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677c57e
1
Parent(s):
6dfda4d
init
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- DeepFace.py +585 -0
- LICENSE +21 -0
- Makefile +8 -0
- Train.py +55 -0
- __init__.py +1 -0
- __pycache__/DeepFace.cpython-312.pyc +0 -0
- __pycache__/__init__.cpython-312.pyc +0 -0
- api/__init__.py +0 -0
- api/__pycache__/__init__.cpython-312.pyc +0 -0
- api/postman/deepface-api.postman_collection.json +102 -0
- api/src/__init__.py +0 -0
- api/src/__pycache__/__init__.cpython-312.pyc +0 -0
- api/src/__pycache__/app.cpython-312.pyc +0 -0
- api/src/api.py +10 -0
- api/src/app.py +11 -0
- api/src/modules/__init__.py +0 -0
- api/src/modules/__pycache__/__init__.cpython-312.pyc +0 -0
- api/src/modules/core/__init__.py +0 -0
- api/src/modules/core/__pycache__/__init__.cpython-312.pyc +0 -0
- api/src/modules/core/__pycache__/routes.cpython-312.pyc +0 -0
- api/src/modules/core/__pycache__/service.cpython-312.pyc +0 -0
- api/src/modules/core/routes.py +207 -0
- api/src/modules/core/service.py +84 -0
- basemodels/ArcFace.py +179 -0
- basemodels/DeepID.py +99 -0
- basemodels/Dlib.py +89 -0
- basemodels/Facenet.py +1715 -0
- basemodels/FbDeepFace.py +105 -0
- basemodels/GhostFaceNet.py +312 -0
- basemodels/OpenFace.py +397 -0
- basemodels/SFace.py +87 -0
- basemodels/VGGFace.py +160 -0
- basemodels/__init__.py +0 -0
- basemodels/__pycache__/ArcFace.cpython-312.pyc +0 -0
- basemodels/__pycache__/DeepID.cpython-312.pyc +0 -0
- basemodels/__pycache__/Dlib.cpython-312.pyc +0 -0
- basemodels/__pycache__/Facenet.cpython-312.pyc +0 -0
- basemodels/__pycache__/FbDeepFace.cpython-312.pyc +0 -0
- basemodels/__pycache__/GhostFaceNet.cpython-312.pyc +0 -0
- basemodels/__pycache__/OpenFace.cpython-312.pyc +0 -0
- basemodels/__pycache__/SFace.cpython-312.pyc +0 -0
- basemodels/__pycache__/VGGFace.cpython-312.pyc +0 -0
- basemodels/__pycache__/__init__.cpython-312.pyc +0 -0
- commons/__init__.py +0 -0
- commons/__pycache__/__init__.cpython-312.pyc +0 -0
- commons/__pycache__/file_utils.cpython-312.pyc +0 -0
- commons/__pycache__/folder_utils.cpython-312.pyc +0 -0
- commons/__pycache__/image_utils.cpython-312.pyc +0 -0
- commons/__pycache__/logger.cpython-312.pyc +0 -0
- commons/__pycache__/os_path.cpython-312.pyc +0 -0
DeepFace.py
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1 |
+
# common dependencies
|
2 |
+
import os
|
3 |
+
import warnings
|
4 |
+
import logging
|
5 |
+
from typing import Any, Dict, List, Union, Optional
|
6 |
+
from deepface.commons.os_path import os_path
|
7 |
+
|
8 |
+
# this has to be set before importing tensorflow
|
9 |
+
os.environ["TF_USE_LEGACY_KERAS"] = "1"
|
10 |
+
|
11 |
+
# pylint: disable=wrong-import-position
|
12 |
+
|
13 |
+
# 3rd party dependencies
|
14 |
+
import numpy as np
|
15 |
+
import pandas as pd
|
16 |
+
import tensorflow as tf
|
17 |
+
|
18 |
+
# package dependencies
|
19 |
+
from deepface.commons import package_utils, folder_utils
|
20 |
+
from deepface.commons import logger as log
|
21 |
+
from deepface.modules import (
|
22 |
+
modeling,
|
23 |
+
representation,
|
24 |
+
verification,
|
25 |
+
recognition,
|
26 |
+
demography,
|
27 |
+
detection,
|
28 |
+
streaming,
|
29 |
+
preprocessing,
|
30 |
+
cloudservice,
|
31 |
+
)
|
32 |
+
from deepface import __version__
|
33 |
+
|
34 |
+
logger = log.get_singletonish_logger()
|
35 |
+
|
36 |
+
# -----------------------------------
|
37 |
+
# configurations for dependencies
|
38 |
+
|
39 |
+
# users should install tf_keras package if they are using tf 2.16 or later versions
|
40 |
+
package_utils.validate_for_keras3()
|
41 |
+
|
42 |
+
warnings.filterwarnings("ignore")
|
43 |
+
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
|
44 |
+
tf_version = package_utils.get_tf_major_version()
|
45 |
+
if tf_version == 2:
|
46 |
+
tf.get_logger().setLevel(logging.ERROR)
|
47 |
+
# -----------------------------------
|
48 |
+
|
49 |
+
# create required folders if necessary to store model weights
|
50 |
+
folder_utils.initialize_folder()
|
51 |
+
|
52 |
+
|
53 |
+
def build_model(model_name: str) -> Any:
|
54 |
+
"""
|
55 |
+
This function builds a deepface model
|
56 |
+
Args:
|
57 |
+
model_name (string): face recognition or facial attribute model
|
58 |
+
VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition
|
59 |
+
Age, Gender, Emotion, Race for facial attributes
|
60 |
+
Returns:
|
61 |
+
built_model
|
62 |
+
"""
|
63 |
+
return modeling.build_model(model_name=model_name)
|
64 |
+
|
65 |
+
|
66 |
+
def verify(
|
67 |
+
img1_path: Union[str, np.ndarray, List[float]],
|
68 |
+
img2_path: Union[str, np.ndarray, List[float]],
|
69 |
+
model_name: str = "VGG-Face",
|
70 |
+
detector_backend: str = "opencv",
|
71 |
+
distance_metric: str = "cosine",
|
72 |
+
enforce_detection: bool = True,
|
73 |
+
align: bool = True,
|
74 |
+
expand_percentage: int = 0,
|
75 |
+
normalization: str = "base",
|
76 |
+
silent: bool = False,
|
77 |
+
) -> Dict[str, Any]:
|
78 |
+
"""
|
79 |
+
Verify if an image pair represents the same person or different persons.
|
80 |
+
Args:
|
81 |
+
img1_path (str or np.ndarray or List[float]): Path to the first image.
|
82 |
+
Accepts exact image path as a string, numpy array (BGR), base64 encoded images
|
83 |
+
or pre-calculated embeddings.
|
84 |
+
|
85 |
+
img2_path (str or np.ndarray or List[float]): Path to the second image.
|
86 |
+
Accepts exact image path as a string, numpy array (BGR), base64 encoded images
|
87 |
+
or pre-calculated embeddings.
|
88 |
+
|
89 |
+
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
90 |
+
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
|
91 |
+
|
92 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
93 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
94 |
+
(default is opencv).
|
95 |
+
|
96 |
+
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
|
97 |
+
'euclidean', 'euclidean_l2' (default is cosine).
|
98 |
+
|
99 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
100 |
+
Set to False to avoid the exception for low-resolution images (default is True).
|
101 |
+
|
102 |
+
align (bool): Flag to enable face alignment (default is True).
|
103 |
+
|
104 |
+
expand_percentage (int): expand detected facial area with a percentage (default is 0).
|
105 |
+
|
106 |
+
normalization (string): Normalize the input image before feeding it to the model.
|
107 |
+
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
|
108 |
+
|
109 |
+
silent (boolean): Suppress or allow some log messages for a quieter analysis process
|
110 |
+
(default is False).
|
111 |
+
|
112 |
+
Returns:
|
113 |
+
result (dict): A dictionary containing verification results with following keys.
|
114 |
+
|
115 |
+
- 'verified' (bool): Indicates whether the images represent the same person (True)
|
116 |
+
or different persons (False).
|
117 |
+
|
118 |
+
- 'distance' (float): The distance measure between the face vectors.
|
119 |
+
A lower distance indicates higher similarity.
|
120 |
+
|
121 |
+
- 'max_threshold_to_verify' (float): The maximum threshold used for verification.
|
122 |
+
If the distance is below this threshold, the images are considered a match.
|
123 |
+
|
124 |
+
- 'model' (str): The chosen face recognition model.
|
125 |
+
|
126 |
+
- 'distance_metric' (str): The chosen similarity metric for measuring distances.
|
127 |
+
|
128 |
+
- 'facial_areas' (dict): Rectangular regions of interest for faces in both images.
|
129 |
+
- 'img1': {'x': int, 'y': int, 'w': int, 'h': int}
|
130 |
+
Region of interest for the first image.
|
131 |
+
- 'img2': {'x': int, 'y': int, 'w': int, 'h': int}
|
132 |
+
Region of interest for the second image.
|
133 |
+
|
134 |
+
- 'time' (float): Time taken for the verification process in seconds.
|
135 |
+
"""
|
136 |
+
|
137 |
+
return verification.verify(
|
138 |
+
img1_path=img1_path,
|
139 |
+
img2_path=img2_path,
|
140 |
+
model_name=model_name,
|
141 |
+
detector_backend=detector_backend,
|
142 |
+
distance_metric=distance_metric,
|
143 |
+
enforce_detection=enforce_detection,
|
144 |
+
align=align,
|
145 |
+
expand_percentage=expand_percentage,
|
146 |
+
normalization=normalization,
|
147 |
+
silent=silent,
|
148 |
+
)
|
149 |
+
|
150 |
+
|
151 |
+
def analyze(
|
152 |
+
img_path: Union[str, np.ndarray],
|
153 |
+
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
|
154 |
+
enforce_detection: bool = True,
|
155 |
+
detector_backend: str = "opencv",
|
156 |
+
align: bool = True,
|
157 |
+
expand_percentage: int = 0,
|
158 |
+
silent: bool = False,
|
159 |
+
) -> List[Dict[str, Any]]:
|
160 |
+
"""
|
161 |
+
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
|
162 |
+
Args:
|
163 |
+
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
|
164 |
+
or a base64 encoded image. If the source image contains multiple faces, the result will
|
165 |
+
include information for each detected face.
|
166 |
+
|
167 |
+
actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race').
|
168 |
+
You can exclude some of these attributes from the analysis if needed.
|
169 |
+
|
170 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
171 |
+
Set to False to avoid the exception for low-resolution images (default is True).
|
172 |
+
|
173 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
174 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
175 |
+
(default is opencv).
|
176 |
+
|
177 |
+
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
|
178 |
+
'euclidean', 'euclidean_l2' (default is cosine).
|
179 |
+
|
180 |
+
align (boolean): Perform alignment based on the eye positions (default is True).
|
181 |
+
|
182 |
+
expand_percentage (int): expand detected facial area with a percentage (default is 0).
|
183 |
+
|
184 |
+
silent (boolean): Suppress or allow some log messages for a quieter analysis process
|
185 |
+
(default is False).
|
186 |
+
|
187 |
+
Returns:
|
188 |
+
results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
|
189 |
+
the analysis results for a detected face. Each dictionary in the list contains the
|
190 |
+
following keys:
|
191 |
+
|
192 |
+
- 'region' (dict): Represents the rectangular region of the detected face in the image.
|
193 |
+
- 'x': x-coordinate of the top-left corner of the face.
|
194 |
+
- 'y': y-coordinate of the top-left corner of the face.
|
195 |
+
- 'w': Width of the detected face region.
|
196 |
+
- 'h': Height of the detected face region.
|
197 |
+
|
198 |
+
- 'age' (float): Estimated age of the detected face.
|
199 |
+
|
200 |
+
- 'face_confidence' (float): Confidence score for the detected face.
|
201 |
+
Indicates the reliability of the face detection.
|
202 |
+
|
203 |
+
- 'dominant_gender' (str): The dominant gender in the detected face.
|
204 |
+
Either "Man" or "Woman".
|
205 |
+
|
206 |
+
- 'gender' (dict): Confidence scores for each gender category.
|
207 |
+
- 'Man': Confidence score for the male gender.
|
208 |
+
- 'Woman': Confidence score for the female gender.
|
209 |
+
|
210 |
+
- 'dominant_emotion' (str): The dominant emotion in the detected face.
|
211 |
+
Possible values include "sad," "angry," "surprise," "fear," "happy,"
|
212 |
+
"disgust," and "neutral"
|
213 |
+
|
214 |
+
- 'emotion' (dict): Confidence scores for each emotion category.
|
215 |
+
- 'sad': Confidence score for sadness.
|
216 |
+
- 'angry': Confidence score for anger.
|
217 |
+
- 'surprise': Confidence score for surprise.
|
218 |
+
- 'fear': Confidence score for fear.
|
219 |
+
- 'happy': Confidence score for happiness.
|
220 |
+
- 'disgust': Confidence score for disgust.
|
221 |
+
- 'neutral': Confidence score for neutrality.
|
222 |
+
|
223 |
+
- 'dominant_race' (str): The dominant race in the detected face.
|
224 |
+
Possible values include "indian," "asian," "latino hispanic,"
|
225 |
+
"black," "middle eastern," and "white."
|
226 |
+
|
227 |
+
- 'race' (dict): Confidence scores for each race category.
|
228 |
+
- 'indian': Confidence score for Indian ethnicity.
|
229 |
+
- 'asian': Confidence score for Asian ethnicity.
|
230 |
+
- 'latino hispanic': Confidence score for Latino/Hispanic ethnicity.
|
231 |
+
- 'black': Confidence score for Black ethnicity.
|
232 |
+
- 'middle eastern': Confidence score for Middle Eastern ethnicity.
|
233 |
+
- 'white': Confidence score for White ethnicity.
|
234 |
+
"""
|
235 |
+
return demography.analyze(
|
236 |
+
img_path=img_path,
|
237 |
+
actions=actions,
|
238 |
+
enforce_detection=enforce_detection,
|
239 |
+
detector_backend=detector_backend,
|
240 |
+
align=align,
|
241 |
+
expand_percentage=expand_percentage,
|
242 |
+
silent=silent,
|
243 |
+
)
|
244 |
+
|
245 |
+
|
246 |
+
def find(
|
247 |
+
img_path: Union[str, np.ndarray],
|
248 |
+
db_path: str,
|
249 |
+
model_name: str = "VGG-Face",
|
250 |
+
distance_metric: str = "cosine",
|
251 |
+
enforce_detection: bool = True,
|
252 |
+
detector_backend: str = "opencv",
|
253 |
+
align: bool = True,
|
254 |
+
expand_percentage: int = 0,
|
255 |
+
threshold: Optional[float] = None,
|
256 |
+
normalization: str = "base",
|
257 |
+
silent: bool = False,
|
258 |
+
) -> List[pd.DataFrame]:
|
259 |
+
"""
|
260 |
+
Identify individuals in a database
|
261 |
+
Args:
|
262 |
+
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
|
263 |
+
or a base64 encoded image. If the source image contains multiple faces, the result will
|
264 |
+
include information for each detected face.
|
265 |
+
|
266 |
+
db_path (string): Path to the folder containing image files. All detected faces
|
267 |
+
in the database will be considered in the decision-making process.
|
268 |
+
|
269 |
+
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
270 |
+
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
|
271 |
+
|
272 |
+
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
|
273 |
+
'euclidean', 'euclidean_l2' (default is cosine).
|
274 |
+
|
275 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
276 |
+
Set to False to avoid the exception for low-resolution images (default is True).
|
277 |
+
|
278 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
279 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
280 |
+
(default is opencv).
|
281 |
+
|
282 |
+
align (boolean): Perform alignment based on the eye positions (default is True).
|
283 |
+
|
284 |
+
expand_percentage (int): expand detected facial area with a percentage (default is 0).
|
285 |
+
|
286 |
+
threshold (float): Specify a threshold to determine whether a pair represents the same
|
287 |
+
person or different individuals. This threshold is used for comparing distances.
|
288 |
+
If left unset, default pre-tuned threshold values will be applied based on the specified
|
289 |
+
model name and distance metric (default is None).
|
290 |
+
|
291 |
+
normalization (string): Normalize the input image before feeding it to the model.
|
292 |
+
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base).
|
293 |
+
|
294 |
+
silent (boolean): Suppress or allow some log messages for a quieter analysis process
|
295 |
+
(default is False).
|
296 |
+
|
297 |
+
Returns:
|
298 |
+
results (List[pd.DataFrame]): A list of pandas dataframes. Each dataframe corresponds
|
299 |
+
to the identity information for an individual detected in the source image.
|
300 |
+
The DataFrame columns include:
|
301 |
+
|
302 |
+
- 'identity': Identity label of the detected individual.
|
303 |
+
|
304 |
+
- 'target_x', 'target_y', 'target_w', 'target_h': Bounding box coordinates of the
|
305 |
+
target face in the database.
|
306 |
+
|
307 |
+
- 'source_x', 'source_y', 'source_w', 'source_h': Bounding box coordinates of the
|
308 |
+
detected face in the source image.
|
309 |
+
|
310 |
+
- 'threshold': threshold to determine a pair whether same person or different persons
|
311 |
+
|
312 |
+
- 'distance': Similarity score between the faces based on the
|
313 |
+
specified model and distance metric
|
314 |
+
"""
|
315 |
+
return recognition.find(
|
316 |
+
img_path=img_path,
|
317 |
+
db_path=db_path,
|
318 |
+
model_name=model_name,
|
319 |
+
distance_metric=distance_metric,
|
320 |
+
enforce_detection=enforce_detection,
|
321 |
+
detector_backend=detector_backend,
|
322 |
+
align=align,
|
323 |
+
expand_percentage=expand_percentage,
|
324 |
+
threshold=threshold,
|
325 |
+
normalization=normalization,
|
326 |
+
silent=silent,
|
327 |
+
)
|
328 |
+
|
329 |
+
|
330 |
+
def represent(
|
331 |
+
img_path: Union[str, np.ndarray],
|
332 |
+
model_name: str = "VGG-Face",
|
333 |
+
enforce_detection: bool = True,
|
334 |
+
detector_backend: str = "opencv",
|
335 |
+
align: bool = True,
|
336 |
+
expand_percentage: int = 0,
|
337 |
+
normalization: str = "base",
|
338 |
+
) -> List[Dict[str, Any]]:
|
339 |
+
"""
|
340 |
+
Represent facial images as multi-dimensional vector embeddings.
|
341 |
+
|
342 |
+
Args:
|
343 |
+
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
|
344 |
+
or a base64 encoded image. If the source image contains multiple faces, the result will
|
345 |
+
include information for each detected face.
|
346 |
+
|
347 |
+
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
348 |
+
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
|
349 |
+
(default is VGG-Face.).
|
350 |
+
|
351 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
352 |
+
Default is True. Set to False to avoid the exception for low-resolution images
|
353 |
+
(default is True).
|
354 |
+
|
355 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
356 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
357 |
+
(default is opencv).
|
358 |
+
|
359 |
+
align (boolean): Perform alignment based on the eye positions (default is True).
|
360 |
+
|
361 |
+
expand_percentage (int): expand detected facial area with a percentage (default is 0).
|
362 |
+
|
363 |
+
normalization (string): Normalize the input image before feeding it to the model.
|
364 |
+
Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
|
365 |
+
(default is base).
|
366 |
+
|
367 |
+
Returns:
|
368 |
+
results (List[Dict[str, Any]]): A list of dictionaries, each containing the
|
369 |
+
following fields:
|
370 |
+
|
371 |
+
- embedding (List[float]): Multidimensional vector representing facial features.
|
372 |
+
The number of dimensions varies based on the reference model
|
373 |
+
(e.g., FaceNet returns 128 dimensions, VGG-Face returns 4096 dimensions).
|
374 |
+
|
375 |
+
- facial_area (dict): Detected facial area by face detection in dictionary format.
|
376 |
+
Contains 'x' and 'y' as the left-corner point, and 'w' and 'h'
|
377 |
+
as the width and height. If `detector_backend` is set to 'skip', it represents
|
378 |
+
the full image area and is nonsensical.
|
379 |
+
|
380 |
+
- face_confidence (float): Confidence score of face detection. If `detector_backend` is set
|
381 |
+
to 'skip', the confidence will be 0 and is nonsensical.
|
382 |
+
"""
|
383 |
+
return representation.represent(
|
384 |
+
img_path=img_path,
|
385 |
+
model_name=model_name,
|
386 |
+
enforce_detection=enforce_detection,
|
387 |
+
detector_backend=detector_backend,
|
388 |
+
align=align,
|
389 |
+
expand_percentage=expand_percentage,
|
390 |
+
normalization=normalization,
|
391 |
+
)
|
392 |
+
|
393 |
+
|
394 |
+
def stream(
|
395 |
+
db_path: str = "",
|
396 |
+
model_name: str = "VGG-Face",
|
397 |
+
detector_backend: str = "opencv",
|
398 |
+
distance_metric: str = "cosine",
|
399 |
+
enable_face_analysis: bool = True,
|
400 |
+
source: Any = 0,
|
401 |
+
time_threshold: int = 5,
|
402 |
+
frame_threshold: int = 5,
|
403 |
+
) -> None:
|
404 |
+
"""
|
405 |
+
Run real time face recognition and facial attribute analysis
|
406 |
+
|
407 |
+
Args:
|
408 |
+
db_path (string): Path to the folder containing image files. All detected faces
|
409 |
+
in the database will be considered in the decision-making process.
|
410 |
+
|
411 |
+
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
412 |
+
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
|
413 |
+
|
414 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
415 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
416 |
+
(default is opencv).
|
417 |
+
|
418 |
+
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
|
419 |
+
'euclidean', 'euclidean_l2' (default is cosine).
|
420 |
+
|
421 |
+
enable_face_analysis (bool): Flag to enable face analysis (default is True).
|
422 |
+
|
423 |
+
source (Any): The source for the video stream (default is 0, which represents the
|
424 |
+
default camera).
|
425 |
+
|
426 |
+
time_threshold (int): The time threshold (in seconds) for face recognition (default is 5).
|
427 |
+
|
428 |
+
frame_threshold (int): The frame threshold for face recognition (default is 5).
|
429 |
+
Returns:
|
430 |
+
None
|
431 |
+
"""
|
432 |
+
|
433 |
+
time_threshold = max(time_threshold, 1)
|
434 |
+
frame_threshold = max(frame_threshold, 1)
|
435 |
+
|
436 |
+
streaming.analysis(
|
437 |
+
db_path=db_path,
|
438 |
+
model_name=model_name,
|
439 |
+
detector_backend=detector_backend,
|
440 |
+
distance_metric=distance_metric,
|
441 |
+
enable_face_analysis=enable_face_analysis,
|
442 |
+
source=source,
|
443 |
+
time_threshold=time_threshold,
|
444 |
+
frame_threshold=frame_threshold,
|
445 |
+
)
|
446 |
+
|
447 |
+
|
448 |
+
def extract_faces(
|
449 |
+
img_path: Union[str, np.ndarray],
|
450 |
+
detector_backend: str = "opencv",
|
451 |
+
enforce_detection: bool = True,
|
452 |
+
align: bool = True,
|
453 |
+
expand_percentage: int = 0,
|
454 |
+
grayscale: bool = False,
|
455 |
+
) -> List[Dict[str, Any]]:
|
456 |
+
"""
|
457 |
+
Extract faces from a given image
|
458 |
+
|
459 |
+
Args:
|
460 |
+
img_path (str or np.ndarray): Path to the first image. Accepts exact image path
|
461 |
+
as a string, numpy array (BGR), or base64 encoded images.
|
462 |
+
|
463 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
464 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
465 |
+
(default is opencv).
|
466 |
+
|
467 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
468 |
+
Set to False to avoid the exception for low-resolution images (default is True).
|
469 |
+
|
470 |
+
align (bool): Flag to enable face alignment (default is True).
|
471 |
+
|
472 |
+
expand_percentage (int): expand detected facial area with a percentage (default is 0).
|
473 |
+
|
474 |
+
grayscale (boolean): Flag to convert the image to grayscale before
|
475 |
+
processing (default is False).
|
476 |
+
|
477 |
+
Returns:
|
478 |
+
results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary contains:
|
479 |
+
|
480 |
+
- "face" (np.ndarray): The detected face as a NumPy array.
|
481 |
+
|
482 |
+
- "facial_area" (Dict[str, Any]): The detected face's regions as a dictionary containing:
|
483 |
+
- keys 'x', 'y', 'w', 'h' with int values
|
484 |
+
- keys 'left_eye', 'right_eye' with a tuple of 2 ints as values. left and right eyes
|
485 |
+
are eyes on the left and right respectively with respect to the person itself
|
486 |
+
instead of observer.
