Marmelator/image-classification-service
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refactoring

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This commit is contained in:
Matthias Bisping 2022-03-31 19:17:48 +02:00
parent 5caa9807e2
commit 268b83a1ff
10 changed files with 259 additions and 86 deletions
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4
.coveragerc
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@ -11,6 +11,8 @@ omit =
*/env/*
*/build_venv/*
*/build_env/*
*/utils/banner.py
*/utils/logger.py
source =
image_prediction
src
@ -44,6 +46,8 @@ omit =
*/env/*
*/build_venv/*
*/build_env/*
*/utils/banner.py
*/utils/logger.py
ignore_errors = True

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banner.txt Normal file
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..... . ... ..
.d88888Neu. 'L xH88"`~ .x8X x .d88" oec :
F""""*8888888F .. . : :8888 .f"8888Hf 5888R @88888
* `"*88*" .888: x888 x888. :8888> X8L ^""` '888R 8"*88%
-.... ue=:. ~`8888~'888X`?888f` X8888 X888h 888R 8b.
:88N ` X888 888X '888> 88888 !88888. 888R u888888>
9888L X888 888X '888> 88888 %88888 888R 8888R
uzu. `8888L X888 888X '888> 88888 '> `8888> 888R 8888P
,""888i ?8888 X888 888X '888> `8888L % ?888 ! 888R *888>
4 9888L %888> "*88%""*88" '888!` `8888 `-*"" / .888B . 4888
' '8888 '88% `~ " `"` "888. :" ^*888% '888
"*8Nu.z*" `""***~"` "% 88R
88>
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'8

122
deprecated/predictor.py Normal file
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@ -0,0 +1,122 @@
from itertools import chain
from operator import itemgetter
from typing import List, Dict, Iterable
import numpy as np
from image_prediction.config import CONFIG
from image_prediction.locations import MLRUNS_DIR, BASE_WEIGHTS
from image_prediction.utils import temporary_pdf_file, get_logger
from incl.redai_image.redai.redai.backend.model.model_handle import ModelHandle
from incl.redai_image.redai.redai.backend.pdf.image_extraction import extract_and_stitch
from incl.redai_image.redai.redai.utils.mlflow_reader import MlflowModelReader
from incl.redai_image.redai.redai.utils.shared import chunk_iterable
logger = get_logger()
class Predictor:
"""`ModelHandle` wrapper. Forwards to wrapped service_estimator handle for prediction and produces structured output that is
interpretable independently of the wrapped service_estimator (e.g. with regard to a .classes_ attribute).
"""
def __init__(self, model_handle: ModelHandle = None):
"""Initializes a ServiceEstimator.
Args:
model_handle: ModelHandle object to forward to for prediction. By default, a service_estimator handle is loaded from the
mlflow database via CONFIG.service.run_id.
"""
try:
if model_handle is None:
reader = MlflowModelReader(run_id=CONFIG.service.run_id, mlruns_dir=MLRUNS_DIR)
self.model_handle = reader.get_model_handle(BASE_WEIGHTS)
else:
self.model_handle = model_handle
self.classes = self.model_handle.model.classes_
self.classes_readable = np.array(self.model_handle.classes)
self.classes_readable_aligned = self.classes_readable[self.classes[list(range(len(self.classes)))]]
except Exception as e:
logger.info(f"Service estimator initialization failed: {e}")
def __make_predictions_human_readable(self, probs: np.ndarray) -> List[Dict[str, float]]:
"""Translates an n x m matrix of probabilities over classes into an n-element list of mappings from classes to
probabilities.
Args:
probs: probability matrix (items x classes)
Returns:
list of mappings from classes to probabilities.
"""
classes = np.argmax(probs, axis=1)
classes = self.classes[classes]
classes_readable = [self.model_handle.classes[c] for c in classes]
return classes_readable
def predict(self, images: List, probabilities: bool = False, **kwargs):
"""Gathers predictions for list of images. Assigns each image a class and optionally a probability distribution
over all classes.
Args:
images (List[PIL.Image]) : Images to gather predictions for.
probabilities: Whether to return dictionaries of the following form instead of strings:
{
"class": predicted class,
"probabilities": {
"class 1" : class 1 probability,
"class 2" : class 2 probability,
...
}
}
Returns:
By default the return value is a list of classes (meaningful class name strings). Alternatively a list of
dictionaries with an additional probability field for estimated class probabilities per image can be
returned.
"""
X = self.model_handle.prep_images(list(images))
probs_per_item = self.model_handle.model.predict_proba(X, **kwargs).astype(float)
classes = self.__make_predictions_human_readable(probs_per_item)
class2prob_per_item = [dict(zip(self.classes_readable_aligned, probs)) for probs in probs_per_item]
class2prob_per_item = [
dict(sorted(c2p.items(), key=itemgetter(1), reverse=True)) for c2p in class2prob_per_item
]
predictions = [{"class": c, "probabilities": c2p} for c, c2p in zip(classes, class2prob_per_item)]
return predictions if probabilities else classes
def predict_pdf(self, pdf, verbose=False):
with temporary_pdf_file(pdf) as pdf_path:
image_metadata_pairs = self.__extract_image_metadata_pairs(pdf_path, verbose=verbose)
return self.__predict_images(image_metadata_pairs)
def __predict_images(self, image_metadata_pairs: Iterable, batch_size: int = CONFIG.service.batch_size):
def process_chunk(chunk):
images, metadata = zip(*chunk)
predictions = self.predict(images, probabilities=True)
return predictions, metadata
def predict(image_metadata_pair_generator):
chunks = chunk_iterable(image_metadata_pair_generator, n=batch_size)
return map(chain.from_iterable, zip(*map(process_chunk, chunks)))
try:
predictions, metadata = predict(image_metadata_pairs)
return predictions, metadata
except ValueError:
return [], []
@staticmethod
def __extract_image_metadata_pairs(pdf_path: str, **kwargs):
def image_is_large_enough(metadata: dict):
x1, x2, y1, y2 = itemgetter("x1", "x2", "y1", "y2")(metadata)
return abs(x1 - x2) > 2 and abs(y1 - y2) > 2
yield from extract_and_stitch(pdf_path, convert_to_rgb=True, filter_fn=image_is_large_enough, **kwargs)

