mirror of
https://github.com/serengil/deepface.git
synced 2025-06-09 21:07:09 +00:00
Pass custom path argument while loading model
Add `model_path` parameter in `loadModel()` function. This adds more flexiblity while loading the models. Also, refactor code using standard `os.path.join` which will make sure to join the paths correctly.
This commit is contained in:
NISH1001
parent
892cbae56a
commit
2c0a507994
@ -1,5 +1,6 @@
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from keras.preprocessing import image
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import warnings
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warnings.filterwarnings("ignore")
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import time
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import os
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@ -20,8 +21,15 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.commons import functions, realtime, distance as dst
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def verify(img1_path, img2_path=''
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
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def verify(
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img1_path,
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img2_path="",
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model_name="VGG-Face",
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distance_metric="cosine",
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model=None,
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enforce_detection=True,
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):
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tic = time.time()
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@ -35,19 +43,19 @@ def verify(img1_path, img2_path=''
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# ------------------------------
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if model == None:
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if model_name == 'VGG-Face':
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if model_name == "VGG-Face":
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print("Using VGG-Face model backend and", distance_metric, "distance.")
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model = VGGFace.loadModel()
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elif model_name == 'OpenFace':
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elif model_name == "OpenFace":
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print("Using OpenFace model backend", distance_metric, "distance.")
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model = OpenFace.loadModel()
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elif model_name == 'Facenet':
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elif model_name == "Facenet":
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print("Using Facenet model backend", distance_metric, "distance.")
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model = Facenet.loadModel()
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elif model_name == 'DeepFace':
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elif model_name == "DeepFace":
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print("Using FB DeepFace model backend", distance_metric, "distance.")
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model = FbDeepFace.loadModel()
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@ -66,7 +74,7 @@ def verify(img1_path, img2_path=''
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threshold = functions.findThreshold(model_name, distance_metric)
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# ------------------------------
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pbar = tqdm(range(0,len(img_list)), desc='Verification')
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pbar = tqdm(range(0, len(img_list)), desc="Verification")
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resp_objects = []
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@ -82,8 +90,12 @@ def verify(img1_path, img2_path=''
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# ----------------------
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# crop and align faces
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img1 = functions.detectFace(img1_path, input_shape, enforce_detection = enforce_detection)
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img2 = functions.detectFace(img2_path, input_shape, enforce_detection = enforce_detection)
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img1 = functions.detectFace(
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img1_path, input_shape, enforce_detection=enforce_detection
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)
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img2 = functions.detectFace(
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img2_path, input_shape, enforce_detection=enforce_detection
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)
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# ----------------------
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# find embeddings
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@ -94,12 +106,19 @@ def verify(img1_path, img2_path=''
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# ----------------------
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# find distances between embeddings
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if distance_metric == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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if distance_metric == "cosine":
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distance = dst.findCosineDistance(
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img1_representation, img2_representation
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)
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elif distance_metric == "euclidean":
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distance = dst.findEuclideanDistance(
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img1_representation, img2_representation
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)
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elif distance_metric == "euclidean_l2":
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distance = dst.findEuclideanDistance(
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dst.l2_normalize(img1_representation),
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dst.l2_normalize(img2_representation),
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)
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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@ -115,11 +134,11 @@ def verify(img1_path, img2_path=''
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# response object
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resp_obj = "{"
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resp_obj += "\"verified\": "+identified
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resp_obj += ", \"distance\": "+str(distance)
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resp_obj += ", \"max_threshold_to_verify\": "+str(threshold)
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resp_obj += ", \"model\": \""+model_name+"\""
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resp_obj += ", \"similarity_metric\": \""+distance_metric+"\""
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resp_obj += '"verified": ' + identified
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resp_obj += ', "distance": ' + str(distance)
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resp_obj += ', "max_threshold_to_verify": ' + str(threshold)
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resp_obj += ', "model": "' + model_name + '"'
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resp_obj += ', "similarity_metric": "' + distance_metric + '"'
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resp_obj += "}"
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resp_obj = json.loads(resp_obj) # string to json
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@ -149,7 +168,7 @@ def verify(img1_path, img2_path=''
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"pair_"+str(i+1)+"\": "+resp_item
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resp_obj += '"pair_' + str(i + 1) + '": ' + resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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@ -169,44 +188,44 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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# if a specific target is not passed, then find them all
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if len(actions) == 0:
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actions= ['emotion', 'age', 'gender', 'race']
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actions = ["emotion", "age", "gender", "race"]
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print("Actions to do: ", actions)
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# ---------------------------------
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if 'emotion' in actions:
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if 'emotion' in models:
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if "emotion" in actions:
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if "emotion" in models:
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print("already built emotion model is passed")
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emotion_model = models['emotion']
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emotion_model = models["emotion"]
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else:
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emotion_model = Emotion.loadModel()
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if 'age' in actions:
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if 'age' in models:
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if "age" in actions:
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if "age" in models:
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print("already built age model is passed")
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age_model = models['age']
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age_model = models["age"]
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else:
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age_model = Age.loadModel()
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if 'gender' in actions:
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if 'gender' in models:
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if "gender" in actions:
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if "gender" in models:
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print("already built gender model is passed")
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gender_model = models['gender']
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gender_model = models["gender"]
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else:
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gender_model = Gender.loadModel()
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if 'race' in actions:
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if 'race' in models:
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if "race" in actions:
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if "race" in models:
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print("already built race model is passed")
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race_model = models['race']
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race_model = models["race"]
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else:
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race_model = Race.loadModel()
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# ---------------------------------
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resp_objects = []
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global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing')
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global_pbar = tqdm(range(0, len(img_paths)), desc="Analyzing")
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# for img_path in img_paths:
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for j in global_pbar:
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@ -216,7 +235,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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# TO-DO: do this in parallel
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pbar = tqdm(range(0,len(actions)), desc='Finding actions')
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pbar = tqdm(range(0, len(actions)), desc="Finding actions")
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action_idx = 0
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img_224 = None # Set to prevent re-detection
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@ -228,41 +247,69 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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if action_idx > 0:
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resp_obj += ", "
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if action == 'emotion':
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emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
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img = functions.detectFace(img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection)
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if action == "emotion":
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emotion_labels = [
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"angry",
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"disgust",
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"fear",
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"happy",
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"sad",
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"surprise",
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"neutral",
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]
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img = functions.detectFace(
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img_path,
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target_size=(48, 48),
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grayscale=True,
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enforce_detection=enforce_detection,
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)
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emotion_predictions = emotion_model.predict(img)[0, :]
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sum_of_predictions = emotion_predictions.sum()
