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Copy pathFacenet.py
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1793 lines (1725 loc) · 62.5 KB
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# --------------------------------
# dependency configuration
import os
import sys
import tensorflow as tf
import onnx
import tf2onnx
import cv2 # 添加opencv导入
import numpy as np # 添加numpy导入
import onnxruntime # 新增导入
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
# pylint:disable=line-too-long
FACENET128_WEIGHTS = (
"https://github.com/serengil/deepface_models/releases/download/v1.0/facenet_weights.h5"
)
FACENET512_WEIGHTS = (
"https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5"
)
# pylint: disable=too-few-public-methods
class FaceNet128dClient():
"""
FaceNet-128d model class
"""
def __init__(self):
self.model = load_facenet128d_model()
self.model_name = "FaceNet-128d"
self.input_shape = (160, 160)
self.output_shape = 128
class FaceNet512dClient():
"""
FaceNet-512d model class
"""
def __init__(self):
self.model = load_facenet512d_model()
self.model_name = "FaceNet-512d"
self.input_shape = (160, 160)
self.output_shape = 512
def scaling(x, scale):
return x * scale
def InceptionResNetV1(dimension: int = 128) -> Model:
"""
InceptionResNetV1 model heavily inspired from
github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
As mentioned in Sandberg's repo's readme, pre-trained models are using Inception ResNet v1
Besides training process is documented at
sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/
Args:
dimension (int): number of dimensions in the embedding layer
Returns:
model (Model)
"""
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",