49
deprecated/serve.py Normal file
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@ -0,0 +1,49 @@
import logging
from waitress import serve
from image_prediction.config import CONFIG
from image_prediction.flask import make_prediction_server
from image_prediction.predictor import Predictor
from image_prediction.response import build_response
from image_prediction.utils import get_logger, show_banner
logger = get_logger()
def main():
def predict(pdf):
# Keras service_estimator.predict stalls when service_estimator was loaded in different process
# https://stackoverflow.com/questions/42504669/keras-tensorflow-and-multiprocessing-in-python
predictor = Predictor()
predictions, metadata = predictor.predict_pdf(pdf, verbose=CONFIG.service.progressbar)
response = build_response(predictions, metadata)
return response
logger.info("Predictor ready.")
prediction_server = make_prediction_server(predict)
run_prediction_server(prediction_server, mode=CONFIG.webserver.mode)
def run_prediction_server(app, mode="development"):
if mode == "development":
app.run(host=CONFIG.webserver.host, port=CONFIG.webserver.port, debug=True)
elif mode == "production":
serve(app, host=CONFIG.webserver.host, port=CONFIG.webserver.port)
if __name__ == "__main__":
logging_level = CONFIG.service.logging_level
logging.basicConfig(level=logging_level)
logging.getLogger("flask").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("werkzeug").setLevel(logging.ERROR)
logging.getLogger("waitress").setLevel(logging.ERROR)
logging.getLogger("PIL").setLevel(logging.ERROR)
logging.getLogger("h5py").setLevel(logging.ERROR)
show_banner()
main()

1
doc/tests.drawio Normal file
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@ -0,0 +1 @@
<mxfile host="app.diagrams.net" modified="2022-03-17T15:35:10.371Z" agent="5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36" etag="b-CbBXg6FXQ9T3Px-oLc" version="17.1.1" type="device"><diagram id="tS3WR_Pr6QhNVK3FqSUP" name="Page-1">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</diagram></mxfile>

