76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
import os
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import zipfile
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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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# --------------------------------
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# dependency configuration
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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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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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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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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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# -------------------------------------
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# pylint: disable=line-too-long
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def loadModel(
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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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):
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base_model = Sequential()
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base_model.add(
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Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))
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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(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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# ---------------------------------
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home = functions.get_deepface_home()
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if os.path.isfile(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5") != True:
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print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
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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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# unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
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with zipfile.ZipFile(output, "r") as zip_ref:
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zip_ref.extractall(home + "/.deepface/weights/")
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base_model.load_weights(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5")
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# drop F8 and D0. F7 is the representation layer.
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deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
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return deepface_model
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