120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
import os
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import gdown
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import tensorflow as tf
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from deepface.commons import functions
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from deepface.commons.logger import Logger
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logger = Logger(module="basemodels.VGGFace")
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# ---------------------------------------
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tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
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if tf_version == 1:
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from keras.models import Model, Sequential
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from keras.layers import (
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Convolution2D,
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ZeroPadding2D,
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MaxPooling2D,
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Flatten,
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Dropout,
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Activation,
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Lambda,
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)
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from keras import backend as K
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else:
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import (
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Convolution2D,
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ZeroPadding2D,
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MaxPooling2D,
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Flatten,
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Dropout,
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Activation,
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Lambda,
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)
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from tensorflow.keras import backend as K
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# ---------------------------------------
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def baseModel() -> Sequential:
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model = Sequential()
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model.add(ZeroPadding2D((1, 1), input_shape=(224, 224, 3)))
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model.add(Convolution2D(64, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(64, (3, 3), activation="relu"))
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model.add(MaxPooling2D((2, 2), strides=(2, 2)))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(128, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(128, (3, 3), activation="relu"))
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model.add(MaxPooling2D((2, 2), strides=(2, 2)))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(256, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(256, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(256, (3, 3), activation="relu"))
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model.add(MaxPooling2D((2, 2), strides=(2, 2)))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(MaxPooling2D((2, 2), strides=(2, 2)))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(ZeroPadding2D((1, 1)))
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model.add(Convolution2D(512, (3, 3), activation="relu"))
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model.add(MaxPooling2D((2, 2), strides=(2, 2)))
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model.add(Convolution2D(4096, (7, 7), activation="relu"))
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model.add(Dropout(0.5))
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model.add(Convolution2D(4096, (1, 1), activation="relu"))
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model.add(Dropout(0.5))
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model.add(Convolution2D(2622, (1, 1)))
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model.add(Flatten())
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model.add(Activation("softmax"))
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return model
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def loadModel(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5",
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) -> Model:
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model = baseModel()
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home = functions.get_deepface_home()
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output = home + "/.deepface/weights/vgg_face_weights.h5"
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if os.path.isfile(output) != True:
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logger.info("vgg_face_weights.h5 will be downloaded...")
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gdown.download(url, output, quiet=False)
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model.load_weights(output)
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# 2622d dimensional model
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# vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
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# 4096 dimensional model offers 6% to 14% increasement on accuracy!
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# - softmax causes underfitting
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# - added normalization layer to avoid underfitting with euclidean
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# as described here: https://github.com/serengil/deepface/issues/944
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base_model_output = Sequential()
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base_model_output = Flatten()(model.layers[-5].output)
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base_model_output = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(
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base_model_output
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)
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vgg_face_descriptor = Model(inputs=model.input, outputs=base_model_output)
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return vgg_face_descriptor
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