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GhostFace model added
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Sefik Ilkin Serengil
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5825e2aa88
319
deepface/basemodels/GhostFaceNet.py
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319
deepface/basemodels/GhostFaceNet.py
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# built-in dependencies
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import os
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from typing import List
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# 3rd party dependencies
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import gdown
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import numpy as np
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import tensorflow as tf
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# project dependencies
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from deepface.commons import package_utils, folder_utils
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from deepface.models.FacialRecognition import FacialRecognition
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from deepface.commons.logger import Logger
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logger = Logger(module="basemodels.VGGFace")
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tf_major = package_utils.get_tf_major_version()
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if tf_major == 1:
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import keras
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from keras import backend as K
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from keras.models import Model
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from keras.layers import (
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Activation,
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Add,
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BatchNormalization,
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Concatenate,
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Conv2D,
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DepthwiseConv2D,
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GlobalAveragePooling2D,
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Input,
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Reshape,
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Multiply,
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ReLU,
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PReLU,
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)
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else:
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from tensorflow import keras
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from tensorflow.keras import backend as K
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from tensorflow.keras.models import Model
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from tensorflow.keras.layers import (
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Activation,
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Add,
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BatchNormalization,
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Concatenate,
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Conv2D,
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DepthwiseConv2D,
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GlobalAveragePooling2D,
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Input,
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Reshape,
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Multiply,
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ReLU,
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PReLU,
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)
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# pylint: disable=line-too-long, too-few-public-methods, no-else-return, unsubscriptable-object
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PRETRAINED_WEIGHTS = "https://github.com/HamadYA/GhostFaceNets/releases/download/v1.2/GhostFaceNet_W1.3_S1_ArcFace.h5"
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class GhostFaceNetClient(FacialRecognition):
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"""
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GhostFaceNet model (GhostFaceNetV1 backbone)
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Repo: https://github.com/HamadYA/GhostFaceNets
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Pre-trained weights: https://github.com/HamadYA/GhostFaceNets/releases/tag/v1.2
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GhostFaceNet_W1.3_S1_ArcFace.h5 ~ 16.5MB
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Author declared that this backbone and pre-trained weights got 99.7667% accuracy on LFW
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"""
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def __init__(self):
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self.model_name = "GhostFaceNet"
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self.input_shape = (112, 112)
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self.output_shape = 512
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self.model = load_model()
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def find_embeddings(self, img: np.ndarray) -> List[float]:
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# model.predict causes memory issue when it is called in a for loop
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# embedding = model.predict(img, verbose=0)[0].tolist()
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return self.model(img, training=False).numpy()[0].tolist()
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def load_model():
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model = GhostFaceNetV1()
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home = folder_utils.get_deepface_home()
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output = home + "/.deepface/weights/ghostfacenet_v1.h5"
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if os.path.isfile(output) is not True:
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logger.info("Pre-trained weights is downloaded from {PRETRAINED_WEIGHTS} to {output}")
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gdown.download(PRETRAINED_WEIGHTS, output, quiet=False)
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logger.info(f"Pre-trained weights is just downloaded to {output}")
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model.load_weights(output)
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return model
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def GhostFaceNetV1() -> Model:
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"""
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Build GhostFaceNetV1 model. Refactored from
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github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
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Returns:
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model (Model)
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"""
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inputs = Input(shape=(112, 112, 3))
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out_channel = 20
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nn = Conv2D(
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out_channel,
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(3, 3),
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strides=1,
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padding="same",
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use_bias=False,
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(inputs)
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nn = BatchNormalization(axis=-1)(nn)
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nn = Activation("relu")(nn)
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dwkernels = [3, 3, 3, 5, 5, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5]
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exps = [20, 64, 92, 92, 156, 312, 260, 240, 240, 624, 872, 872, 1248, 1248, 1248, 664]
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outs = [20, 32, 32, 52, 52, 104, 104, 104, 104, 144, 144, 208, 208, 208, 208, 208]
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strides_set = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1]
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reductions = [0, 0, 0, 24, 40, 0, 0, 0, 0, 156, 220, 220, 0, 312, 0, 168]
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pre_out = out_channel
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for dwk, stride, exp, out, reduction in zip(dwkernels, strides_set, exps, outs, reductions):
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shortcut = not (out == pre_out and stride == 1)
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nn = ghost_bottleneck(nn, dwk, stride, exp, out, reduction, shortcut)
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pre_out = out
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nn = Conv2D(
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664,
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(1, 1),
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strides=(1, 1),
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padding="valid",
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use_bias=False,
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(nn)
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nn = BatchNormalization(axis=-1)(nn)
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nn = Activation("relu")(nn)
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xx = Model(inputs=inputs, outputs=nn, name="GhostFaceNetV1")
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# post modelling
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inputs = xx.inputs[0]
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nn = xx.outputs[0]
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nn = keras.layers.DepthwiseConv2D(nn.shape[1], use_bias=False, name="GDC_dw")(nn)
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nn = keras.layers.BatchNormalization(momentum=0.99, epsilon=0.001, name="GDC_batchnorm")(nn)
