refactoring

.coveragerc

@@ -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
 

banner.txt

@@ -0,0 +1,15 @@
+    .....     .                            ...                ..
+  .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

deprecated/predictor.py

@@ -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)

deprecated/serve.py

@@ -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()

doc/tests.drawio

@@ -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>

image_prediction/locations.py

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

image_prediction/utils.py

@@ -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)

image_prediction/utils/__init__.py

@@ -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)))))

image_prediction/utils/banner.py

@@ -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)

image_prediction/utils/logger.py

@@ -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()
+1