Sefik Ilkin Serengil 7a978d29c5 linting
2023-01-29 00:45:25 +00:00

53 lines
1.6 KiB
Python

import os
import gdown
import tensorflow as tf
from deepface.basemodels import VGGFace
from deepface.commons import functions
# --------------------------
# pylint: disable=line-too-long
# --------------------------
# dependency configurations
tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
if tf_version == 1:
from keras.models import Model, Sequential
from keras.layers import Convolution2D, Flatten, Activation
elif tf_version == 2:
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Convolution2D, Flatten, Activation
# --------------------------
def loadModel(
url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
):
model = VGGFace.baseModel()
# --------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name="predictions")(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation("softmax")(base_model_output)
# --------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
# --------------------------
# load weights
home = functions.get_deepface_home()
if os.path.isfile(home + "/.deepface/weights/race_model_single_batch.h5") != True:
print("race_model_single_batch.h5 will be downloaded...")
output = home + "/.deepface/weights/race_model_single_batch.h5"
gdown.download(url, output, quiet=False)
race_model.load_weights(home + "/.deepface/weights/race_model_single_batch.h5")
return race_model