Add pytorch version

embedding-calculator/requirements.txt

@@ -86,7 +86,7 @@ requests==2.31.0
 pylama==8.4.1
 
 # dependencies for both scanner backends
-Pillow==8.3.2
+Pillow==9.0.0
 imagecodecs==2020.5.30
 numpy==1.19.5
 scipy==1.4.1
@@ -95,5 +95,16 @@ scikit-image==0.17.2
 scikit-learn==0.23.2
 joblib==0.17.0
 
+# dependencies for pytorch version
+torch==1.13.1
+pytorch-lightning==1.8.6
+tqdm==4.66.1
+bcolz-zipline==1.2.6
+prettytable==3.10.0
+menpo==0.11.0
+mxnet==1.9.0
+torchvision==0.14.1
+pandas==1.4.4
+
 # web server
 uWSGI==2.0.19

embedding-calculator/src/_endpoints.py

@@ -26,6 +26,7 @@
 from src.services.imgtools.read_img import read_img
 from src.services.utils.pyutils import Constants
 from src.services.imgtools.test.files import IMG_DIR
+from src.services.facescan.plugins.adaface.inference import *
 import base64
 from src.constants import SKIPPED_PLUGINS
 
@@ -44,18 +45,18 @@ def face_detection_skip_check(face_plugins):
 
 
 def endpoints(app):
-    @app.before_first_request
-    def init_model() -> None:
-        detector = managers.plugin_manager.detector
-        face_plugins = managers.plugin_manager.face_plugins
-        face_plugins = face_detection_skip_check(face_plugins)
-        detector(
-            img=read_img(str(IMG_DIR / 'einstein.jpeg')),
-            det_prob_threshold=_get_det_prob_threshold(),
-            face_plugins=face_plugins
-        )
-        print("Starting to load ML models")
-        return None
+    #@app.before_first_request
+    #def init_model() -> None:
+    #    detector = managers.plugin_manager.detector
+    #    face_plugins = managers.plugin_manager.face_plugins
+    #    face_plugins = face_detection_skip_check(face_plugins)
+    #    detector(
+    #        img=read_img(str(IMG_DIR / 'einstein.jpeg')),
+    #        det_prob_threshold=_get_det_prob_threshold(),
+    #        face_plugins=face_plugins
+    #    )
+    #    print("Starting to load ML models")
+    #    return None
 
     @app.route('/healthcheck')
     def healthcheck():
@@ -77,50 +78,67 @@ def status_get():
 
     @app.route('/find_faces_base64', methods=['POST'])
     def find_faces_base64_post():
-        detector = managers.plugin_manager.detector
-        face_plugins = managers.plugin_manager.filter_face_plugins(
-            _get_face_plugin_names()
-        )
-        face_plugins = face_detection_skip_check(face_plugins)
-        rawfile = base64.b64decode(request.get_json()["file"])
-
-        faces = detector(
-            img=read_img(rawfile),
-            det_prob_threshold=_get_det_prob_threshold(),
-            face_plugins=face_plugins
-        )
-        plugins_versions = {p.slug: str(p) for p in [detector] + face_plugins}
-        faces = _limit(faces, request.values.get(ARG.LIMIT))
-        FaceDetection.SKIPPING_FACE_DETECTION = False
-        return jsonify(plugins_versions=plugins_versions, result=faces)
+        if ENV.SWITCHER:
+            rawfile = base64.b64decode(request.get_json()["file"])
+            return jsonify(inference_detector(image_path = rawfile))
+        else:
+            detector = managers.plugin_manager.detector
+            face_plugins = managers.plugin_manager.filter_face_plugins(
+                _get_face_plugin_names()
+            )
+            face_plugins = face_detection_skip_check(face_plugins)
+            rawfile = base64.b64decode(request.get_json()["file"])
+
+            faces = detector(
+                img=read_img(rawfile),
+                det_prob_threshold=_get_det_prob_threshold(),
+                face_plugins=face_plugins
+            )
+            plugins_versions = {p.slug: str(p) for p in [detector] + face_plugins}
+            faces = _limit(faces, request.values.get(ARG.LIMIT))
+            FaceDetection.SKIPPING_FACE_DETECTION = False
+            return jsonify(plugins_versions=plugins_versions, result=faces)
 
     @app.route('/find_faces', methods=['POST'])
     @needs_attached_file
     def find_faces_post():
-        detector = managers.plugin_manager.detector
-        face_plugins = managers.plugin_manager.filter_face_plugins(
-            _get_face_plugin_names()
-        )
-        face_plugins = face_detection_skip_check(face_plugins)
-        faces = detector(
-            img=read_img(request.files['file']),
-            det_prob_threshold=_get_det_prob_threshold(),
-            face_plugins=face_plugins
-        )
-        plugins_versions = {p.slug: str(p) for p in [detector] + face_plugins}
-        faces = _limit(faces, request.values.get(ARG.LIMIT))
-        FaceDetection.SKIPPING_FACE_DETECTION = False
-        return jsonify(plugins_versions=plugins_versions, result=faces)
+        if ENV.SWITCHER:
+            img = request.files['file']
+            return jsonify(inference_detector(image_path = img))
+        else:
+            detector = managers.plugin_manager.detector
+            face_plugins = managers.plugin_manager.filter_face_plugins(
+                _get_face_plugin_names()
+            )
+            face_plugins = face_detection_skip_check(face_plugins)
+            faces = detector(
+                img=read_img(request.files['file']),
+                det_prob_threshold=_get_det_prob_threshold(),
+                face_plugins=face_plugins
+            )
+            plugins_versions = {p.slug: str(p) for p in [detector] + face_plugins}
+            faces = _limit(faces, request.values.get(ARG.LIMIT))
+            FaceDetection.SKIPPING_FACE_DETECTION = False
+            return jsonify(plugins_versions=plugins_versions, result=faces)
+
+    # ----------------------------------------------------------------
+    # ----------------------------------------------------------------
+    # ----------------------------------------------------------------
+
 