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emotion_obj = "\"emotion\": {"
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emotion_obj = '"emotion": {'
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for i in range(0, len(emotion_labels)):
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emotion_label = emotion_labels[i]
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emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
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emotion_prediction = (
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100 * emotion_predictions[i] / sum_of_predictions
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)
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if i > 0: emotion_obj += ", "
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if i > 0:
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emotion_obj += ", "
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emotion_obj += "\"%s\": %s" % (emotion_label, emotion_prediction)
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emotion_obj += '"%s": %s' % (emotion_label, emotion_prediction)
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emotion_obj += "}"
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emotion_obj += ", \"dominant_emotion\": \"%s\"" % (emotion_labels[np.argmax(emotion_predictions)])
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emotion_obj += ', "dominant_emotion": "%s"' % (
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emotion_labels[np.argmax(emotion_predictions)]
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)
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resp_obj += emotion_obj
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elif action == 'age':
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elif action == "age":
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.detectFace(
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img_path,
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target_size=(224, 224),
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grayscale=False,
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enforce_detection=enforce_detection,
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) # just emotion model expects grayscale images
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# print("age prediction")
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age_predictions = age_model.predict(img_224)[0, :]
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apparent_age = Age.findApparentAge(age_predictions)
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resp_obj += "\"age\": %s" % (apparent_age)
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resp_obj += '"age": %s' % (apparent_age)
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elif action == 'gender':
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elif action == "gender":
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.detectFace(
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img_path,
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target_size=(224, 224),
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grayscale=False,
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enforce_detection=enforce_detection,
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) # just emotion model expects grayscale images
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# print("gender prediction")
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gender_prediction = gender_model.predict(img_224)[0, :]
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@ -272,27 +319,42 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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elif np.argmax(gender_prediction) == 1:
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gender = "Man"
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resp_obj += "\"gender\": \"%s\"" % (gender)
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resp_obj += '"gender": "%s"' % (gender)
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elif action == 'race':
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elif action == "race":
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.detectFace(
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img_path,
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target_size=(224, 224),
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grayscale=False,
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enforce_detection=enforce_detection,
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) # just emotion model expects grayscale images
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race_predictions = race_model.predict(img_224)[0, :]
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race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
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race_labels = [
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"asian",
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"indian",
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"black",
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"white",
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"middle eastern",
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"latino hispanic",
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]
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sum_of_predictions = race_predictions.sum()
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race_obj = "\"race\": {"
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race_obj = '"race": {'
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for i in range(0, len(race_labels)):
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race_label = race_labels[i]
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race_prediction = 100 * race_predictions[i] / sum_of_predictions
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if i > 0: race_obj += ", "
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if i > 0:
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race_obj += ", "
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race_obj += "\"%s\": %s" % (race_label, race_prediction)
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race_obj += '"%s": %s' % (race_label, race_prediction)
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race_obj += "}"
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race_obj += ", \"dominant_race\": \"%s\"" % (race_labels[np.argmax(race_predictions)])
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race_obj += ', "dominant_race": "%s"' % (
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race_labels[np.argmax(race_predictions)]
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)
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resp_obj += race_obj
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@ -316,7 +378,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
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resp_obj += '"instance_' + str(i + 1) + '": ' + resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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@ -328,14 +390,20 @@ def detectFace(img_path):
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return img[:, :, ::-1] # bgr to rgb
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def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
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def stream(
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db_path="",
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model_name="VGG-Face",
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distance_metric="cosine",
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enable_face_analysis=True,
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):
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realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
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def allocateMemory():
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print("Analyzing your system...")
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functions.allocateMemory()
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functions.initializeFolder()
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# ---------------------------
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File diff suppressed because it is too large
Load Diff
@ -3,44 +3,71 @@ from pathlib import Path
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import gdown
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import keras
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from keras.models import Model, Sequential
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from keras.layers import Convolution2D, LocallyConnected2D, MaxPooling2D, Flatten, Dense, Dropout
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from keras.layers import (
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Convolution2D,
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LocallyConnected2D,
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MaxPooling2D,
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Flatten,
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Dense,
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Dropout,
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)
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import zipfile
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# -------------------------------------
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def loadModel():
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def get_base_model():
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base_model = Sequential()
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base_model.add(Convolution2D(32, (11, 11), activation='relu', name='C1', input_shape=(152, 152, 3)))
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base_model.add(MaxPooling2D(pool_size=3, strides=2, padding='same', name='M2'))
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base_model.add(Convolution2D(16, (9, 9), activation='relu', name='C3'))
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base_model.add(LocallyConnected2D(16, (9, 9), activation='relu', name='L4'))
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base_model.add(LocallyConnected2D(16, (7, 7), strides=2, activation='relu', name='L5') )
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base_model.add(LocallyConnected2D(16, (5, 5), activation='relu', name='L6'))
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base_model.add(Flatten(name='F0'))
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base_model.add(Dense(4096, activation='relu', name='F7'))
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base_model.add(Dropout(rate=0.5, name='D0'))
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base_model.add(Dense(8631, activation='softmax', name='F8'))
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base_model.add(
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Convolution2D(
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32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3)
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)
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)
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base_model.add(MaxPooling2D(pool_size=3, strides=2, padding="same", name="M2"))
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base_model.add(Convolution2D(16, (9, 9), activation="relu", name="C3"))
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base_model.add(LocallyConnected2D(16, (9, 9), activation="relu", name="L4"))
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base_model.add(
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LocallyConnected2D(16, (7, 7), strides=2, activation="relu", name="L5")
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)
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base_model.add(LocallyConnected2D(16, (5, 5), activation="relu", name="L6"))
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base_model.add(Flatten(name="F0"))
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base_model.add(Dense(4096, activation="relu", name="F7"))
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base_model.add(Dropout(rate=0.5, name="D0"))
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base_model.add(Dense(8631, activation="softmax", name="F8"))
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return base_model
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def loadModel(model_path=""):
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# ---------------------------------
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home = str(Path.home())
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if os.path.isfile(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5') != True:
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if model_path:
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assert Path(model_path).exists()
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assert model_path.endswith(".h5")
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else:
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home = Path.home().as_posix()
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model_path = os.path.join(
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home, ".deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5"
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)
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if not os.path.isfile(model_path):
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print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
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url = 'https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
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url = "https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
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output = home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
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gdown.download(url, output, quiet=False)
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zip_path = os.path.join(
|
||||
home, ".deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
|
||||
)
|
||||
gdown.download(url, zip_path, quiet=False)
|
||||
|
||||
# unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
|
||||
with zipfile.ZipFile(output, 'r') as zip_ref:
|
||||
zip_ref.extractall(home+'/.deepface/weights/')
|
||||
with zipfile.ZipFile(zip_path, "r") as zip_ref:
|
||||
zip_ref.extractall(os.path.join(home, "/.deepface/weights/"))
|
||||
|
||||
base_model.load_weights(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5')
|
||||
print(f"Loading model from {model_path}")
|
||||
base_model = get_base_model()
|
||||
base_model.load_weights(model_path)