|
487 |
+
|
488 |
+
- "confidence" (float): The confidence score associated with the detected face.
|
489 |
+
"""
|
490 |
+
|
491 |
+
return detection.extract_faces(
|
492 |
+
img_path=img_path,
|
493 |
+
detector_backend=detector_backend,
|
494 |
+
enforce_detection=enforce_detection,
|
495 |
+
align=align,
|
496 |
+
expand_percentage=expand_percentage,
|
497 |
+
grayscale=grayscale,
|
498 |
+
)
|
499 |
+
|
500 |
+
|
501 |
+
def cli() -> None:
|
502 |
+
"""
|
503 |
+
command line interface function will be offered in this block
|
504 |
+
"""
|
505 |
+
import fire
|
506 |
+
|
507 |
+
fire.Fire()
|
508 |
+
|
509 |
+
|
510 |
+
# deprecated function(s)
|
511 |
+
|
512 |
+
|
513 |
+
def detectFace(
|
514 |
+
img_path: Union[str, np.ndarray],
|
515 |
+
target_size: tuple = (224, 224),
|
516 |
+
detector_backend: str = "opencv",
|
517 |
+
enforce_detection: bool = True,
|
518 |
+
align: bool = True,
|
519 |
+
) -> Union[np.ndarray, None]:
|
520 |
+
"""
|
521 |
+
Deprecated face detection function. Use extract_faces for same functionality.
|
522 |
+
|
523 |
+
Args:
|
524 |
+
img_path (str or np.ndarray): Path to the first image. Accepts exact image path
|
525 |
+
as a string, numpy array (BGR), or base64 encoded images.
|
526 |
+
|
527 |
+
target_size (tuple): final shape of facial image. black pixels will be
|
528 |
+
added to resize the image (default is (224, 224)).
|
529 |
+
|
530 |
+
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
|
531 |
+
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
|
532 |
+
(default is opencv).
|
533 |
+
|
534 |
+
enforce_detection (boolean): If no face is detected in an image, raise an exception.
|
535 |
+
Set to False to avoid the exception for low-resolution images (default is True).
|
536 |
+
|
537 |
+
align (bool): Flag to enable face alignment (default is True).
|
538 |
+
|
539 |
+
Returns:
|
540 |
+
img (np.ndarray): detected (and aligned) facial area image as numpy array
|
541 |
+
"""
|
542 |
+
logger.warn("Function detectFace is deprecated. Use extract_faces instead.")
|
543 |
+
face_objs = extract_faces(
|
544 |
+
img_path=img_path,
|
545 |
+
detector_backend=detector_backend,
|
546 |
+
enforce_detection=enforce_detection,
|
547 |
+
align=align,
|
548 |
+
grayscale=False,
|
549 |
+
)
|
550 |
+
extracted_face = None
|
551 |
+
if len(face_objs) > 0:
|
552 |
+
extracted_face = face_objs[0]["face"]
|
553 |
+
extracted_face = preprocessing.resize_image(img=extracted_face, target_size=target_size)
|
554 |
+
return extracted_face
|
555 |
+
|
556 |
+
|
557 |
+
def sync_datasets():
|
558 |
+
# Set the local directories
|
559 |
+
base_dir = os_path.get_main_directory()
|
560 |
+
|
561 |
+
missing_dir = os.path.join(base_dir, 'mafqoud', 'images', 'missing_people')
|
562 |
+
founded_dir = os.path.join(base_dir, 'mafqoud', 'images', 'founded_people')
|
563 |
+
|
564 |
+
# Ensure the directories exist
|
565 |
+
os.makedirs(missing_dir, exist_ok=True)
|
566 |
+
os.makedirs(founded_dir, exist_ok=True)
|
567 |
+
|
568 |
+
missing_people = cloudservice.sync_folder('missing_people', missing_dir)
|
569 |
+
|
570 |
+
founded_people = cloudservice.sync_folder('founded_people', founded_dir)
|
571 |
+
|
572 |
+
def delete_pkls():
|
573 |
+
# Set the local directories
|
574 |
+
base_dir = os_path.get_main_directory()
|
575 |
+
|
576 |
+
missing_dir = os.path.join(base_dir, 'mafqoud', 'images', 'missing_people')
|
577 |
+
founded_dir = os.path.join(base_dir, 'mafqoud', 'images', 'founded_people')
|
578 |
+
|
579 |
+
# Ensure the directories exist
|
580 |
+
os.makedirs(missing_dir, exist_ok=True)
|
581 |
+
os.makedirs(founded_dir, exist_ok=True)
|
582 |
+
|
583 |
+
cloudservice.delete_pkl_files(missing_dir)
|
584 |
+
cloudservice.delete_pkl_files(founded_dir)
|
585 |
+
|
LICENSE
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
MIT License
|
2 |
+
|
3 |
+
Copyright (c) 2019 Sefik Ilkin Serengil
|
4 |
+
|
5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
6 |
+
of this software and associated documentation files (the "Software"), to deal
|
7 |
+
in the Software without restriction, including without limitation the rights
|
8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
9 |
+
copies of the Software, and to permit persons to whom the Software is
|
10 |
+
furnished to do so, subject to the following conditions:
|
11 |
+
|
12 |
+
The above copyright notice and this permission notice shall be included in all
|
13 |
+
copies or substantial portions of the Software.
|
14 |
+
|
15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
21 |
+
SOFTWARE.
|
Makefile
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
test:
|
2 |
+
cd tests && python -m pytest . -s --disable-warnings
|
3 |
+
|
4 |
+
lint:
|
5 |
+
python -m pylint deepface/ --fail-under=10
|
6 |
+
|
7 |
+
coverage:
|
8 |
+
pip install pytest-cov && cd tests && python -m pytest --cov=deepface
|
Train.py
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
# from deepface import DeepFace
|
3 |
+
# import os
|
4 |
+
# models = [
|
5 |
+
# "VGG-Face",
|
6 |
+
# "Facenet",
|
7 |
+
# "Facenet512",
|
8 |
+
# "OpenFace",
|
9 |
+
# "DeepFace",
|
10 |
+
# "DeepID",
|
11 |
+
# "ArcFace",
|
12 |
+
# "Dlib",
|
13 |
+
# "SFace",
|
14 |
+
# ]
|
15 |
+
|
16 |
+
# metrics = ["cosine", "euclidean", "euclidean_l2"]
|
17 |
+
|
18 |
+
# backends = [
|
19 |
+
# 'opencv',
|
20 |
+
# 'ssd',
|
21 |
+
# 'dlib',
|
22 |
+
# 'mtcnn',
|
23 |
+
# 'retinaface',
|
24 |
+
# 'mediapipe',
|
25 |
+
# 'yolov8',
|
26 |
+
# 'yunet',
|
27 |
+
# 'fastmtcnn',
|
28 |
+
# ]
|
29 |
+
|
30 |
+
# # df = DeepFace.find(img_path='F:/projects/python/mafqoud/dataset/missing_people/m0.jpg'
|
31 |
+
# # , db_path='F:/projects/python/mafqoud/dataset/founded_people'
|
32 |
+
# # , enforce_detection = True
|
33 |
+
# # , model_name = models[2]
|
34 |
+
# # , distance_metric = metrics[2]
|
35 |
+
# # , detector_backend = backends[3])
|
36 |
+
|
37 |
+
# DeepFace.stream(db_path = "F:/deepface")
|
38 |
+
|
39 |
+
# base_dir = os.path.abspath(os.path.dirname(__file__))
|
40 |
+
# # base_dir = "f:\\"
|
41 |
+
# founded_dir = os.path.join(base_dir, 'mafqoud', 'images', 'founded_people')
|
42 |
+
# def get_main_directory():
|
43 |
+
# path = os.path.abspath(__file__)
|
44 |
+
# drive, _ = os.path.splitdrive(path)
|
45 |
+
# if not drive.endswith(os.path.sep):
|
46 |
+
# drive += os.path.sep
|
47 |
+
# return drive
|
48 |
+
|
49 |
+
# base_dir = get_main_directory()
|
50 |
+
# missing_dir = os.path.join(base_dir, 'mafqoud', 'images', 'missing_people')
|
51 |
+
# print(missing_dir)
|
52 |
+
|
53 |
+
# print(base_dir)
|
54 |
+
# print(missing_dir)
|
55 |
+
# print(founded_dir)
|
__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
__version__ = "0.0.90"
|
__pycache__/DeepFace.cpython-312.pyc
ADDED
Binary file (23.7 kB). View file
|
|
__pycache__/__init__.cpython-312.pyc
ADDED
Binary file (186 Bytes). View file
|
|
api/__init__.py
ADDED
File without changes
|
api/__pycache__/__init__.cpython-312.pyc
ADDED
Binary file (173 Bytes). View file
|
|
api/postman/deepface-api.postman_collection.json
ADDED
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"info": {
|
3 |
+
"_postman_id": "4c0b144e-4294-4bdd-8072-bcb326b1fed2",
|
4 |
+
"name": "deepface-api",
|
5 |
+
"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
|
6 |
+
},
|
7 |
+
"item": [
|
8 |
+
{
|
9 |
+
"name": "Represent",
|
10 |
+
"request": {
|
11 |
+
"method": "POST",
|
12 |
+
"header": [],
|
13 |
+
"body": {
|
14 |
+
"mode": "raw",
|
15 |
+
"raw": "{\n \"model_name\": \"Facenet\",\n \"img\": \"/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg\"\n}",
|
16 |
+
"options": {
|
17 |
+
"raw": {
|
18 |
+
"language": "json"
|
19 |
+
}
|
20 |
+
}
|
21 |
+
},
|
22 |
+
"url": {
|
23 |
+
"raw": "http://127.0.0.1:5000/represent",
|
24 |
+
"protocol": "http",
|
25 |
+
"host": [
|
26 |
+
"127",
|
27 |
+
"0",
|
28 |
+
"0",
|
29 |
+
"1"
|
30 |
+
],
|
31 |
+
"port": "5000",
|
32 |
+
"path": [
|
33 |
+
"represent"
|
34 |
+
]
|
35 |
+
}
|
36 |
+
},
|
37 |
+
"response": []
|
38 |
+
},
|
39 |
+
{
|
40 |
+
"name": "Face verification",
|
41 |
+
"request": {
|
42 |
+
"method": "POST",
|
43 |
+
"header": [],
|
44 |
+
"body": {
|
45 |
+
"mode": "raw",
|
46 |
+
"raw": " {\n \t\"img1_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/img1.jpg\",\n \"img2_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/img2.jpg\",\n \"model_name\": \"Facenet\",\n \"detector_backend\": \"mtcnn\",\n \"distance_metric\": \"euclidean\"\n }",
|
47 |
+
"options": {
|
48 |
+
"raw": {
|
49 |
+
"language": "json"
|
50 |
+
}
|
51 |
+
}
|
52 |
+
},
|
53 |
+
"url": {
|
54 |
+
"raw": "http://127.0.0.1:5000/verify",
|
55 |
+
"protocol": "http",
|
56 |
+
"host": [
|
57 |
+
"127",
|
58 |
+
"0",
|
59 |
+
"0",
|
60 |
+
"1"
|
61 |
+
],
|
62 |
+
"port": "5000",
|
63 |
+
"path": [
|
64 |
+
"verify"
|
65 |
+
]
|
66 |
+
}
|
67 |
+
},
|
68 |
+
"response": []
|
69 |
+
},
|
70 |
+
{
|
71 |
+
"name": "Face analysis",
|
72 |
+
"request": {
|
73 |
+
"method": "POST",
|
74 |
+
"header": [],
|
75 |
+
"body": {
|
76 |
+
"mode": "raw",
|
77 |
+
"raw": "{\n \"img_path\": \"/Users/sefik/Desktop/deepface/tests/dataset/couple.jpg\",\n \"actions\": [\"age\", \"gender\", \"emotion\", \"race\"]\n}",
|
78 |
+
"options": {
|
79 |
+
"raw": {
|
80 |
+
"language": "json"
|
81 |
+
}
|
82 |
+
}
|
83 |
+
},
|
84 |
+
"url": {
|
85 |
+
"raw": "http://127.0.0.1:5000/analyze",
|
86 |
+
"protocol": "http",
|
87 |
+
"host": [
|
88 |
+
"127",
|
89 |
+
"0",
|
90 |
+
"0",
|
91 |
+
"1"
|
92 |
+
],
|
93 |
+
"port": "5000",
|
94 |
+
"path": [
|
95 |
+
"analyze"
|
96 |
+
]
|
97 |
+
}
|
98 |
+
},
|
99 |
+
"response": []
|
100 |
+
}
|
101 |
+
]
|
102 |
+
}
|
api/src/__init__.py
ADDED
File without changes
|
api/src/__pycache__/__init__.cpython-312.pyc
ADDED
Binary file (177 Bytes). View file
|
|
api/src/__pycache__/app.cpython-312.pyc
ADDED
Binary file (585 Bytes). View file
|
|
api/src/api.py
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import app
|
3 |
+
import os
|
4 |
+
|
5 |
+
if __name__ == "__main__":
|
6 |
+
deepface_app = app.create_app()
|
7 |
+
parser = argparse.ArgumentParser()
|
8 |
+
parser.add_argument("-p", "--port", type=int, default=int(os.getenv('DEFAULT_PORT')), help="Port of serving api")
|
9 |
+
args = parser.parse_args()
|
10 |
+
deepface_app.run(host="0.0.0.0", port=args.port)
|
api/src/app.py
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# 3rd parth dependencies
|
2 |
+
from flask import Flask
|
3 |
+
from deepface.api.src.modules.core.routes import blueprint
|
4 |
+
|
5 |
+
|
6 |
+
def create_app():
|
7 |
+
app = Flask(__name__)
|
8 |
+
app.register_blueprint(blueprint)
|
9 |
+
print(app.url_map)
|
10 |
+
return app
|
11 |
+
|
api/src/modules/__init__.py
ADDED
File without changes
|
api/src/modules/__pycache__/__init__.cpython-312.pyc
ADDED
Binary file (185 Bytes). View file
|
|
api/src/modules/core/__init__.py
ADDED
File without changes
|
api/src/modules/core/__pycache__/__init__.cpython-312.pyc
ADDED
Binary file (190 Bytes). View file
|
|
api/src/modules/core/__pycache__/routes.cpython-312.pyc
ADDED
Binary file (8.62 kB). View file
|
|
api/src/modules/core/__pycache__/service.cpython-312.pyc
ADDED
Binary file (3.1 kB). View file
|
|
api/src/modules/core/routes.py
ADDED
@@ -0,0 +1,207 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from flask import Blueprint, request , jsonify
|
2 |
+
from deepface.api.src.modules.core import service
|
3 |
+
from deepface.commons.logger import Logger
|
4 |
+
from deepface.commons.os_path import os_path
|
5 |
+
import json
|
6 |
+
import os
|
7 |
+
|
8 |
+
logger = Logger(module="api/src/routes.py")
|
9 |
+
|
10 |
+
blueprint = Blueprint("routes", __name__)
|
11 |
+
|
12 |
+
|
13 |
+
@blueprint.route("/")
|
14 |
+
def home():
|
15 |
+
return "<h1>Welcome to DeepFace API!</h1>"
|
16 |
+
|
17 |
+
|
18 |
+
@blueprint.route("/represent", methods=["POST"])
|
19 |
+
def represent():
|
20 |
+
input_args = request.get_json()
|
21 |
+
|
22 |
+
if input_args is None:
|
23 |
+
return {"message": "empty input set passed"}
|
24 |
+
|
25 |
+
img_path = input_args.get("img") or input_args.get("img_path")
|
26 |
+
if img_path is None:
|
27 |
+
return {"message": "you must pass img_path input"}
|
28 |
+
|
29 |
+
model_name = input_args.get("model_name", "VGG-Face")
|
30 |
+
detector_backend = input_args.get("detector_backend", "opencv")
|
31 |
+
enforce_detection = input_args.get("enforce_detection", True)
|
32 |
+
align = input_args.get("align", True)
|
33 |
+
|
34 |
+
obj = service.represent(
|
35 |
+
img_path=img_path,
|
36 |
+
model_name=model_name,
|
37 |
+
detector_backend=detector_backend,
|
38 |
+
enforce_detection=enforce_detection,
|
39 |
+
align=align,
|
40 |
+
)
|
41 |
+
|
42 |
+
logger.debug(obj)
|
43 |
+
|
44 |
+
return obj
|
45 |
+
|
46 |
+
|
47 |
+
@blueprint.route("/verify", methods=["POST"])
|
48 |
+
def verify():
|
49 |
+
input_args = request.get_json()
|
50 |
+
|
51 |
+
if input_args is None:
|
52 |
+
return {"message": "empty input set passed"}
|
53 |
+
|
54 |
+
img1_path = input_args.get("img1") or input_args.get("img1_path")
|
55 |
+
img2_path = input_args.get("img2") or input_args.get("img2_path")
|
56 |
+
|
57 |
+
if img1_path is None:
|
58 |
+
return {"message": "you must pass img1_path input"}
|
59 |
+
|
60 |
+
if img2_path is None:
|
61 |
+
return {"message": "you must pass img2_path input"}
|
62 |
+
|
63 |
+
model_name = input_args.get("model_name", "VGG-Face")
|
64 |
+
detector_backend = input_args.get("detector_backend", "opencv")
|
65 |
+
enforce_detection = input_args.get("enforce_detection", True)
|
66 |
+
distance_metric = input_args.get("distance_metric", "cosine")
|
67 |
+
align = input_args.get("align", True)
|
68 |
+
|
69 |
+
verification = service.verify(
|
70 |
+
img1_path=img1_path,
|
71 |
+
img2_path=img2_path,
|
72 |
+
model_name=model_name,
|
73 |
+
detector_backend=detector_backend,
|
74 |
+
distance_metric=distance_metric,
|
75 |
+
align=align,
|
76 |
+
enforce_detection=enforce_detection,
|
77 |
+
)
|
78 |
+
|
79 |
+
logger.debug(verification)
|
80 |
+
|
81 |
+
return verification
|
82 |
+
|
83 |
+
|
84 |
+
@blueprint.route("/analyze", methods=["POST"])
|
85 |
+
def analyze():
|
86 |
+
input_args = request.get_json()
|
87 |
+
|
88 |
+
if input_args is None:
|
89 |
+
return {"message": "empty input set passed"}
|
90 |
+
|
91 |
+
img_path = input_args.get("img") or input_args.get("img_path")
|
92 |
+
if img_path is None:
|
93 |
+
return {"message": "you must pass img_path input"}
|
94 |
+
|
95 |
+
detector_backend = input_args.get("detector_backend", "opencv")
|
96 |
+
enforce_detection = input_args.get("enforce_detection", True)
|
97 |
+
align = input_args.get("align", True)
|
98 |
+
actions = input_args.get("actions", ["age", "gender", "emotion", "race"])
|
99 |
+
|
100 |
+
demographies = service.analyze(
|
101 |
+
img_path=img_path,
|
102 |
+
actions=actions,
|
103 |
+
detector_backend=detector_backend,
|
104 |
+
enforce_detection=enforce_detection,
|
105 |
+
align=align,
|
106 |
+
)
|
107 |
+
|
108 |
+
logger.debug(demographies)
|
109 |
+
|
110 |
+
return demographies
|
111 |
+
|
112 |
+
@blueprint.route("/find", methods=["POST"])
|
113 |
+
def find():
|
114 |
+
input_args = request.get_json()
|
115 |
+
|
116 |
+
if input_args is None:
|
117 |
+
response = jsonify({'error': 'empty input set passed'})
|
118 |
+
response.status_code = 500
|
119 |
+
return response
|
120 |
+
|
121 |
+
img_name = input_args.get("img") or input_args.get("img_name")
|
122 |
+
img_type = input_args.get("img_type")
|
123 |
+
|
124 |
+
if img_name is None:
|
125 |
+
response = jsonify({'error': 'you must pass img_name input'})
|
126 |
+
response.status_code = 404
|
127 |
+
return response
|
128 |
+
|
129 |
+
if img_type == "missing" or img_type == "missing_person" or img_type == "missing_people" or img_type == "missing person" or img_type == "missing people" :
|
130 |
+
|
131 |
+
img_path = os.path.join( os_path.get_main_directory() , 'mafqoud' , 'images' , "missing_people" , img_name)
|
132 |
+
db_path = os.path.join( os_path.get_main_directory() , 'mafqoud' , 'images' , "founded_people")
|
133 |
+
|
134 |
+
elif img_type == "founded" or img_type == "founded_person" or img_type == "founded_people" or img_type == "founded person" or img_type == "founded people" :
|
135 |
+
|
136 |
+
img_path = os.path.join( os_path.get_main_directory() , 'mafqoud' , 'images' , "founded_people" , img_name)
|
137 |
+
db_path = os.path.join( os_path.get_main_directory() , 'mafqoud' , 'images' , "missing_people")
|
138 |
+
|
139 |
+
else :
|
140 |
+
|
141 |
+
