mirror of
https://github.com/serengil/deepface.git
synced 2025-06-04 02:20:06 +00:00
1714 lines
60 KiB
Python
1714 lines
60 KiB
Python
import os
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import gdown
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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()
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# --------------------------------
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# dependency configuration
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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 Activation
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from keras.layers import BatchNormalization
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from keras.layers import Concatenate
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from keras.layers import Conv2D
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from keras.layers import Dense
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from keras.layers import Dropout
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from keras.layers import GlobalAveragePooling2D
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from keras.layers import Input
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from keras.layers import Lambda
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from keras.layers import MaxPooling2D
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from keras.layers import add
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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 Activation
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from tensorflow.keras.layers import BatchNormalization
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from tensorflow.keras.layers import Concatenate
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from tensorflow.keras.layers import Conv2D
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.layers import Dropout
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from tensorflow.keras.layers import GlobalAveragePooling2D
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from tensorflow.keras.layers import Input
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from tensorflow.keras.layers import Lambda
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from tensorflow.keras.layers import MaxPooling2D
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from tensorflow.keras.layers import add
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from tensorflow.keras import backend as K
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# --------------------------------
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# pylint: disable=too-few-public-methods
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class FaceNet128dClient(FacialRecognition):
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"""
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FaceNet-128d model class
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"""
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def __init__(self):
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self.model = load_facenet128d_model()
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self.model_name = "FaceNet-128d"
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self.input_shape = (160, 160)
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self.output_shape = 128
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class FaceNet512dClient(FacialRecognition):
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"""
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FaceNet-1512d model class
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"""
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def __init__(self):
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self.model = load_facenet512d_model()
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self.model_name = "FaceNet-512d"
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self.input_shape = (160, 160)
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self.output_shape = 512
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def scaling(x, scale):
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return x * scale
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def InceptionResNetV1(dimension: int = 128) -> Model:
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"""
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InceptionResNetV1 model heavily inspired from
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github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
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As mentioned in Sandberg's repo's readme, pre-trained models are using Inception ResNet v1
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Besides training process is documented at
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sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/
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Args:
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dimension (int): number of dimensions in the embedding layer
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Returns:
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model (Model)
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"""
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inputs = Input(shape=(160, 160, 3))
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x = Conv2D(32, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_1a_3x3")(inputs)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_1a_3x3_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_1a_3x3_Activation")(x)
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x = Conv2D(32, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_2a_3x3")(x)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2a_3x3_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_2a_3x3_Activation")(x)
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x = Conv2D(64, 3, strides=1, padding="same", use_bias=False, name="Conv2d_2b_3x3")(x)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2b_3x3_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_2b_3x3_Activation")(x)
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x = MaxPooling2D(3, strides=2, name="MaxPool_3a_3x3")(x)
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x = Conv2D(80, 1, strides=1, padding="valid", use_bias=False, name="Conv2d_3b_1x1")(x)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_3b_1x1_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_3b_1x1_Activation")(x)
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x = Conv2D(192, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_4a_3x3")(x)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4a_3x3_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_4a_3x3_Activation")(x)
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x = Conv2D(256, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_4b_3x3")(x)
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x = BatchNormalization(
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axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4b_3x3_BatchNorm"
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)(x)
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x = Activation("relu", name="Conv2d_4b_3x3_Activation")(x)
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# 5x Block35 (Inception-ResNet-A block):
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branch_0 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_0_Conv2d_1x1"
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)(x)
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branch_0 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_0_Conv2d_1x1_BatchNorm",
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)(branch_0)
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branch_0 = Activation("relu", name="Block35_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
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branch_1 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0a_1x1"
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)(x)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
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branch_1 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0b_3x3"
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)(branch_1)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
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branch_2 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0a_1x1"
