def __init__(self, args):
self.model = preprocessor_worker.FaceModel(args)
self.feature_extractor = base_server.BaseServer(
model_fp=configs.face_describer_model_fp,
input_tensor_names=configs.face_describer_input_tensor_names,
output_tensor_names=configs.face_describer_output_tensor_names,
device=configs.face_describer_device)
import cv2
import numpy as np
from models import base_server
from configs import configs
# Read example image
test_img = cv2.imread(configs.test_img_fp)
test_img = cv2.resize(test_img, (112, 112))
# Define input tensors feed to session graph
dropout_rate = 0.5
input_data = [np.expand_dims(test_img, axis=0), dropout_rate]
# Define a Base Server
srv = base_server.BaseServer(model_fp=configs.model_fp,
input_tensor_names=configs.input_tensor_names,
output_tensor_names=configs.output_tensor_names,
device=configs.device)
# Run prediction
prediction = srv.inference(data=input_data)
# Print results
print('512-D Features are \n{}'.format(prediction))
import cv2
import numpy as np
from models import base_server
from configs import configs
# Read example image
test_img = cv2.imread(configs.test_img_fp)
test_img = cv2.resize(test_img, configs.face_describer_tensor_shape)
# Define input tensors feed to session graph
#dropout_rate = 0.5
input_data = np.array([np.expand_dims(test_img, axis=0)])
print(input_data.shape)
# Define a Base Server
srv = base_server.BaseServer(
model_fp=configs.face_describer_model_fp,
input_tensor_names=configs.face_describer_input_tensor_names,
output_tensor_names=configs.face_describer_output_tensor_names,
device=configs.face_describer_device)
# Run prediction
prediction = srv.inference(data=input_data)
# Print results
print('512-D Features are \n{}'.format(prediction))