response = jsonify({'error': 'the type of the image is not correct and it should be one of those : ( missing , missing_people , missing_people , missing person , missing people ) or ( founded , founded_people , founded_people , founded person , founded people )'})
|
142 |
+
response.status_code = 400
|
143 |
+
return response
|
144 |
+
|
145 |
+
print(img_path)
|
146 |
+
if not os.path.exists(img_path) or not os.path.isfile(img_path):
|
147 |
+
# If the image does not exist, return a JSON response with status code 404
|
148 |
+
response = jsonify({'error': 'Image not found'})
|
149 |
+
response.status_code = 404
|
150 |
+
return response
|
151 |
+
|
152 |
+
|
153 |
+
model_name = input_args.get("model_name", "Facenet512")
|
154 |
+
detector_backend = input_args.get("detector_backend", "mtcnn")
|
155 |
+
enforce_detection = input_args.get("enforce_detection", True)
|
156 |
+
distance_metric = input_args.get("distance_metric", "euclidean_l2")
|
157 |
+
align = input_args.get("align", True)
|
158 |
+
|
159 |
+
if img_name is None:
|
160 |
+
return {"message": "you must pass img1_path input"}
|
161 |
+
|
162 |
+
if db_path is None:
|
163 |
+
dataset_path = os.path.join(path.get_parent_path(), 'dataset')
|
164 |
+
if img_type == "missing_person":
|
165 |
+
img_path = os.path.join(dataset_path, 'missing_people', img_name)
|
166 |
+
db_path = os.path.join(dataset_path, 'founded_people')
|
167 |
+
elif img_type == "founded_people":
|
168 |
+
img_path = os.path.join(dataset_path, 'founded_people', img_name)
|
169 |
+
db_path = os.path.join(dataset_path, 'missing_people')
|
170 |
+
|
171 |
+
results = service.find(
|
172 |
+
img_path=img_path,
|
173 |
+
db_path=db_path,
|
174 |
+
model_name=model_name,
|
175 |
+
detector_backend=detector_backend,
|
176 |
+
distance_metric=distance_metric,
|
177 |
+
align=align,
|
178 |
+
enforce_detection=enforce_detection,
|
179 |
+
)
|
180 |
+
|
181 |
+
# Calculate similarity_percentage for each row
|
182 |
+
results[0]['similarity_percentage'] =100 - ((results[0]['distance'] / results[0]['threshold']) * 100)
|
183 |
+
|
184 |
+
data = []
|
185 |
+
for _, row in results[0].iterrows():
|
186 |
+
data.append({
|
187 |
+
"identity": row['identity'],
|
188 |
+
"similarity_percentage": row['similarity_percentage']
|
189 |
+
})
|
190 |
+
|
191 |
+
json_data = json.dumps(data, indent=4)
|
192 |
+
|
193 |
+
|
194 |
+
logger.debug(json_data)
|
195 |
+
return json_data
|
196 |
+
|
197 |
+
|
198 |
+
@blueprint.route("/dataset/sync", methods=["GET"])
|
199 |
+
def sync_datasets():
|
200 |
+
result = service.sync_datasets()
|
201 |
+
return jsonify(result)
|
202 |
+
|
203 |
+
|
204 |
+
@blueprint.route("/delete/pkls", methods=["GET"])
|
205 |
+
def delete_pkls():
|
206 |
+
result = service.delete_pkls()
|
207 |
+
return jsonify(result)
|
api/src/modules/core/service.py
ADDED
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from deepface import DeepFace
|
2 |
+
|
3 |
+
# pylint: disable=broad-except
|
4 |
+
|
5 |
+
|
6 |
+
def represent(img_path, model_name, detector_backend, enforce_detection, align):
|
7 |
+
try:
|
8 |
+
result = {}
|
9 |
+
embedding_objs = DeepFace.represent(
|
10 |
+
img_path=img_path,
|
11 |
+
model_name=model_name,
|
12 |
+
detector_backend=detector_backend,
|
13 |
+
enforce_detection=enforce_detection,
|
14 |
+
align=align,
|
15 |
+
)
|
16 |
+
result["results"] = embedding_objs
|
17 |
+
return result
|
18 |
+
except Exception as err:
|
19 |
+
return {"error": f"Exception while representing: {str(err)}"}, 400
|
20 |
+
|
21 |
+
|
22 |
+
def verify(
|
23 |
+
img1_path, img2_path, model_name, detector_backend, distance_metric, enforce_detection, align
|
24 |
+
):
|
25 |
+
try:
|
26 |
+
obj = DeepFace.verify(
|
27 |
+
img1_path=img1_path,
|
28 |
+
img2_path=img2_path,
|
29 |
+
model_name=model_name,
|
30 |
+
detector_backend=detector_backend,
|
31 |
+
distance_metric=distance_metric,
|
32 |
+
align=align,
|
33 |
+
enforce_detection=enforce_detection,
|
34 |
+
)
|
35 |
+
return obj
|
36 |
+
except Exception as err:
|
37 |
+
return {"error": f"Exception while verifying: {str(err)}"}, 400
|
38 |
+
|
39 |
+
|
40 |
+
def analyze(img_path, actions, detector_backend, enforce_detection, align):
|
41 |
+
try:
|
42 |
+
result = {}
|
43 |
+
demographies = DeepFace.analyze(
|
44 |
+
img_path=img_path,
|
45 |
+
actions=actions,
|
46 |
+
detector_backend=detector_backend,
|
47 |
+
enforce_detection=enforce_detection,
|
48 |
+
align=align,
|
49 |
+
silent=True,
|
50 |
+
)
|
51 |
+
result["results"] = demographies
|
52 |
+
return result
|
53 |
+
except Exception as err:
|
54 |
+
return {"error": f"Exception while analyzing: {str(err)}"}, 400
|
55 |
+
|
56 |
+
def find(img_path, db_path, model_name, detector_backend, distance_metric, enforce_detection, align):
|
57 |
+
try:
|
58 |
+
obj = DeepFace.find(
|
59 |
+
img_path=img_path,
|
60 |
+
db_path=db_path,
|
61 |
+
model_name=model_name,
|
62 |
+
detector_backend=detector_backend,
|
63 |
+
distance_metric=distance_metric,
|
64 |
+
align=align,
|
65 |
+
enforce_detection=enforce_detection,
|
66 |
+
)
|
67 |
+
return obj
|
68 |
+
except Exception as err:
|
69 |
+
return {"error": f"Exception while Findind: {str(err)}"}, 400
|
70 |
+
|
71 |
+
|
72 |
+
def sync_datasets():
|
73 |
+
try:
|
74 |
+
DeepFace.sync_datasets()
|
75 |
+
return {'data': 'synced successfully'}, 200
|
76 |
+
except Exception as e:
|
77 |
+
return {'error': str(e)}, 400
|
78 |
+
|
79 |
+
def delete_pkls():
|
80 |
+
try:
|
81 |
+
DeepFace.delete_pkls()
|
82 |
+
return {'data': 'pkl files deleted successfully'}, 200
|
83 |
+
except Exception as e:
|
84 |
+
return {'error': str(e)}, 400
|
basemodels/ArcFace.py
ADDED
@@ -0,0 +1,179 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import gdown
|
3 |
+
from deepface.commons import package_utils, folder_utils
|
4 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
5 |
+
|
6 |
+
from deepface.commons import logger as log
|
7 |
+
|
8 |
+
logger = log.get_singletonish_logger()
|
9 |
+
|
10 |
+
# pylint: disable=unsubscriptable-object
|
11 |
+
|
12 |
+
# --------------------------------
|
13 |
+
# dependency configuration
|
14 |
+
|
15 |
+
tf_version = package_utils.get_tf_major_version()
|
16 |
+
|
17 |
+
if tf_version == 1:
|
18 |
+
from keras.models import Model
|
19 |
+
from keras.engine import training
|
20 |
+
from keras.layers import (
|
21 |
+
ZeroPadding2D,
|
22 |
+
Input,
|
23 |
+
Conv2D,
|
24 |
+
BatchNormalization,
|
25 |
+
PReLU,
|
26 |
+
Add,
|
27 |
+
Dropout,
|
28 |
+
Flatten,
|
29 |
+
Dense,
|
30 |
+
)
|
31 |
+
else:
|
32 |
+
from tensorflow.keras.models import Model
|
33 |
+
from tensorflow.python.keras.engine import training
|
34 |
+
from tensorflow.keras.layers import (
|
35 |
+
ZeroPadding2D,
|
36 |
+
Input,
|
37 |
+
Conv2D,
|
38 |
+
BatchNormalization,
|
39 |
+
PReLU,
|
40 |
+
Add,
|
41 |
+
Dropout,
|
42 |
+
Flatten,
|
43 |
+
Dense,
|
44 |
+
)
|
45 |
+
|
46 |
+
# pylint: disable=too-few-public-methods
|
47 |
+
class ArcFaceClient(FacialRecognition):
|
48 |
+
"""
|
49 |
+
ArcFace model class
|
50 |
+
"""
|
51 |
+
|
52 |
+
def __init__(self):
|
53 |
+
self.model = load_model()
|
54 |
+
self.model_name = "ArcFace"
|
55 |
+
self.input_shape = (112, 112)
|
56 |
+
self.output_shape = 512
|
57 |
+
|
58 |
+
|
59 |
+
def load_model(
|
60 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5",
|
61 |
+
) -> Model:
|
62 |
+
"""
|
63 |
+
Construct ArcFace model, download its weights and load
|
64 |
+
Returns:
|
65 |
+
model (Model)
|
66 |
+
"""
|
67 |
+
base_model = ResNet34()
|
68 |
+
inputs = base_model.inputs[0]
|
69 |
+
arcface_model = base_model.outputs[0]
|
70 |
+
arcface_model = BatchNormalization(momentum=0.9, epsilon=2e-5)(arcface_model)
|
71 |
+
arcface_model = Dropout(0.4)(arcface_model)
|
72 |
+
arcface_model = Flatten()(arcface_model)
|
73 |
+
arcface_model = Dense(512, activation=None, use_bias=True, kernel_initializer="glorot_normal")(
|
74 |
+
arcface_model
|
75 |
+
)
|
76 |
+
embedding = BatchNormalization(momentum=0.9, epsilon=2e-5, name="embedding", scale=True)(
|
77 |
+
arcface_model
|
78 |
+
)
|
79 |
+
model = Model(inputs, embedding, name=base_model.name)
|
80 |
+
|
81 |
+
# ---------------------------------------
|
82 |
+
# check the availability of pre-trained weights
|
83 |
+
|
84 |
+
home = folder_utils.get_deepface_home()
|
85 |
+
|
86 |
+
file_name = "arcface_weights.h5"
|
87 |
+
output = home + "/.deepface/weights/" + file_name
|
88 |
+
|
89 |
+
if os.path.isfile(output) != True:
|
90 |
+
|
91 |
+
logger.info(f"{file_name} will be downloaded to {output}")
|
92 |
+
gdown.download(url, output, quiet=False)
|
93 |
+
|
94 |
+
# ---------------------------------------
|
95 |
+
|
96 |
+
model.load_weights(output)
|
97 |
+
|
98 |
+
return model
|
99 |
+
|
100 |
+
|
101 |
+
def ResNet34() -> Model:
|
102 |
+
"""
|
103 |
+
ResNet34 model
|
104 |
+
Returns:
|
105 |
+
model (Model)
|
106 |
+
"""
|
107 |
+
img_input = Input(shape=(112, 112, 3))
|
108 |
+
|
109 |
+
x = ZeroPadding2D(padding=1, name="conv1_pad")(img_input)
|
110 |
+
x = Conv2D(
|
111 |
+
64, 3, strides=1, use_bias=False, kernel_initializer="glorot_normal", name="conv1_conv"
|
112 |
+
)(x)
|
113 |
+
x = BatchNormalization(axis=3, epsilon=2e-5, momentum=0.9, name="conv1_bn")(x)
|
114 |
+
x = PReLU(shared_axes=[1, 2], name="conv1_prelu")(x)
|
115 |
+
x = stack_fn(x)
|
116 |
+
|
117 |
+
model = training.Model(img_input, x, name="ResNet34")
|
118 |
+
|
119 |
+
return model
|
120 |
+
|
121 |
+
|
122 |
+
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
|
123 |
+
bn_axis = 3
|
124 |
+
|
125 |
+
if conv_shortcut:
|
126 |
+
shortcut = Conv2D(
|
127 |
+
filters,
|
128 |
+
1,
|
129 |
+
strides=stride,
|
130 |
+
use_bias=False,
|
131 |
+
kernel_initializer="glorot_normal",
|
132 |
+
name=name + "_0_conv",
|
133 |
+
)(x)
|
134 |
+
shortcut = BatchNormalization(
|
135 |
+
axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + "_0_bn"
|
136 |
+
)(shortcut)
|
137 |
+
else:
|
138 |
+
shortcut = x
|
139 |
+
|
140 |
+
x = BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + "_1_bn")(x)
|
141 |
+
x = ZeroPadding2D(padding=1, name=name + "_1_pad")(x)
|
142 |
+
x = Conv2D(
|
143 |
+
filters,
|
144 |
+
3,
|
145 |
+
strides=1,
|
146 |
+
kernel_initializer="glorot_normal",
|
147 |
+
use_bias=False,
|
148 |
+
name=name + "_1_conv",
|
149 |
+
)(x)
|
150 |
+
x = BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + "_2_bn")(x)
|
151 |
+
x = PReLU(shared_axes=[1, 2], name=name + "_1_prelu")(x)
|
152 |
+
|
153 |
+
x = ZeroPadding2D(padding=1, name=name + "_2_pad")(x)
|
154 |
+
x = Conv2D(
|
155 |
+
filters,
|
156 |
+
kernel_size,
|
157 |
+
strides=stride,
|
158 |
+
kernel_initializer="glorot_normal",
|
159 |
+
use_bias=False,
|
160 |
+
name=name + "_2_conv",
|
161 |
+
)(x)
|
162 |
+
x = BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + "_3_bn")(x)
|
163 |
+
|
164 |
+
x = Add(name=name + "_add")([shortcut, x])
|
165 |
+
return x
|
166 |
+
|
167 |
+
|
168 |
+
def stack1(x, filters, blocks, stride1=2, name=None):
|
169 |
+
x = block1(x, filters, stride=stride1, name=name + "_block1")
|
170 |
+
for i in range(2, blocks + 1):
|
171 |
+
x = block1(x, filters, conv_shortcut=False, name=name + "_block" + str(i))
|
172 |
+
return x
|
173 |
+
|
174 |
+
|
175 |
+
def stack_fn(x):
|
176 |
+
x = stack1(x, 64, 3, name="conv2")
|
177 |
+
x = stack1(x, 128, 4, name="conv3")
|
178 |
+
x = stack1(x, 256, 6, name="conv4")
|
179 |
+
return stack1(x, 512, 3, name="conv5")
|
basemodels/DeepID.py
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import gdown
|
3 |
+
from deepface.commons import package_utils, folder_utils
|
4 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
5 |
+
from deepface.commons import logger as log
|
6 |
+
|
7 |
+
logger = log.get_singletonish_logger()
|
8 |
+
|
9 |
+
tf_version = package_utils.get_tf_major_version()
|
10 |
+
|
11 |
+
if tf_version == 1:
|
12 |
+
from keras.models import Model
|
13 |
+
from keras.layers import (
|
14 |
+
Conv2D,
|
15 |
+
Activation,
|
16 |
+
Input,
|
17 |
+
Add,
|
18 |
+
MaxPooling2D,
|
19 |
+
Flatten,
|
20 |
+
Dense,
|
21 |
+
Dropout,
|
22 |
+
)
|
23 |
+
else:
|
24 |
+
from tensorflow.keras.models import Model
|
25 |
+
from tensorflow.keras.layers import (
|
26 |
+
Conv2D,
|
27 |
+
Activation,
|
28 |
+
Input,
|
29 |
+
Add,
|
30 |
+
MaxPooling2D,
|
31 |
+
Flatten,
|
32 |
+
Dense,
|
33 |
+
Dropout,
|
34 |
+
)
|
35 |
+
|
36 |
+
# pylint: disable=line-too-long
|
37 |
+
|
38 |
+
|
39 |
+
# -------------------------------------
|
40 |
+
|
41 |
+
# pylint: disable=too-few-public-methods
|
42 |
+
class DeepIdClient(FacialRecognition):
|
43 |
+
"""
|
44 |
+
DeepId model class
|
45 |
+
"""
|
46 |
+
|
47 |
+
def __init__(self):
|
48 |
+
self.model = load_model()
|
49 |
+
self.model_name = "DeepId"
|
50 |
+
self.input_shape = (47, 55)
|
51 |
+
self.output_shape = 160
|
52 |
+
|
53 |
+
|
54 |
+
def load_model(
|
55 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",
|
56 |
+
) -> Model:
|
57 |
+
"""
|
58 |
+
Construct DeepId model, download its weights and load
|
59 |
+
"""
|
60 |
+
|
61 |
+
myInput = Input(shape=(55, 47, 3))
|
62 |
+
|
63 |
+
x = Conv2D(20, (4, 4), name="Conv1", activation="relu", input_shape=(55, 47, 3))(myInput)
|
64 |
+
x = MaxPooling2D(pool_size=2, strides=2, name="Pool1")(x)
|
65 |
+
x = Dropout(rate=0.99, name="D1")(x)
|
66 |
+
|
67 |
+
x = Conv2D(40, (3, 3), name="Conv2", activation="relu")(x)
|
68 |
+
x = MaxPooling2D(pool_size=2, strides=2, name="Pool2")(x)
|
69 |
+
x = Dropout(rate=0.99, name="D2")(x)
|
70 |
+
|
71 |
+
x = Conv2D(60, (3, 3), name="Conv3", activation="relu")(x)
|
72 |
+
x = MaxPooling2D(pool_size=2, strides=2, name="Pool3")(x)
|
73 |
+
x = Dropout(rate=0.99, name="D3")(x)
|
74 |
+
|
75 |
+
x1 = Flatten()(x)
|
76 |
+
fc11 = Dense(160, name="fc11")(x1)
|
77 |
+
|
78 |
+
x2 = Conv2D(80, (2, 2), name="Conv4", activation="relu")(x)
|
79 |
+
x2 = Flatten()(x2)
|
80 |
+
fc12 = Dense(160, name="fc12")(x2)
|
81 |
+
|
82 |
+
y = Add()([fc11, fc12])
|
83 |
+
y = Activation("relu", name="deepid")(y)
|
84 |
+
|
85 |
+
model = Model(inputs=[myInput], outputs=y)
|
86 |
+
|
87 |
+
# ---------------------------------
|
88 |
+
|
89 |
+
home = folder_utils.get_deepface_home()
|
90 |
+
|
91 |
+
if os.path.isfile(home + "/.deepface/weights/deepid_keras_weights.h5") != True:
|
92 |
+
logger.info("deepid_keras_weights.h5 will be downloaded...")
|
93 |
+
|
94 |
+
output = home + "/.deepface/weights/deepid_keras_weights.h5"
|
95 |
+
gdown.download(url, output, quiet=False)
|
96 |
+
|
97 |
+
model.load_weights(home + "/.deepface/weights/deepid_keras_weights.h5")
|
98 |
+
|
99 |
+
return model
|
basemodels/Dlib.py
ADDED
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import List
|
2 |
+
import os
|
3 |
+
import bz2
|
4 |
+
import gdown
|
5 |
+
import numpy as np
|
6 |
+
from deepface.commons import folder_utils
|
7 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
8 |
+
from deepface.commons import logger as log
|
9 |
+
|
10 |
+
logger = log.get_singletonish_logger()
|
11 |
+
|
12 |
+
# pylint: disable=too-few-public-methods
|
13 |
+
|
14 |
+
|
15 |
+
class DlibClient(FacialRecognition):
|
16 |
+
"""
|
17 |
+
Dlib model class
|
18 |
+
"""
|
19 |
+
|
20 |
+
def __init__(self):
|
21 |
+
self.model = DlibResNet()
|
22 |
+
self.model_name = "Dlib"
|
23 |
+
self.input_shape = (150, 150)
|
24 |
+
self.output_shape = 128
|
25 |
+
|
26 |
+
def forward(self, img: np.ndarray) -> List[float]:
|
27 |
+
"""
|
28 |
+
Find embeddings with Dlib model.
|
29 |
+
This model necessitates the override of the forward method
|
30 |
+
because it is not a keras model.
|
31 |
+
Args:
|
32 |
+
img (np.ndarray): pre-loaded image in BGR
|
33 |
+
Returns
|
34 |
+
embeddings (list): multi-dimensional vector
|
35 |
+
"""
|
36 |
+
# return self.model.predict(img)[0].tolist()
|
37 |
+
|
38 |
+
# extract_faces returns 4 dimensional images
|
39 |
+
if len(img.shape) == 4:
|
40 |
+
img = img[0]
|
41 |
+
|
42 |
+
# bgr to rgb
|
43 |
+
img = img[:, :, ::-1] # bgr to rgb
|
44 |
+
|
45 |
+
# img is in scale of [0, 1] but expected [0, 255]
|
46 |
+
if img.max() <= 1:
|
47 |
+
img = img * 255
|
48 |
+
|
49 |
+
img = img.astype(np.uint8)
|
50 |
+
|
51 |
+
img_representation = self.model.model.compute_face_descriptor(img)
|
52 |
+
img_representation = np.array(img_representation)
|
53 |
+
img_representation = np.expand_dims(img_representation, axis=0)
|
54 |
+
return img_representation[0].tolist()
|
55 |
+
|
56 |
+
|
57 |
+
class DlibResNet:
|
58 |
+
def __init__(self):
|
59 |
+
|
60 |
+
## this is not a must dependency. do not import it in the global level.
|
61 |
+
try:
|
62 |
+
import dlib
|
63 |
+
except ModuleNotFoundError as e:
|
64 |
+
raise ImportError(
|
65 |
+
"Dlib is an optional dependency, ensure the library is installed."