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nn = keras.layers.Conv2D(
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512, 1, use_bias=True, kernel_initializer="glorot_normal", name="GDC_conv"
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)(nn)
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nn = keras.layers.Flatten(name="GDC_flatten")(nn)
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embedding = keras.layers.BatchNormalization(
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momentum=0.99, epsilon=0.001, scale=True, name="pre_embedding"
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)(nn)
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embedding_fp32 = keras.layers.Activation("linear", dtype="float32", name="embedding")(embedding)
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model = keras.models.Model(inputs, embedding_fp32, name=xx.name)
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model = replace_relu_with_prelu(model=model)
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return model
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def se_module(inputs, reduction):
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"""
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Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
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"""
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# get the channel axis
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channel_axis = 1 if K.image_data_format() == "channels_first" else -1
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# filters = channel axis shape
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filters = inputs.shape[channel_axis]
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# from None x H x W x C to None x C
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se = GlobalAveragePooling2D()(inputs)
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# Reshape None x C to None 1 x 1 x C
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se = Reshape((1, 1, filters))(se)
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# Squeeze by using C*se_ratio. The size will be 1 x 1 x C*se_ratio
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se = Conv2D(
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reduction,
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kernel_size=1,
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use_bias=True,
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(se)
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se = Activation("relu")(se)
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# Excitation using C filters. The size will be 1 x 1 x C
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se = Conv2D(
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filters,
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kernel_size=1,
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use_bias=True,
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(se)
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se = Activation("hard_sigmoid")(se)
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return Multiply()([inputs, se])
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def ghost_module(inputs, out, convkernel=1, dwkernel=3, add_activation=True):
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"""
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Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
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"""
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conv_out_channel = out // 2
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cc = Conv2D(
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conv_out_channel,
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convkernel,
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use_bias=False,
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strides=(1, 1),
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padding="same",
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(inputs)
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cc = BatchNormalization(axis=-1)(cc)
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if add_activation:
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cc = Activation("relu")(cc)
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nn = DepthwiseConv2D(
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dwkernel,
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1,
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padding="same",
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use_bias=False,
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depthwise_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(cc)
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nn = BatchNormalization(axis=-1)(nn)
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if add_activation:
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nn = Activation("relu")(nn)
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return Concatenate()([cc, nn])
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def ghost_bottleneck(inputs, dwkernel, strides, exp, out, reduction, shortcut=True):
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"""
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Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
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"""
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nn = ghost_module(inputs, exp, add_activation=True)
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if strides > 1:
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# Extra depth conv if strides higher than 1
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nn = DepthwiseConv2D(
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dwkernel,
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strides,
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padding="same",
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use_bias=False,
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depthwise_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(nn)
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nn = BatchNormalization(axis=-1)(nn)
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if reduction > 0:
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# Squeeze and excite
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nn = se_module(nn, reduction)
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# Point-wise linear projection
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nn = ghost_module(nn, out, add_activation=False) # ghost2 = GhostModule(exp, out, relu=False)
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if shortcut:
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xx = DepthwiseConv2D(
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dwkernel,
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strides,
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padding="same",
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use_bias=False,
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depthwise_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(inputs)
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xx = BatchNormalization(axis=-1)(xx)
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xx = Conv2D(
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out,
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(1, 1),
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strides=(1, 1),
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padding="valid",
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use_bias=False,
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kernel_initializer=keras.initializers.VarianceScaling(
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scale=2.0, mode="fan_out", distribution="truncated_normal"
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),
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)(xx)
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xx = BatchNormalization(axis=-1)(xx)
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else:
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xx = inputs
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return Add()([xx, nn])
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def replace_relu_with_prelu(model) -> Model:
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"""
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Replaces relu activation function in the built model with prelu.
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Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py
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Args:
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model (Model): built model with relu activation functions
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Returns
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model (Model): built model with prelu activation functions
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"""
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def convert_relu(layer):
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if isinstance(layer, ReLU) or (
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isinstance(layer, Activation) and layer.activation == keras.activations.relu
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):
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layer_name = layer.name.replace("_relu", "_prelu")
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return PReLU(
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shared_axes=[1, 2],
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alpha_initializer=tf.initializers.Constant(0.25),
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name=layer_name,
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)
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return layer
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input_tensors = keras.layers.Input(model.input_shape[1:])
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return keras.models.clone_model(model, input_tensors=input_tensors, clone_function=convert_relu)
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