|
||||
|
||||
# drop F8 and D0. F7 is the representation layer.
|
||||
deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
|
||||
deepface_model = Model(
|
||||
inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output
|
||||
)
|
||||
|
||||
return deepface_model
|
||||
@ -15,235 +15,394 @@ from keras import backend as K
|
||||
|
||||
# ---------------------------------------
|
||||
|
||||
def loadModel():
|
||||
|
||||
def get_base_model():
|
||||
myInput = Input(shape=(96, 96, 3))
|
||||
|
||||
x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
|
||||
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
|
||||
x = Activation('relu')(x)
|
||||
x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x)
|
||||
x = Activation("relu")(x)
|
||||
x = ZeroPadding2D(padding=(1, 1))(x)
|
||||
x = MaxPooling2D(pool_size=3, strides=2)(x)
|
||||
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_1')(x)
|
||||
x = Conv2D(64, (1, 1), name='conv2')(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
|
||||
x = Activation('relu')(x)
|
||||
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1")(x)
|
||||
x = Conv2D(64, (1, 1), name="conv2")(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x)
|
||||
x = Activation("relu")(x)
|
||||
x = ZeroPadding2D(padding=(1, 1))(x)
|
||||
x = Conv2D(192, (3, 3), name='conv3')(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
|
||||
x = Activation('relu')(x)
|
||||
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x) #x is equal added
|
||||
x = Conv2D(192, (3, 3), name="conv3")(x)
|
||||
x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x)
|
||||
x = Activation("relu")(x)
|
||||
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2")(
|
||||
x
|
||||
) # x is equal added
|
||||
x = ZeroPadding2D(padding=(1, 1))(x)
|
||||
x = MaxPooling2D(pool_size=3, strides=2)(x)
|
||||
|
||||
# Inception3a
|
||||
inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
|
||||
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
|
||||
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
|
||||
inception_3a_3x3 = Conv2D(96, (1, 1), name="inception_3a_3x3_conv1")(x)
|
||||
inception_3a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1"
|
||||
)(inception_3a_3x3)
|
||||
inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
|
||||
inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
|
||||
inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
|
||||
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
|
||||
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
|
||||
inception_3a_3x3 = Conv2D(128, (3, 3), name="inception_3a_3x3_conv2")(
|
||||
inception_3a_3x3
|
||||
)
|
||||
inception_3a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2"
|
||||
)(inception_3a_3x3)
|
||||
inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
|
||||
|
||||
inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
|
||||
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
|
||||
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
|
||||
inception_3a_5x5 = Conv2D(16, (1, 1), name="inception_3a_5x5_conv1")(x)
|
||||
inception_3a_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1"
|
||||
)(inception_3a_5x5)
|
||||
inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
|
||||
inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
|
||||
inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
|
||||
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
|
||||
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
|
||||
inception_3a_5x5 = Conv2D(32, (5, 5), name="inception_3a_5x5_conv2")(
|
||||
inception_3a_5x5
|
||||
)
|
||||
inception_3a_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2"
|
||||
)(inception_3a_5x5)
|
||||
inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
|
||||
|
||||
inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
|
||||
inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
|
||||
inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
|
||||
inception_3a_pool = Activation('relu')(inception_3a_pool)
|
||||
inception_3a_pool = Conv2D(32, (1, 1), name="inception_3a_pool_conv")(
|
||||
inception_3a_pool
|
||||
)
|
||||
inception_3a_pool = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_pool_bn"
|
||||
)(inception_3a_pool)
|
||||
inception_3a_pool = Activation("relu")(inception_3a_pool)
|
||||
inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
|
||||
|
||||
inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
|
||||
inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
|
||||
inception_3a_1x1 = Activation('relu')(inception_3a_1x1)
|
||||
inception_3a_1x1 = Conv2D(64, (1, 1), name="inception_3a_1x1_conv")(x)
|
||||
inception_3a_1x1 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3a_1x1_bn"
|
||||
)(inception_3a_1x1)
|
||||
inception_3a_1x1 = Activation("relu")(inception_3a_1x1)
|
||||
|
||||
inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)
|
||||
inception_3a = concatenate(
|
||||
[inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1],
|
||||
axis=3,
|
||||
)
|
||||
|
||||
# Inception3b
|
||||
inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
|
||||
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
|
||||
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
|
||||
inception_3b_3x3 = Conv2D(96, (1, 1), name="inception_3b_3x3_conv1")(inception_3a)
|
||||
inception_3b_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1"
|
||||
)(inception_3b_3x3)
|
||||
inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
|
||||
inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
|
||||
inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
|
||||
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
|
||||
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
|
||||
inception_3b_3x3 = Conv2D(128, (3, 3), name="inception_3b_3x3_conv2")(
|
||||
inception_3b_3x3
|
||||
)
|
||||
inception_3b_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2"
|
||||
)(inception_3b_3x3)
|
||||
inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
|
||||
|
||||
inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
|
||||
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
|
||||
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
|
||||
inception_3b_5x5 = Conv2D(32, (1, 1), name="inception_3b_5x5_conv1")(inception_3a)
|
||||
inception_3b_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1"
|
||||
)(inception_3b_5x5)
|
||||
inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
|
||||
inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
|
||||
inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
|
||||
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
|
||||
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
|
||||
inception_3b_5x5 = Conv2D(64, (5, 5), name="inception_3b_5x5_conv2")(
|
||||
inception_3b_5x5
|
||||
)
|
||||
inception_3b_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2"
|
||||
)(inception_3b_5x5)
|
||||
inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
|
||||
|
||||
inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
|
||||
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
|
||||
inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
|
||||