|
66 |
+
"Please install using 'pip install dlib' "
|
67 |
+
) from e
|
68 |
+
|
69 |
+
home = folder_utils.get_deepface_home()
|
70 |
+
weight_file = home + "/.deepface/weights/dlib_face_recognition_resnet_model_v1.dat"
|
71 |
+
|
72 |
+
# download pre-trained model if it does not exist
|
73 |
+
if os.path.isfile(weight_file) != True:
|
74 |
+
logger.info("dlib_face_recognition_resnet_model_v1.dat is going to be downloaded")
|
75 |
+
|
76 |
+
file_name = "dlib_face_recognition_resnet_model_v1.dat.bz2"
|
77 |
+
url = f"http://dlib.net/files/{file_name}"
|
78 |
+
output = f"{home}/.deepface/weights/{file_name}"
|
79 |
+
gdown.download(url, output, quiet=False)
|
80 |
+
|
81 |
+
zipfile = bz2.BZ2File(output)
|
82 |
+
data = zipfile.read()
|
83 |
+
newfilepath = output[:-4] # discard .bz2 extension
|
84 |
+
with open(newfilepath, "wb") as f:
|
85 |
+
f.write(data)
|
86 |
+
|
87 |
+
self.model = dlib.face_recognition_model_v1(weight_file)
|
88 |
+
|
89 |
+
# return None # classes must return None
|
basemodels/Facenet.py
ADDED
@@ -0,0 +1,1715 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
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|
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|
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|
1 |
+
import os
|
2 |
+
import gdown
|
3 |
+
from deepface.commons import package_utils, folder_utils
|
4 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
5 |
+
from deepface.commons import logger as log
|
6 |
+
|
7 |
+
logger = log.get_singletonish_logger()
|
8 |
+
|
9 |
+
# --------------------------------
|
10 |
+
# dependency configuration
|
11 |
+
|
12 |
+
tf_version = package_utils.get_tf_major_version()
|
13 |
+
|
14 |
+
if tf_version == 1:
|
15 |
+
from keras.models import Model
|
16 |
+
from keras.layers import Activation
|
17 |
+
from keras.layers import BatchNormalization
|
18 |
+
from keras.layers import Concatenate
|
19 |
+
from keras.layers import Conv2D
|
20 |
+
from keras.layers import Dense
|
21 |
+
from keras.layers import Dropout
|
22 |
+
from keras.layers import GlobalAveragePooling2D
|
23 |
+
from keras.layers import Input
|
24 |
+
from keras.layers import Lambda
|
25 |
+
from keras.layers import MaxPooling2D
|
26 |
+
from keras.layers import add
|
27 |
+
from keras import backend as K
|
28 |
+
else:
|
29 |
+
from tensorflow.keras.models import Model
|
30 |
+
from tensorflow.keras.layers import Activation
|
31 |
+
from tensorflow.keras.layers import BatchNormalization
|
32 |
+
from tensorflow.keras.layers import Concatenate
|
33 |
+
from tensorflow.keras.layers import Conv2D
|
34 |
+
from tensorflow.keras.layers import Dense
|
35 |
+
from tensorflow.keras.layers import Dropout
|
36 |
+
from tensorflow.keras.layers import GlobalAveragePooling2D
|
37 |
+
from tensorflow.keras.layers import Input
|
38 |
+
from tensorflow.keras.layers import Lambda
|
39 |
+
from tensorflow.keras.layers import MaxPooling2D
|
40 |
+
from tensorflow.keras.layers import add
|
41 |
+
from tensorflow.keras import backend as K
|
42 |
+
|
43 |
+
# --------------------------------
|
44 |
+
|
45 |
+
# pylint: disable=too-few-public-methods
|
46 |
+
class FaceNet128dClient(FacialRecognition):
|
47 |
+
"""
|
48 |
+
FaceNet-128d model class
|
49 |
+
"""
|
50 |
+
|
51 |
+
def __init__(self):
|
52 |
+
self.model = load_facenet128d_model()
|
53 |
+
self.model_name = "FaceNet-128d"
|
54 |
+
self.input_shape = (160, 160)
|
55 |
+
self.output_shape = 128
|
56 |
+
|
57 |
+
|
58 |
+
class FaceNet512dClient(FacialRecognition):
|
59 |
+
"""
|
60 |
+
FaceNet-1512d model class
|
61 |
+
"""
|
62 |
+
|
63 |
+
def __init__(self):
|
64 |
+
self.model = load_facenet512d_model()
|
65 |
+
self.model_name = "FaceNet-512d"
|
66 |
+
self.input_shape = (160, 160)
|
67 |
+
self.output_shape = 512
|
68 |
+
|
69 |
+
|
70 |
+
def scaling(x, scale):
|
71 |
+
return x * scale
|
72 |
+
|
73 |
+
|
74 |
+
def InceptionResNetV1(dimension: int = 128) -> Model:
|
75 |
+
"""
|
76 |
+
InceptionResNetV1 model heavily inspired from
|
77 |
+
github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
|
78 |
+
As mentioned in Sandberg's repo's readme, pre-trained models are using Inception ResNet v1
|
79 |
+
Besides training process is documented at
|
80 |
+
sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/
|
81 |
+
|
82 |
+
Args:
|
83 |
+
dimension (int): number of dimensions in the embedding layer
|
84 |
+
Returns:
|
85 |
+
model (Model)
|
86 |
+
"""
|
87 |
+
|
88 |
+
inputs = Input(shape=(160, 160, 3))
|
89 |
+
x = Conv2D(32, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_1a_3x3")(inputs)
|
90 |
+
x = BatchNormalization(
|
91 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_1a_3x3_BatchNorm"
|
92 |
+
)(x)
|
93 |
+
x = Activation("relu", name="Conv2d_1a_3x3_Activation")(x)
|
94 |
+
x = Conv2D(32, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_2a_3x3")(x)
|
95 |
+
x = BatchNormalization(
|
96 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2a_3x3_BatchNorm"
|
97 |
+
)(x)
|
98 |
+
x = Activation("relu", name="Conv2d_2a_3x3_Activation")(x)
|
99 |
+
x = Conv2D(64, 3, strides=1, padding="same", use_bias=False, name="Conv2d_2b_3x3")(x)
|
100 |
+
x = BatchNormalization(
|
101 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2b_3x3_BatchNorm"
|
102 |
+
)(x)
|
103 |
+
x = Activation("relu", name="Conv2d_2b_3x3_Activation")(x)
|
104 |
+
x = MaxPooling2D(3, strides=2, name="MaxPool_3a_3x3")(x)
|
105 |
+
x = Conv2D(80, 1, strides=1, padding="valid", use_bias=False, name="Conv2d_3b_1x1")(x)
|
106 |
+
x = BatchNormalization(
|
107 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_3b_1x1_BatchNorm"
|
108 |
+
)(x)
|
109 |
+
x = Activation("relu", name="Conv2d_3b_1x1_Activation")(x)
|
110 |
+
x = Conv2D(192, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_4a_3x3")(x)
|
111 |
+
x = BatchNormalization(
|
112 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4a_3x3_BatchNorm"
|
113 |
+
)(x)
|
114 |
+
x = Activation("relu", name="Conv2d_4a_3x3_Activation")(x)
|
115 |
+
x = Conv2D(256, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_4b_3x3")(x)
|
116 |
+
x = BatchNormalization(
|
117 |
+
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4b_3x3_BatchNorm"
|
118 |
+
)(x)
|
119 |
+
x = Activation("relu", name="Conv2d_4b_3x3_Activation")(x)
|
120 |
+
|
121 |
+
# 5x Block35 (Inception-ResNet-A block):
|
122 |
+
branch_0 = Conv2D(
|
123 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_0_Conv2d_1x1"
|
124 |
+
)(x)
|
125 |
+
branch_0 = BatchNormalization(
|
126 |
+
axis=3,
|
127 |
+
momentum=0.995,
|
128 |
+
epsilon=0.001,
|
129 |
+
scale=False,
|
130 |
+
name="Block35_1_Branch_0_Conv2d_1x1_BatchNorm",
|
131 |
+
)(branch_0)
|
132 |
+
branch_0 = Activation("relu", name="Block35_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
133 |
+
branch_1 = Conv2D(
|
134 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0a_1x1"
|
135 |
+
)(x)
|
136 |
+
branch_1 = BatchNormalization(
|
137 |
+
axis=3,
|
138 |
+
momentum=0.995,
|
139 |
+
epsilon=0.001,
|
140 |
+
scale=False,
|
141 |
+
name="Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
142 |
+
)(branch_1)
|
143 |
+
branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
144 |
+
branch_1 = Conv2D(
|
145 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0b_3x3"
|
146 |
+
)(branch_1)
|
147 |
+
branch_1 = BatchNormalization(
|
148 |
+
axis=3,
|
149 |
+
momentum=0.995,
|
150 |
+
epsilon=0.001,
|
151 |
+
scale=False,
|
152 |
+
name="Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
153 |
+
)(branch_1)
|
154 |
+
branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
155 |
+
branch_2 = Conv2D(
|
156 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0a_1x1"
|
157 |
+
)(x)
|
158 |
+
branch_2 = BatchNormalization(
|
159 |
+
axis=3,
|
160 |
+
momentum=0.995,
|
161 |
+
epsilon=0.001,
|
162 |
+
scale=False,
|
163 |
+
name="Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
164 |
+
)(branch_2)
|
165 |
+
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
166 |
+
branch_2 = Conv2D(
|
167 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0b_3x3"
|
168 |
+
)(branch_2)
|
169 |
+
branch_2 = BatchNormalization(
|
170 |
+
axis=3,
|
171 |
+
momentum=0.995,
|
172 |
+
epsilon=0.001,
|
173 |
+
scale=False,
|
174 |
+
name="Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
175 |
+
)(branch_2)
|
176 |
+
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
177 |
+
branch_2 = Conv2D(
|
178 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0c_3x3"
|
179 |
+
)(branch_2)
|
180 |
+
branch_2 = BatchNormalization(
|
181 |
+
axis=3,
|
182 |
+
momentum=0.995,
|
183 |
+
epsilon=0.001,
|
184 |
+
scale=False,
|
185 |
+
name="Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
186 |
+
)(branch_2)
|
187 |
+
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
188 |
+
branches = [branch_0, branch_1, branch_2]
|
189 |
+
mixed = Concatenate(axis=3, name="Block35_1_Concatenate")(branches)
|
190 |
+
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_1_Conv2d_1x1")(
|
191 |
+
mixed
|
192 |
+
)
|
193 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
194 |
+
x = add([x, up])
|
195 |
+
x = Activation("relu", name="Block35_1_Activation")(x)
|
196 |
+
|
197 |
+
branch_0 = Conv2D(
|
198 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_0_Conv2d_1x1"
|
199 |
+
)(x)
|
200 |
+
branch_0 = BatchNormalization(
|
201 |
+
axis=3,
|
202 |
+
momentum=0.995,
|
203 |
+
epsilon=0.001,
|
204 |
+
scale=False,
|
205 |
+
name="Block35_2_Branch_0_Conv2d_1x1_BatchNorm",
|
206 |
+
)(branch_0)
|
207 |
+
branch_0 = Activation("relu", name="Block35_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
208 |
+
branch_1 = Conv2D(
|
209 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0a_1x1"
|
210 |
+
)(x)
|
211 |
+
branch_1 = BatchNormalization(
|
212 |
+
axis=3,
|
213 |
+
momentum=0.995,
|
214 |
+
epsilon=0.001,
|
215 |
+
scale=False,
|
216 |
+
name="Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
217 |
+
)(branch_1)
|
218 |
+
branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
219 |
+
branch_1 = Conv2D(
|
220 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0b_3x3"
|
221 |
+
)(branch_1)
|
222 |
+
branch_1 = BatchNormalization(
|
223 |
+
axis=3,
|
224 |
+
momentum=0.995,
|
225 |
+
epsilon=0.001,
|
226 |
+
scale=False,
|
227 |
+
name="Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
228 |
+
)(branch_1)
|
229 |
+
branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
230 |
+
branch_2 = Conv2D(
|
231 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0a_1x1"
|
232 |
+
)(x)
|
233 |
+
branch_2 = BatchNormalization(
|
234 |
+
axis=3,
|
235 |
+
momentum=0.995,
|
236 |
+
epsilon=0.001,
|
237 |
+
scale=False,
|
238 |
+
name="Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
239 |
+
)(branch_2)
|
240 |
+
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
241 |
+
branch_2 = Conv2D(
|
242 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0b_3x3"
|
243 |
+
)(branch_2)
|
244 |
+
branch_2 = BatchNormalization(
|
245 |
+
axis=3,
|
246 |
+
momentum=0.995,
|
247 |
+
epsilon=0.001,
|
248 |
+
scale=False,
|
249 |
+
name="Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
250 |
+
)(branch_2)
|
251 |
+
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
252 |
+
branch_2 = Conv2D(
|
253 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0c_3x3"
|
254 |
+
)(branch_2)
|
255 |
+
branch_2 = BatchNormalization(
|
256 |
+
axis=3,
|
257 |
+
momentum=0.995,
|
258 |
+
epsilon=0.001,
|
259 |
+
scale=False,
|
260 |
+
name="Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
261 |
+
)(branch_2)
|
262 |
+
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
263 |
+
branches = [branch_0, branch_1, branch_2]
|
264 |
+
mixed = Concatenate(axis=3, name="Block35_2_Concatenate")(branches)
|
265 |
+
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_2_Conv2d_1x1")(
|
266 |
+
mixed
|
267 |
+
)
|
268 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
269 |
+
x = add([x, up])
|
270 |
+
x = Activation("relu", name="Block35_2_Activation")(x)
|
271 |
+
|
272 |
+
branch_0 = Conv2D(
|
273 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_0_Conv2d_1x1"
|
274 |
+
)(x)
|
275 |
+
branch_0 = BatchNormalization(
|
276 |
+
axis=3,
|
277 |
+
momentum=0.995,
|
278 |
+
epsilon=0.001,
|
279 |
+
scale=False,
|
280 |
+
name="Block35_3_Branch_0_Conv2d_1x1_BatchNorm",
|
281 |
+
)(branch_0)
|
282 |
+
branch_0 = Activation("relu", name="Block35_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
283 |
+
branch_1 = Conv2D(
|
284 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0a_1x1"
|
285 |
+
)(x)
|
286 |
+
branch_1 = BatchNormalization(
|
287 |
+
axis=3,
|
288 |
+
momentum=0.995,
|
289 |
+
epsilon=0.001,
|
290 |
+
scale=False,
|
291 |
+
name="Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
292 |
+
)(branch_1)
|
293 |
+
branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
294 |
+
branch_1 = Conv2D(
|
295 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0b_3x3"
|
296 |
+
)(branch_1)
|
297 |
+
branch_1 = BatchNormalization(
|
298 |
+
axis=3,
|
299 |
+
momentum=0.995,
|
300 |
+
epsilon=0.001,
|
301 |
+
scale=False,
|
302 |
+
name="Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
303 |
+
)(branch_1)
|
304 |
+
branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
305 |
+
branch_2 = Conv2D(
|
306 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0a_1x1"
|
307 |
+
)(x)
|
308 |
+
branch_2 = BatchNormalization(
|
309 |
+
axis=3,
|
310 |
+
momentum=0.995,
|
311 |
+
epsilon=0.001,
|
312 |
+
scale=False,
|
313 |
+
name="Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
314 |
+
)(branch_2)
|
315 |
+
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
316 |
+
branch_2 = Conv2D(
|
317 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0b_3x3"
|
318 |
+
)(branch_2)
|
319 |
+
branch_2 = BatchNormalization(
|
320 |
+
axis=3,
|
321 |
+
momentum=0.995,
|
322 |
+
epsilon=0.001,
|
323 |
+
scale=False,
|
324 |
+
name="Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
325 |
+
)(branch_2)
|
326 |
+
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
327 |
+
branch_2 = Conv2D(
|
328 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0c_3x3"
|
329 |
+
)(branch_2)
|
330 |
+
branch_2 = BatchNormalization(
|
331 |
+
axis=3,
|
332 |
+
momentum=0.995,
|
333 |
+
epsilon=0.001,
|
334 |
+
scale=False,
|
335 |
+
name="Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
336 |
+
)(branch_2)
|
337 |
+
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
338 |
+
branches = [branch_0, branch_1, branch_2]
|
339 |
+
mixed = Concatenate(axis=3, name="Block35_3_Concatenate")(branches)
|
340 |
+
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_3_Conv2d_1x1")(
|
341 |
+
mixed
|
342 |
+
)
|
343 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
344 |
+
x = add([x, up])
|
345 |
+
x = Activation("relu", name="Block35_3_Activation")(x)
|
346 |
+
|
347 |
+
branch_0 = Conv2D(
|
348 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_0_Conv2d_1x1"
|
349 |
+
)(x)
|
350 |
+
branch_0 = BatchNormalization(
|
351 |
+
axis=3,
|
352 |
+
momentum=0.995,
|
353 |
+
epsilon=0.001,
|
354 |
+
scale=False,
|
355 |
+
name="Block35_4_Branch_0_Conv2d_1x1_BatchNorm",
|
356 |
+
)(branch_0)
|
357 |
+
branch_0 = Activation("relu", name="Block35_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
358 |
+
branch_1 = Conv2D(
|
359 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0a_1x1"
|
360 |
+
)(x)
|
361 |
+
branch_1 = BatchNormalization(
|
362 |
+
axis=3,
|
363 |
+
momentum=0.995,
|
364 |
+
epsilon=0.001,
|
365 |
+
scale=False,
|
366 |
+
name="Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
367 |
+
)(branch_1)
|
368 |
+
branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
369 |
+
branch_1 = Conv2D(
|
370 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0b_3x3"
|
371 |
+
)(branch_1)
|
372 |
+
branch_1 = BatchNormalization(
|
373 |
+
axis=3,
|
374 |
+
momentum=0.995,
|
375 |
+
epsilon=0.001,
|
376 |
+
scale=False,
|
377 |
+
name="Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
378 |
+
)(branch_1)
|
379 |
+
branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
380 |
+
branch_2 = Conv2D(
|
381 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0a_1x1"
|
382 |
+
)(x)
|
383 |
+
branch_2 = BatchNormalization(
|
384 |
+
axis=3,
|
385 |
+
momentum=0.995,
|
386 |
+
epsilon=0.001,
|
387 |
+
scale=False,
|
388 |
+
name="Block35_4_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
389 |
+
)(branch_2)
|
390 |
+
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
391 |
+
branch_2 = Conv2D(
|
392 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0b_3x3"
|
393 |
+
)(branch_2)
|
394 |
+
branch_2 = BatchNormalization(
|
395 |
+
axis=3,
|
396 |
+
momentum=0.995,
|
397 |
+
epsilon=0.001,
|
398 |
+
scale=False,
|
399 |
+
name="Block35_4_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
400 |
+
)(branch_2)
|
401 |
+
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
402 |
+
branch_2 = Conv2D(
|
403 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0c_3x3"
|
404 |
+
)(branch_2)
|
405 |
+
branch_2 = BatchNormalization(
|
406 |
+
axis=3,
|
407 |
+
momentum=0.995,
|
408 |
+
epsilon=0.001,
|
409 |
+
scale=False,
|
410 |
+
name="Block35_4_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
411 |
+
)(branch_2)
|
412 |
+
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
413 |
+
branches = [branch_0, branch_1, branch_2]
|
414 |
+
mixed = Concatenate(axis=3, name="Block35_4_Concatenate")(branches)
|
415 |
+
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_4_Conv2d_1x1")(
|
416 |
+
mixed
|
417 |
+
)
|
418 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
419 |
+
x = add([x, up])
|
420 |
+
x = Activation("relu", name="Block35_4_Activation")(x)
|
421 |
+
|
422 |
+
branch_0 = Conv2D(
|
423 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_0_Conv2d_1x1"
|
424 |
+
)(x)
|
425 |
+
branch_0 = BatchNormalization(
|
426 |
+
axis=3,
|
427 |
+
momentum=0.995,
|
428 |
+
epsilon=0.001,
|
429 |
+
scale=False,
|
430 |
+
name="Block35_5_Branch_0_Conv2d_1x1_BatchNorm",
|
431 |
+
)(branch_0)
|
432 |
+
branch_0 = Activation("relu", name="Block35_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
433 |
+
branch_1 = Conv2D(
|
434 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_1_Conv2d_0a_1x1"
|
435 |
+
)(x)
|
436 |
+
branch_1 = BatchNormalization(
|
437 |
+
axis=3,
|
438 |
+
momentum=0.995,
|
439 |
+
epsilon=0.001,
|
440 |
+
scale=False,
|
441 |
+
name="Block35_5_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
442 |
+
)(branch_1)
|
443 |
+
branch_1 = Activation("relu", name="Block35_5_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
444 |
+
branch_1 = Conv2D(
|
445 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_1_Conv2d_0b_3x3"
|
446 |
+
)(branch_1)
|
447 |
+
branch_1 = BatchNormalization(
|
448 |
+
axis=3,
|
449 |
+
momentum=0.995,
|
450 |
+
epsilon=0.001,
|
451 |
+
scale=False,
|
452 |
+
name="Block35_5_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
453 |
+
)(branch_1)
|
454 |
+
branch_1 = Activation("relu", name="Block35_5_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
455 |
+
branch_2 = Conv2D(
|
456 |
+
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0a_1x1"
|
457 |
+
)(x)
|
458 |
+
branch_2 = BatchNormalization(
|
459 |
+
axis=3,
|
460 |
+
momentum=0.995,
|
461 |
+
epsilon=0.001,
|
462 |
+
scale=False,
|
463 |
+
name="Block35_5_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
464 |
+
)(branch_2)
|
465 |
+
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
466 |
+
branch_2 = Conv2D(
|
467 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0b_3x3"
|
468 |
+
)(branch_2)
|
469 |
+
branch_2 = BatchNormalization(
|
470 |
+
axis=3,
|
471 |
+
momentum=0.995,
|
472 |
+
epsilon=0.001,
|
473 |
+
scale=False,
|
474 |
+
name="Block35_5_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
475 |
+
)(branch_2)
|
476 |
+
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
477 |
+
branch_2 = Conv2D(
|
478 |
+
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0c_3x3"
|
479 |
+
)(branch_2)
|
480 |
+
branch_2 = BatchNormalization(
|
481 |
+
axis=3,
|
482 |
+
momentum=0.995,
|
483 |
+
epsilon=0.001,
|
484 |
+
scale=False,
|
485 |
+
name="Block35_5_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
486 |
+
)(branch_2)
|
487 |
+
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
488 |
+
branches = [branch_0, branch_1, branch_2]
|
489 |
+
mixed = Concatenate(axis=3, name="Block35_5_Concatenate")(branches)
|
490 |
+
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_5_Conv2d_1x1")(
|
491 |
+
mixed
|
492 |
+
)
|
493 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
494 |
+
x = add([x, up])
|
495 |
+
x = Activation("relu", name="Block35_5_Activation")(x)
|
496 |
+
|
497 |
+
# Mixed 6a (Reduction-A block):
|
498 |
+
branch_0 = Conv2D(
|
499 |
+
384, 3, strides=2, padding="valid", use_bias=False, name="Mixed_6a_Branch_0_Conv2d_1a_3x3"
|
500 |
+
)(x)
|
501 |
+
branch_0 = BatchNormalization(
|
502 |
+
axis=3,
|
503 |
+
momentum=0.995,
|
504 |
+
epsilon=0.001,
|
505 |
+
scale=False,
|
506 |
+
name="Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm",
|
507 |
+
)(branch_0)
|
508 |
+
branch_0 = Activation("relu", name="Mixed_6a_Branch_0_Conv2d_1a_3x3_Activation")(branch_0)
|
509 |
+
branch_1 = Conv2D(
|
510 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_0a_1x1"
|
511 |
+
)(x)
|
512 |
+
branch_1 = BatchNormalization(
|
513 |
+
axis=3,
|
514 |
+
momentum=0.995,
|
515 |
+
epsilon=0.001,
|
516 |
+
scale=False,
|
517 |
+
name="Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
518 |
+
)(branch_1)
|
519 |
+
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
520 |
+
branch_1 = Conv2D(