     @app.route('/scan_faces', methods=['POST'])
     @needs_attached_file
     def scan_faces_post():
-        faces = scanner.scan(
-            img=read_img(request.files['file']),
-            det_prob_threshold=_get_det_prob_threshold()
-        )
-        faces = _limit(faces, request.values.get(ARG.LIMIT))
-        return jsonify(calculator_version=scanner.ID, result=faces)
+        if ENV.SWITCHER:
+            img = request.files['file']
+            return jsonify(inference_scaner(image_path=img))
+        else:
+            faces = scanner.scan(
+                img=read_img(request.files['file']),
+                det_prob_threshold=_get_det_prob_threshold()
+            )
+            faces = _limit(faces, request.values.get(ARG.LIMIT))
+            return jsonify(calculator_version=scanner.ID, result=faces)
 
 
 def _get_det_prob_threshold():

embedding-calculator/src/constants.py

@@ -23,8 +23,10 @@ class ENV(Constants):
     ML_PORT = int(get_env('ML_PORT', '3000'))
     IMG_LENGTH_LIMIT = int(get_env('IMG_LENGTH_LIMIT', '640'))
 
-    FACE_DETECTION_PLUGIN = get_env('FACE_DETECTION_PLUGIN', 'facenet.FaceDetector')
-    CALCULATION_PLUGIN = get_env('CALCULATION_PLUGIN', 'facenet.Calculator')
+    #FACE_DETECTION_PLUGIN = get_env('FACE_DETECTION_PLUGIN', 'facenet.FaceDetector')
+    FACE_DETECTION_PLUGIN = get_env('FACE_DETECTION_PLUGIN', 'insightface.FaceDetector@retinaface_mnet025_v1')
+    #FACE_DETECTION_PLUGIN = get_env('FACE_DETECTION_PLUGIN', 'adaface.FaceDetector@mtcnn')
+    CALCULATION_PLUGIN = get_env('CALCULATION_PLUGIN', 'insightface.Calculator@arcface_mobilefacenet')
     EXTRA_PLUGINS = get_env_split('EXTRA_PLUGINS', 'facenet.LandmarksDetector,agegender.AgeDetector,agegender.GenderDetector,facenet.facemask.MaskDetector,facenet.PoseEstimator')
 
     LOGGING_LEVEL_NAME = get_env('LOGGING_LEVEL_NAME', 'debug').upper()
@@ -36,7 +38,17 @@ class ENV(Constants):
 
     RUN_MODE = get_env_bool('RUN_MODE', False)
 
+    # PyTorch settings
+    # SWITCHER = False    - using all standard CompreFace settings
+    # SWITCHER = True     - using PyTorch settings
+    SWITCHER = get_env_bool('SWITCHER', True)
+    DEVICE = get_env('DEVICE', 'cpu')
+    RECOGNITION_MODEL = get_env('RECOGNITION_MODEL', 'ir_50')
+    RECOGNITION_MODEL_PATH = get_env('RECOGNITION_MODEL_PATH', 'services/facescan/plugins/adaface/pretrained/adaface_ir50_ms1mv2.ckpt')
+    DETECTOR_NAME = get_env('DETECTOR_NAME', 'mtcnn')
+
 
 LOGGING_LEVEL = logging._nameToLevel[ENV.LOGGING_LEVEL_NAME]
 ENV_MAIN = ENV
 SKIPPED_PLUGINS = ["insightface.PoseEstimator", "facemask.MaskDetector", "facenet.PoseEstimator"]
+

embedding-calculator/src/services/facescan/plugins/adaface/.gitignore

@@ -0,0 +1,11 @@
+*.pyc
+.vscode
+*.ckpt
+.idea
+experiments
+playground
+validation_ijbs
+*.pkl
+*.csv
+result
+scripts/run_debug*

embedding-calculator/src/services/facescan/plugins/adaface/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Minchul Kim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

embedding-calculator/src/services/facescan/plugins/adaface/Pipfile

@@ -0,0 +1,23 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pytorch-lightning = "==1.8.6"
+torch = "<=1.13.1"
+tqdm = "*"
+bcolz-zipline = "*"
+prettytable = "*"
+menpo = "*"
+mxnet = "*"
+opencv-python = "*"
+torchvision = "*"
+pandas = "*"
+scikit-learn = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.9"
+python_full_version = "3.9.16"