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check face_detector global variable initialized

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serengil 2020-11-29 22:02:31 +03:00
parent b663ac641b
commit 6e4cf06738
2 changed files with 9 additions and 90 deletions
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7
deepface/DeepFace.py
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@ -858,12 +858,11 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
return None
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
functions.initialize_detector(detector_backend = 'opencv')
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
def allocateMemory():
print("Analyzing your system...")
functions.allocateMemory()
def DlibResNet_():
#this is not a regular Keras model.
from deepface.basemodels.DlibResNet import DlibResNet

92
deepface/commons/functions.py
View File

@ -170,6 +170,12 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
home = str(Path.home())
#TODO: what if I directly call detect_face? In this case, initialize_detector hadn't been called before...
#if functions.preproces_face is called directly, then face_detector global variable might not been initialized.
if not "face_detector" in globals():
initialize_detector(detector_backend = detector_backend)
if detector_backend == 'opencv':
faces = []
@ -443,89 +449,3 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
img_pixels /= 255 #normalize input in [0, 1]
return img_pixels
def allocateMemory():
#find allocated memories
gpu_indexes = []
memory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = []
power_usages = []; power_capacities = []
try:
result = subprocess.check_output(['nvidia-smi'])
dashboard = result.decode("utf-8").split("=|")
dashboard = dashboard[1].split("\n")
gpu_idx = 0
for line in dashboard:
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_usages.append(power_usage)
power_capacities.append(power_capacity)
#----------------------------
memory_info = line.split("|")[2].replace("MiB","").split("/")
utilization_info = int(line.split("|")[3].split("%")[0])
allocated = int(memory_info[0])
total_memory = int(memory_info[1])
available_memory = total_memory - allocated
total_memories.append(total_memory)
available_memories.append(available_memory)
memory_usage_percentages.append(round(100*int(allocated)/int(total_memory), 4))
utilizations.append(utilization_info)
gpu_indexes.append(gpu_idx)
gpu_idx = gpu_idx + 1
gpu_count = gpu_idx * 1
except Exception as err:
gpu_count = 0
#print(str(err))
#------------------------------
df = pd.DataFrame(gpu_indexes, columns = ["gpu_index"])
df["total_memories_in_mb"] = total_memories
df["available_memories_in_mb"] = available_memories
df["memory_usage_percentage"] = memory_usage_percentages
df["utilizations"] = utilizations
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)
#------------------------------
required_memory = 10000 #All deepface models require 9016 MiB
if df.shape[0] > 0: #has gpu
if df.iloc[0].available_memories_in_mb > required_memory:
my_gpu = str(int(df.iloc[0].gpu_index))
os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu
#------------------------------
#tf allocates all memory by default
#this block avoids greedy approach
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
keras.backend.set_session(session)
print("DeepFace will run on GPU (gpu_", my_gpu,")")
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("DeepFace will run on CPU")
else:
print("DeepFace will run on CPU")