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)(x)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0b_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0c_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
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branches = [branch_0, branch_1, branch_2]
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mixed = Concatenate(axis=3, name="Block35_1_Concatenate")(branches)
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up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_1_Conv2d_1x1")(
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mixed
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)
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up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
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x = add([x, up])
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x = Activation("relu", name="Block35_1_Activation")(x)
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branch_0 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_0_Conv2d_1x1"
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)(x)
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branch_0 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_0_Conv2d_1x1_BatchNorm",
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)(branch_0)
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branch_0 = Activation("relu", name="Block35_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
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branch_1 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0a_1x1"
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)(x)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
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branch_1 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0b_3x3"
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)(branch_1)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
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branch_2 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0a_1x1"
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)(x)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0b_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0c_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
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branches = [branch_0, branch_1, branch_2]
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mixed = Concatenate(axis=3, name="Block35_2_Concatenate")(branches)
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up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_2_Conv2d_1x1")(
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mixed
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)
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up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
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x = add([x, up])
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x = Activation("relu", name="Block35_2_Activation")(x)
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branch_0 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_0_Conv2d_1x1"
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)(x)
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branch_0 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_0_Conv2d_1x1_BatchNorm",
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)(branch_0)
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branch_0 = Activation("relu", name="Block35_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
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branch_1 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0a_1x1"
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)(x)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
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branch_1 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0b_3x3"
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)(branch_1)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
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branch_2 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0a_1x1"
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)(x)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0b_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
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branch_2 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0c_3x3"
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)(branch_2)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm",
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)(branch_2)
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branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
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branches = [branch_0, branch_1, branch_2]
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mixed = Concatenate(axis=3, name="Block35_3_Concatenate")(branches)
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up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_3_Conv2d_1x1")(
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mixed
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)
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up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
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x = add([x, up])
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x = Activation("relu", name="Block35_3_Activation")(x)
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branch_0 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_0_Conv2d_1x1"
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)(x)
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branch_0 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_4_Branch_0_Conv2d_1x1_BatchNorm",
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)(branch_0)
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branch_0 = Activation("relu", name="Block35_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
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branch_1 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0a_1x1"
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)(x)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
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branch_1 = Conv2D(
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32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0b_3x3"
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)(branch_1)
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branch_1 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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scale=False,
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name="Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm",
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)(branch_1)
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branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
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branch_2 = Conv2D(
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32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0a_1x1"
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)(x)
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branch_2 = BatchNormalization(
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axis=3,
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momentum=0.995,
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epsilon=0.001,
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|
scale=False,
|
|
name="Block35_4_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0b_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_4_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0c_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_4_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_4_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
|
branches = [branch_0, branch_1, branch_2]
|
|
mixed = Concatenate(axis=3, name="Block35_4_Concatenate")(branches)
|
|
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_4_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block35_4_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block35_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block35_5_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_1_Conv2d_0b_3x3"
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block35_5_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
|
branch_2 = Conv2D(
|
|
32, 1, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0b_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
