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backend.py
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backend.py
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from keras.models import Model
import tensorflow as tf
from keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.merge import concatenate
from keras.applications.mobilenet import MobileNet
from keras.applications import InceptionV3
from keras.applications.vgg16 import VGG16
from keras.applications.resnet50 import ResNet50
FULL_YOLO_BACKEND_PATH = "full_yolo_backend.h5" # should be hosted on a server
TINY_YOLO_BACKEND_PATH = "tiny_yolo_backend.h5" # should be hosted on a server
SQUEEZENET_BACKEND_PATH = "squeezenet_backend.h5" # should be hosted on a server
MOBILENET_BACKEND_PATH = "mobilenet_backend.h5" # should be hosted on a server
INCEPTION3_BACKEND_PATH = "inception_backend.h5" # should be hosted on a server
VGG16_BACKEND_PATH = "vgg16_backend.h5" # should be hosted on a server
RESNET50_BACKEND_PATH = "resnet50_backend.h5" # should be hosted on a server
class BaseFeatureExtractor(object):
"""docstring for ClassName"""
# to be defined in each subclass
def __init__(self, input_size):
raise NotImplementedError("error message")
# to be defined in each subclass
def normalize(self, image):
raise NotImplementedError("error message")
def get_output_shape(self):
return self.feature_extractor.get_output_shape_at(-1)[1:3]
def extract(self, input_image):
return self.feature_extractor(input_image)
class FullYoloFeature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
input_image = Input(shape=(input_size, input_size, 3))
# the function to implement the orgnization layer (thanks to github.com/allanzelener/YAD2K)
def space_to_depth_x2(x):
return tf.space_to_depth(x, block_size=2)
# Layer 1
x = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input_image)
x = BatchNormalization(name='norm_1')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 2
x = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_2', use_bias=False)(x)
x = BatchNormalization(name='norm_2')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 3
x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_3', use_bias=False)(x)
x = BatchNormalization(name='norm_3')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 4
x = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_4', use_bias=False)(x)
x = BatchNormalization(name='norm_4')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 5
x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_5', use_bias=False)(x)
x = BatchNormalization(name='norm_5')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 6
x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x)
x = BatchNormalization(name='norm_6')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 7
x = Conv2D(128, (1,1), strides=(1,1), padding='same', name='conv_7', use_bias=False)(x)
x = BatchNormalization(name='norm_7')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 8
x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_8', use_bias=False)(x)
x = BatchNormalization(name='norm_8')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 9
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_9', use_bias=False)(x)
x = BatchNormalization(name='norm_9')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 10
x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_10', use_bias=False)(x)
x = BatchNormalization(name='norm_10')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 11
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_11', use_bias=False)(x)
x = BatchNormalization(name='norm_11')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 12
x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_12', use_bias=False)(x)
x = BatchNormalization(name='norm_12')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 13
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_13', use_bias=False)(x)
x = BatchNormalization(name='norm_13')(x)
x = LeakyReLU(alpha=0.1)(x)
skip_connection = x
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 14
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_14', use_bias=False)(x)
x = BatchNormalization(name='norm_14')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 15
x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_15', use_bias=False)(x)
x = BatchNormalization(name='norm_15')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 16
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_16', use_bias=False)(x)
x = BatchNormalization(name='norm_16')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 17
x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_17', use_bias=False)(x)
x = BatchNormalization(name='norm_17')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 18
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_18', use_bias=False)(x)
x = BatchNormalization(name='norm_18')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 19
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_19', use_bias=False)(x)
x = BatchNormalization(name='norm_19')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 20
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_20', use_bias=False)(x)
x = BatchNormalization(name='norm_20')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 21
skip_connection = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_21', use_bias=False)(skip_connection)
skip_connection = BatchNormalization(name='norm_21')(skip_connection)
skip_connection = LeakyReLU(alpha=0.1)(skip_connection)
skip_connection = Lambda(space_to_depth_x2)(skip_connection)
x = concatenate([skip_connection, x])
# Layer 22
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_22', use_bias=False)(x)
x = BatchNormalization(name='norm_22')(x)
x = LeakyReLU(alpha=0.1)(x)
self.feature_extractor = Model(input_image, x)
self.feature_extractor.load_weights(FULL_YOLO_BACKEND_PATH)
def normalize(self, image):
return image / 255.
