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import os |
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import gdown |
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import tensorflow as tf |
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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 import logger as log |
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logger = log.get_singletonish_logger() |
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tf_version = package_utils.get_tf_major_version() |
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if tf_version == 1: |
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from keras.models import Model |
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from keras.layers import Conv2D, ZeroPadding2D, Input, concatenate |
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from keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization |
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from keras.layers import MaxPooling2D, AveragePooling2D |
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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 |
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from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Input, concatenate |
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from tensorflow.keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization |
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from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D |
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from tensorflow.keras import backend as K |
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class OpenFaceClient(FacialRecognition): |
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""" |
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OpenFace model class |
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""" |
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def __init__(self): |
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self.model = load_model() |
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self.model_name = "OpenFace" |
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self.input_shape = (96, 96) |
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self.output_shape = 128 |
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def load_model( |
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5", |
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) -> Model: |
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""" |
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Consturct OpenFace model, download its weights and load |
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Returns: |
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model (Model) |
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""" |
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myInput = Input(shape=(96, 96, 3)) |
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x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput) |
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x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x) |
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x) |
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x = Activation("relu")(x) |
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x = ZeroPadding2D(padding=(1, 1))(x) |
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x = MaxPooling2D(pool_size=3, strides=2)(x) |
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x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1")(x) |
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x = Conv2D(64, (1, 1), name="conv2")(x) |
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x) |
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x = Activation("relu")(x) |
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x = ZeroPadding2D(padding=(1, 1))(x) |
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x = Conv2D(192, (3, 3), name="conv3")(x) |
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x) |
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x = Activation("relu")(x) |
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x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2")(x) |
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x = ZeroPadding2D(padding=(1, 1))(x) |
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x = MaxPooling2D(pool_size=3, strides=2)(x) |
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inception_3a_3x3 = Conv2D(96, (1, 1), name="inception_3a_3x3_conv1")(x) |
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inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1")( |
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inception_3a_3x3 |
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) |
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inception_3a_3x3 = Activation("relu")(inception_3a_3x3) |
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inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3) |
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inception_3a_3x3 = Conv2D(128, (3, 3), name="inception_3a_3x3_conv2")(inception_3a_3x3) |
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inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2")( |
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inception_3a_3x3 |
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) |
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inception_3a_3x3 = Activation("relu")(inception_3a_3x3) |
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inception_3a_5x5 = Conv2D(16, (1, 1), name="inception_3a_5x5_conv1")(x) |
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inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1")( |
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inception_3a_5x5 |
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) |
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inception_3a_5x5 = Activation("relu")(inception_3a_5x5) |
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inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5) |
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inception_3a_5x5 = Conv2D(32, (5, 5), name="inception_3a_5x5_conv2")(inception_3a_5x5) |
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inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2")( |
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inception_3a_5x5 |
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) |
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inception_3a_5x5 = Activation("relu")(inception_3a_5x5) |
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inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x) |
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inception_3a_pool = Conv2D(32, (1, 1), name="inception_3a_pool_conv")(inception_3a_pool) |
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inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_pool_bn")( |
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inception_3a_pool |
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) |
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inception_3a_pool = Activation("relu")(inception_3a_pool) |
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inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool) |
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inception_3a_1x1 = Conv2D(64, (1, 1), name="inception_3a_1x1_conv")(x) |
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inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_1x1_bn")( |
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inception_3a_1x1 |
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) |
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inception_3a_1x1 = Activation("relu")(inception_3a_1x1) |
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inception_3a = concatenate( |
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[inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3 |
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) |
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inception_3b_3x3 = Conv2D(96, (1, 1), name="inception_3b_3x3_conv1")(inception_3a) |
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inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1")( |
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inception_3b_3x3 |
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) |
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inception_3b_3x3 = Activation("relu")(inception_3b_3x3) |
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inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3) |
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inception_3b_3x3 = Conv2D(128, (3, 3), name="inception_3b_3x3_conv2")(inception_3b_3x3) |
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inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2")( |
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inception_3b_3x3 |
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) |
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inception_3b_3x3 = Activation("relu")(inception_3b_3x3) |
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inception_3b_5x5 = Conv2D(32, (1, 1), name="inception_3b_5x5_conv1")(inception_3a) |
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inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1")( |
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inception_3b_5x5 |
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) |
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inception_3b_5x5 = Activation("relu")(inception_3b_5x5) |
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inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5) |
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inception_3b_5x5 = Conv2D(64, (5, 5), name="inception_3b_5x5_conv2")(inception_3b_5x5) |