|
521 |
+
192, 3, strides=1, padding="same", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_0b_3x3"
|
522 |
+
)(branch_1)
|
523 |
+
branch_1 = BatchNormalization(
|
524 |
+
axis=3,
|
525 |
+
momentum=0.995,
|
526 |
+
epsilon=0.001,
|
527 |
+
scale=False,
|
528 |
+
name="Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
529 |
+
)(branch_1)
|
530 |
+
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
531 |
+
branch_1 = Conv2D(
|
532 |
+
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_1a_3x3"
|
533 |
+
)(branch_1)
|
534 |
+
branch_1 = BatchNormalization(
|
535 |
+
axis=3,
|
536 |
+
momentum=0.995,
|
537 |
+
epsilon=0.001,
|
538 |
+
scale=False,
|
539 |
+
name="Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm",
|
540 |
+
)(branch_1)
|
541 |
+
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_1a_3x3_Activation")(branch_1)
|
542 |
+
branch_pool = MaxPooling2D(
|
543 |
+
3, strides=2, padding="valid", name="Mixed_6a_Branch_2_MaxPool_1a_3x3"
|
544 |
+
)(x)
|
545 |
+
branches = [branch_0, branch_1, branch_pool]
|
546 |
+
x = Concatenate(axis=3, name="Mixed_6a")(branches)
|
547 |
+
|
548 |
+
# 10x Block17 (Inception-ResNet-B block):
|
549 |
+
branch_0 = Conv2D(
|
550 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_1_Branch_0_Conv2d_1x1"
|
551 |
+
)(x)
|
552 |
+
branch_0 = BatchNormalization(
|
553 |
+
axis=3,
|
554 |
+
momentum=0.995,
|
555 |
+
epsilon=0.001,
|
556 |
+
scale=False,
|
557 |
+
name="Block17_1_Branch_0_Conv2d_1x1_BatchNorm",
|
558 |
+
)(branch_0)
|
559 |
+
branch_0 = Activation("relu", name="Block17_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
560 |
+
branch_1 = Conv2D(
|
561 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_1_Branch_1_Conv2d_0a_1x1"
|
562 |
+
)(x)
|
563 |
+
branch_1 = BatchNormalization(
|
564 |
+
axis=3,
|
565 |
+
momentum=0.995,
|
566 |
+
epsilon=0.001,
|
567 |
+
scale=False,
|
568 |
+
name="Block17_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
569 |
+
)(branch_1)
|
570 |
+
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
571 |
+
branch_1 = Conv2D(
|
572 |
+
128,
|
573 |
+
[1, 7],
|
574 |
+
strides=1,
|
575 |
+
padding="same",
|
576 |
+
use_bias=False,
|
577 |
+
name="Block17_1_Branch_1_Conv2d_0b_1x7",
|
578 |
+
)(branch_1)
|
579 |
+
branch_1 = BatchNormalization(
|
580 |
+
axis=3,
|
581 |
+
momentum=0.995,
|
582 |
+
epsilon=0.001,
|
583 |
+
scale=False,
|
584 |
+
name="Block17_1_Branch_1_Conv2d_0b_1x7_BatchNorm",
|
585 |
+
)(branch_1)
|
586 |
+
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0b_1x7_Activation")(branch_1)
|
587 |
+
branch_1 = Conv2D(
|
588 |
+
128,
|
589 |
+
[7, 1],
|
590 |
+
strides=1,
|
591 |
+
padding="same",
|
592 |
+
use_bias=False,
|
593 |
+
name="Block17_1_Branch_1_Conv2d_0c_7x1",
|
594 |
+
)(branch_1)
|
595 |
+
branch_1 = BatchNormalization(
|
596 |
+
axis=3,
|
597 |
+
momentum=0.995,
|
598 |
+
epsilon=0.001,
|
599 |
+
scale=False,
|
600 |
+
name="Block17_1_Branch_1_Conv2d_0c_7x1_BatchNorm",
|
601 |
+
)(branch_1)
|
602 |
+
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0c_7x1_Activation")(branch_1)
|
603 |
+
branches = [branch_0, branch_1]
|
604 |
+
mixed = Concatenate(axis=3, name="Block17_1_Concatenate")(branches)
|
605 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_1_Conv2d_1x1")(
|
606 |
+
mixed
|
607 |
+
)
|
608 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
609 |
+
x = add([x, up])
|
610 |
+
x = Activation("relu", name="Block17_1_Activation")(x)
|
611 |
+
|
612 |
+
branch_0 = Conv2D(
|
613 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_2_Branch_0_Conv2d_1x1"
|
614 |
+
)(x)
|
615 |
+
branch_0 = BatchNormalization(
|
616 |
+
axis=3,
|
617 |
+
momentum=0.995,
|
618 |
+
epsilon=0.001,
|
619 |
+
scale=False,
|
620 |
+
name="Block17_2_Branch_0_Conv2d_1x1_BatchNorm",
|
621 |
+
)(branch_0)
|
622 |
+
branch_0 = Activation("relu", name="Block17_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
623 |
+
branch_1 = Conv2D(
|
624 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_2_Branch_2_Conv2d_0a_1x1"
|
625 |
+
)(x)
|
626 |
+
branch_1 = BatchNormalization(
|
627 |
+
axis=3,
|
628 |
+
momentum=0.995,
|
629 |
+
epsilon=0.001,
|
630 |
+
scale=False,
|
631 |
+
name="Block17_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
632 |
+
)(branch_1)
|
633 |
+
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_1)
|
634 |
+
branch_1 = Conv2D(
|
635 |
+
128,
|
636 |
+
[1, 7],
|
637 |
+
strides=1,
|
638 |
+
padding="same",
|
639 |
+
use_bias=False,
|
640 |
+
name="Block17_2_Branch_2_Conv2d_0b_1x7",
|
641 |
+
)(branch_1)
|
642 |
+
branch_1 = BatchNormalization(
|
643 |
+
axis=3,
|
644 |
+
momentum=0.995,
|
645 |
+
epsilon=0.001,
|
646 |
+
scale=False,
|
647 |
+
name="Block17_2_Branch_2_Conv2d_0b_1x7_BatchNorm",
|
648 |
+
)(branch_1)
|
649 |
+
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0b_1x7_Activation")(branch_1)
|
650 |
+
branch_1 = Conv2D(
|
651 |
+
128,
|
652 |
+
[7, 1],
|
653 |
+
strides=1,
|
654 |
+
padding="same",
|
655 |
+
use_bias=False,
|
656 |
+
name="Block17_2_Branch_2_Conv2d_0c_7x1",
|
657 |
+
)(branch_1)
|
658 |
+
branch_1 = BatchNormalization(
|
659 |
+
axis=3,
|
660 |
+
momentum=0.995,
|
661 |
+
epsilon=0.001,
|
662 |
+
scale=False,
|
663 |
+
name="Block17_2_Branch_2_Conv2d_0c_7x1_BatchNorm",
|
664 |
+
)(branch_1)
|
665 |
+
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0c_7x1_Activation")(branch_1)
|
666 |
+
branches = [branch_0, branch_1]
|
667 |
+
mixed = Concatenate(axis=3, name="Block17_2_Concatenate")(branches)
|
668 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_2_Conv2d_1x1")(
|
669 |
+
mixed
|
670 |
+
)
|
671 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
672 |
+
x = add([x, up])
|
673 |
+
x = Activation("relu", name="Block17_2_Activation")(x)
|
674 |
+
|
675 |
+
branch_0 = Conv2D(
|
676 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_3_Branch_0_Conv2d_1x1"
|
677 |
+
)(x)
|
678 |
+
branch_0 = BatchNormalization(
|
679 |
+
axis=3,
|
680 |
+
momentum=0.995,
|
681 |
+
epsilon=0.001,
|
682 |
+
scale=False,
|
683 |
+
name="Block17_3_Branch_0_Conv2d_1x1_BatchNorm",
|
684 |
+
)(branch_0)
|
685 |
+
branch_0 = Activation("relu", name="Block17_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
686 |
+
branch_1 = Conv2D(
|
687 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_3_Branch_3_Conv2d_0a_1x1"
|
688 |
+
)(x)
|
689 |
+
branch_1 = BatchNormalization(
|
690 |
+
axis=3,
|
691 |
+
momentum=0.995,
|
692 |
+
epsilon=0.001,
|
693 |
+
scale=False,
|
694 |
+
name="Block17_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
|
695 |
+
)(branch_1)
|
696 |
+
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0a_1x1_Activation")(branch_1)
|
697 |
+
branch_1 = Conv2D(
|
698 |
+
128,
|
699 |
+
[1, 7],
|
700 |
+
strides=1,
|
701 |
+
padding="same",
|
702 |
+
use_bias=False,
|
703 |
+
name="Block17_3_Branch_3_Conv2d_0b_1x7",
|
704 |
+
)(branch_1)
|
705 |
+
branch_1 = BatchNormalization(
|
706 |
+
axis=3,
|
707 |
+
momentum=0.995,
|
708 |
+
epsilon=0.001,
|
709 |
+
scale=False,
|
710 |
+
name="Block17_3_Branch_3_Conv2d_0b_1x7_BatchNorm",
|
711 |
+
)(branch_1)
|
712 |
+
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0b_1x7_Activation")(branch_1)
|
713 |
+
branch_1 = Conv2D(
|
714 |
+
128,
|
715 |
+
[7, 1],
|
716 |
+
strides=1,
|
717 |
+
padding="same",
|
718 |
+
use_bias=False,
|
719 |
+
name="Block17_3_Branch_3_Conv2d_0c_7x1",
|
720 |
+
)(branch_1)
|
721 |
+
branch_1 = BatchNormalization(
|
722 |
+
axis=3,
|
723 |
+
momentum=0.995,
|
724 |
+
epsilon=0.001,
|
725 |
+
scale=False,
|
726 |
+
name="Block17_3_Branch_3_Conv2d_0c_7x1_BatchNorm",
|
727 |
+
)(branch_1)
|
728 |
+
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0c_7x1_Activation")(branch_1)
|
729 |
+
branches = [branch_0, branch_1]
|
730 |
+
mixed = Concatenate(axis=3, name="Block17_3_Concatenate")(branches)
|
731 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_3_Conv2d_1x1")(
|
732 |
+
mixed
|
733 |
+
)
|
734 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
735 |
+
x = add([x, up])
|
736 |
+
x = Activation("relu", name="Block17_3_Activation")(x)
|
737 |
+
|
738 |
+
branch_0 = Conv2D(
|
739 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_4_Branch_0_Conv2d_1x1"
|
740 |
+
)(x)
|
741 |
+
branch_0 = BatchNormalization(
|
742 |
+
axis=3,
|
743 |
+
momentum=0.995,
|
744 |
+
epsilon=0.001,
|
745 |
+
scale=False,
|
746 |
+
name="Block17_4_Branch_0_Conv2d_1x1_BatchNorm",
|
747 |
+
)(branch_0)
|
748 |
+
branch_0 = Activation("relu", name="Block17_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
749 |
+
branch_1 = Conv2D(
|
750 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_4_Branch_4_Conv2d_0a_1x1"
|
751 |
+
)(x)
|
752 |
+
branch_1 = BatchNormalization(
|
753 |
+
axis=3,
|
754 |
+
momentum=0.995,
|
755 |
+
epsilon=0.001,
|
756 |
+
scale=False,
|
757 |
+
name="Block17_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
|
758 |
+
)(branch_1)
|
759 |
+
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0a_1x1_Activation")(branch_1)
|
760 |
+
branch_1 = Conv2D(
|
761 |
+
128,
|
762 |
+
[1, 7],
|
763 |
+
strides=1,
|
764 |
+
padding="same",
|
765 |
+
use_bias=False,
|
766 |
+
name="Block17_4_Branch_4_Conv2d_0b_1x7",
|
767 |
+
)(branch_1)
|
768 |
+
branch_1 = BatchNormalization(
|
769 |
+
axis=3,
|
770 |
+
momentum=0.995,
|
771 |
+
epsilon=0.001,
|
772 |
+
scale=False,
|
773 |
+
name="Block17_4_Branch_4_Conv2d_0b_1x7_BatchNorm",
|
774 |
+
)(branch_1)
|
775 |
+
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0b_1x7_Activation")(branch_1)
|
776 |
+
branch_1 = Conv2D(
|
777 |
+
128,
|
778 |
+
[7, 1],
|
779 |
+
strides=1,
|
780 |
+
padding="same",
|
781 |
+
use_bias=False,
|
782 |
+
name="Block17_4_Branch_4_Conv2d_0c_7x1",
|
783 |
+
)(branch_1)
|
784 |
+
branch_1 = BatchNormalization(
|
785 |
+
axis=3,
|
786 |
+
momentum=0.995,
|
787 |
+
epsilon=0.001,
|
788 |
+
scale=False,
|
789 |
+
name="Block17_4_Branch_4_Conv2d_0c_7x1_BatchNorm",
|
790 |
+
)(branch_1)
|
791 |
+
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0c_7x1_Activation")(branch_1)
|
792 |
+
branches = [branch_0, branch_1]
|
793 |
+
mixed = Concatenate(axis=3, name="Block17_4_Concatenate")(branches)
|
794 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_4_Conv2d_1x1")(
|
795 |
+
mixed
|
796 |
+
)
|
797 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
798 |
+
x = add([x, up])
|
799 |
+
x = Activation("relu", name="Block17_4_Activation")(x)
|
800 |
+
|
801 |
+
branch_0 = Conv2D(
|
802 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_5_Branch_0_Conv2d_1x1"
|
803 |
+
)(x)
|
804 |
+
branch_0 = BatchNormalization(
|
805 |
+
axis=3,
|
806 |
+
momentum=0.995,
|
807 |
+
epsilon=0.001,
|
808 |
+
scale=False,
|
809 |
+
name="Block17_5_Branch_0_Conv2d_1x1_BatchNorm",
|
810 |
+
)(branch_0)
|
811 |
+
branch_0 = Activation("relu", name="Block17_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
812 |
+
branch_1 = Conv2D(
|
813 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_5_Branch_5_Conv2d_0a_1x1"
|
814 |
+
)(x)
|
815 |
+
branch_1 = BatchNormalization(
|
816 |
+
axis=3,
|
817 |
+
momentum=0.995,
|
818 |
+
epsilon=0.001,
|
819 |
+
scale=False,
|
820 |
+
name="Block17_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
|
821 |
+
)(branch_1)
|
822 |
+
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0a_1x1_Activation")(branch_1)
|
823 |
+
branch_1 = Conv2D(
|
824 |
+
128,
|
825 |
+
[1, 7],
|
826 |
+
strides=1,
|
827 |
+
padding="same",
|
828 |
+
use_bias=False,
|
829 |
+
name="Block17_5_Branch_5_Conv2d_0b_1x7",
|
830 |
+
)(branch_1)
|
831 |
+
branch_1 = BatchNormalization(
|
832 |
+
axis=3,
|
833 |
+
momentum=0.995,
|
834 |
+
epsilon=0.001,
|
835 |
+
scale=False,
|
836 |
+
name="Block17_5_Branch_5_Conv2d_0b_1x7_BatchNorm",
|
837 |
+
)(branch_1)
|
838 |
+
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0b_1x7_Activation")(branch_1)
|
839 |
+
branch_1 = Conv2D(
|
840 |
+
128,
|
841 |
+
[7, 1],
|
842 |
+
strides=1,
|
843 |
+
padding="same",
|
844 |
+
use_bias=False,
|
845 |
+
name="Block17_5_Branch_5_Conv2d_0c_7x1",
|
846 |
+
)(branch_1)
|
847 |
+
branch_1 = BatchNormalization(
|
848 |
+
axis=3,
|
849 |
+
momentum=0.995,
|
850 |
+
epsilon=0.001,
|
851 |
+
scale=False,
|
852 |
+
name="Block17_5_Branch_5_Conv2d_0c_7x1_BatchNorm",
|
853 |
+
)(branch_1)
|
854 |
+
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0c_7x1_Activation")(branch_1)
|
855 |
+
branches = [branch_0, branch_1]
|
856 |
+
mixed = Concatenate(axis=3, name="Block17_5_Concatenate")(branches)
|
857 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_5_Conv2d_1x1")(
|
858 |
+
mixed
|
859 |
+
)
|
860 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
861 |
+
x = add([x, up])
|
862 |
+
x = Activation("relu", name="Block17_5_Activation")(x)
|
863 |
+
|
864 |
+
branch_0 = Conv2D(
|
865 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_6_Branch_0_Conv2d_1x1"
|
866 |
+
)(x)
|
867 |
+
branch_0 = BatchNormalization(
|
868 |
+
axis=3,
|
869 |
+
momentum=0.995,
|
870 |
+
epsilon=0.001,
|
871 |
+
scale=False,
|
872 |
+
name="Block17_6_Branch_0_Conv2d_1x1_BatchNorm",
|
873 |
+
)(branch_0)
|
874 |
+
branch_0 = Activation("relu", name="Block17_6_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
875 |
+
branch_1 = Conv2D(
|
876 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_6_Branch_6_Conv2d_0a_1x1"
|
877 |
+
)(x)
|
878 |
+
branch_1 = BatchNormalization(
|
879 |
+
axis=3,
|
880 |
+
momentum=0.995,
|
881 |
+
epsilon=0.001,
|
882 |
+
scale=False,
|
883 |
+
name="Block17_6_Branch_6_Conv2d_0a_1x1_BatchNorm",
|
884 |
+
)(branch_1)
|
885 |
+
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0a_1x1_Activation")(branch_1)
|
886 |
+
branch_1 = Conv2D(
|
887 |
+
128,
|
888 |
+
[1, 7],
|
889 |
+
strides=1,
|
890 |
+
padding="same",
|
891 |
+
use_bias=False,
|
892 |
+
name="Block17_6_Branch_6_Conv2d_0b_1x7",
|
893 |
+
)(branch_1)
|
894 |
+
branch_1 = BatchNormalization(
|
895 |
+
axis=3,
|
896 |
+
momentum=0.995,
|
897 |
+
epsilon=0.001,
|
898 |
+
scale=False,
|
899 |
+
name="Block17_6_Branch_6_Conv2d_0b_1x7_BatchNorm",
|
900 |
+
)(branch_1)
|
901 |
+
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0b_1x7_Activation")(branch_1)
|
902 |
+
branch_1 = Conv2D(
|
903 |
+
128,
|
904 |
+
[7, 1],
|
905 |
+
strides=1,
|
906 |
+
padding="same",
|
907 |
+
use_bias=False,
|
908 |
+
name="Block17_6_Branch_6_Conv2d_0c_7x1",
|
909 |
+
)(branch_1)
|
910 |
+
branch_1 = BatchNormalization(
|
911 |
+
axis=3,
|
912 |
+
momentum=0.995,
|
913 |
+
epsilon=0.001,
|
914 |
+
scale=False,
|
915 |
+
name="Block17_6_Branch_6_Conv2d_0c_7x1_BatchNorm",
|
916 |
+
)(branch_1)
|
917 |
+
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0c_7x1_Activation")(branch_1)
|
918 |
+
branches = [branch_0, branch_1]
|
919 |
+
mixed = Concatenate(axis=3, name="Block17_6_Concatenate")(branches)
|
920 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_6_Conv2d_1x1")(
|
921 |
+
mixed
|
922 |
+
)
|
923 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
924 |
+
x = add([x, up])
|
925 |
+
x = Activation("relu", name="Block17_6_Activation")(x)
|
926 |
+
|
927 |
+
branch_0 = Conv2D(
|
928 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_7_Branch_0_Conv2d_1x1"
|
929 |
+
)(x)
|
930 |
+
branch_0 = BatchNormalization(
|
931 |
+
axis=3,
|
932 |
+
momentum=0.995,
|
933 |
+
epsilon=0.001,
|
934 |
+
scale=False,
|
935 |
+
name="Block17_7_Branch_0_Conv2d_1x1_BatchNorm",
|
936 |
+
)(branch_0)
|
937 |
+
branch_0 = Activation("relu", name="Block17_7_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
938 |
+
branch_1 = Conv2D(
|
939 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_7_Branch_7_Conv2d_0a_1x1"
|
940 |
+
)(x)
|
941 |
+
branch_1 = BatchNormalization(
|
942 |
+
axis=3,
|
943 |
+
momentum=0.995,
|
944 |
+
epsilon=0.001,
|
945 |
+
scale=False,
|
946 |
+
name="Block17_7_Branch_7_Conv2d_0a_1x1_BatchNorm",
|
947 |
+
)(branch_1)
|
948 |
+
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0a_1x1_Activation")(branch_1)
|
949 |
+
branch_1 = Conv2D(
|
950 |
+
128,
|
951 |
+
[1, 7],
|
952 |
+
strides=1,
|
953 |
+
padding="same",
|
954 |
+
use_bias=False,
|
955 |
+
name="Block17_7_Branch_7_Conv2d_0b_1x7",
|
956 |
+
)(branch_1)
|
957 |
+
branch_1 = BatchNormalization(
|
958 |
+
axis=3,
|
959 |
+
momentum=0.995,
|
960 |
+
epsilon=0.001,
|
961 |
+
scale=False,
|
962 |
+
name="Block17_7_Branch_7_Conv2d_0b_1x7_BatchNorm",
|
963 |
+
)(branch_1)
|
964 |
+
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0b_1x7_Activation")(branch_1)
|
965 |
+
branch_1 = Conv2D(
|
966 |
+
128,
|
967 |
+
[7, 1],
|
968 |
+
strides=1,
|
969 |
+
padding="same",
|
970 |
+
use_bias=False,
|
971 |
+
name="Block17_7_Branch_7_Conv2d_0c_7x1",
|
972 |
+
)(branch_1)
|
973 |
+
branch_1 = BatchNormalization(
|
974 |
+
axis=3,
|
975 |
+
momentum=0.995,
|
976 |
+
epsilon=0.001,
|
977 |
+
scale=False,
|
978 |
+
name="Block17_7_Branch_7_Conv2d_0c_7x1_BatchNorm",
|
979 |
+
)(branch_1)
|
980 |
+
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0c_7x1_Activation")(branch_1)
|
981 |
+
branches = [branch_0, branch_1]
|
982 |
+
mixed = Concatenate(axis=3, name="Block17_7_Concatenate")(branches)
|
983 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_7_Conv2d_1x1")(
|
984 |
+
mixed
|
985 |
+
)
|
986 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
987 |
+
x = add([x, up])
|
988 |
+
x = Activation("relu", name="Block17_7_Activation")(x)
|
989 |
+
|
990 |
+
branch_0 = Conv2D(
|
991 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_8_Branch_0_Conv2d_1x1"
|
992 |
+
)(x)
|
993 |
+
branch_0 = BatchNormalization(
|
994 |
+
axis=3,
|
995 |
+
momentum=0.995,
|
996 |
+
epsilon=0.001,
|
997 |
+
scale=False,
|
998 |
+
name="Block17_8_Branch_0_Conv2d_1x1_BatchNorm",
|
999 |
+
)(branch_0)
|
1000 |
+
branch_0 = Activation("relu", name="Block17_8_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1001 |
+
branch_1 = Conv2D(
|
1002 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_8_Branch_8_Conv2d_0a_1x1"
|
1003 |
+
)(x)
|
1004 |
+
branch_1 = BatchNormalization(
|
1005 |
+
axis=3,
|
1006 |
+
momentum=0.995,
|
1007 |
+
epsilon=0.001,
|
1008 |
+
scale=False,
|
1009 |
+
name="Block17_8_Branch_8_Conv2d_0a_1x1_BatchNorm",
|
1010 |
+
)(branch_1)
|
1011 |
+
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0a_1x1_Activation")(branch_1)
|
1012 |
+
branch_1 = Conv2D(
|
1013 |
+
128,
|
1014 |
+
[1, 7],
|
1015 |
+
strides=1,
|
1016 |
+
padding="same",
|
1017 |
+
use_bias=False,
|
1018 |
+
name="Block17_8_Branch_8_Conv2d_0b_1x7",
|
1019 |
+
)(branch_1)
|
1020 |
+
branch_1 = BatchNormalization(
|
1021 |
+
axis=3,
|
1022 |
+
momentum=0.995,
|
1023 |
+
epsilon=0.001,
|
1024 |
+
scale=False,
|
1025 |
+
name="Block17_8_Branch_8_Conv2d_0b_1x7_BatchNorm",
|
1026 |
+
)(branch_1)
|
1027 |
+
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0b_1x7_Activation")(branch_1)
|
1028 |
+
branch_1 = Conv2D(
|
1029 |
+
128,
|
1030 |
+
[7, 1],
|
1031 |
+
strides=1,
|
1032 |
+
padding="same",
|
1033 |
+
use_bias=False,
|
1034 |
+
name="Block17_8_Branch_8_Conv2d_0c_7x1",
|
1035 |
+
)(branch_1)
|
1036 |
+
branch_1 = BatchNormalization(
|
1037 |
+
axis=3,
|
1038 |
+
momentum=0.995,
|
1039 |
+
epsilon=0.001,
|
1040 |
+
scale=False,
|
1041 |
+
name="Block17_8_Branch_8_Conv2d_0c_7x1_BatchNorm",
|
1042 |
+
)(branch_1)
|
1043 |
+
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0c_7x1_Activation")(branch_1)
|
1044 |
+
branches = [branch_0, branch_1]
|
1045 |
+
mixed = Concatenate(axis=3, name="Block17_8_Concatenate")(branches)
|
1046 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_8_Conv2d_1x1")(
|
1047 |
+
mixed
|
1048 |
+
)
|
1049 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
1050 |
+
x = add([x, up])
|
1051 |
+
x = Activation("relu", name="Block17_8_Activation")(x)
|
1052 |
+
|
1053 |
+
branch_0 = Conv2D(
|
1054 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_9_Branch_0_Conv2d_1x1"
|
1055 |
+
)(x)
|
1056 |
+
branch_0 = BatchNormalization(
|
1057 |
+
axis=3,
|
1058 |
+
momentum=0.995,
|
1059 |
+
epsilon=0.001,
|
1060 |
+
scale=False,
|
1061 |
+
name="Block17_9_Branch_0_Conv2d_1x1_BatchNorm",
|
1062 |
+
)(branch_0)
|
1063 |
+
branch_0 = Activation("relu", name="Block17_9_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1064 |
+
branch_1 = Conv2D(
|
1065 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_9_Branch_9_Conv2d_0a_1x1"
|
1066 |
+
)(x)
|
1067 |
+
branch_1 = BatchNormalization(
|
1068 |
+
axis=3,
|
1069 |
+
momentum=0.995,
|
1070 |
+
epsilon=0.001,
|
1071 |
+
scale=False,
|
1072 |
+
name="Block17_9_Branch_9_Conv2d_0a_1x1_BatchNorm",
|
1073 |
+
)(branch_1)
|
1074 |
+
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0a_1x1_Activation")(branch_1)
|
1075 |
+
branch_1 = Conv2D(
|
1076 |
+
128,
|
1077 |
+
[1, 7],
|
1078 |
+
strides=1,
|
1079 |
+
padding="same",
|
1080 |
+
use_bias=False,
|
1081 |
+
name="Block17_9_Branch_9_Conv2d_0b_1x7",
|
1082 |
+
)(branch_1)
|
1083 |
+
branch_1 = BatchNormalization(
|
1084 |
+
axis=3,
|
1085 |
+
momentum=0.995,
|
1086 |
+
epsilon=0.001,
|
1087 |
+
scale=False,
|
1088 |
+
name="Block17_9_Branch_9_Conv2d_0b_1x7_BatchNorm",
|
1089 |
+
)(branch_1)
|
1090 |
+
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0b_1x7_Activation")(branch_1)
|
1091 |
+
branch_1 = Conv2D(
|
1092 |
+
128,
|
1093 |
+
[7, 1],
|
1094 |
+
strides=1,
|
1095 |
+
padding="same",
|
1096 |
+
use_bias=False,
|
1097 |
+
name="Block17_9_Branch_9_Conv2d_0c_7x1",
|
1098 |
+
)(branch_1)
|
1099 |
+
branch_1 = BatchNormalization(
|
1100 |
+
axis=3,
|
1101 |
+
momentum=0.995,
|
1102 |
+
epsilon=0.001,
|
1103 |
+
scale=False,
|
1104 |
+
name="Block17_9_Branch_9_Conv2d_0c_7x1_BatchNorm",
|
1105 |
+
)(branch_1)
|
1106 |
+
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0c_7x1_Activation")(branch_1)
|
1107 |
+
branches = [branch_0, branch_1]
|