class TinyYoloFeature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
input_image = Input(shape=(input_size, input_size, 3))
# Layer 1
x = Conv2D(16, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input_image)
x = BatchNormalization(name='norm_1')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 2 - 5
for i in range(0,4):
x = Conv2D(32*(2**i), (3,3), strides=(1,1), padding='same', name='conv_' + str(i+2), use_bias=False)(x)
x = BatchNormalization(name='norm_' + str(i+2))(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 6
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x)
x = BatchNormalization(name='norm_6')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(1,1), padding='same')(x)
# Layer 7 - 8
for i in range(0,2):
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_' + str(i+7), use_bias=False)(x)
x = BatchNormalization(name='norm_' + str(i+7))(x)
x = LeakyReLU(alpha=0.1)(x)
self.feature_extractor = Model(input_image, x)
self.feature_extractor.load_weights(TINY_YOLO_BACKEND_PATH)
def normalize(self, image):
return image / 255.
class MobileNetFeature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
input_image = Input(shape=(input_size, input_size, 3))
mobilenet = MobileNet(input_shape=(224,224,3), include_top=False)
mobilenet.load_weights(MOBILENET_BACKEND_PATH)
x = mobilenet(input_image)
self.feature_extractor = Model(input_image, x)
def normalize(self, image):
image = image / 255.
image = image - 0.5
image = image * 2.
return image
class SqueezeNetFeature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
# define some auxiliary variables and the fire module
sq1x1 = "squeeze1x1"
exp1x1 = "expand1x1"
exp3x3 = "expand3x3"
relu = "relu_"
def fire_module(x, fire_id, squeeze=16, expand=64):
s_id = 'fire' + str(fire_id) + '/'
x = Conv2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x)
x = Activation('relu', name=s_id + relu + sq1x1)(x)
left = Conv2D(expand, (1, 1), padding='valid', name=s_id + exp1x1)(x)
left = Activation('relu', name=s_id + relu + exp1x1)(left)
right = Conv2D(expand, (3, 3), padding='same', name=s_id + exp3x3)(x)
right = Activation('relu', name=s_id + relu + exp3x3)(right)
x = concatenate([left, right], axis=3, name=s_id + 'concat')
return x
# define the model of SqueezeNet
input_image = Input(shape=(input_size, input_size, 3))
x = Conv2D(64, (3, 3), strides=(2, 2), padding='valid', name='conv1')(input_image)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = fire_module(x, fire_id=2, squeeze=16, expand=64)
x = fire_module(x, fire_id=3, squeeze=16, expand=64)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool3')(x)
x = fire_module(x, fire_id=4, squeeze=32, expand=128)
x = fire_module(x, fire_id=5, squeeze=32, expand=128)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool5')(x)
x = fire_module(x, fire_id=6, squeeze=48, expand=192)
x = fire_module(x, fire_id=7, squeeze=48, expand=192)
x = fire_module(x, fire_id=8, squeeze=64, expand=256)
x = fire_module(x, fire_id=9, squeeze=64, expand=256)
self.feature_extractor = Model(input_image, x)
self.feature_extractor.load_weights(SQUEEZENET_BACKEND_PATH)
def normalize(self, image):
image = image[..., ::-1]
image = image.astype('float')
image[..., 0] -= 103.939
image[..., 1] -= 116.779
image[..., 2] -= 123.68
return image
class Inception3Feature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
input_image = Input(shape=(input_size, input_size, 3))
inception = InceptionV3(input_shape=(input_size,input_size,3), include_top=False)
inception.load_weights(INCEPTION3_BACKEND_PATH)
x = inception(input_image)
self.feature_extractor = Model(input_image, x)
def normalize(self, image):
image = image / 255.
image = image - 0.5
image = image * 2.
return image
class VGG16Feature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
vgg16 = VGG16(input_shape=(input_size, input_size, 3), include_top=False)
#vgg16.load_weights(VGG16_BACKEND_PATH)
self.feature_extractor = vgg16
def normalize(self, image):
image = image[..., ::-1]
image = image.astype('float')
image[..., 0] -= 103.939
image[..., 1] -= 116.779
image[..., 2] -= 123.68
return image
class ResNet50Feature(BaseFeatureExtractor):
"""docstring for ClassName"""
def __init__(self, input_size):
resnet50 = ResNet50(input_shape=(input_size, input_size, 3), include_top=False)
resnet50.layers.pop() # remove the average pooling layer
#resnet50.load_weights(RESNET50_BACKEND_PATH)
self.feature_extractor = Model(resnet50.layers[0].input, resnet50.layers[-1].output)
def normalize(self, image):
image = image[..., ::-1]
image = image.astype('float')
image[..., 0] -= 103.939
image[..., 1] -= 116.779
image[..., 2] -= 123.68
return image