32, 3, strides=1, padding="same", use_bias=False, name="Block35_5_Branch_2_Conv2d_0c_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block35_5_Branch_2_Conv2d_0c_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Block35_5_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
|
|
branches = [branch_0, branch_1, branch_2]
|
|
mixed = Concatenate(axis=3, name="Block35_5_Concatenate")(branches)
|
|
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_5_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.17})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block35_5_Activation")(x)
|
|
|
|
# Mixed 6a (Reduction-A block):
|
|
branch_0 = Conv2D(
|
|
384, 3, strides=2, padding="valid", use_bias=False, name="Mixed_6a_Branch_0_Conv2d_1a_3x3"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Mixed_6a_Branch_0_Conv2d_1a_3x3_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192, 3, strides=1, padding="same", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_0b_3x3"
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_6a_Branch_1_Conv2d_1a_3x3"
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Mixed_6a_Branch_1_Conv2d_1a_3x3_Activation")(branch_1)
|
|
branch_pool = MaxPooling2D(
|
|
3, strides=2, padding="valid", name="Mixed_6a_Branch_2_MaxPool_1a_3x3"
|
|
)(x)
|
|
branches = [branch_0, branch_1, branch_pool]
|
|
x = Concatenate(axis=3, name="Mixed_6a")(branches)
|
|
|
|
# 10x Block17 (Inception-ResNet-B block):
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_1_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_1_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_1_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_1_Branch_1_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_1_Branch_1_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_1_Branch_1_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_1_Branch_1_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_1_Branch_1_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_1_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_1_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_1_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_2_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_2_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_2_Branch_2_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_2_Branch_2_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_2_Branch_2_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_2_Branch_2_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_2_Branch_2_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_2_Branch_2_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_2_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_2_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_2_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_3_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_3_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_3_Branch_3_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_3_Branch_3_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_3_Branch_3_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_3_Branch_3_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_3_Branch_3_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_3_Branch_3_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_3_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_3_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_3_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_4_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_4_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_4_Branch_4_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_4_Branch_4_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_4_Branch_4_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_4_Branch_4_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_4_Branch_4_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_4_Branch_4_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_4_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_4_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_4_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_5_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_5_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_5_Branch_5_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_5_Branch_5_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_5_Branch_5_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_5_Branch_5_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_5_Branch_5_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_5_Branch_5_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_5_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_5_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_5_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_6_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_6_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_6_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_6_Branch_6_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_6_Branch_6_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_6_Branch_6_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_6_Branch_6_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_6_Branch_6_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_6_Branch_6_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_6_Branch_6_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_6_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_6_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_6_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_7_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_7_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_7_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_7_Branch_7_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_7_Branch_7_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_7_Branch_7_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_7_Branch_7_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_7_Branch_7_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_7_Branch_7_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_7_Branch_7_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_7_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_7_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_7_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_8_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_8_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_8_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_8_Branch_8_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_8_Branch_8_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_8_Branch_8_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_8_Branch_8_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_8_Branch_8_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_8_Branch_8_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_8_Branch_8_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_8_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_8_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_8_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_9_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_9_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_9_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_9_Branch_9_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_9_Branch_9_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_9_Branch_9_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_9_Branch_9_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_9_Branch_9_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_9_Branch_9_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_9_Branch_9_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_9_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_9_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_9_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_10_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_10_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block17_10_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
128, 1, strides=1, padding="same", use_bias=False, name="Block17_10_Branch_10_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_10_Branch_10_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[1, 7],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_10_Branch_10_Conv2d_0b_1x7",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_10_Branch_10_Conv2d_0b_1x7_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0b_1x7_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
128,
|
|