1108 |
+
mixed = Concatenate(axis=3, name="Block17_9_Concatenate")(branches)
|
1109 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_9_Conv2d_1x1")(
|
1110 |
+
mixed
|
1111 |
+
)
|
1112 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
1113 |
+
x = add([x, up])
|
1114 |
+
x = Activation("relu", name="Block17_9_Activation")(x)
|
1115 |
+
|
1116 |
+
branch_0 = Conv2D(
|
1117 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_10_Branch_0_Conv2d_1x1"
|
1118 |
+
)(x)
|
1119 |
+
branch_0 = BatchNormalization(
|
1120 |
+
axis=3,
|
1121 |
+
momentum=0.995,
|
1122 |
+
epsilon=0.001,
|
1123 |
+
scale=False,
|
1124 |
+
name="Block17_10_Branch_0_Conv2d_1x1_BatchNorm",
|
1125 |
+
)(branch_0)
|
1126 |
+
branch_0 = Activation("relu", name="Block17_10_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1127 |
+
branch_1 = Conv2D(
|
1128 |
+
128, 1, strides=1, padding="same", use_bias=False, name="Block17_10_Branch_10_Conv2d_0a_1x1"
|
1129 |
+
)(x)
|
1130 |
+
branch_1 = BatchNormalization(
|
1131 |
+
axis=3,
|
1132 |
+
momentum=0.995,
|
1133 |
+
epsilon=0.001,
|
1134 |
+
scale=False,
|
1135 |
+
name="Block17_10_Branch_10_Conv2d_0a_1x1_BatchNorm",
|
1136 |
+
)(branch_1)
|
1137 |
+
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0a_1x1_Activation")(branch_1)
|
1138 |
+
branch_1 = Conv2D(
|
1139 |
+
128,
|
1140 |
+
[1, 7],
|
1141 |
+
strides=1,
|
1142 |
+
padding="same",
|
1143 |
+
use_bias=False,
|
1144 |
+
name="Block17_10_Branch_10_Conv2d_0b_1x7",
|
1145 |
+
)(branch_1)
|
1146 |
+
branch_1 = BatchNormalization(
|
1147 |
+
axis=3,
|
1148 |
+
momentum=0.995,
|
1149 |
+
epsilon=0.001,
|
1150 |
+
scale=False,
|
1151 |
+
name="Block17_10_Branch_10_Conv2d_0b_1x7_BatchNorm",
|
1152 |
+
)(branch_1)
|
1153 |
+
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0b_1x7_Activation")(branch_1)
|
1154 |
+
branch_1 = Conv2D(
|
1155 |
+
128,
|
1156 |
+
[7, 1],
|
1157 |
+
strides=1,
|
1158 |
+
padding="same",
|
1159 |
+
use_bias=False,
|
1160 |
+
name="Block17_10_Branch_10_Conv2d_0c_7x1",
|
1161 |
+
)(branch_1)
|
1162 |
+
branch_1 = BatchNormalization(
|
1163 |
+
axis=3,
|
1164 |
+
momentum=0.995,
|
1165 |
+
epsilon=0.001,
|
1166 |
+
scale=False,
|
1167 |
+
name="Block17_10_Branch_10_Conv2d_0c_7x1_BatchNorm",
|
1168 |
+
)(branch_1)
|
1169 |
+
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0c_7x1_Activation")(branch_1)
|
1170 |
+
branches = [branch_0, branch_1]
|
1171 |
+
mixed = Concatenate(axis=3, name="Block17_10_Concatenate")(branches)
|
1172 |
+
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_10_Conv2d_1x1")(
|
1173 |
+
mixed
|
1174 |
+
)
|
1175 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
1176 |
+
x = add([x, up])
|
1177 |
+
x = Activation("relu", name="Block17_10_Activation")(x)
|
1178 |
+
|
1179 |
+
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
|
1180 |
+
branch_0 = Conv2D(
|
1181 |
+
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_0_Conv2d_0a_1x1"
|
1182 |
+
)(x)
|
1183 |
+
branch_0 = BatchNormalization(
|
1184 |
+
axis=3,
|
1185 |
+
momentum=0.995,
|
1186 |
+
epsilon=0.001,
|
1187 |
+
scale=False,
|
1188 |
+
name="Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm",
|
1189 |
+
)(branch_0)
|
1190 |
+
branch_0 = Activation("relu", name="Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation")(branch_0)
|
1191 |
+
branch_0 = Conv2D(
|
1192 |
+
384, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_0_Conv2d_1a_3x3"
|
1193 |
+
)(branch_0)
|
1194 |
+
branch_0 = BatchNormalization(
|
1195 |
+
axis=3,
|
1196 |
+
momentum=0.995,
|
1197 |
+
epsilon=0.001,
|
1198 |
+
scale=False,
|
1199 |
+
name="Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm",
|
1200 |
+
)(branch_0)
|
1201 |
+
branch_0 = Activation("relu", name="Mixed_7a_Branch_0_Conv2d_1a_3x3_Activation")(branch_0)
|
1202 |
+
branch_1 = Conv2D(
|
1203 |
+
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_1_Conv2d_0a_1x1"
|
1204 |
+
)(x)
|
1205 |
+
branch_1 = BatchNormalization(
|
1206 |
+
axis=3,
|
1207 |
+
momentum=0.995,
|
1208 |
+
epsilon=0.001,
|
1209 |
+
scale=False,
|
1210 |
+
name="Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
1211 |
+
)(branch_1)
|
1212 |
+
branch_1 = Activation("relu", name="Mixed_7a_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
1213 |
+
branch_1 = Conv2D(
|
1214 |
+
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_1_Conv2d_1a_3x3"
|
1215 |
+
)(branch_1)
|
1216 |
+
branch_1 = BatchNormalization(
|
1217 |
+
axis=3,
|
1218 |
+
momentum=0.995,
|
1219 |
+
epsilon=0.001,
|
1220 |
+
scale=False,
|
1221 |
+
name="Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm",
|
1222 |
+
)(branch_1)
|
1223 |
+
branch_1 = Activation("relu", name="Mixed_7a_Branch_1_Conv2d_1a_3x3_Activation")(branch_1)
|
1224 |
+
branch_2 = Conv2D(
|
1225 |
+
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_0a_1x1"
|
1226 |
+
)(x)
|
1227 |
+
branch_2 = BatchNormalization(
|
1228 |
+
axis=3,
|
1229 |
+
momentum=0.995,
|
1230 |
+
epsilon=0.001,
|
1231 |
+
scale=False,
|
1232 |
+
name="Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
1233 |
+
)(branch_2)
|
1234 |
+
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
1235 |
+
branch_2 = Conv2D(
|
1236 |
+
256, 3, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_0b_3x3"
|
1237 |
+
)(branch_2)
|
1238 |
+
branch_2 = BatchNormalization(
|
1239 |
+
axis=3,
|
1240 |
+
momentum=0.995,
|
1241 |
+
epsilon=0.001,
|
1242 |
+
scale=False,
|
1243 |
+
name="Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
1244 |
+
)(branch_2)
|
1245 |
+
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
1246 |
+
branch_2 = Conv2D(
|
1247 |
+
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_1a_3x3"
|
1248 |
+
)(branch_2)
|
1249 |
+
branch_2 = BatchNormalization(
|
1250 |
+
axis=3,
|
1251 |
+
momentum=0.995,
|
1252 |
+
epsilon=0.001,
|
1253 |
+
scale=False,
|
1254 |
+
name="Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm",
|
1255 |
+
)(branch_2)
|
1256 |
+
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_1a_3x3_Activation")(branch_2)
|
1257 |
+
branch_pool = MaxPooling2D(
|
1258 |
+
3, strides=2, padding="valid", name="Mixed_7a_Branch_3_MaxPool_1a_3x3"
|
1259 |
+
)(x)
|
1260 |
+
branches = [branch_0, branch_1, branch_2, branch_pool]
|
1261 |
+
x = Concatenate(axis=3, name="Mixed_7a")(branches)
|
1262 |
+
|
1263 |
+
# 5x Block8 (Inception-ResNet-C block):
|
1264 |
+
|
1265 |
+
branch_0 = Conv2D(
|
1266 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_1_Branch_0_Conv2d_1x1"
|
1267 |
+
)(x)
|
1268 |
+
branch_0 = BatchNormalization(
|
1269 |
+
axis=3,
|
1270 |
+
momentum=0.995,
|
1271 |
+
epsilon=0.001,
|
1272 |
+
scale=False,
|
1273 |
+
name="Block8_1_Branch_0_Conv2d_1x1_BatchNorm",
|
1274 |
+
)(branch_0)
|
1275 |
+
branch_0 = Activation("relu", name="Block8_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1276 |
+
branch_1 = Conv2D(
|
1277 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_1_Branch_1_Conv2d_0a_1x1"
|
1278 |
+
)(x)
|
1279 |
+
branch_1 = BatchNormalization(
|
1280 |
+
axis=3,
|
1281 |
+
momentum=0.995,
|
1282 |
+
epsilon=0.001,
|
1283 |
+
scale=False,
|
1284 |
+
name="Block8_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
1285 |
+
)(branch_1)
|
1286 |
+
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
1287 |
+
branch_1 = Conv2D(
|
1288 |
+
192,
|
1289 |
+
[1, 3],
|
1290 |
+
strides=1,
|
1291 |
+
padding="same",
|
1292 |
+
use_bias=False,
|
1293 |
+
name="Block8_1_Branch_1_Conv2d_0b_1x3",
|
1294 |
+
)(branch_1)
|
1295 |
+
branch_1 = BatchNormalization(
|
1296 |
+
axis=3,
|
1297 |
+
momentum=0.995,
|
1298 |
+
epsilon=0.001,
|
1299 |
+
scale=False,
|
1300 |
+
name="Block8_1_Branch_1_Conv2d_0b_1x3_BatchNorm",
|
1301 |
+
)(branch_1)
|
1302 |
+
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0b_1x3_Activation")(branch_1)
|
1303 |
+
branch_1 = Conv2D(
|
1304 |
+
192,
|
1305 |
+
[3, 1],
|
1306 |
+
strides=1,
|
1307 |
+
padding="same",
|
1308 |
+
use_bias=False,
|
1309 |
+
name="Block8_1_Branch_1_Conv2d_0c_3x1",
|
1310 |
+
)(branch_1)
|
1311 |
+
branch_1 = BatchNormalization(
|
1312 |
+
axis=3,
|
1313 |
+
momentum=0.995,
|
1314 |
+
epsilon=0.001,
|
1315 |
+
scale=False,
|
1316 |
+
name="Block8_1_Branch_1_Conv2d_0c_3x1_BatchNorm",
|
1317 |
+
)(branch_1)
|
1318 |
+
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0c_3x1_Activation")(branch_1)
|
1319 |
+
branches = [branch_0, branch_1]
|
1320 |
+
mixed = Concatenate(axis=3, name="Block8_1_Concatenate")(branches)
|
1321 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_1_Conv2d_1x1")(
|
1322 |
+
mixed
|
1323 |
+
)
|
1324 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
1325 |
+
x = add([x, up])
|
1326 |
+
x = Activation("relu", name="Block8_1_Activation")(x)
|
1327 |
+
|
1328 |
+
branch_0 = Conv2D(
|
1329 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_2_Branch_0_Conv2d_1x1"
|
1330 |
+
)(x)
|
1331 |
+
branch_0 = BatchNormalization(
|
1332 |
+
axis=3,
|
1333 |
+
momentum=0.995,
|
1334 |
+
epsilon=0.001,
|
1335 |
+
scale=False,
|
1336 |
+
name="Block8_2_Branch_0_Conv2d_1x1_BatchNorm",
|
1337 |
+
)(branch_0)
|
1338 |
+
branch_0 = Activation("relu", name="Block8_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1339 |
+
branch_1 = Conv2D(
|
1340 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_2_Branch_2_Conv2d_0a_1x1"
|
1341 |
+
)(x)
|
1342 |
+
branch_1 = BatchNormalization(
|
1343 |
+
axis=3,
|
1344 |
+
momentum=0.995,
|
1345 |
+
epsilon=0.001,
|
1346 |
+
scale=False,
|
1347 |
+
name="Block8_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
1348 |
+
)(branch_1)
|
1349 |
+
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_1)
|
1350 |
+
branch_1 = Conv2D(
|
1351 |
+
192,
|
1352 |
+
[1, 3],
|
1353 |
+
strides=1,
|
1354 |
+
padding="same",
|
1355 |
+
use_bias=False,
|
1356 |
+
name="Block8_2_Branch_2_Conv2d_0b_1x3",
|
1357 |
+
)(branch_1)
|
1358 |
+
branch_1 = BatchNormalization(
|
1359 |
+
axis=3,
|
1360 |
+
momentum=0.995,
|
1361 |
+
epsilon=0.001,
|
1362 |
+
scale=False,
|
1363 |
+
name="Block8_2_Branch_2_Conv2d_0b_1x3_BatchNorm",
|
1364 |
+
)(branch_1)
|
1365 |
+
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0b_1x3_Activation")(branch_1)
|
1366 |
+
branch_1 = Conv2D(
|
1367 |
+
192,
|
1368 |
+
[3, 1],
|
1369 |
+
strides=1,
|
1370 |
+
padding="same",
|
1371 |
+
use_bias=False,
|
1372 |
+
name="Block8_2_Branch_2_Conv2d_0c_3x1",
|
1373 |
+
)(branch_1)
|
1374 |
+
branch_1 = BatchNormalization(
|
1375 |
+
axis=3,
|
1376 |
+
momentum=0.995,
|
1377 |
+
epsilon=0.001,
|
1378 |
+
scale=False,
|
1379 |
+
name="Block8_2_Branch_2_Conv2d_0c_3x1_BatchNorm",
|
1380 |
+
)(branch_1)
|
1381 |
+
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0c_3x1_Activation")(branch_1)
|
1382 |
+
branches = [branch_0, branch_1]
|
1383 |
+
mixed = Concatenate(axis=3, name="Block8_2_Concatenate")(branches)
|
1384 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_2_Conv2d_1x1")(
|
1385 |
+
mixed
|
1386 |
+
)
|
1387 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
1388 |
+
x = add([x, up])
|
1389 |
+
x = Activation("relu", name="Block8_2_Activation")(x)
|
1390 |
+
|
1391 |
+
branch_0 = Conv2D(
|
1392 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_3_Branch_0_Conv2d_1x1"
|
1393 |
+
)(x)
|
1394 |
+
branch_0 = BatchNormalization(
|
1395 |
+
axis=3,
|
1396 |
+
momentum=0.995,
|
1397 |
+
epsilon=0.001,
|
1398 |
+
scale=False,
|
1399 |
+
name="Block8_3_Branch_0_Conv2d_1x1_BatchNorm",
|
1400 |
+
)(branch_0)
|
1401 |
+
branch_0 = Activation("relu", name="Block8_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1402 |
+
branch_1 = Conv2D(
|
1403 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_3_Branch_3_Conv2d_0a_1x1"
|
1404 |
+
)(x)
|
1405 |
+
branch_1 = BatchNormalization(
|
1406 |
+
axis=3,
|
1407 |
+
momentum=0.995,
|
1408 |
+
epsilon=0.001,
|
1409 |
+
scale=False,
|
1410 |
+
name="Block8_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
|
1411 |
+
)(branch_1)
|
1412 |
+
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0a_1x1_Activation")(branch_1)
|
1413 |
+
branch_1 = Conv2D(
|
1414 |
+
192,
|
1415 |
+
[1, 3],
|
1416 |
+
strides=1,
|
1417 |
+
padding="same",
|
1418 |
+
use_bias=False,
|
1419 |
+
name="Block8_3_Branch_3_Conv2d_0b_1x3",
|
1420 |
+
)(branch_1)
|
1421 |
+
branch_1 = BatchNormalization(
|
1422 |
+
axis=3,
|
1423 |
+
momentum=0.995,
|
1424 |
+
epsilon=0.001,
|
1425 |
+
scale=False,
|
1426 |
+
name="Block8_3_Branch_3_Conv2d_0b_1x3_BatchNorm",
|
1427 |
+
)(branch_1)
|
1428 |
+
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0b_1x3_Activation")(branch_1)
|
1429 |
+
branch_1 = Conv2D(
|
1430 |
+
192,
|
1431 |
+
[3, 1],
|
1432 |
+
strides=1,
|
1433 |
+
padding="same",
|
1434 |
+
use_bias=False,
|
1435 |
+
name="Block8_3_Branch_3_Conv2d_0c_3x1",
|
1436 |
+
)(branch_1)
|
1437 |
+
branch_1 = BatchNormalization(
|
1438 |
+
axis=3,
|
1439 |
+
momentum=0.995,
|
1440 |
+
epsilon=0.001,
|
1441 |
+
scale=False,
|
1442 |
+
name="Block8_3_Branch_3_Conv2d_0c_3x1_BatchNorm",
|
1443 |
+
)(branch_1)
|
1444 |
+
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0c_3x1_Activation")(branch_1)
|
1445 |
+
branches = [branch_0, branch_1]
|
1446 |
+
mixed = Concatenate(axis=3, name="Block8_3_Concatenate")(branches)
|
1447 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_3_Conv2d_1x1")(
|
1448 |
+
mixed
|
1449 |
+
)
|
1450 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
1451 |
+
x = add([x, up])
|
1452 |
+
x = Activation("relu", name="Block8_3_Activation")(x)
|
1453 |
+
|
1454 |
+
branch_0 = Conv2D(
|
1455 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_4_Branch_0_Conv2d_1x1"
|
1456 |
+
)(x)
|
1457 |
+
branch_0 = BatchNormalization(
|
1458 |
+
axis=3,
|
1459 |
+
momentum=0.995,
|
1460 |
+
epsilon=0.001,
|
1461 |
+
scale=False,
|
1462 |
+
name="Block8_4_Branch_0_Conv2d_1x1_BatchNorm",
|
1463 |
+
)(branch_0)
|
1464 |
+
branch_0 = Activation("relu", name="Block8_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1465 |
+
branch_1 = Conv2D(
|
1466 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_4_Branch_4_Conv2d_0a_1x1"
|
1467 |
+
)(x)
|
1468 |
+
branch_1 = BatchNormalization(
|
1469 |
+
axis=3,
|
1470 |
+
momentum=0.995,
|
1471 |
+
epsilon=0.001,
|
1472 |
+
scale=False,
|
1473 |
+
name="Block8_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
|
1474 |
+
)(branch_1)
|
1475 |
+
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0a_1x1_Activation")(branch_1)
|
1476 |
+
branch_1 = Conv2D(
|
1477 |
+
192,
|
1478 |
+
[1, 3],
|
1479 |
+
strides=1,
|
1480 |
+
padding="same",
|
1481 |
+
use_bias=False,
|
1482 |
+
name="Block8_4_Branch_4_Conv2d_0b_1x3",
|
1483 |
+
)(branch_1)
|
1484 |
+
branch_1 = BatchNormalization(
|
1485 |
+
axis=3,
|
1486 |
+
momentum=0.995,
|
1487 |
+
epsilon=0.001,
|
1488 |
+
scale=False,
|
1489 |
+
name="Block8_4_Branch_4_Conv2d_0b_1x3_BatchNorm",
|
1490 |
+
)(branch_1)
|
1491 |
+
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0b_1x3_Activation")(branch_1)
|
1492 |
+
branch_1 = Conv2D(
|
1493 |
+
192,
|
1494 |
+
[3, 1],
|
1495 |
+
strides=1,
|
1496 |
+
padding="same",
|
1497 |
+
use_bias=False,
|
1498 |
+
name="Block8_4_Branch_4_Conv2d_0c_3x1",
|
1499 |
+
)(branch_1)
|
1500 |
+
branch_1 = BatchNormalization(
|
1501 |
+
axis=3,
|
1502 |
+
momentum=0.995,
|
1503 |
+
epsilon=0.001,
|
1504 |
+
scale=False,
|
1505 |
+
name="Block8_4_Branch_4_Conv2d_0c_3x1_BatchNorm",
|
1506 |
+
)(branch_1)
|
1507 |
+
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0c_3x1_Activation")(branch_1)
|
1508 |
+
branches = [branch_0, branch_1]
|
1509 |
+
mixed = Concatenate(axis=3, name="Block8_4_Concatenate")(branches)
|
1510 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_4_Conv2d_1x1")(
|
1511 |
+
mixed
|
1512 |
+
)
|
1513 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
1514 |
+
x = add([x, up])
|
1515 |
+
x = Activation("relu", name="Block8_4_Activation")(x)
|
1516 |
+
|
1517 |
+
branch_0 = Conv2D(
|
1518 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_5_Branch_0_Conv2d_1x1"
|
1519 |
+
)(x)
|
1520 |
+
branch_0 = BatchNormalization(
|
1521 |
+
axis=3,
|
1522 |
+
momentum=0.995,
|
1523 |
+
epsilon=0.001,
|
1524 |
+
scale=False,
|
1525 |
+
name="Block8_5_Branch_0_Conv2d_1x1_BatchNorm",
|
1526 |
+
)(branch_0)
|
1527 |
+
branch_0 = Activation("relu", name="Block8_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1528 |
+
branch_1 = Conv2D(
|
1529 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_5_Branch_5_Conv2d_0a_1x1"
|
1530 |
+
)(x)
|
1531 |
+
branch_1 = BatchNormalization(
|
1532 |
+
axis=3,
|
1533 |
+
momentum=0.995,
|
1534 |
+
epsilon=0.001,
|
1535 |
+
scale=False,
|
1536 |
+
name="Block8_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
|
1537 |
+
)(branch_1)
|
1538 |
+
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0a_1x1_Activation")(branch_1)
|
1539 |
+
branch_1 = Conv2D(
|
1540 |
+
192,
|
1541 |
+
[1, 3],
|
1542 |
+
strides=1,
|
1543 |
+
padding="same",
|
1544 |
+
use_bias=False,
|
1545 |
+
name="Block8_5_Branch_5_Conv2d_0b_1x3",
|
1546 |
+
)(branch_1)
|
1547 |
+
branch_1 = BatchNormalization(
|
1548 |
+
axis=3,
|
1549 |
+
momentum=0.995,
|
1550 |
+
epsilon=0.001,
|
1551 |
+
scale=False,
|
1552 |
+
name="Block8_5_Branch_5_Conv2d_0b_1x3_BatchNorm",
|
1553 |
+
)(branch_1)
|
1554 |
+
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0b_1x3_Activation")(branch_1)
|
1555 |
+
branch_1 = Conv2D(
|
1556 |
+
192,
|
1557 |
+
[3, 1],
|
1558 |
+
strides=1,
|
1559 |
+
padding="same",
|
1560 |
+
use_bias=False,
|
1561 |
+
name="Block8_5_Branch_5_Conv2d_0c_3x1",
|
1562 |
+
)(branch_1)
|
1563 |
+
branch_1 = BatchNormalization(
|
1564 |
+
axis=3,
|
1565 |
+
momentum=0.995,
|
1566 |
+
epsilon=0.001,
|
1567 |
+
scale=False,
|
1568 |
+
name="Block8_5_Branch_5_Conv2d_0c_3x1_BatchNorm",
|
1569 |
+
)(branch_1)
|
1570 |
+
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0c_3x1_Activation")(branch_1)
|
1571 |
+
branches = [branch_0, branch_1]
|
1572 |
+
mixed = Concatenate(axis=3, name="Block8_5_Concatenate")(branches)
|
1573 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_5_Conv2d_1x1")(
|
1574 |
+
mixed
|
1575 |
+
)
|
1576 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
1577 |
+
x = add([x, up])
|
1578 |
+
x = Activation("relu", name="Block8_5_Activation")(x)
|
1579 |
+
|
1580 |
+
branch_0 = Conv2D(
|
1581 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_6_Branch_0_Conv2d_1x1"
|
1582 |
+
)(x)
|
1583 |
+
branch_0 = BatchNormalization(
|
1584 |
+
axis=3,
|
1585 |
+
momentum=0.995,
|
1586 |
+
epsilon=0.001,
|
1587 |
+
scale=False,
|
1588 |
+
name="Block8_6_Branch_0_Conv2d_1x1_BatchNorm",
|
1589 |
+
)(branch_0)
|
1590 |
+
branch_0 = Activation("relu", name="Block8_6_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
1591 |
+
branch_1 = Conv2D(
|
1592 |
+
192, 1, strides=1, padding="same", use_bias=False, name="Block8_6_Branch_1_Conv2d_0a_1x1"
|
1593 |
+
)(x)
|
1594 |
+
branch_1 = BatchNormalization(
|
1595 |
+
axis=3,
|
1596 |
+
momentum=0.995,
|
1597 |
+
epsilon=0.001,
|
1598 |
+
scale=False,
|
1599 |
+
name="Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
1600 |
+
)(branch_1)
|
1601 |
+
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
1602 |
+
branch_1 = Conv2D(
|
1603 |
+
192,
|
1604 |
+
[1, 3],
|
1605 |
+
strides=1,
|
1606 |
+
padding="same",
|
1607 |
+
use_bias=False,
|
1608 |
+
name="Block8_6_Branch_1_Conv2d_0b_1x3",
|
1609 |
+
)(branch_1)
|
1610 |
+
branch_1 = BatchNormalization(
|
1611 |
+
axis=3,
|
1612 |
+
momentum=0.995,
|
1613 |
+
epsilon=0.001,
|
1614 |
+
scale=False,
|
1615 |
+
name="Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm",
|
1616 |
+
)(branch_1)
|
1617 |
+
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0b_1x3_Activation")(branch_1)
|
1618 |
+
branch_1 = Conv2D(
|
1619 |
+
192,
|
1620 |
+
[3, 1],
|
1621 |
+
strides=1,
|
1622 |
+
padding="same",
|
1623 |
+
use_bias=False,
|
1624 |
+
name="Block8_6_Branch_1_Conv2d_0c_3x1",
|
1625 |
+
)(branch_1)
|
1626 |
+
branch_1 = BatchNormalization(
|
1627 |
+
axis=3,
|
1628 |
+
momentum=0.995,
|
1629 |
+
epsilon=0.001,
|
1630 |
+
scale=False,
|
1631 |
+
name="Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm",
|
1632 |
+
)(branch_1)
|
1633 |
+
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0c_3x1_Activation")(branch_1)
|
1634 |
+
branches = [branch_0, branch_1]
|
1635 |
+
mixed = Concatenate(axis=3, name="Block8_6_Concatenate")(branches)
|
1636 |
+
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_6_Conv2d_1x1")(
|
1637 |
+
mixed
|
1638 |
+
)
|
1639 |
+
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 1})(up)
|
1640 |
+
x = add([x, up])
|
1641 |
+
|
1642 |
+
# Classification block
|
1643 |
+
x = GlobalAveragePooling2D(name="AvgPool")(x)
|
1644 |
+
x = Dropout(1.0 - 0.8, name="Dropout")(x)
|
1645 |
+
# Bottleneck
|
1646 |
+
x = Dense(dimension, use_bias=False, name="Bottleneck")(x)
|
1647 |
+
x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False, name="Bottleneck_BatchNorm")(
|
1648 |
+
x
|
1649 |
+
)
|
1650 |
+
|
1651 |
+
# Create model
|
1652 |
+
model = Model(inputs, x, name="inception_resnet_v1")
|
1653 |
+
|
1654 |
+
return model
|
1655 |
+
|
1656 |
+
|
1657 |
+
def load_facenet128d_model(
|
1658 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5",
|
1659 |
+
) -> Model:
|
1660 |
+
"""
|
1661 |
+
Construct FaceNet-128d model, download weights and then load weights
|
1662 |
+
Args:
|
1663 |
+
dimension (int): construct FaceNet-128d or FaceNet-512d models
|
1664 |
+
Returns:
|
1665 |
+
model (Model)
|
1666 |
+
"""
|
1667 |
+
model = InceptionResNetV1()
|
1668 |
+
|
1669 |
+
# -----------------------------------
|
1670 |
+
|
1671 |
+
home = folder_utils.get_deepface_home()
|
1672 |
+
|
1673 |
+
if os.path.isfile(home + "/.deepface/weights/facenet_weights.h5") != True:
|
1674 |
+
logger.info("facenet_weights.h5 will be downloaded...")