20
image_prediction/locations.py
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@ -1,11 +1,17 @@
from os import path
"""Defines constant paths relative to the module root path."""
MODULE_DIR = path.dirname(path.abspath(__file__))
PACKAGE_ROOT_DIR = path.dirname(MODULE_DIR)
from pathlib import Path
CONFIG_FILE = path.join(PACKAGE_ROOT_DIR, "config.yaml")
MODULE_DIR = Path(__file__).resolve().parents[0]
DATA_DIR = path.join(PACKAGE_ROOT_DIR, "data")
MLRUNS_DIR = path.join(DATA_DIR, "mlruns")
PACKAGE_ROOT_DIR = MODULE_DIR.parents[0]
TEST_DATA_DIR = path.join(PACKAGE_ROOT_DIR, "test", "data")
CONFIG_FILE = PACKAGE_ROOT_DIR / "config.yaml"
BANNER_FILE = PACKAGE_ROOT_DIR / "banner.txt"
DATA_DIR = PACKAGE_ROOT_DIR / "data"
MLRUNS_DIR = str(DATA_DIR / "mlruns")
TEST_DATA_DIR = PACKAGE_ROOT_DIR / "test" / "data"

79
image_prediction/utils.py
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@ -1,82 +1,3 @@
import logging
import tempfile
from contextlib import contextmanager
from functools import reduce
from itertools import takewhile, starmap, islice, repeat
from operator import truth
from image_prediction.config import CONFIG
from redai.utils import export
@contextmanager
def temporary_pdf_file(pdf: bytes):
with tempfile.NamedTemporaryFile() as f:
f.write(pdf)
yield f.name
def make_logger_getter():
logger = logging.getLogger("imclf")
logger.propagate = False
handler = logging.StreamHandler()
handler.setLevel(CONFIG.service.logging_level)
log_format = "[%(levelname)s]: %(message)s"
formatter = logging.Formatter(log_format)
handler.setFormatter(formatter)
logger.addHandler(handler)
def get_logger():
return logger
return get_logger
get_logger = make_logger_getter()
def show_banner():
banner = '''
..... . ... ..
.d88888Neu. 'L xH88"`~ .x8X x .d88" oec :
F""""*8888888F .. . : :8888 .f"8888Hf 5888R @88888
* `"*88*" .888: x888 x888. :8888> X8L ^""` '888R 8"*88%
-.... ue=:. ~`8888~'888X`?888f` X8888 X888h 888R 8b.
:88N ` X888 888X '888> 88888 !88888. 888R u888888>
9888L X888 888X '888> 88888 %88888 888R 8888R
uzu. `8888L X888 888X '888> 88888 '> `8888> 888R 8888P
,""888i ?8888 X888 888X '888> `8888L % ?888 ! 888R *888>
4 9888L %888> "*88%""*88" '888!` `8888 `-*"" / .888B . 4888
' '8888 '88% `~ " `"` "888. :" ^*888% '888
"*8Nu.z*" `""***~"` "% 88R
88>
48
'8
'''
logger = logging.getLogger(__name__)
logger.propagate = False
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
formatter = logging.Formatter("")
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info(banner)
@export
def chunk_iterable(iterable, chunk_size):
return takewhile(truth, map(tuple, starmap(islice, repeat((iter(iterable), chunk_size)))))
def compose(func, *funcs):
funcs = [func, *funcs]
return lambda x: reduce(lambda acc, f: f(acc), funcs, x)

8
image_prediction/utils/__init__.py Normal file
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@ -0,0 +1,8 @@
from itertools import takewhile, starmap, islice, repeat
from operator import truth
from .logger import get_logger
def chunk_iterable(iterable, chunk_size):
return takewhile(truth, map(tuple, starmap(islice, repeat((iter(iterable), chunk_size)))))

21
image_prediction/utils/banner.py Normal file
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@ -0,0 +1,21 @@
import logging
from image_prediction.locations import BANNER_FILE
def show_banner():
with open(BANNER_FILE) as f:
banner = "\n" + "".join(f.readlines()) + "\n"
logger = logging.getLogger(__name__)
logger.propagate = False
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
formatter = logging.Formatter("")
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info(banner)

26
image_prediction/utils/logger.py Normal file
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@ -0,0 +1,26 @@
import logging
from image_prediction.config import CONFIG
def make_logger_getter():
logger = logging.getLogger("imclf")
logger.propagate = False
handler = logging.StreamHandler()
handler.setLevel(CONFIG.service.logging_level)
log_format = "[%(levelname)s]: %(message)s"
formatter = logging.Formatter(log_format)
handler.setFormatter(formatter)
logger.addHandler(handler)
def get_logger():
return logger
return get_logger
get_logger = make_logger_getter()
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