGE", | ||
output_file_grp="OCR-D-GT-PAGE-BIN", | ||
**workspace_aufklaerung, | ||
) | ||
run_processor(KrakenRecognize, | ||
# re-use layout, overwrite text: | ||
input_file_grp="OCR-D-GT-PAGE-BIN", | ||
output_file_grp="OCR-D-OCR-KRAKEN", | ||
parameter={'overwrite_text': True}, | ||
**workspace_aufklaerung, | ||
) | ||
ws = workspace_aufklaerung['workspace'] | ||
ws.save_mets() | ||
assert os.path.isdir(os.path.join(ws.directory, 'OCR-D-OCR-KRAKEN')) | ||
results = ws.find_files(file_grp='OCR-D-OCR-KRAKEN', mimetype=MIMETYPE_PAGE) | ||
result0 = next(results, False) | ||
assert result0, "found no output PAGE file" | ||
result0 = page_from_file(result0) | ||
text0 = result0.etree.xpath('//page:Glyph/page:TextEquiv/page:Unicode', namespaces=NAMESPACES) | ||
assert len(text0) > 0, "found no glyph text in output PAGE file" |
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And here the consumer part.
@@ -68,7 +68,7 @@ docker: | |||
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# Run test | |||
test: tests/assets | |||
$(PYTHON) -m pytest tests $(PYTEST_ARGS) | |||
$(PYTHON) -m pytest tests --durations=0 $(PYTEST_ARGS) |
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And with this we get to see what difference in performance these settings make:
93.35s call tests/test_recognize.py::test_recognize[pageparallel+metscache]
92.28s call tests/test_recognize.py::test_recognize[pageparallel]
76.19s call tests/test_recognize.py::test_recognize[]
74.83s call tests/test_recognize.py::test_recognize[metscache]
55.92s call tests/test_segment.py::test_run_blla[metscache]
55.11s call tests/test_segment.py::test_run_blla[]
48.43s call tests/test_segment.py::test_run_blla[pageparallel+metscache]
41.80s call tests/test_segment.py::test_run_blla[pageparallel]
(In this case, it was only 2 pages – the scaling factor is not so great.)
- during `setup`, instead of loading models in the processor directly, instantiate and spawn a singleton predictor subprocess with the given parameters (after resolving the model path name), communicating via shared (task and result) queues to synchronize processor and predictor processes; the predictor will then load models in its own address space - at runtime, the processor merely calls the predictor with the respective arguments for that page, which translates into - putting the arguments on the task queue - getting the results from the result queue, blocking - at runtime, the predictor loops into: - receiving inputs from the task queue, blocking - calling `predict` on them - putting outputs on the result queue - in the predictor, tasks and results are identified via page id, so results get retrieved for their respective task only, implemented via shared dict to synchronize forked processor workers - during `shutdown`, tell the predictor to shut down as well (terminating the subprocess); the predictor will then exit its loop and close the queues - abstract from kraken.pageseg, kraken.blla, and kraken.rpred differences in initialization phase and inference phase via shared `common.KrakenPredictor` class, override specifics in - `recognize.KrakenRecognizePredictor`: - during `setup`, after loading the model, submit a special "task" to query the model's `one_channel_mode` attribute - at runtime, translate the model into a `defaultdict` for `mm_rpred`, but picklable to be compatible with mp.Queue; for the same reason, exhaust the result generator immediately - `segment.KrakenSegmentPredictor`: during `setup`, map the given parameters and inputs to kwargs as applicable by either `pageseg.segment` or `blla.segment`
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