|
1675 |
+
|
1676 |
+
output = home + "/.deepface/weights/facenet_weights.h5"
|
1677 |
+
gdown.download(url, output, quiet=False)
|
1678 |
+
|
1679 |
+
# -----------------------------------
|
1680 |
+
|
1681 |
+
model.load_weights(home + "/.deepface/weights/facenet_weights.h5")
|
1682 |
+
|
1683 |
+
# -----------------------------------
|
1684 |
+
|
1685 |
+
return model
|
1686 |
+
|
1687 |
+
|
1688 |
+
def load_facenet512d_model(
|
1689 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5",
|
1690 |
+
) -> Model:
|
1691 |
+
"""
|
1692 |
+
Construct FaceNet-512d model, download its weights and load
|
1693 |
+
Returns:
|
1694 |
+
model (Model)
|
1695 |
+
"""
|
1696 |
+
|
1697 |
+
model = InceptionResNetV1(dimension=512)
|
1698 |
+
|
1699 |
+
# -------------------------
|
1700 |
+
|
1701 |
+
home = folder_utils.get_deepface_home()
|
1702 |
+
|
1703 |
+
if os.path.isfile(home + "/.deepface/weights/facenet512_weights.h5") != True:
|
1704 |
+
logger.info("facenet512_weights.h5 will be downloaded...")
|
1705 |
+
|
1706 |
+
output = home + "/.deepface/weights/facenet512_weights.h5"
|
1707 |
+
gdown.download(url, output, quiet=False)
|
1708 |
+
|
1709 |
+
# -------------------------
|
1710 |
+
|
1711 |
+
model.load_weights(home + "/.deepface/weights/facenet512_weights.h5")
|
1712 |
+
|
1713 |
+
# -------------------------
|
1714 |
+
|
1715 |
+
return model
|
basemodels/FbDeepFace.py
ADDED
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import zipfile
|
3 |
+
import gdown
|
4 |
+
from deepface.commons import package_utils, folder_utils
|
5 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
6 |
+
from deepface.commons import logger as log
|
7 |
+
|
8 |
+
logger = log.get_singletonish_logger()
|
9 |
+
|
10 |
+
# --------------------------------
|
11 |
+
# dependency configuration
|
12 |
+
|
13 |
+
tf_major = package_utils.get_tf_major_version()
|
14 |
+
tf_minor = package_utils.get_tf_minor_version()
|
15 |
+
|
16 |
+
if tf_major == 1:
|
17 |
+
from keras.models import Model, Sequential
|
18 |
+
from keras.layers import (
|
19 |
+
Convolution2D,
|
20 |
+
MaxPooling2D,
|
21 |
+
Flatten,
|
22 |
+
Dense,
|
23 |
+
Dropout,
|
24 |
+
)
|
25 |
+
else:
|
26 |
+
from tensorflow.keras.models import Model, Sequential
|
27 |
+
from tensorflow.keras.layers import (
|
28 |
+
Convolution2D,
|
29 |
+
MaxPooling2D,
|
30 |
+
Flatten,
|
31 |
+
Dense,
|
32 |
+
Dropout,
|
33 |
+
)
|
34 |
+
|
35 |
+
|
36 |
+
# -------------------------------------
|
37 |
+
# pylint: disable=line-too-long, too-few-public-methods
|
38 |
+
class DeepFaceClient(FacialRecognition):
|
39 |
+
"""
|
40 |
+
Fb's DeepFace model class
|
41 |
+
"""
|
42 |
+
|
43 |
+
def __init__(self):
|
44 |
+
# DeepFace requires tf 2.12 or less
|
45 |
+
if tf_major == 2 and tf_minor > 12:
|
46 |
+
# Ref: https://github.com/serengil/deepface/pull/1079
|
47 |
+
raise ValueError(
|
48 |
+
"DeepFace model requires LocallyConnected2D but it is no longer supported"
|
49 |
+
f" after tf 2.12 but you have {tf_major}.{tf_minor}. You need to downgrade your tf."
|
50 |
+
)
|
51 |
+
|
52 |
+
self.model = load_model()
|
53 |
+
self.model_name = "DeepFace"
|
54 |
+
self.input_shape = (152, 152)
|
55 |
+
self.output_shape = 4096
|
56 |
+
|
57 |
+
|
58 |
+
def load_model(
|
59 |
+
url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
|
60 |
+
) -> Model:
|
61 |
+
"""
|
62 |
+
Construct DeepFace model, download its weights and load
|
63 |
+
"""
|
64 |
+
# we have some checks for this dependency in the init of client
|
65 |
+
# putting this in global causes library initialization
|
66 |
+
if tf_major == 1:
|
67 |
+
from keras.layers import LocallyConnected2D
|
68 |
+
else:
|
69 |
+
from tensorflow.keras.layers import LocallyConnected2D
|
70 |
+
|
71 |
+
base_model = Sequential()
|
72 |
+
base_model.add(
|
73 |
+
Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))
|
74 |
+
)
|
75 |
+
base_model.add(MaxPooling2D(pool_size=3, strides=2, padding="same", name="M2"))
|
76 |
+
base_model.add(Convolution2D(16, (9, 9), activation="relu", name="C3"))
|
77 |
+
base_model.add(LocallyConnected2D(16, (9, 9), activation="relu", name="L4"))
|
78 |
+
base_model.add(LocallyConnected2D(16, (7, 7), strides=2, activation="relu", name="L5"))
|
79 |
+
base_model.add(LocallyConnected2D(16, (5, 5), activation="relu", name="L6"))
|
80 |
+
base_model.add(Flatten(name="F0"))
|
81 |
+
base_model.add(Dense(4096, activation="relu", name="F7"))
|
82 |
+
base_model.add(Dropout(rate=0.5, name="D0"))
|
83 |
+
base_model.add(Dense(8631, activation="softmax", name="F8"))
|
84 |
+
|
85 |
+
# ---------------------------------
|
86 |
+
|
87 |
+
home = folder_utils.get_deepface_home()
|
88 |
+
|
89 |
+
if os.path.isfile(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5") != True:
|
90 |
+
logger.info("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
|
91 |
+
|
92 |
+
output = home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
|
93 |
+
|
94 |
+
gdown.download(url, output, quiet=False)
|
95 |
+
|
96 |
+
# unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
|
97 |
+
with zipfile.ZipFile(output, "r") as zip_ref:
|
98 |
+
zip_ref.extractall(home + "/.deepface/weights/")
|
99 |
+
|
100 |
+
base_model.load_weights(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5")
|
101 |
+
|
102 |
+
# drop F8 and D0. F7 is the representation layer.
|
103 |
+
deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
|
104 |
+
|
105 |
+
return deepface_model
|
basemodels/GhostFaceNet.py
ADDED
@@ -0,0 +1,312 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# built-in dependencies
|
2 |
+
import os
|
3 |
+
|
4 |
+
# 3rd party dependencies
|
5 |
+
import gdown
|
6 |
+
import tensorflow as tf
|
7 |
+
|
8 |
+
# project dependencies
|
9 |
+
from deepface.commons import package_utils, folder_utils
|
10 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
11 |
+
from deepface.commons import logger as log
|
12 |
+
|
13 |
+
logger = log.get_singletonish_logger()
|
14 |
+
|
15 |
+
tf_major = package_utils.get_tf_major_version()
|
16 |
+
if tf_major == 1:
|
17 |
+
import keras
|
18 |
+
from keras import backend as K
|
19 |
+
from keras.models import Model
|
20 |
+
from keras.layers import (
|
21 |
+
Activation,
|
22 |
+
Add,
|
23 |
+
BatchNormalization,
|
24 |
+
Concatenate,
|
25 |
+
Conv2D,
|
26 |
+
DepthwiseConv2D,
|
27 |
+
GlobalAveragePooling2D,
|
28 |
+
Input,
|
29 |
+
Reshape,
|
30 |
+
Multiply,
|
31 |
+
ReLU,
|
32 |
+
PReLU,
|
33 |
+
)
|
34 |
+
else:
|
35 |
+
from tensorflow import keras
|
36 |
+
from tensorflow.keras import backend as K
|
37 |
+
from tensorflow.keras.models import Model
|
38 |
+
from tensorflow.keras.layers import (
|
39 |
+
Activation,
|
40 |
+
Add,
|
41 |
+
BatchNormalization,
|
42 |
+
Concatenate,
|
43 |
+
Conv2D,
|
44 |
+
DepthwiseConv2D,
|
45 |
+
GlobalAveragePooling2D,
|
46 |
+
Input,
|
47 |
+
Reshape,
|
48 |
+
Multiply,
|
49 |
+
ReLU,
|
50 |
+
PReLU,
|
51 |
+
)
|
52 |
+
|
53 |
+
|
54 |
+
# pylint: disable=line-too-long, too-few-public-methods, no-else-return, unsubscriptable-object, comparison-with-callable
|
55 |
+
PRETRAINED_WEIGHTS = "https://github.com/HamadYA/GhostFaceNets/releases/download/v1.2/GhostFaceNet_W1.3_S1_ArcFace.h5"
|
56 |
+
|
57 |
+
|
58 |
+
class GhostFaceNetClient(FacialRecognition):
|
59 |
+
"""
|
60 |
+
GhostFaceNet model (GhostFaceNetV1 backbone)
|
61 |
+
Repo: https://github.com/HamadYA/GhostFaceNets
|
62 |
+
Pre-trained weights: https://github.com/HamadYA/GhostFaceNets/releases/tag/v1.2
|
63 |
+
GhostFaceNet_W1.3_S1_ArcFace.h5 ~ 16.5MB
|
64 |
+
Author declared that this backbone and pre-trained weights got 99.7667% accuracy on LFW
|
65 |
+
"""
|
66 |
+
|
67 |
+
def __init__(self):
|
68 |
+
self.model_name = "GhostFaceNet"
|
69 |
+
self.input_shape = (112, 112)
|
70 |
+
self.output_shape = 512
|
71 |
+
self.model = load_model()
|
72 |
+
|
73 |
+
|
74 |
+
def load_model():
|
75 |
+
model = GhostFaceNetV1()
|
76 |
+
|
77 |
+
home = folder_utils.get_deepface_home()
|
78 |
+
output = home + "/.deepface/weights/ghostfacenet_v1.h5"
|
79 |
+
|
80 |
+
if os.path.isfile(output) is not True:
|
81 |
+
logger.info(f"Pre-trained weights is downloaded from {PRETRAINED_WEIGHTS} to {output}")
|
82 |
+
gdown.download(PRETRAINED_WEIGHTS, output, quiet=False)
|
83 |
+
logger.info(f"Pre-trained weights is just downloaded to {output}")
|
84 |
+
|
85 |
+
model.load_weights(output)
|
86 |
+
|
87 |
+
return model
|
88 |
+
|
89 |
+
|
90 |
+
def GhostFaceNetV1() -> Model:
|
91 |
+
"""
|
92 |
+
Build GhostFaceNetV1 model. Refactored from
|
93 |
+
github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
|
94 |
+
Returns:
|
95 |
+
model (Model)
|
96 |
+
"""
|
97 |
+
inputs = Input(shape=(112, 112, 3))
|
98 |
+
|
99 |
+
out_channel = 20
|
100 |
+
|
101 |
+
nn = Conv2D(
|
102 |
+
out_channel,
|
103 |
+
(3, 3),
|
104 |
+
strides=1,
|
105 |
+
padding="same",
|
106 |
+
use_bias=False,
|
107 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
108 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
109 |
+
),
|
110 |
+
)(inputs)
|
111 |
+
|
112 |
+
nn = BatchNormalization(axis=-1)(nn)
|
113 |
+
nn = Activation("relu")(nn)
|
114 |
+
|
115 |
+
dwkernels = [3, 3, 3, 5, 5, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5]
|
116 |
+
exps = [20, 64, 92, 92, 156, 312, 260, 240, 240, 624, 872, 872, 1248, 1248, 1248, 664]
|
117 |
+
outs = [20, 32, 32, 52, 52, 104, 104, 104, 104, 144, 144, 208, 208, 208, 208, 208]
|
118 |
+
strides_set = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1]
|
119 |
+
reductions = [0, 0, 0, 24, 40, 0, 0, 0, 0, 156, 220, 220, 0, 312, 0, 168]
|
120 |
+
|
121 |
+
pre_out = out_channel
|
122 |
+
for dwk, stride, exp, out, reduction in zip(dwkernels, strides_set, exps, outs, reductions):
|
123 |
+
shortcut = not (out == pre_out and stride == 1)
|
124 |
+
nn = ghost_bottleneck(nn, dwk, stride, exp, out, reduction, shortcut)
|
125 |
+
pre_out = out
|
126 |
+
|
127 |
+
nn = Conv2D(
|
128 |
+
664,
|
129 |
+
(1, 1),
|
130 |
+
strides=(1, 1),
|
131 |
+
padding="valid",
|
132 |
+
use_bias=False,
|
133 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
134 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
135 |
+
),
|
136 |
+
)(nn)
|
137 |
+
nn = BatchNormalization(axis=-1)(nn)
|
138 |
+
nn = Activation("relu")(nn)
|
139 |
+
|
140 |
+
xx = Model(inputs=inputs, outputs=nn, name="GhostFaceNetV1")
|
141 |
+
|
142 |
+
# post modelling
|
143 |
+
inputs = xx.inputs[0]
|
144 |
+
nn = xx.outputs[0]
|
145 |
+
|
146 |
+
nn = keras.layers.DepthwiseConv2D(nn.shape[1], use_bias=False, name="GDC_dw")(nn)
|
147 |
+
nn = keras.layers.BatchNormalization(momentum=0.99, epsilon=0.001, name="GDC_batchnorm")(nn)
|
148 |
+
nn = keras.layers.Conv2D(
|
149 |
+
512, 1, use_bias=True, kernel_initializer="glorot_normal", name="GDC_conv"
|
150 |
+
)(nn)
|
151 |
+
nn = keras.layers.Flatten(name="GDC_flatten")(nn)
|
152 |
+
|
153 |
+
embedding = keras.layers.BatchNormalization(
|
154 |
+
momentum=0.99, epsilon=0.001, scale=True, name="pre_embedding"
|
155 |
+
)(nn)
|
156 |
+
embedding_fp32 = keras.layers.Activation("linear", dtype="float32", name="embedding")(embedding)
|
157 |
+
|
158 |
+
model = keras.models.Model(inputs, embedding_fp32, name=xx.name)
|
159 |
+
model = replace_relu_with_prelu(model=model)
|
160 |
+
return model
|
161 |
+
|
162 |
+
|
163 |
+
def se_module(inputs, reduction):
|
164 |
+
"""
|
165 |
+
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
|
166 |
+
"""
|
167 |
+
# get the channel axis
|
168 |
+
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
|
169 |
+
# filters = channel axis shape
|
170 |
+
filters = inputs.shape[channel_axis]
|
171 |
+
|
172 |
+
# from None x H x W x C to None x C
|
173 |
+
se = GlobalAveragePooling2D()(inputs)
|
174 |
+
|
175 |
+
# Reshape None x C to None 1 x 1 x C
|
176 |
+
se = Reshape((1, 1, filters))(se)
|
177 |
+
|
178 |
+
# Squeeze by using C*se_ratio. The size will be 1 x 1 x C*se_ratio
|
179 |
+
se = Conv2D(
|
180 |
+
reduction,
|
181 |
+
kernel_size=1,
|
182 |
+
use_bias=True,
|
183 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
184 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
185 |
+
),
|
186 |
+
)(se)
|
187 |
+
se = Activation("relu")(se)
|
188 |
+
|
189 |
+
# Excitation using C filters. The size will be 1 x 1 x C
|
190 |
+
se = Conv2D(
|
191 |
+
filters,
|
192 |
+
kernel_size=1,
|
193 |
+
use_bias=True,
|
194 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
195 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
196 |
+
),
|
197 |
+
)(se)
|
198 |
+
se = Activation("hard_sigmoid")(se)
|
199 |
+
|
200 |
+
return Multiply()([inputs, se])
|
201 |
+
|
202 |
+
|
203 |
+
def ghost_module(inputs, out, convkernel=1, dwkernel=3, add_activation=True):
|
204 |
+
"""
|
205 |
+
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
|
206 |
+
"""
|
207 |
+
conv_out_channel = out // 2
|
208 |
+
cc = Conv2D(
|
209 |
+
conv_out_channel,
|
210 |
+
convkernel,
|
211 |
+
use_bias=False,
|
212 |
+
strides=(1, 1),
|
213 |
+
padding="same",
|
214 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
215 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
216 |
+
),
|
217 |
+
)(inputs)
|
218 |
+
cc = BatchNormalization(axis=-1)(cc)
|
219 |
+
if add_activation:
|
220 |
+
cc = Activation("relu")(cc)
|
221 |
+
|
222 |
+
nn = DepthwiseConv2D(
|
223 |
+
dwkernel,
|
224 |
+
1,
|
225 |
+
padding="same",
|
226 |
+
use_bias=False,
|
227 |
+
depthwise_initializer=keras.initializers.VarianceScaling(
|
228 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
229 |
+
),
|
230 |
+
)(cc)
|
231 |
+
nn = BatchNormalization(axis=-1)(nn)
|
232 |
+
if add_activation:
|
233 |
+
nn = Activation("relu")(nn)
|
234 |
+
return Concatenate()([cc, nn])
|
235 |
+
|
236 |
+
|
237 |
+
def ghost_bottleneck(inputs, dwkernel, strides, exp, out, reduction, shortcut=True):
|
238 |
+
"""
|
239 |
+
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
|
240 |
+
"""
|
241 |
+
nn = ghost_module(inputs, exp, add_activation=True)
|
242 |
+
if strides > 1:
|
243 |
+
# Extra depth conv if strides higher than 1
|
244 |
+
nn = DepthwiseConv2D(
|
245 |
+
dwkernel,
|
246 |
+
strides,
|
247 |
+
padding="same",
|
248 |
+
use_bias=False,
|
249 |
+
depthwise_initializer=keras.initializers.VarianceScaling(
|
250 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
251 |
+
),
|
252 |
+
)(nn)
|
253 |
+
nn = BatchNormalization(axis=-1)(nn)
|
254 |
+
|
255 |
+
if reduction > 0:
|
256 |
+
# Squeeze and excite
|
257 |
+
nn = se_module(nn, reduction)
|
258 |
+
|
259 |
+
# Point-wise linear projection
|
260 |
+
nn = ghost_module(nn, out, add_activation=False) # ghost2 = GhostModule(exp, out, relu=False)
|
261 |
+
|
262 |
+
if shortcut:
|
263 |
+
xx = DepthwiseConv2D(
|
264 |
+
dwkernel,
|
265 |
+
strides,
|
266 |
+
padding="same",
|
267 |
+
use_bias=False,
|
268 |
+
depthwise_initializer=keras.initializers.VarianceScaling(
|
269 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
270 |
+
),
|
271 |
+
)(inputs)
|
272 |
+
xx = BatchNormalization(axis=-1)(xx)
|
273 |
+
xx = Conv2D(
|
274 |
+
out,
|
275 |
+
(1, 1),
|
276 |
+
strides=(1, 1),
|
277 |
+
padding="valid",
|
278 |
+
use_bias=False,
|
279 |
+
kernel_initializer=keras.initializers.VarianceScaling(
|
280 |
+
scale=2.0, mode="fan_out", distribution="truncated_normal"
|
281 |
+
),
|
282 |
+
)(xx)
|
283 |
+
xx = BatchNormalization(axis=-1)(xx)
|
284 |
+
else:
|
285 |
+
xx = inputs
|
286 |
+
return Add()([xx, nn])
|
287 |
+
|
288 |
+
|
289 |
+
def replace_relu_with_prelu(model) -> Model:
|
290 |
+
"""
|
291 |
+
Replaces relu activation function in the built model with prelu.