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
|
||||
inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
|
||||
inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
|
||||
inception_3b_pool = Activation('relu')(inception_3b_pool)
|
||||
inception_3b_pool = Lambda(lambda x: x ** 2, name="power2_3b")(inception_3a)
|
||||
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(
|
||||
inception_3b_pool
|
||||
)
|
||||
inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(inception_3b_pool)
|
||||
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(inception_3b_pool)
|
||||
inception_3b_pool = Conv2D(64, (1, 1), name="inception_3b_pool_conv")(
|
||||
inception_3b_pool
|
||||
)
|
||||
inception_3b_pool = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_pool_bn"
|
||||
)(inception_3b_pool)
|
||||
inception_3b_pool = Activation("relu")(inception_3b_pool)
|
||||
inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
|
||||
|
||||
inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
|
||||
inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
|
||||
inception_3b_1x1 = Activation('relu')(inception_3b_1x1)
|
||||
inception_3b_1x1 = Conv2D(64, (1, 1), name="inception_3b_1x1_conv")(inception_3a)
|
||||
inception_3b_1x1 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3b_1x1_bn"
|
||||
)(inception_3b_1x1)
|
||||
inception_3b_1x1 = Activation("relu")(inception_3b_1x1)
|
||||
|
||||
inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)
|
||||
inception_3b = concatenate(
|
||||
[inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1],
|
||||
axis=3,
|
||||
)
|
||||
|
||||
# Inception3c
|
||||
inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name='inception_3c_3x3_conv1')(inception_3b)
|
||||
inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn1')(inception_3c_3x3)
|
||||
inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
|
||||
inception_3c_3x3 = Conv2D(
|
||||
128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1"
|
||||
)(inception_3b)
|
||||
inception_3c_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1"
|
||||
)(inception_3c_3x3)
|
||||
inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
|
||||
inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
|
||||
inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name='inception_3c_3x3_conv'+'2')(inception_3c_3x3)
|
||||
inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn'+'2')(inception_3c_3x3)
|
||||
inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
|
||||
inception_3c_3x3 = Conv2D(
|
||||
256, (3, 3), strides=(2, 2), name="inception_3c_3x3_conv" + "2"
|
||||
)(inception_3c_3x3)
|
||||
inception_3c_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2"
|
||||
)(inception_3c_3x3)
|
||||
inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
|
||||
|
||||
inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name='inception_3c_5x5_conv1')(inception_3b)
|
||||
inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn1')(inception_3c_5x5)
|
||||
inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
|
||||
inception_3c_5x5 = Conv2D(
|
||||
32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1"
|
||||
)(inception_3b)
|
||||
inception_3c_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1"
|
||||
)(inception_3c_5x5)
|
||||
inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
|
||||
inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
|
||||
inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name='inception_3c_5x5_conv'+'2')(inception_3c_5x5)
|
||||
inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn'+'2')(inception_3c_5x5)
|
||||
inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
|
||||
inception_3c_5x5 = Conv2D(
|
||||
64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2"
|
||||
)(inception_3c_5x5)
|
||||
inception_3c_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2"
|
||||
)(inception_3c_5x5)
|
||||
inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
|
||||
|
||||
inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
|
||||
inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
|
||||
|
||||
inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
|
||||
inception_3c = concatenate(
|
||||
[inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3
|
||||
)
|
||||
|
||||
# inception 4a
|
||||
inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name='inception_4a_3x3_conv'+'1')(inception_3c)
|
||||
inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'1')(inception_4a_3x3)
|
||||
inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
|
||||
inception_4a_3x3 = Conv2D(
|
||||
96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1"
|
||||
)(inception_3c)
|
||||
inception_4a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1"
|
||||
)(inception_4a_3x3)
|
||||
inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
|
||||
inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
|
||||
inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name='inception_4a_3x3_conv'+'2')(inception_4a_3x3)
|
||||
inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'2')(inception_4a_3x3)
|
||||
inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
|
||||
inception_4a_3x3 = Conv2D(
|
||||
192, (3, 3), strides=(1, 1), name="inception_4a_3x3_conv" + "2"
|
||||
)(inception_4a_3x3)
|
||||
inception_4a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2"
|
||||
)(inception_4a_3x3)
|
||||
inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
|
||||
|
||||
inception_4a_5x5 = Conv2D(32, (1,1), strides=(1,1), name='inception_4a_5x5_conv1')(inception_3c)
|
||||
inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn1')(inception_4a_5x5)
|
||||
inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
|
||||
inception_4a_5x5 = Conv2D(
|
||||
32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1"
|
||||
)(inception_3c)
|
||||
inception_4a_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1"
|
||||
)(inception_4a_5x5)
|
||||
inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
|
||||
inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5)
|
||||
inception_4a_5x5 = Conv2D(64, (5,5), strides=(1,1), name='inception_4a_5x5_conv'+'2')(inception_4a_5x5)
|
||||
inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn'+'2')(inception_4a_5x5)
|
||||
inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
|
||||
inception_4a_5x5 = Conv2D(
|
||||
64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2"
|
||||
)(inception_4a_5x5)
|
||||
inception_4a_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2"
|
||||
)(inception_4a_5x5)
|
||||
inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
|
||||
|
||||
inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
|
||||
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
|
||||
inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
|
||||
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
|
||||
inception_4a_pool = Lambda(lambda x: x ** 2, name="power2_4a")(inception_3c)
|
||||
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(
|
||||
inception_4a_pool
|
||||
)
|
||||
inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(inception_4a_pool)
|
||||
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(inception_4a_pool)
