def import_feature_extractor(backend, input_size):
if backend == 'Inception3':
feature_extractor = Inception3Feature(input_size)
elif backend == 'SqueezeNet':
feature_extractor = SqueezeNetFeature(input_size)
elif backend == 'MobileNet':
feature_extractor = MobileNetFeature(input_size)
elif backend == 'Full Yolo':
feature_extractor = FullYoloFeature(input_size)
elif backend == 'Tiny Yolo':
feature_extractor = TinyYoloFeature(input_size)
elif backend == 'VGG16':
feature_extractor = VGG16Feature(input_size)
elif backend == 'ResNet50':
feature_extractor = ResNet50Feature(input_size)
elif os.path.dirname(backend) != "":
basePath = os.path.dirname(backend)
sys.path.append(basePath)
custom_backend_name = os.path.basename(backend)
custom_backend = import_dynamically(custom_backend_name)
feature_extractor = custom_backend(input_size)
if not issubclass(custom_backend,BaseFeatureExtractor):
raise RuntimeError('You are trying to import a custom backend, your backend must'
' be in inherited from "backend.BaseFeatureExtractor".')
print('Using a custom backend called {}.'.format(custom_backend_name))
else:
raise RuntimeError('Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet,'
'SqueezeNet, VGG16, ResNet50, or Inception3 at the moment!')
return feature_extractor
def __init__(self, architecture, input_size, labels, max_box_per_image,
anchors):
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = 5
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# make the feature extractor layers
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
if architecture == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_size)
elif architecture == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(self.input_size)
else:
raise Exception(
'Architecture not supported! Please use Full Yolo or Tiny Yolo!'
)
print(self.feature_extractor.get_output_shape())
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image)
# make the object detection layer
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class), (1, 1),
strides=(1, 1),
padding='same',
name='conv_23',
kernel_initializer='lecun_normal')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box,
4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h *
self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_h *
self.grid_w)
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
self.model.summary()
def __init__(self, backend, input_size, labels, max_box_per_image,
anchors):
"""
:param backend: 特征提取器
:param input_size: 输入图像的维度
:param labels: 标签
:param max_box_per_image: 每张图像最多所拥有的框数量
:param anchors: 锚框
"""
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = len(anchors) // 2
self.class_wt = np.ones(self.nb_class, dtype=np.float32)
self.anchors = anchors
self.max_box_per_image = max_box_per_image
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
self.feature_extractor = FullYoloFeature(self.input_size)
print(self.feature_extractor.get_output_shape())
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image)
# 创建物体检测层
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class), (1, 1),
strides=(1, 1),
padding='same',
name='DetectionLayer',
kernel_initializer='lecun_normal')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box,
4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
# 初始化物体检测层的参数
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h *
self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_w *
self.grid_h)
layer.set_weights([new_kernel, new_bias])
# 打印模型的摘要信息
self.model.summary()
def __init__(self, backend,
input_size,
labels,
max_box_per_image,
anchors):
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = len(anchors) // 2
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# make the feature extractor layers
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
if backend == 'Inception3':
self.feature_extractor = Inception3Feature(self.input_size)
elif backend == 'SqueezeNet':
self.feature_extractor = SqueezeNetFeature(self.input_size)
elif backend == 'MobileNet':
self.feature_extractor = MobileNetFeature(self.input_size)
elif backend == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_size)
elif backend == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(self.input_size)
elif backend == 'VGG16':
self.feature_extractor = VGG16Feature(self.input_size)
elif backend == 'ResNet50':
self.feature_extractor = ResNet50Feature(self.input_size)
else:
raise Exception(
'Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!')
print(self.feature_extractor.get_output_shape())
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image)
# make the object detection layer
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class),
(1, 1), strides=(1, 1),
padding='same',
name='DetectionLayer',
kernel_initializer='lecun_normal')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box, 4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h * self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_h * self.grid_w)
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
self.model.summary()
def __init__(self, backend, input_size, labels, max_box_per_image, anchors, verbose=1):
##########################
# Save the network parameters
##########################
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = len(anchors) // 2
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# make the feature extractor layers
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
if backend == 'Inception3':
self.feature_extractor = Inception3Feature(input_size=self.input_size)
elif backend == 'Squeezenet':
self.feature_extractor = SqueezeNetFeature(input_size=self.input_size)
elif backend == 'MobileNet':
self.feature_extractor = MobileNetFeature(input_size=self.input_size)
elif backend == 'Full Yolo':
self.feature_extractor = FullYoloFeature(input_size=self.input_size)
elif backend == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(input_size=self.input_size)
elif backend == 'VGG16':
self.feature_extractor = VGG16Feature(input_size=self.input_size)
elif backend == 'ResNet50':
self.feature_extractor = ResNet50Feature(input_size=self.input_size)
else:
raise Exception('Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!')