|
292 |
+
Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
|
293 |
+
Args:
|
294 |
+
model (Model): built model with relu activation functions
|
295 |
+
Returns
|
296 |
+
model (Model): built model with prelu activation functions
|
297 |
+
"""
|
298 |
+
|
299 |
+
def convert_relu(layer):
|
300 |
+
if isinstance(layer, ReLU) or (
|
301 |
+
isinstance(layer, Activation) and layer.activation == keras.activations.relu
|
302 |
+
):
|
303 |
+
layer_name = layer.name.replace("_relu", "_prelu")
|
304 |
+
return PReLU(
|
305 |
+
shared_axes=[1, 2],
|
306 |
+
alpha_initializer=tf.initializers.Constant(0.25),
|
307 |
+
name=layer_name,
|
308 |
+
)
|
309 |
+
return layer
|
310 |
+
|
311 |
+
input_tensors = keras.layers.Input(model.input_shape[1:])
|
312 |
+
return keras.models.clone_model(model, input_tensors=input_tensors, clone_function=convert_relu)
|
basemodels/OpenFace.py
ADDED
@@ -0,0 +1,397 @@
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import gdown
|
3 |
+
import tensorflow as tf
|
4 |
+
from deepface.commons import package_utils, folder_utils
|
5 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
6 |
+
from deepface.commons import logger as log
|
7 |
+
|
8 |
+
logger = log.get_singletonish_logger()
|
9 |
+
|
10 |
+
tf_version = package_utils.get_tf_major_version()
|
11 |
+
if tf_version == 1:
|
12 |
+
from keras.models import Model
|
13 |
+
from keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
|
14 |
+
from keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
|
15 |
+
from keras.layers import MaxPooling2D, AveragePooling2D
|
16 |
+
from keras import backend as K
|
17 |
+
else:
|
18 |
+
from tensorflow.keras.models import Model
|
19 |
+
from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
|
20 |
+
from tensorflow.keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
|
21 |
+
from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D
|
22 |
+
from tensorflow.keras import backend as K
|
23 |
+
|
24 |
+
# pylint: disable=unnecessary-lambda
|
25 |
+
|
26 |
+
# ---------------------------------------
|
27 |
+
|
28 |
+
# pylint: disable=too-few-public-methods
|
29 |
+
class OpenFaceClient(FacialRecognition):
|
30 |
+
"""
|
31 |
+
OpenFace model class
|
32 |
+
"""
|
33 |
+
|
34 |
+
def __init__(self):
|
35 |
+
self.model = load_model()
|
36 |
+
self.model_name = "OpenFace"
|
37 |
+
self.input_shape = (96, 96)
|
38 |
+
self.output_shape = 128
|
39 |
+
|
40 |
+
|
41 |
+
def load_model(
|
42 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",
|
43 |
+
) -> Model:
|
44 |
+
"""
|
45 |
+
Consturct OpenFace model, download its weights and load
|
46 |
+
Returns:
|
47 |
+
model (Model)
|
48 |
+
"""
|
49 |
+
myInput = Input(shape=(96, 96, 3))
|
50 |
+
|
51 |
+
x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
|
52 |
+
x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x)
|
53 |
+
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x)
|
54 |
+
x = Activation("relu")(x)
|
55 |
+
x = ZeroPadding2D(padding=(1, 1))(x)
|
56 |
+
x = MaxPooling2D(pool_size=3, strides=2)(x)
|
57 |
+
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1")(x)
|
58 |
+
x = Conv2D(64, (1, 1), name="conv2")(x)
|
59 |
+
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x)
|
60 |
+
x = Activation("relu")(x)
|
61 |
+
x = ZeroPadding2D(padding=(1, 1))(x)
|
62 |
+
x = Conv2D(192, (3, 3), name="conv3")(x)
|
63 |
+
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x)
|
64 |
+
x = Activation("relu")(x)
|
65 |
+
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2")(x) # x is equal added
|
66 |
+
x = ZeroPadding2D(padding=(1, 1))(x)
|
67 |
+
x = MaxPooling2D(pool_size=3, strides=2)(x)
|
68 |
+
|
69 |
+
# Inception3a
|
70 |
+
inception_3a_3x3 = Conv2D(96, (1, 1), name="inception_3a_3x3_conv1")(x)
|
71 |
+
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1")(
|
72 |
+
inception_3a_3x3
|
73 |
+
)
|
74 |
+
inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
|
75 |
+
inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
|
76 |
+
inception_3a_3x3 = Conv2D(128, (3, 3), name="inception_3a_3x3_conv2")(inception_3a_3x3)
|
77 |
+
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2")(
|
78 |
+
inception_3a_3x3
|
79 |
+
)
|
80 |
+
inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
|
81 |
+
|
82 |
+
inception_3a_5x5 = Conv2D(16, (1, 1), name="inception_3a_5x5_conv1")(x)
|
83 |
+
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1")(
|
84 |
+
inception_3a_5x5
|
85 |
+
)
|
86 |
+
inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
|
87 |
+
inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
|
88 |
+
inception_3a_5x5 = Conv2D(32, (5, 5), name="inception_3a_5x5_conv2")(inception_3a_5x5)
|
89 |
+
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2")(
|
90 |
+
inception_3a_5x5
|
91 |
+
)
|
92 |
+
inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
|
93 |
+
|
94 |
+
inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
|
95 |
+
inception_3a_pool = Conv2D(32, (1, 1), name="inception_3a_pool_conv")(inception_3a_pool)
|
96 |
+
inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_pool_bn")(
|
97 |
+
inception_3a_pool
|
98 |
+
)
|
99 |
+
inception_3a_pool = Activation("relu")(inception_3a_pool)
|
100 |
+
inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
|
101 |
+
|
102 |
+
inception_3a_1x1 = Conv2D(64, (1, 1), name="inception_3a_1x1_conv")(x)
|
103 |
+
inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_1x1_bn")(
|
104 |
+
inception_3a_1x1
|
105 |
+
)
|
106 |
+
inception_3a_1x1 = Activation("relu")(inception_3a_1x1)
|
107 |
+
|
108 |
+
inception_3a = concatenate(
|
109 |
+
[inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3
|
110 |
+
)
|
111 |
+
|
112 |
+
# Inception3b
|
113 |
+
inception_3b_3x3 = Conv2D(96, (1, 1), name="inception_3b_3x3_conv1")(inception_3a)
|
114 |
+
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1")(
|
115 |
+
inception_3b_3x3
|
116 |
+
)
|
117 |
+
inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
|
118 |
+
inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
|
119 |
+
inception_3b_3x3 = Conv2D(128, (3, 3), name="inception_3b_3x3_conv2")(inception_3b_3x3)
|
120 |
+
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2")(
|
121 |
+
inception_3b_3x3
|
122 |
+
)
|
123 |
+
inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
|
124 |
+
|
125 |
+
inception_3b_5x5 = Conv2D(32, (1, 1), name="inception_3b_5x5_conv1")(inception_3a)
|
126 |
+
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1")(
|
127 |
+
inception_3b_5x5
|
128 |
+
)
|
129 |
+
inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
|
130 |
+
inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
|
131 |
+
inception_3b_5x5 = Conv2D(64, (5, 5), name="inception_3b_5x5_conv2")(inception_3b_5x5)
|
132 |
+
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2")(
|
133 |
+
inception_3b_5x5
|
134 |
+
)
|
135 |
+
inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
|
136 |
+
|
137 |
+
inception_3b_pool = Lambda(lambda x: x**2, name="power2_3b")(inception_3a)
|
138 |
+
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
|
139 |
+
inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(inception_3b_pool)
|
140 |
+
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(inception_3b_pool)
|
141 |
+
inception_3b_pool = Conv2D(64, (1, 1), name="inception_3b_pool_conv")(inception_3b_pool)
|
142 |
+
inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_pool_bn")(
|
143 |
+
inception_3b_pool
|
144 |
+
)
|
145 |
+
inception_3b_pool = Activation("relu")(inception_3b_pool)
|
146 |
+
inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
|
147 |
+
|
148 |
+
inception_3b_1x1 = Conv2D(64, (1, 1), name="inception_3b_1x1_conv")(inception_3a)
|
149 |
+
inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_1x1_bn")(
|
150 |
+
inception_3b_1x1
|
151 |
+
)
|
152 |
+
inception_3b_1x1 = Activation("relu")(inception_3b_1x1)
|
153 |
+
|
154 |
+
inception_3b = concatenate(
|
155 |
+
[inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3
|
156 |
+
)
|
157 |
+
|
158 |
+
# Inception3c
|
159 |
+
inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1")(
|
160 |
+
inception_3b
|
161 |
+
)
|
162 |
+
inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1")(
|
163 |
+
inception_3c_3x3
|
164 |
+
)
|
165 |
+
inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
|
166 |
+
inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
|
167 |
+
inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_3c_3x3_conv" + "2")(
|
168 |
+
inception_3c_3x3
|
169 |
+
)
|
170 |
+
inception_3c_3x3 = BatchNormalization(
|
171 |
+
axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2"
|
172 |
+
)(inception_3c_3x3)
|
173 |
+
inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
|
174 |
+
|
175 |
+
inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1")(
|
176 |
+
inception_3b
|
177 |
+
)
|
178 |
+
inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1")(
|
179 |
+
inception_3c_5x5
|
180 |
+
)
|
181 |
+
inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
|
182 |
+
inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
|
183 |
+
inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2")(
|
184 |
+
inception_3c_5x5
|
185 |
+
)
|
186 |
+
inception_3c_5x5 = BatchNormalization(
|
187 |
+
axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2"
|
188 |
+
)(inception_3c_5x5)
|
189 |
+
inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
|
190 |
+
|
191 |
+
inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
|
192 |
+
inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
|
193 |
+
|
194 |
+
inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
|
195 |
+
|
196 |
+
# inception 4a
|
197 |
+
inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1")(
|
198 |
+
inception_3c
|
199 |
+
)
|
200 |
+
inception_4a_3x3 = BatchNormalization(
|
201 |
+
axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1"
|
202 |
+
)(inception_4a_3x3)
|
203 |
+
inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
|
204 |
+
inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
|
205 |
+
inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name="inception_4a_3x3_conv" + "2")(
|
206 |
+
inception_4a_3x3
|
207 |
+
)
|
208 |
+
inception_4a_3x3 = BatchNormalization(
|
209 |
+
axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2"
|
210 |
+
)(inception_4a_3x3)
|
211 |
+
inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
|
212 |
+
|
213 |
+
inception_4a_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1")(
|
214 |
+
inception_3c
|
215 |
+
)
|
216 |
+
inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1")(
|
217 |
+
inception_4a_5x5
|
218 |
+
)
|
219 |
+
inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
|
220 |
+
inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5)
|
221 |
+
inception_4a_5x5 = Conv2D(64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2")(
|
222 |
+
inception_4a_5x5
|
223 |
+
)
|
224 |
+
inception_4a_5x5 = BatchNormalization(
|
225 |
+
axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2"
|
226 |
+
)(inception_4a_5x5)
|
227 |
+
inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
|
228 |
+
|
229 |
+
inception_4a_pool = Lambda(lambda x: x**2, name="power2_4a")(inception_3c)
|
230 |
+
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
|
231 |
+
inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(inception_4a_pool)
|
232 |
+
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(inception_4a_pool)
|
233 |
+
|
234 |
+
inception_4a_pool = Conv2D(128, (1, 1), strides=(1, 1), name="inception_4a_pool_conv" + "")(
|
235 |
+
inception_4a_pool
|
236 |
+
)
|
237 |
+
inception_4a_pool = BatchNormalization(
|
238 |
+
axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + ""
|
239 |
+
)(inception_4a_pool)
|
240 |
+
inception_4a_pool = Activation("relu")(inception_4a_pool)
|
241 |
+
inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
|
242 |
+
|
243 |
+
inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + "")(
|
244 |
+
inception_3c
|
245 |
+
)
|
246 |
+
inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + "")(
|
247 |
+
inception_4a_1x1
|
248 |
+
)
|
249 |
+
inception_4a_1x1 = Activation("relu")(inception_4a_1x1)
|
250 |
+
|
251 |
+
inception_4a = concatenate(
|
252 |
+
[inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3
|
253 |
+
)
|
254 |
+
|
255 |
+
# inception4e
|
256 |
+
inception_4e_3x3 = Conv2D(160, (1, 1), strides=(1, 1), name="inception_4e_3x3_conv" + "1")(
|
257 |
+
inception_4a
|
258 |
+
)
|
259 |
+
inception_4e_3x3 = BatchNormalization(
|
260 |
+
axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1"
|
261 |
+
)(inception_4e_3x3)
|
262 |
+
inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
|
263 |
+
inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
|
264 |
+
inception_4e_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_4e_3x3_conv" + "2")(
|
265 |
+
inception_4e_3x3
|
266 |
+
)
|
267 |
+
inception_4e_3x3 = BatchNormalization(
|
268 |
+
axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2"
|
269 |
+
)(inception_4e_3x3)
|
270 |
+
inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
|
271 |
+
|
272 |
+
inception_4e_5x5 = Conv2D(64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1")(
|
273 |
+
inception_4a
|
274 |
+
)
|
275 |
+
inception_4e_5x5 = BatchNormalization(
|
276 |
+
axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1"
|
277 |
+
)(inception_4e_5x5)
|
278 |
+
inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
|
279 |
+
inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
|
280 |
+
inception_4e_5x5 = Conv2D(128, (5, 5), strides=(2, 2), name="inception_4e_5x5_conv" + "2")(
|
281 |
+
inception_4e_5x5
|
282 |
+
)
|
283 |
+
inception_4e_5x5 = BatchNormalization(
|
284 |
+
axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2"
|
285 |
+
)(inception_4e_5x5)
|
286 |
+
inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
|
287 |
+
|
288 |
+
inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
|
289 |
+
inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
|
290 |
+
|
291 |
+
inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
|
292 |
+
|
293 |
+
# inception5a
|
294 |
+
inception_5a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1")(
|
295 |
+
inception_4e
|
296 |
+
)
|
297 |
+
inception_5a_3x3 = BatchNormalization(
|
298 |
+
axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1"
|
299 |
+
)(inception_5a_3x3)
|
300 |
+
inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
|
301 |
+
inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
|
302 |
+
inception_5a_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5a_3x3_conv" + "2")(
|
303 |
+
inception_5a_3x3
|
304 |
+
)
|
305 |
+
inception_5a_3x3 = BatchNormalization(
|
306 |
+
axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2"
|
307 |
+
)(inception_5a_3x3)
|
308 |
+
inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
|
309 |
+
|
310 |
+
inception_5a_pool = Lambda(lambda x: x**2, name="power2_5a")(inception_4e)
|
311 |
+
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
|
312 |
+
inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(inception_5a_pool)
|
313 |
+
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(inception_5a_pool)
|
314 |
+
|
315 |
+
inception_5a_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + "")(
|
316 |
+
inception_5a_pool
|
317 |
+
)
|
318 |
+
inception_5a_pool = BatchNormalization(
|
319 |
+
axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + ""
|
320 |
+
)(inception_5a_pool)
|
321 |
+
inception_5a_pool = Activation("relu")(inception_5a_pool)
|
322 |
+
inception_5a_pool = ZeroPadding2D(padding=(1, 1))(inception_5a_pool)
|
323 |
+
|
324 |
+
inception_5a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + "")(
|
325 |
+
inception_4e
|
326 |
+
)
|
327 |
+
inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + "")(
|
328 |
+
inception_5a_1x1
|
329 |
+
)
|
330 |
+
inception_5a_1x1 = Activation("relu")(inception_5a_1x1)
|
331 |
+
|
332 |
+
inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
|
333 |
+
|
334 |
+
# inception_5b
|
335 |
+
inception_5b_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1")(
|
336 |
+
inception_5a
|
337 |
+
)
|
338 |
+
inception_5b_3x3 = BatchNormalization(
|
339 |
+
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1"
|
340 |
+
)(inception_5b_3x3)
|
341 |
+
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
342 |
+
inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3)
|
343 |
+
inception_5b_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5b_3x3_conv" + "2")(
|
344 |
+
inception_5b_3x3
|
345 |
+
)
|
346 |
+
inception_5b_3x3 = BatchNormalization(
|
347 |
+
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2"
|
348 |
+
)(inception_5b_3x3)
|
349 |
+
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
350 |
+
|
351 |
+
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
|
352 |
+
|
353 |
+
inception_5b_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + "")(
|
354 |
+
inception_5b_pool
|
355 |
+
)
|
356 |
+
inception_5b_pool = BatchNormalization(
|
357 |
+
axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + ""
|
358 |
+
)(inception_5b_pool)
|
359 |
+
inception_5b_pool = Activation("relu")(inception_5b_pool)
|
360 |
+
|
361 |
+
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
|
362 |
+
|
363 |
+
inception_5b_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + "")(
|
364 |
+
inception_5a
|
365 |
+
)
|
366 |
+
inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + "")(
|
367 |
+
inception_5b_1x1
|
368 |
+
)
|
369 |
+
inception_5b_1x1 = Activation("relu")(inception_5b_1x1)
|
370 |
+
|
371 |
+
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
|
372 |
+
|
373 |
+
av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
|
374 |
+
reshape_layer = Flatten()(av_pool)
|
375 |
+
dense_layer = Dense(128, name="dense_layer")(reshape_layer)
|
376 |
+
norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(dense_layer)
|
377 |
+
|
378 |
+
# Final Model
|
379 |
+
model = Model(inputs=[myInput], outputs=norm_layer)
|
380 |
+
|
381 |
+
# -----------------------------------
|
382 |
+
|
383 |
+
home = folder_utils.get_deepface_home()
|
384 |
+
|
385 |
+
if os.path.isfile(home + "/.deepface/weights/openface_weights.h5") != True:
|
386 |
+
logger.info("openface_weights.h5 will be downloaded...")
|
387 |
+
|
388 |
+
output = home + "/.deepface/weights/openface_weights.h5"
|
389 |
+
gdown.download(url, output, quiet=False)
|
390 |
+
|
391 |
+
# -----------------------------------
|
392 |
+
|
393 |
+
model.load_weights(home + "/.deepface/weights/openface_weights.h5")
|
394 |
+
|
395 |
+
# -----------------------------------
|
396 |
+
|
397 |
+
return model
|
basemodels/SFace.py
ADDED
@@ -0,0 +1,87 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# built-in dependencies
|
2 |
+
import os
|
3 |
+
from typing import Any, List
|
4 |
+
|
5 |
+
# 3rd party dependencies
|
6 |
+
import numpy as np
|
7 |
+
import cv2 as cv
|
8 |
+
import gdown
|
9 |
+
|
10 |
+
# project dependencies
|
11 |
+
from deepface.commons import folder_utils
|
12 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
13 |
+
from deepface.commons import logger as log
|
14 |
+
|
15 |
+
logger = log.get_singletonish_logger()
|
16 |
+
|
17 |
+
# pylint: disable=line-too-long, too-few-public-methods
|
18 |
+
|
19 |
+
|
20 |
+
class SFaceClient(FacialRecognition):
|
21 |
+
"""
|
22 |
+
SFace model class
|
23 |
+
"""
|
24 |
+
|
25 |
+
def __init__(self):
|
26 |
+
self.model = load_model()
|
27 |
+
self.model_name = "SFace"
|
28 |
+
self.input_shape = (112, 112)
|
29 |
+
self.output_shape = 128
|
30 |
+
|
31 |
+
def forward(self, img: np.ndarray) -> List[float]:
|
32 |
+
"""
|
33 |
+
Find embeddings with SFace model
|
34 |
+
This model necessitates the override of the forward method
|
35 |
+
because it is not a keras model.
|
36 |
+
Args:
|
37 |
+
img (np.ndarray): pre-loaded image in BGR
|
38 |
+
Returns
|
39 |
+
embeddings (list): multi-dimensional vector
|
40 |
+
"""
|
41 |
+
# return self.model.predict(img)[0].tolist()
|
42 |
+
|
43 |
+
# revert the image to original format and preprocess using the model
|
44 |
+
input_blob = (img[0] * 255).astype(np.uint8)
|
45 |
+
|
46 |
+
embeddings = self.model.model.feature(input_blob)
|
47 |
+
|
48 |
+
return embeddings[0].tolist()
|
49 |
+
|
50 |
+
|
51 |
+
def load_model(
|
52 |
+
url="https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx",
|
53 |
+
) -> Any:
|
54 |
+
"""
|
55 |
+
Construct SFace model, download its weights and load
|
56 |
+
"""
|
57 |
+
|
58 |
+
home = folder_utils.get_deepface_home()
|
59 |
+
|
60 |
+
file_name = home + "/.deepface/weights/face_recognition_sface_2021dec.onnx"
|
61 |
+
|
62 |
+
if not os.path.isfile(file_name):
|
63 |
+
|
64 |
+
logger.info("sface weights will be downloaded...")
|
65 |
+
|
66 |
+
gdown.download(url, file_name, quiet=False)
|
67 |
+
|
68 |
+
model = SFaceWrapper(model_path=file_name)
|
69 |
+
|
70 |
+
return model
|
71 |
+
|
72 |
+
|
73 |
+
class SFaceWrapper:
|
74 |
+
def __init__(self, model_path):
|
75 |
+
"""
|
76 |
+
SFace wrapper covering model construction, layer infos and predict
|
77 |
+
"""
|
78 |
+
try:
|
79 |
+
self.model = cv.FaceRecognizerSF.create(
|
80 |
+
model=model_path, config="", backend_id=0, target_id=0
|
81 |
+
)
|
82 |
+
except Exception as err:
|
83 |
+
raise ValueError(
|
84 |
+
"Exception while calling opencv.FaceRecognizerSF module."
|
85 |
+
+ "This is an optional dependency."
|
86 |
+
+ "You can install it as pip install opencv-contrib-python."
|
87 |
+
) from err
|
basemodels/VGGFace.py
ADDED
@@ -0,0 +1,160 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import List
|
2 |
+
import os
|
3 |
+
import gdown
|
4 |
+
import numpy as np
|
5 |
+
from deepface.commons import package_utils, folder_utils
|
6 |
+
from deepface.modules import verification
|
7 |
+
from deepface.models.FacialRecognition import FacialRecognition
|
8 |
+
from deepface.commons import logger as log
|
9 |
+
|
10 |
+
logger = log.get_singletonish_logger()
|
11 |
+
|
12 |
+
# ---------------------------------------
|
13 |
+
|
14 |
+
tf_version = package_utils.get_tf_major_version()
|
15 |
+
if tf_version == 1:
|
16 |
+
from keras.models import Model, Sequential
|
17 |
+
from keras.layers import (
|
18 |
+
Convolution2D,
|
19 |
+
ZeroPadding2D,
|
20 |
+
MaxPooling2D,
|
21 |
+
Flatten,
|
22 |
+
Dropout,
|
23 |
+
Activation,
|
24 |
+
)
|
25 |
+
else:
|
26 |
+
from tensorflow.keras.models import Model, Sequential
|
27 |
+
from tensorflow.keras.layers import (
|
28 |
+
Convolution2D,
|
29 |
+
ZeroPadding2D,
|
30 |
+
MaxPooling2D,
|
31 |
+
Flatten,
|
32 |
+
Dropout,
|
33 |
+
Activation,
|
34 |
+
)
|
35 |
+
|
36 |
+
# ---------------------------------------
|
37 |
+
|
38 |
+
# pylint: disable=too-few-public-methods
|
39 |
+
class VggFaceClient(FacialRecognition):
|
40 |
+
"""
|
41 |
+
VGG-Face model class
|
42 |
+
"""
|
43 |
+
|
44 |
+
def __init__(self):
|
45 |
+
self.model = load_model()
|
46 |
+
self.model_name = "VGG-Face"
|
47 |
+
self.input_shape = (224, 224)
|
48 |
+
self.output_shape = 4096
|
49 |
+
|
50 |
+
def forward(self, img: np.ndarray) -> List[float]:
|
51 |
+
"""
|
52 |
+
Generates embeddings using the VGG-Face model.
|
53 |
+
This method incorporates an additional normalization layer,
|
54 |
+
necessitating the override of the forward method.
|
55 |
+
|
56 |
+
Args:
|
57 |
+
img (np.ndarray): pre-loaded image in BGR
|
58 |
+
Returns
|
59 |
+
embeddings (list): multi-dimensional vector
|
60 |
+
"""
|
61 |
+
# model.predict causes memory issue when it is called in a for loop
|
62 |
+
# embedding = model.predict(img, verbose=0)[0].tolist()
|
63 |
+
|
64 |
+
# having normalization layer in descriptor troubles for some gpu users (e.g. issue 957, 966)
|
65 |
+
# instead we are now calculating it with traditional way not with keras backend
|
66 |
+
embedding = self.model(img, training=False).numpy()[0].tolist()
|
67 |
+
embedding = verification.l2_normalize(embedding)
|
68 |
+
return embedding.tolist()
|
69 |
+
|
70 |
+
|
71 |
+
def base_model() -> Sequential:
|
72 |
+
"""
|
73 |
+
Base model of VGG-Face being used for classification - not to find embeddings
|
74 |
+
Returns:
|
75 |
+
model (Sequential): model was trained to classify 2622 identities
|
76 |
+
"""
|
77 |
+
model = Sequential()
|
78 |
+
model.add(ZeroPadding2D((1, 1), input_shape=(224, 224, 3)))
|
79 |
+
model.add(Convolution2D(64, (3, 3), activation="relu"))
|
80 |
+
model.add(ZeroPadding2D((1, 1)))
|
81 |
+
model.add(Convolution2D(64, (3, 3), activation="relu"))
|
82 |
+
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
83 |
+
|
84 |
+
model.add(ZeroPadding2D((1, 1)))
|
85 |
+
model.add(Convolution2D(128, (3, 3), activation="relu"))
|
86 |
+
model.add(ZeroPadding2D((1, 1)))
|
87 |
+
model.add(Convolution2D(128, (3, 3), activation="relu"))
|
88 |
+
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
89 |
+
|
90 |
+
model.add(ZeroPadding2D((1, 1)))
|
91 |
+
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
92 |
+
model.add(ZeroPadding2D((1, 1)))
|
93 |
+
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
94 |
+
model.add(ZeroPadding2D((1, 1)))
|
95 |
+
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
96 |
+
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
97 |
+
|
98 |
+
model.add(ZeroPadding2D((1, 1)))
|
99 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
100 |
+
model.add(ZeroPadding2D((1, 1)))
|
101 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
102 |
+
model.add(ZeroPadding2D((1, 1)))
|
103 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
104 |
+
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
105 |
+
|
106 |
+
model.add(ZeroPadding2D((1, 1)))
|
107 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
108 |
+
model.add(ZeroPadding2D((1, 1)))
|
109 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
110 |
+
model.add(ZeroPadding2D((1, 1)))
|
111 |
+
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
112 |
+
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
113 |
+
|
114 |
+
model.add(Convolution2D(4096, (7, 7), activation="relu"))
|
115 |
+
model.add(Dropout(0.5))
|
116 |
+
model.add(Convolution2D(4096, (1, 1), activation="relu"))
|
117 |
+
model.add(Dropout(0.5))
|
118 |
+
model.add(Convolution2D(2622, (1, 1)))
|
119 |
+
model.add(Flatten())
|
120 |
+
model.add(Activation("softmax"))
|
121 |
+
|
122 |
+
return model
|
123 |
+
|
124 |
+
|
125 |
+
def load_model(
|
126 |
+
url="https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5",
|
127 |
+
) -> Model:
|
128 |
+
"""
|
129 |
+
Final VGG-Face model being used for finding embeddings
|
130 |
+
Returns:
|
131 |
+
model (Model): returning 4096 dimensional vectors
|
132 |
+
"""
|
133 |
+
|
134 |
+
model = base_model()
|
135 |
+
|
136 |
+
home = folder_utils.get_deepface_home()
|
137 |
+
output = home + "/.deepface/weights/vgg_face_weights.h5"
|
138 |
+
|
139 |
+
if os.path.isfile(output) != True:
|
140 |
+
logger.info("vgg_face_weights.h5 will be downloaded...")
|
141 |
+
gdown.download(url, output, quiet=False)
|
142 |
+
|
143 |
+
model.load_weights(output)
|
144 |
+
|
145 |
+
# 2622d dimensional model
|
146 |
+
# vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
|
147 |
+
|
148 |
+
# 4096 dimensional model offers 6% to 14% increasement on accuracy!
|
149 |
+
# - softmax causes underfitting
|
150 |
+
# - added normalization layer to avoid underfitting with euclidean
|
151 |
+
# as described here: https://github.com/serengil/deepface/issues/944
|
152 |
+
base_model_output = Sequential()
|
153 |
+
base_model_output = Flatten()(model.layers[-5].output)
|
154 |
+
# keras backend's l2 normalization layer troubles some gpu users (e.g. issue 957, 966)
|
155 |
+
# base_model_output = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(
|
156 |
+
# base_model_output
|
157 |
+
# )
|
158 |
+
vgg_face_descriptor = Model(inputs=model.input, outputs=base_model_output)
|
159 |
+
|
160 |
+
return vgg_face_descriptor
|
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ADDED
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commons/__pycache__/__init__.cpython-312.pyc
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commons/__pycache__/file_utils.cpython-312.pyc
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commons/__pycache__/folder_utils.cpython-312.pyc
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commons/__pycache__/image_utils.cpython-312.pyc
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commons/__pycache__/logger.cpython-312.pyc
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commons/__pycache__/os_path.cpython-312.pyc
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