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inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2")( |
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inception_3b_5x5 |
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) |
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inception_3b_5x5 = Activation("relu")(inception_3b_5x5) |
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inception_3b_pool = Lambda(lambda x: x**2, name="power2_3b")(inception_3a) |
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inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool) |
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inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(inception_3b_pool) |
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inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(inception_3b_pool) |
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inception_3b_pool = Conv2D(64, (1, 1), name="inception_3b_pool_conv")(inception_3b_pool) |
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inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_pool_bn")( |
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inception_3b_pool |
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) |
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inception_3b_pool = Activation("relu")(inception_3b_pool) |
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inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool) |
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inception_3b_1x1 = Conv2D(64, (1, 1), name="inception_3b_1x1_conv")(inception_3a) |
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inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_1x1_bn")( |
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inception_3b_1x1 |
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) |
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inception_3b_1x1 = Activation("relu")(inception_3b_1x1) |
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inception_3b = concatenate( |
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[inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3 |
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) |
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inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1")( |
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inception_3b |
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) |
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inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1")( |
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inception_3c_3x3 |
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) |
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inception_3c_3x3 = Activation("relu")(inception_3c_3x3) |
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inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3) |
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inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_3c_3x3_conv" + "2")( |
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inception_3c_3x3 |
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) |
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inception_3c_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2" |
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)(inception_3c_3x3) |
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inception_3c_3x3 = Activation("relu")(inception_3c_3x3) |
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inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1")( |
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inception_3b |
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) |
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inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1")( |
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inception_3c_5x5 |
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) |
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inception_3c_5x5 = Activation("relu")(inception_3c_5x5) |
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inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5) |
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inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2")( |
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inception_3c_5x5 |
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) |
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inception_3c_5x5 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2" |
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)(inception_3c_5x5) |
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inception_3c_5x5 = Activation("relu")(inception_3c_5x5) |
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inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b) |
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inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool) |
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inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3) |
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inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1")( |
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inception_3c |
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) |
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inception_4a_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1" |
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)(inception_4a_3x3) |
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inception_4a_3x3 = Activation("relu")(inception_4a_3x3) |
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inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3) |
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inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name="inception_4a_3x3_conv" + "2")( |
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inception_4a_3x3 |
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) |
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inception_4a_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2" |
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)(inception_4a_3x3) |
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inception_4a_3x3 = Activation("relu")(inception_4a_3x3) |
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inception_4a_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1")( |
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inception_3c |
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) |
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inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1")( |
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inception_4a_5x5 |
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) |
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inception_4a_5x5 = Activation("relu")(inception_4a_5x5) |
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inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5) |
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inception_4a_5x5 = Conv2D(64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2")( |
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inception_4a_5x5 |
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) |
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inception_4a_5x5 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2" |
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)(inception_4a_5x5) |
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inception_4a_5x5 = Activation("relu")(inception_4a_5x5) |
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inception_4a_pool = Lambda(lambda x: x**2, name="power2_4a")(inception_3c) |
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inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool) |
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inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(inception_4a_pool) |
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inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(inception_4a_pool) |
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inception_4a_pool = Conv2D(128, (1, 1), strides=(1, 1), name="inception_4a_pool_conv" + "")( |
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inception_4a_pool |
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) |
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inception_4a_pool = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + "" |
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)(inception_4a_pool) |
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inception_4a_pool = Activation("relu")(inception_4a_pool) |
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inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool) |
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inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + "")( |
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inception_3c |
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) |
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inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + "")( |
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inception_4a_1x1 |
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) |
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inception_4a_1x1 = Activation("relu")(inception_4a_1x1) |
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inception_4a = concatenate( |
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[inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3 |
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) |
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inception_4e_3x3 = Conv2D(160, (1, 1), strides=(1, 1), name="inception_4e_3x3_conv" + "1")( |
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inception_4a |
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) |