print('Feature extractor shape: {}'.format(self.feature_extractor.get_output_shape()))
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image=input_image)
# make the object detection layer
output = Conv2D(filters=self.nb_box * (4 + 1 + self.nb_class),
kernel_size=(1, 1),
strides=(1, 1),
padding='same',
kernel_initializer='lecun_normal',
name='DetectionLayer')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box, 4 + 1 + self.nb_class))(output)
# small hack to allow true_boxes to be registered when Keras build the model
# for more information: https://github.com/fchollet/keras/issues/2790
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
# ??? Why have to redefine Conv2D_DetectionLayer as below?
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h * self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_h * self.grid_w)
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
if verbose: self.model.summary()
def __init__(self, backend, input_width, input_height, input_channel,
labels, max_box_per_image, anchors, saved_config_name):
self.input_width = input_width
self.input_height = input_height
self.input_channel = input_channel
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = len(anchors) // 2 # each anchor has 2 (w,h) number.
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# models.model_1(self.input_height, self.input_width, self.input_channel, \
# self.max_box_per_image, self.nb_box, self.nb_class)
# make the feature extractor layers
input_image = Input(shape=(self.input_height, self.input_width,
self.input_channel))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
if backend == 'Inception3':
self.feature_extractor = Inception3Feature(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'SqueezeNet':
self.feature_extractor = SqueezeNetFeature(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'MobileNet':
self.feature_extractor = MobileNetFeature(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo_1':
self.feature_extractor = TinyYoloFeature_1(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo_2':
self.feature_extractor = TinyYoloFeature_2(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo_3':
self.feature_extractor = TinyYoloFeature_3(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo_4':
self.feature_extractor = TinyYoloFeature_4(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'Tiny Yolo_5':
self.feature_extractor = TinyYoloFeature_5(self.input_height,
self.input_width,
self.input_channel)
elif backend == 'VGG16':
self.feature_extractor = VGG16Feature(self.input_height,
self.input_width)
elif backend == 'ResNet50':
self.feature_extractor = ResNet50Feature(self.input_height,
self.input_width)
elif backend == 'My Yolo':
self.feature_extractor = MyYoloFeature(self.input_height,
self.input_width)
else:
raise Exception(
'Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!'
)
# print(self.feature_extractor.get_output_shape())
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
#features = self.feature_extractor.extract(input_image)
features = self.feature_extractor.feature_extractor.output
# make the object detection layer
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class), (1, 1),
strides=(1, 1),
padding='same',
name='DetectionLayer',
kernel_initializer='lecun_normal')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box,
4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
#self.model = Model([input_image, self.true_boxes], output)
self.model = Model(
[self.feature_extractor.feature_extractor.input, self.true_boxes],
output)
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h *
self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_h *
self.grid_w)
layer.set_weights([new_kernel, new_bias])
# save model config
model_json = self.model.to_json()
with open(str(saved_config_name), "w") as json_file:
json_file.write(model_json)
# print a summary of the whole model
self.feature_extractor.feature_extractor.summary()
self.model.summary()
def __init__(self, architecture, input_size, labels, max_box_per_image,
anchors):
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = 5
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# make the feature extractor layers
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
if architecture == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_size)
else:
raise Exception(
'Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!'
