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deepface/deepface/basemodels/Facenet.py
Sefik Ilkin Serengil 125895398e modules added into logger
2023-12-06 20:55:00 +00:00

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import os
import gdown
import tensorflow as tf
from deepface.commons import functions
from deepface.commons.logger import Logger
logger = Logger(module="basemodels.Facenet")
# --------------------------------
# dependency configuration
tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
if tf_version == 1:
from keras.models import Model
from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import Concatenate
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers import Lambda
from keras.layers import MaxPooling2D
from keras.layers import add
from keras import backend as K
else:
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Concatenate
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Lambda
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import add
from tensorflow.keras import backend as K
# --------------------------------
def scaling(x, scale):
return x * scale
def InceptionResNetV2(dimension=128):
inputs = Input(shape=(160, 160, 3))
x = Conv2D(32, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_1a_3x3")(inputs)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_1a_3x3_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_1a_3x3_Activation")(x)
x = Conv2D(32, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_2a_3x3")(x)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2a_3x3_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_2a_3x3_Activation")(x)
x = Conv2D(64, 3, strides=1, padding="same", use_bias=False, name="Conv2d_2b_3x3")(x)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_2b_3x3_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_2b_3x3_Activation")(x)
x = MaxPooling2D(3, strides=2, name="MaxPool_3a_3x3")(x)
x = Conv2D(80, 1, strides=1, padding="valid", use_bias=False, name="Conv2d_3b_1x1")(x)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_3b_1x1_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_3b_1x1_Activation")(x)
x = Conv2D(192, 3, strides=1, padding="valid", use_bias=False, name="Conv2d_4a_3x3")(x)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4a_3x3_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_4a_3x3_Activation")(x)
x = Conv2D(256, 3, strides=2, padding="valid", use_bias=False, name="Conv2d_4b_3x3")(x)
x = BatchNormalization(
axis=3, momentum=0.995, epsilon=0.001, scale=False, name="Conv2d_4b_3x3_BatchNorm"
)(x)
x = Activation("relu", name="Conv2d_4b_3x3_Activation")(x)
# 5x Block35 (Inception-ResNet-A block):
branch_0 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_0_Conv2d_1x1"
)(x)
branch_0 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_0_Conv2d_1x1_BatchNorm",
)(branch_0)
branch_0 = Activation("relu", name="Block35_1_Branch_0_Conv2d_1x1_Activation")(branch_0)
branch_1 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0a_1x1"
)(x)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
branch_1 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_1_Conv2d_0b_3x3"
)(branch_1)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_1_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
branch_2 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0a_1x1"
)(x)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0b_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_1_Branch_2_Conv2d_0c_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_1_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name="Block35_1_Concatenate")(branches)
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_1_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_1_Activation")(x)
branch_0 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_0_Conv2d_1x1"
)(x)
branch_0 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_0_Conv2d_1x1_BatchNorm",
)(branch_0)
branch_0 = Activation("relu", name="Block35_2_Branch_0_Conv2d_1x1_Activation")(branch_0)
branch_1 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0a_1x1"
)(x)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
branch_1 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_1_Conv2d_0b_3x3"
)(branch_1)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_2_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
branch_2 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0a_1x1"
)(x)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0b_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_2_Branch_2_Conv2d_0c_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_2_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name="Block35_2_Concatenate")(branches)
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_2_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_2_Activation")(x)
branch_0 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_0_Conv2d_1x1"
)(x)
branch_0 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_0_Conv2d_1x1_BatchNorm",
)(branch_0)
branch_0 = Activation("relu", name="Block35_3_Branch_0_Conv2d_1x1_Activation")(branch_0)
branch_1 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0a_1x1"
)(x)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
branch_1 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_1_Conv2d_0b_3x3"
)(branch_1)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_3_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
branch_2 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0a_1x1"
)(x)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0a_1x1_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0b_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0b_3x3_Activation")(branch_2)
branch_2 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_3_Branch_2_Conv2d_0c_3x3"
)(branch_2)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm",
)(branch_2)
branch_2 = Activation("relu", name="Block35_3_Branch_2_Conv2d_0c_3x3_Activation")(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name="Block35_3_Concatenate")(branches)
up = Conv2D(256, 1, strides=1, padding="same", use_bias=True, name="Block35_3_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_3_Activation")(x)
branch_0 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_0_Conv2d_1x1"
)(x)
branch_0 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_4_Branch_0_Conv2d_1x1_BatchNorm",
)(branch_0)
branch_0 = Activation("relu", name="Block35_4_Branch_0_Conv2d_1x1_Activation")(branch_0)
branch_1 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0a_1x1"
)(x)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0a_1x1_Activation")(branch_1)
branch_1 = Conv2D(
32, 3, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_1_Conv2d_0b_3x3"
)(branch_1)
branch_1 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
scale=False,
name="Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm",
)(branch_1)
branch_1 = Activation("relu", name="Block35_4_Branch_1_Conv2d_0b_3x3_Activation")(branch_1)
branch_2 = Conv2D(
32, 1, strides=1, padding="same", use_bias=False, name="Block35_4_Branch_2_Conv2d_0a_1x1"
)(x)
branch_2 = BatchNormalization(
axis=3,
momentum=0.995,
epsilon=0.001,
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
# url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'
def loadModel(
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5",
):
model = InceptionResNetV2()
# -----------------------------------
home = functions.get_deepface_home()
if os.path.isfile(home + "/.deepface/weights/facenet_weights.h5") != True:
logger.info("facenet_weights.h5 will be downloaded...")
output = home + "/.deepface/weights/facenet_weights.h5"
gdown.download(url, output, quiet=False)
# -----------------------------------
model.load_weights(home + "/.deepface/weights/facenet_weights.h5")
# -----------------------------------
return model
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