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inception_4e_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1" |
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)(inception_4e_3x3) |
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inception_4e_3x3 = Activation("relu")(inception_4e_3x3) |
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inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3) |
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inception_4e_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_4e_3x3_conv" + "2")( |
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inception_4e_3x3 |
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) |
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inception_4e_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2" |
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)(inception_4e_3x3) |
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inception_4e_3x3 = Activation("relu")(inception_4e_3x3) |
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inception_4e_5x5 = Conv2D(64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1")( |
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inception_4a |
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) |
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inception_4e_5x5 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1" |
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)(inception_4e_5x5) |
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inception_4e_5x5 = Activation("relu")(inception_4e_5x5) |
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inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5) |
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inception_4e_5x5 = Conv2D(128, (5, 5), strides=(2, 2), name="inception_4e_5x5_conv" + "2")( |
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inception_4e_5x5 |
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) |
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inception_4e_5x5 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2" |
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)(inception_4e_5x5) |
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inception_4e_5x5 = Activation("relu")(inception_4e_5x5) |
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inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a) |
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inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool) |
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inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3) |
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inception_5a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1")( |
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inception_4e |
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) |
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inception_5a_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1" |
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)(inception_5a_3x3) |
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inception_5a_3x3 = Activation("relu")(inception_5a_3x3) |
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inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3) |
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inception_5a_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5a_3x3_conv" + "2")( |
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inception_5a_3x3 |
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) |
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inception_5a_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2" |
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)(inception_5a_3x3) |
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inception_5a_3x3 = Activation("relu")(inception_5a_3x3) |
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inception_5a_pool = Lambda(lambda x: x**2, name="power2_5a")(inception_4e) |
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inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool) |
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inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(inception_5a_pool) |
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inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(inception_5a_pool) |
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inception_5a_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + "")( |
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inception_5a_pool |
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) |
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inception_5a_pool = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + "" |
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)(inception_5a_pool) |
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inception_5a_pool = Activation("relu")(inception_5a_pool) |
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inception_5a_pool = ZeroPadding2D(padding=(1, 1))(inception_5a_pool) |
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inception_5a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + "")( |
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inception_4e |
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) |
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inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + "")( |
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inception_5a_1x1 |
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) |
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inception_5a_1x1 = Activation("relu")(inception_5a_1x1) |
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inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3) |
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inception_5b_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1")( |
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inception_5a |
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) |
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inception_5b_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1" |
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)(inception_5b_3x3) |
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inception_5b_3x3 = Activation("relu")(inception_5b_3x3) |
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inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3) |
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inception_5b_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5b_3x3_conv" + "2")( |
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inception_5b_3x3 |
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) |
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inception_5b_3x3 = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2" |
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)(inception_5b_3x3) |
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inception_5b_3x3 = Activation("relu")(inception_5b_3x3) |
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inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a) |
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inception_5b_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + "")( |
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inception_5b_pool |
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) |
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inception_5b_pool = BatchNormalization( |
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axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + "" |
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)(inception_5b_pool) |
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inception_5b_pool = Activation("relu")(inception_5b_pool) |
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inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool) |
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inception_5b_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + "")( |
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inception_5a |
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) |
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inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + "")( |
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inception_5b_1x1 |
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) |
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inception_5b_1x1 = Activation("relu")(inception_5b_1x1) |
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inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3) |
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av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b) |
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reshape_layer = Flatten()(av_pool) |
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dense_layer = Dense(128, name="dense_layer")(reshape_layer) |
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norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(dense_layer) |
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model = Model(inputs=[myInput], outputs=norm_layer) |
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home = folder_utils.get_deepface_home() |
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if os.path.isfile(home + "/.deepface/weights/openface_weights.h5") != True: |
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logger.info("openface_weights.h5 will be downloaded...") |
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output = home + "/.deepface/weights/openface_weights.h5" |
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gdown.download(url, output, quiet=False) |
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model.load_weights(home + "/.deepface/weights/openface_weights.h5") |
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return model |
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