)
print self.feature_extractor.get_output_shape()
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image)
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class), (1, 1),
strides=(1, 1),
padding='same',
name='conv_23',
kernel_initializer='lecun_normal')(features)
# output = Conv2D(1024,
# (3,3), strides=(1,1),
# padding='same',
# name='conv_23',
# kernel_initializer='lecun_normal',
# use_bias=False)(features)
# output = Flatten()(output)
# output = Dense(512)(output)
# # output = Dense(16384)(output)
# output = LeakyReLU(alpha=0.1)(output)
# output = Dense(self.grid_h*self.grid_w*self.nb_box*( 4 + 1 + self.nb_class))(output)
# make the object detection layer
output = Reshape((self.grid_h, self.grid_w, self.nb_box,
4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (self.grid_h *
self.grid_w)
new_bias = np.random.normal(size=weights[1].shape) / (self.grid_h *
self.grid_w)
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
self.model.summary()
def __init__(self, backend,
input_size,
labels,
max_box_per_image,
anchors,
threshold,
max_sur):
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = len(anchors)//2 #应该是期望的box数量？还没仔细看paper
self.class_wt = np.ones(self.nb_class, dtype='float32') #这里没有拓展，可以各类型的修改权重
self.anchors = anchors
self.threshold = threshold
self.max_sur = max_sur
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
# make the feature extractor layers
# 构建了一个图片的input层
input_image = Input(shape=(self.input_size, self.input_size, 3))
# 构建了bounding box回归的输入层
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image , 4))
# 从backend获取卷积部分的结构，这里返回的是bankend中定义的不同的几个类的对象，都是BaseFeatureExtractor类的子类
# backend中子类的self.feature_extractor变量就是构建的keras的model对象，在backend中只是加了个包装
# 这里的YOLO类中的变量也叫feature_extractor，但是对应的是backend中的几个子类的对象，不能弄混了
if backend == 'Inception3':
self.feature_extractor = Inception3Feature(self.input_size)
elif backend == 'SqueezeNet':
self.feature_extractor = SqueezeNetFeature(self.input_size)
elif backend == 'MobileNet':
self.feature_extractor = MobileNetFeature(self.input_size)
elif backend == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_size)
elif backend == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(self.input_size)
elif backend == 'VGG16':
self.feature_extractor = VGG16Feature(self.input_size)
elif backend == 'ResNet50':
self.feature_extractor = ResNet50Feature(self.input_size)
else:
raise Exception('Architecture not supported! Only support Full Yolo, Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!')
# 通过在父类定义的get_output_shape()获得输出的特征矩阵的大小
print(issubclass(Model,Layer))
print(self.feature_extractor.get_output_shape())
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
# 这个extract()和get_output_shape()一样也是backend中BaseFeatureExtractor类定义的父类方法
# 这里是把上面定义的图片输入层和模型的特征提取模块接在一起
# 看backen的代码其实这一步有点多余，因为backend已经有输入层了，可能应为方便？我觉得这个操作可以去掉
# 总之，这里的features就是一个构建到一半的模型（的特征提取部分），如果直接调用predict出来的是一个特征矩阵
features = self.feature_extractor.extract(input_image)
# make the object detection layer
# 构造模型的分类层
# 输出的shape为：
# self.nb_box * (4 + 1 + self.nb_class), self.grid_h, self.grid_w)
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class),
(1,1), strides=(1,1),
padding='same',
name='DetectionLayer',
kernel_initializer='lecun_normal')(features)
# 有13*13组对每个bounding box（max/2个）的predict
output = Reshape((self.grid_h, self.grid_w, self.nb_box, 4 + 1 + self.nb_class))(output)
#加的这层lambda层贼奇怪，把true_boxes放进来以后又不取，相当于没放进来，不知道有啥用
#注意，这里隐形的加了一个input-layer，对于true_boxes的输入
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.model = Model([input_image, self.true_boxes], output)
"""
这里要注意的是：
现在的model是一个：
input->BACKEND->Covn2d->Reshape->(input)->Lambda的模型
一共是6层
虽然backend里面的构造很复杂，但是在这里被当成一个layer（因为其实MODEL对象是layer对象的子类）
"""
#输出的shape：(self.grid_h, self.grid_w, max_box_num, dim(也就是4 + 1 + self.nb_class))
print(self.model.layers)
print(f"number of layers:{len(self.model.layers)}")
print(self.model.output_shape)
# initialize the weights of the detection layer
layer = self.model.layers[-4]
weights = layer.get_weights()
print(f"weigth_2D:{weights}")
# 第一个array的shape是（w,h,d(上一层传下来有多少个feature-map),number）
# 第二个array的shape是（number）也就是说每个kernel一个bias
print(f"weigth_2D_shape:{(weights[0].shape,weights[1].shape)}")
new_kernel = np.random.normal(size=weights[0].shape)/(self.grid_h*self.grid_w)
new_bias = np.random.normal(size=weights[1].shape)/(self.grid_h*self.grid_w)
#分类layer是高斯随机的
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
self.model.summary()