|
||||
|
||||
inception_4a_pool = Conv2D(128, (1,1), strides=(1,1), name='inception_4a_pool_conv'+'')(inception_4a_pool)
|
||||
inception_4a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_pool_bn'+'')(inception_4a_pool)
|
||||
inception_4a_pool = Activation('relu')(inception_4a_pool)
|
||||
inception_4a_pool = Conv2D(
|
||||
128, (1, 1), strides=(1, 1), name="inception_4a_pool_conv" + ""
|
||||
)(inception_4a_pool)
|
||||
inception_4a_pool = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + ""
|
||||
)(inception_4a_pool)
|
||||
inception_4a_pool = Activation("relu")(inception_4a_pool)
|
||||
inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
|
||||
|
||||
inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name='inception_4a_1x1_conv'+'')(inception_3c)
|
||||
inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_1x1_bn'+'')(inception_4a_1x1)
|
||||
inception_4a_1x1 = Activation('relu')(inception_4a_1x1)
|
||||
inception_4a_1x1 = Conv2D(
|
||||
256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + ""
|
||||
)(inception_3c)
|
||||
inception_4a_1x1 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + ""
|
||||
)(inception_4a_1x1)
|
||||
inception_4a_1x1 = Activation("relu")(inception_4a_1x1)
|
||||
|
||||
inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)
|
||||
inception_4a = concatenate(
|
||||
[inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1],
|
||||
axis=3,
|
||||
)
|
||||
|
||||
# inception4e
|
||||
inception_4e_3x3 = Conv2D(160, (1,1), strides=(1,1), name='inception_4e_3x3_conv'+'1')(inception_4a)
|
||||
inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'1')(inception_4e_3x3)
|
||||
inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
|
||||
inception_4e_3x3 = Conv2D(
|
||||
160, (1, 1), strides=(1, 1), name="inception_4e_3x3_conv" + "1"
|
||||
)(inception_4a)
|
||||
inception_4e_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1"
|
||||
)(inception_4e_3x3)
|
||||
inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
|
||||
inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
|
||||
inception_4e_3x3 = Conv2D(256, (3,3), strides=(2,2), name='inception_4e_3x3_conv'+'2')(inception_4e_3x3)
|
||||
inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'2')(inception_4e_3x3)
|
||||
inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
|
||||
inception_4e_3x3 = Conv2D(
|
||||
256, (3, 3), strides=(2, 2), name="inception_4e_3x3_conv" + "2"
|
||||
)(inception_4e_3x3)
|
||||
inception_4e_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2"
|
||||
)(inception_4e_3x3)
|
||||
inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
|
||||
|
||||
inception_4e_5x5 = Conv2D(64, (1,1), strides=(1,1), name='inception_4e_5x5_conv'+'1')(inception_4a)
|
||||
inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'1')(inception_4e_5x5)
|
||||
inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
|
||||
inception_4e_5x5 = Conv2D(
|
||||
64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1"
|
||||
)(inception_4a)
|
||||
inception_4e_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1"
|
||||
)(inception_4e_5x5)
|
||||
inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
|
||||
inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
|
||||
inception_4e_5x5 = Conv2D(128, (5,5), strides=(2,2), name='inception_4e_5x5_conv'+'2')(inception_4e_5x5)
|
||||
inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'2')(inception_4e_5x5)
|
||||
inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
|
||||
inception_4e_5x5 = Conv2D(
|
||||
128, (5, 5), strides=(2, 2), name="inception_4e_5x5_conv" + "2"
|
||||
)(inception_4e_5x5)
|
||||
inception_4e_5x5 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2"
|
||||
)(inception_4e_5x5)
|
||||
inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
|
||||
|
||||
inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
|
||||
inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
|
||||
|
||||
inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
|
||||
inception_4e = concatenate(
|
||||
[inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3
|
||||
)
|
||||
|
||||
# inception5a
|
||||
inception_5a_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_3x3_conv'+'1')(inception_4e)
|
||||
inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'1')(inception_5a_3x3)
|
||||
inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
|
||||
inception_5a_3x3 = Conv2D(
|
||||
96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1"
|
||||
)(inception_4e)
|
||||
inception_5a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1"
|
||||
)(inception_5a_3x3)
|
||||
inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
|
||||
inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
|
||||
inception_5a_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5a_3x3_conv'+'2')(inception_5a_3x3)
|
||||
inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'2')(inception_5a_3x3)
|
||||
inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
|
||||
inception_5a_3x3 = Conv2D(
|
||||
384, (3, 3), strides=(1, 1), name="inception_5a_3x3_conv" + "2"
|
||||
)(inception_5a_3x3)
|
||||
inception_5a_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2"
|
||||
)(inception_5a_3x3)
|
||||
inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
|
||||
|
||||
inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
|
||||
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
|
||||
inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
|
||||
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
|
||||
inception_5a_pool = Lambda(lambda x: x ** 2, name="power2_5a")(inception_4e)
|
||||
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(
|
||||
inception_5a_pool
|
||||
)
|
||||
inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(inception_5a_pool)
|
||||
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(inception_5a_pool)
|
||||
|
||||
inception_5a_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_pool_conv'+'')(inception_5a_pool)
|
||||
inception_5a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_pool_bn'+'')(inception_5a_pool)
|
||||
inception_5a_pool = Activation('relu')(inception_5a_pool)
|
||||
inception_5a_pool = Conv2D(
|
||||
96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + ""
|
||||
)(inception_5a_pool)
|
||||
inception_5a_pool = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + ""
|
||||
)(inception_5a_pool)
|
||||
inception_5a_pool = Activation("relu")(inception_5a_pool)
|
||||
inception_5a_pool = ZeroPadding2D(padding=(1, 1))(inception_5a_pool)
|
||||
|
||||
inception_5a_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5a_1x1_conv'+'')(inception_4e)
|
||||
inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_1x1_bn'+'')(inception_5a_1x1)
|
||||
inception_5a_1x1 = Activation('relu')(inception_5a_1x1)
|
||||
inception_5a_1x1 = Conv2D(
|
||||
256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + ""
|
||||
)(inception_4e)
|
||||
inception_5a_1x1 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + ""
|
||||
)(inception_5a_1x1)
|
||||
inception_5a_1x1 = Activation("relu")(inception_5a_1x1)
|
||||
|
||||
inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
|