[7, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block17_10_Branch_10_Conv2d_0c_7x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block17_10_Branch_10_Conv2d_0c_7x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block17_10_Branch_10_Conv2d_0c_7x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block17_10_Concatenate")(branches)
|
|
up = Conv2D(896, 1, strides=1, padding="same", use_bias=True, name="Block17_10_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.1})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block17_10_Activation")(x)
|
|
|
|
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
|
|
branch_0 = Conv2D(
|
|
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_0_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation")(branch_0)
|
|
branch_0 = Conv2D(
|
|
384, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_0_Conv2d_1a_3x3"
|
|
)(branch_0)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Mixed_7a_Branch_0_Conv2d_1a_3x3_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Mixed_7a_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_1_Conv2d_1a_3x3"
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Mixed_7a_Branch_1_Conv2d_1a_3x3_Activation")(branch_1)
|
|
branch_2 = Conv2D(
|
|
256, 1, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
256, 3, strides=1, padding="same", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_0b_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
|
|
branch_2 = Conv2D(
|
|
256, 3, strides=2, padding="valid", use_bias=False, name="Mixed_7a_Branch_2_Conv2d_1a_3x3"
|
|
)(branch_2)
|
|
branch_2 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm",
|
|
)(branch_2)
|
|
branch_2 = Activation("relu", name="Mixed_7a_Branch_2_Conv2d_1a_3x3_Activation")(branch_2)
|
|
branch_pool = MaxPooling2D(
|
|
3, strides=2, padding="valid", name="Mixed_7a_Branch_3_MaxPool_1a_3x3"
|
|
)(x)
|
|
branches = [branch_0, branch_1, branch_2, branch_pool]
|
|
x = Concatenate(axis=3, name="Mixed_7a")(branches)
|
|
|
|
# 5x Block8 (Inception-ResNet-C block):
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_1_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_1_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_1_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_1_Branch_1_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_1_Branch_1_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_1_Branch_1_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_1_Branch_1_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_1_Branch_1_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_1_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_1_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block8_1_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_2_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_2_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_2_Branch_2_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_2_Branch_2_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_2_Branch_2_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_2_Branch_2_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_2_Branch_2_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_2_Branch_2_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_2_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_2_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block8_2_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_3_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_3_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_3_Branch_3_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_3_Branch_3_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_3_Branch_3_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_3_Branch_3_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_3_Branch_3_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_3_Branch_3_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_3_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_3_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block8_3_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_4_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_4_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_4_Branch_4_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_4_Branch_4_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_4_Branch_4_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_4_Branch_4_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_4_Branch_4_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_4_Branch_4_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_4_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_4_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block8_4_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_5_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_5_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_5_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_5_Branch_5_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_5_Branch_5_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_5_Branch_5_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_5_Branch_5_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_5_Branch_5_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_5_Branch_5_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_5_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_5_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 0.2})(up)
|
|
x = add([x, up])
|
|
x = Activation("relu", name="Block8_5_Activation")(x)
|
|
|
|
branch_0 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_6_Branch_0_Conv2d_1x1"
|
|
)(x)
|
|
branch_0 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_6_Branch_0_Conv2d_1x1_BatchNorm",
|
|
)(branch_0)
|
|
branch_0 = Activation("relu", name="Block8_6_Branch_0_Conv2d_1x1_Activation")(branch_0)
|
|
branch_1 = Conv2D(
|
|
192, 1, strides=1, padding="same", use_bias=False, name="Block8_6_Branch_1_Conv2d_0a_1x1"
|
|
)(x)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[1, 3],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_6_Branch_1_Conv2d_0b_1x3",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0b_1x3_Activation")(branch_1)
|
|
branch_1 = Conv2D(
|
|
192,
|
|
[3, 1],
|
|
strides=1,
|
|
padding="same",
|
|
use_bias=False,
|
|
name="Block8_6_Branch_1_Conv2d_0c_3x1",
|
|
)(branch_1)
|
|
branch_1 = BatchNormalization(
|
|
axis=3,
|
|
momentum=0.995,
|
|
epsilon=0.001,
|
|
scale=False,
|
|
name="Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm",
|
|
)(branch_1)
|
|
branch_1 = Activation("relu", name="Block8_6_Branch_1_Conv2d_0c_3x1_Activation")(branch_1)
|
|
branches = [branch_0, branch_1]
|
|
mixed = Concatenate(axis=3, name="Block8_6_Concatenate")(branches)
|
|
up = Conv2D(1792, 1, strides=1, padding="same", use_bias=True, name="Block8_6_Conv2d_1x1")(
|
|
mixed
|
|
)
|
|
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={"scale": 1})(up)
|
|
x = add([x, up])
|
|
|
|
# Classification block
|
|
x = GlobalAveragePooling2D(name="AvgPool")(x)
|
|
x = Dropout(1.0 - 0.8, name="Dropout")(x)
|
|
# Bottleneck
|
|
x = Dense(dimension, use_bias=False, name="Bottleneck")(x)
|
|
x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False, name="Bottleneck_BatchNorm")(
|
|
x
|
|
)
|
|
|
|
# Create model
|
|
model = Model(inputs, x, name="inception_resnet_v1")
|
|
|
|
return model
|
|
|
|
|
|
def load_facenet128d_model(
|
|
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5",
|
|
) -> Model:
|
|
"""
|
|
Construct FaceNet-128d model, download weights and then load weights
|
|
Args:
|
|
dimension (int): construct FaceNet-128d or FaceNet-512d models
|
|
Returns:
|
|
model (Model)
|
|
"""
|
|
model = InceptionResNetV1()
|
|
|
|
# -----------------------------------
|
|
|
|
home = folder_utils.get_deepface_home()
|
|
output = os.path.join(home, ".deepface/weights/facenet_weights.h5")
|
|
|
|
if not os.path.isfile(output):
|
|
logger.info(f"{os.path.basename(output)} will be downloaded...")
|
|
gdown.download(url, output, quiet=False)
|
|
|
|
# -----------------------------------
|
|
|
|
model.load_weights(output)
|
|
|
|
# -----------------------------------
|
|
|
|
return model
|
|
|
|
|
|
def load_facenet512d_model(
|
|
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5",
|
|
) -> Model:
|
|
"""
|
|
Construct FaceNet-512d model, download its weights and load
|
|
Returns:
|
|
model (Model)
|
|
"""
|
|
|
|
model = InceptionResNetV1(dimension=512)
|
|
|
|
# -------------------------
|
|
|
|
home = folder_utils.get_deepface_home()
|
|
output = os.path.join(home, ".deepface/weights/facenet512_weights.h5")
|
|
|
|
if not os.path.isfile(output):
|
|
logger.info(f"{os.path.basename(output)} will be downloaded...")
|
|
gdown.download(url, output, quiet=False)
|
|
|
|
# -------------------------
|
|
|
|
model.load_weights(output)
|
|
|
|
# -------------------------
|
|
|
|
return model
|