||||
inception_5a = concatenate(
|
||||
[inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3
|
||||
)
|
||||
|
||||
# inception_5b
|
||||
inception_5b_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_3x3_conv'+'1')(inception_5a)
|
||||
inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'1')(inception_5b_3x3)
|
||||
inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
|
||||
inception_5b_3x3 = Conv2D(
|
||||
96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1"
|
||||
)(inception_5a)
|
||||
inception_5b_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1"
|
||||
)(inception_5b_3x3)
|
||||
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
||||
inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3)
|
||||
inception_5b_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5b_3x3_conv'+'2')(inception_5b_3x3)
|
||||
inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'2')(inception_5b_3x3)
|
||||
inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
|
||||
inception_5b_3x3 = Conv2D(
|
||||
384, (3, 3), strides=(1, 1), name="inception_5b_3x3_conv" + "2"
|
||||
)(inception_5b_3x3)
|
||||
inception_5b_3x3 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2"
|
||||
)(inception_5b_3x3)
|
||||
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
||||
|
||||
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
|
||||
|
||||
inception_5b_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_pool_conv'+'')(inception_5b_pool)
|
||||
inception_5b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_pool_bn'+'')(inception_5b_pool)
|
||||
inception_5b_pool = Activation('relu')(inception_5b_pool)
|
||||
inception_5b_pool = Conv2D(
|
||||
96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + ""
|
||||
)(inception_5b_pool)
|
||||
inception_5b_pool = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + ""
|
||||
)(inception_5b_pool)
|
||||
inception_5b_pool = Activation("relu")(inception_5b_pool)
|
||||
|
||||
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
|
||||
|
||||
inception_5b_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5b_1x1_conv'+'')(inception_5a)
|
||||
inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_1x1_bn'+'')(inception_5b_1x1)
|
||||
inception_5b_1x1 = Activation('relu')(inception_5b_1x1)
|
||||
inception_5b_1x1 = Conv2D(
|
||||
256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + ""
|
||||
)(inception_5a)
|
||||
inception_5b_1x1 = BatchNormalization(
|
||||
axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + ""
|
||||
)(inception_5b_1x1)
|
||||
inception_5b_1x1 = Activation("relu")(inception_5b_1x1)
|
||||
|
||||
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
|
||||
inception_5b = concatenate(
|
||||
[inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3
|
||||
)
|
||||
|
||||
av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
|
||||
reshape_layer = Flatten()(av_pool)
|
||||
dense_layer = Dense(128, name='dense_layer')(reshape_layer)
|
||||
norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)
|
||||
dense_layer = Dense(128, name="dense_layer")(reshape_layer)
|
||||
norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(
|
||||
dense_layer
|
||||
)
|
||||
|
||||
# Final Model
|
||||
model = Model(inputs=[myInput], outputs=norm_layer)
|
||||
return Model(inputs=[myInput], outputs=norm_layer)
|
||||
|
||||
|
||||
def loadModel(model_path=""):
|
||||
# -----------------------------------
|
||||
if model_path:
|
||||
assert Path(model_path).exists()
|
||||
assert model_path.endswith(".h5")
|
||||
|
||||
else:
|
||||
home = Path.home().as_posix()
|
||||
model_path = home + "/.deepface/weights/openface_weights.h5"
|
||||
if not os.path.isfile(model_path):
|
||||
print(f"openface_weights.h5 will be downloaded to {model_path}")
|
||||
|
||||
url = "https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn"
|
||||
gdown.download(url, model_path, quiet=False)
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
home = str(Path.home())
|
||||
|
||||
if os.path.isfile(home+'/.deepface/weights/openface_weights.h5') != True:
|
||||
print("openface_weights.h5 will be downloaded...")
|
||||
|
||||
url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'
|
||||
output = home+'/.deepface/weights/openface_weights.h5'
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
#-----------------------------------
|
||||
|
||||
model.load_weights(home+'/.deepface/weights/openface_weights.h5')
|
||||
print(f"Loading model from {model_path}")
|
||||
model = get_base_model()
|
||||
model.load_weights(model_path)
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
|
||||
@ -1,81 +1,99 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
from keras.models import Model, Sequential
|
||||
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
|
||||
from keras.layers import (
|
||||
Input,
|
||||
Convolution2D,
|
||||
ZeroPadding2D,
|
||||
MaxPooling2D,
|
||||
Flatten,
|
||||
Dense,
|
||||
Dropout,
|
||||
Activation,
|
||||
)
|
||||
import gdown
|
||||
|
||||
# ---------------------------------------
|
||||
|
||||
def baseModel():
|
||||
|
||||
def get_base_model():
|
||||
model = Sequential()
|
||||
model.add(ZeroPadding2D((1, 1), input_shape=(224, 224, 3)))
|
||||
model.add(Convolution2D(64, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(64, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(64, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(64, (3, 3), activation="relu"))
|
||||
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
||||
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(128, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(128, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(128, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(128, (3, 3), activation="relu"))
|
||||
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
||||
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(256, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(256, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(256, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(256, (3, 3), activation="relu"))
|
||||
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
||||
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
||||
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(ZeroPadding2D((1, 1)))
|
||||
model.add(Convolution2D(512, (3, 3), activation='relu'))
|
||||
model.add(Convolution2D(512, (3, 3), activation="relu"))
|
||||
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
|
||||
|
||||
model.add(Convolution2D(4096, (7, 7), activation='relu'))
|
||||
model.add(Convolution2D(4096, (7, 7), activation="relu"))
|
||||
model.add(Dropout(0.5))
|
||||
model.add(Convolution2D(4096, (1, 1), activation='relu'))
|
||||
model.add(Convolution2D(4096, (1, 1), activation="relu"))
|
||||
model.add(Dropout(0.5))
|
||||
model.add(Convolution2D(2622, (1, 1)))
|
||||
model.add(Flatten())
|
||||
model.add(Activation('softmax'))
|
||||
model.add(Activation("softmax"))
|
||||
|
||||
return model
|
||||
|
||||
def loadModel():
|
||||
|
||||
model = baseModel()
|
||||
def loadModel(model_path=""):
|
||||
"""
|
||||
Args:
|
||||
model_path: str
|
||||
If provided, this path will be used to load the model from.
|
||||
"""
|
||||
if model_path:
|
||||
assert Path(model_path).exists()
|
||||
assert model_path.endswith(".h5")
|
||||
else:
|
||||
home = Path.home().as_posix()
|
||||
model_path = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
|
||||
if not os.path.isfile(model_path):
|
||||
print(f"vgg_face_weights.h5 will be downloaded to {model_path}")
|
||||
|
||||
url = "https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo"
|
||||
gdown.download(url, model_path, quiet=False)
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
home = str(Path.home())
|
||||
|
||||
if os.path.isfile(home+'/.deepface/weights/vgg_face_weights.h5') != True:
|
||||
print("vgg_face_weights.h5 will be downloaded...")
|
||||
|
||||
url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'
|
||||
output = home+'/.deepface/weights/vgg_face_weights.h5'
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
#-----------------------------------
|
||||
|
||||
model.load_weights(home+'/.deepface/weights/vgg_face_weights.h5')
|
||||
print(f"Loading model from {model_path}")
|
||||
model = get_base_model()
|
||||
model.load_weights(model_path)
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
# TO-DO: why?
|
||||
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
|
||||
vgg_face_descriptor = Model(
|
||||
inputs=model.layers[0].input, outputs=model.layers[-2].output
|
||||
)
|
||||
|
||||
return vgg_face_descriptor
|
||||
@ -17,23 +17,30 @@ import subprocess
|
||||
import tensorflow as tf
|
||||
import keras
|
||||
|
||||
|
||||
def loadBase64Img(uri):
|
||||
encoded_data = uri.split(',')[1]
|
||||
encoded_data = uri.split(",")[1]
|
||||
nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
|
||||
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
return img
|
||||
|
||||
|
||||
def distance(a, b):
|
||||
x1 = a[0]; y1 = a[1]
|
||||
x2 = b[0]; y2 = b[1]
|
||||
x1 = a[0]
|
||||
y1 = a[1]
|
||||
x2 = b[0]
|
||||
y2 = b[1]
|
||||
|
||||
return math.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1)))
|
||||
|
||||
|
||||
def findFileHash(file):
|
||||
BLOCK_SIZE = 65536 # The size of each read from the file
|
||||
|
||||
file_hash = hashlib.sha256() # Create the hash object, can use something other than `.sha256()` if you wish
|
||||
with open(file, 'rb') as f: # Open the file to read it's bytes
|
||||
file_hash = (
|
||||
hashlib.sha256()
|
||||
) # Create the hash object, can use something other than `.sha256()` if you wish
|
||||
with open(file, "rb") as f: # Open the file to read it's bytes
|
||||
fb = f.read(BLOCK_SIZE) # Read from the file. Take in the amount declared above
|
||||
while len(fb) > 0: # While there is still data being read from the file
|
||||
file_hash.update(fb) # Update the hash
|
||||
@ -41,6 +48,7 @@ def findFileHash(file):
|
||||
|
||||
return file_hash.hexdigest()
|
||||
|
||||
|
||||
def initializeFolder():
|
||||
|
||||
home = str(Path.home())
|
||||
@ -81,44 +89,46 @@ def initializeFolder():
|
||||
"""
|
||||
# ----------------------------------
|
||||
|
||||
|
||||
def findThreshold(model_name, distance_metric):
|
||||
|
||||
threshold = 0.40
|
||||
|
||||
if model_name == 'VGG-Face':
|
||||
if distance_metric == 'cosine':
|
||||
if model_name == "VGG-Face":
|
||||
if distance_metric == "cosine":
|
||||
threshold = 0.40
|
||||
elif distance_metric == 'euclidean':
|
||||
elif distance_metric == "euclidean":
|
||||
threshold = 0.55
|
||||
elif distance_metric == 'euclidean_l2':
|
||||
elif distance_metric == "euclidean_l2":
|
||||
threshold = 0.75
|
||||
|
||||
elif model_name == 'OpenFace':
|
||||
if distance_metric == 'cosine':
|
||||
elif model_name == "OpenFace":
|
||||
if distance_metric == "cosine":
|
||||
threshold = 0.10
|
||||
elif distance_metric == 'euclidean':
|
||||
elif distance_metric == "euclidean":
|
||||
threshold = 0.55
|
||||
elif distance_metric == 'euclidean_l2':
|
||||
elif distance_metric == "euclidean_l2":
|
||||
threshold = 0.55
|
||||
|
||||
elif model_name == 'Facenet':
|
||||
if distance_metric == 'cosine':
|
||||
elif model_name == "Facenet":
|
||||
if distance_metric == "cosine":
|
||||
threshold = 0.40
|
||||
elif distance_metric == 'euclidean':
|
||||
elif distance_metric == "euclidean":
|
||||
threshold = 10
|
||||
elif distance_metric == 'euclidean_l2':
|
||||
elif distance_metric == "euclidean_l2":
|
||||
threshold = 0.80
|
||||
|
||||
elif model_name == 'DeepFace':
|
||||
if distance_metric == 'cosine':
|
||||
elif model_name == "DeepFace":
|
||||
if distance_metric == "cosine":
|
||||
threshold = 0.23
|
||||
elif distance_metric == 'euclidean':
|
||||
elif distance_metric == "euclidean":
|
||||
threshold = 64
|
||||
elif distance_metric == 'euclidean_l2':
|
||||
elif distance_metric == "euclidean_l2":
|
||||
threshold = 0.64
|
||||
|
||||
return threshold
|
||||
|
||||
|
||||
def get_opencv_path():
|
||||
opencv_home = cv2.__file__
|
||||
folders = opencv_home.split(os.path.sep)[0:-1]
|
||||
@ -131,10 +141,15 @@ def get_opencv_path():
|
||||
eye_detector_path = path + "/data/haarcascade_eye.xml"
|
||||
|
||||
if os.path.isfile(face_detector_path) != True:
|
||||
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
|
||||
raise ValueError(
|
||||
"Confirm that opencv is installed on your environment! Expected path ",
|
||||
face_detector_path,
|
||||
" violated.",
|
||||
)
|
||||
|
||||
return path + "/data/"
|
||||
|
||||
|
||||
def detectFace(img, target_size=(224, 224), grayscale=False, enforce_detection=True):
|
||||
|
||||
img_path = ""
|
||||
@ -156,7 +171,11 @@ def detectFace(img, target_size=(224, 224), grayscale = False, enforce_detection
|
||||
eye_detector_path = opencv_path + "haarcascade_eye.xml"
|
||||
|
||||
if os.path.isfile(face_detector_path) != True:
|
||||
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
|
||||
raise ValueError(
|
||||
"Confirm that opencv is installed on your environment! Expected path ",
|
||||
face_detector_path,
|
||||
" violated.",
|
||||
)
|
||||
|
||||
# --------------------------------
|
||||
|
||||
@ -200,14 +219,17 @@ def detectFace(img, target_size=(224, 224), grayscale = False, enforce_detection
|
||||
item = (base_eyes[i], i)
|
||||
items.append(item)
|
||||
|
||||
df = pd.DataFrame(items, columns = ["length", "idx"]).sort_values(by=['length'], ascending=False)
|
||||
df = pd.DataFrame(items, columns=["length", "idx"]).sort_values(
|
||||
by=["length"], ascending=False
|
||||
)
|
||||
|
||||
eyes = eyes[df.idx.values[0:2]]
|
||||
|
||||
# -----------------------
|
||||
# decide left and right eye
|
||||
|
||||
eye_1 = eyes[0]; eye_2 = eyes[1]
|
||||
eye_1 = eyes[0]
|
||||
eye_2 = eyes[1]
|
||||
|
||||
if eye_1[0] < eye_2[0]:
|
||||
left_eye = eye_1
|
||||
@ -219,11 +241,19 @@ def detectFace(img, target_size=(224, 224), grayscale = False, enforce_detection
|
||||
# -----------------------
|
||||
# find center of eyes
|
||||
|
||||
left_eye_center = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2)))
|
||||
left_eye_x = left_eye_center[0]; left_eye_y = left_eye_center[1]
|
||||
left_eye_center = (
|
||||
int(left_eye[0] + (left_eye[2] / 2)),
|
||||
int(left_eye[1] + (left_eye[3] / 2)),
|
||||
)
|
||||
left_eye_x = left_eye_center[0]
|
||||
left_eye_y = left_eye_center[1]
|
||||
|
||||
right_eye_center = (int(right_eye[0] + (right_eye[2]/2)), int(right_eye[1] + (right_eye[3]/2)))
|
||||
right_eye_x = right_eye_center[0]; right_eye_y = right_eye_center[1]
|
||||
right_eye_center = (
|
||||
int(right_eye[0] + (right_eye[2] / 2)),
|
||||
int(right_eye[1] + (right_eye[3] / 2)),
|
||||
)
|
||||
right_eye_x = right_eye_center[0]
|
||||
right_eye_y = right_eye_center[1]
|
||||
|
||||
# -----------------------
|
||||
# find rotation direction
|
||||
@ -296,17 +326,24 @@ def detectFace(img, target_size=(224, 224), grayscale = False, enforce_detection
|
||||
img_pixels /= 255
|
||||
return img_pixels
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
raise ValueError(
|
||||
"Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False."
|
||||
)
|
||||
|
||||
|
||||
def allocateMemory():
|
||||
|
||||
# find allocated memories
|
||||
gpu_indexes = []
|
||||
memory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = []
|
||||
power_usages = []; power_capacities = []
|
||||
memory_usage_percentages = []
|
||||
available_memories = []
|
||||
total_memories = []
|
||||
utilizations = []
|
||||
power_usages = []
|
||||
power_capacities = []
|
||||
|
||||
try:
|
||||
result = subprocess.check_output(['nvidia-smi'])
|
||||
result = subprocess.check_output(["nvidia-smi"])
|
||||
|
||||
dashboard = result.decode("utf-8").split("=|")
|
||||
|
||||
@ -314,10 +351,12 @@ def allocateMemory():
|
||||
|
||||
gpu_idx = 0
|
||||
for line in dashboard:
|
||||
if ("MiB" in line):
|
||||
if "MiB" in line:
|
||||
power_info = line.split("|")[1]
|
||||
power_capacity = int(power_info.split("/")[-1].replace("W", ""))
|
||||
power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", ""))
|
||||
power_usage = int(
|
||||
(power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", "")
|
||||
)
|
||||
|
||||
power_usages.append(power_usage)
|
||||
power_capacities.append(power_capacity)
|
||||
@ -333,7 +372,9 @@ def allocateMemory():
|
||||
|
||||
total_memories.append(total_memory)
|
||||
available_memories.append(available_memory)
|
||||
memory_usage_percentages.append(round(100*int(allocated)/int(total_memory), 4))
|
||||
memory_usage_percentages.append(
|
||||
round(100 * int(allocated) / int(total_memory), 4)
|
||||
)
|
||||
utilizations.append(utilization_info)
|
||||
gpu_indexes.append(gpu_idx)
|
||||
|
||||
@ -355,7 +396,9 @@ def allocateMemory():
|
||||
df["power_usages_in_watts"] = power_usages
|
||||
df["power_capacities_in_watts"] = power_capacities
|
||||
|
||||
df = df.sort_values(by = ["available_memories_in_mb"], ascending = False).reset_index(drop = True)
|
||||
df = df.sort_values(by=["available_memories_in_mb"], ascending=False).reset_index(
|
||||
drop=True
|
||||
)
|
||||
|
||||
# ------------------------------
|
||||
|
||||
@ -379,7 +422,9 @@ def allocateMemory():
|
||||
else:
|
||||
# this case has gpu but no enough memory to allocate
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "" # run it on cpu
|
||||
print("Even though the system has GPUs, there is no enough space in memory to allocate.")
|
||||
print(
|
||||
"Even though the system has GPUs, there is no enough space in memory to allocate."
|
||||
)
|
||||
print("DeepFace will run on CPU")
|
||||
else:
|
||||
print("DeepFace will run on CPU")
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user