简而言之:

我如何将特征映射从keras中定义的卷积层传递到一个特殊函数(区域提议器),然后该函数被传递到其他keras层(例如softmax分类器)?

长:

我正在尝试实现 Fast R-CNN ( 不 更快的R-CNN)在喀拉斯。原因是我正在尝试实现一个定制的体系结构,如下图所示:

这是上图的代码(不包括候选输入):

from keras.layers import Input, Dense, Conv2D, ZeroPadding2D, MaxPooling2D, BatchNormalization, concatenate
from keras.activations import relu, sigmoid, linear
from keras.initializers import RandomUniform, Constant, TruncatedNormal, RandomNormal, Zeros

#  Network 1, Layer 1
screenshot = Input(shape=(1280, 1280, 0),
                   dtype='float32',
                   name='screenshot')
conv1 = Conv2D(filters=96,
               kernel_size=11,
               strides=(4, 4),
               activation=relu,
               padding='same')(screenshot)
pooling1 = MaxPooling2D(pool_size=(3, 3),
                        strides=(2, 2),
                        padding='same')(conv1)
normalized1 = BatchNormalization()(pooling1)  # https://stats.stackexchange.com/questions/145768/importance-of-local-response-normalization-in-cnn

# Network 1, Layer 2

conv2 = Conv2D(filters=256,
               kernel_size=5,
               activation=relu,
               padding='same')(normalized1)
normalized2 = BatchNormalization()(conv2)
conv3 = Conv2D(filters=384,
               kernel_size=3,
               activation=relu,
               padding='same',
               kernel_initializer=RandomNormal(stddev=0.01),
               bias_initializer=Constant(value=0.1))(normalized2)

# Network 2, Layer 1

textmaps = Input(shape=(160, 160, 128),
                 dtype='float32',
                 name='textmaps')
txt_conv1 = Conv2D(filters=48,
                   kernel_size=1,
                   activation=relu,
                   padding='same',
                   kernel_initializer=RandomNormal(stddev=0.01),
                   bias_initializer=Constant(value=0.1))(textmaps)

# (Network 1 + Network 2), Layer 1

merged = concatenate([conv3, txt_conv1], axis=-1)
merged_padding = ZeroPadding2D(padding=2, data_format=None)(merged)
merged_conv = Conv2D(filters=96,
                     kernel_size=5,
                     activation=relu, padding='same',
                     kernel_initializer=RandomNormal(stddev=0.01),
                     bias_initializer=Constant(value=0.1))(merged_padding)

如上所述,我尝试构建的网络的最后一步是 ROI池 ,在R-CNN中是这样做的:

现在 there is a code for ROI Pooling layer in Keras 但是到那一层我需要通过 区域建议 . 正如您可能已经知道的,区域建议通常是通过称为选择性搜索的算法完成的, which is already implemented in the Python .

问题:

选择性搜索可以很容易地获取正常图像,并向我们提供如下区域建议:

现在 问题 也就是说,我应该从图层传递一个要素图,而不是图像 merged_conv1 如上述代码所示:

merged_conv = Conv2D(filters=96,
                     kernel_size=5,
                     activation=relu, padding='same',
                     kernel_initializer=RandomNormal(stddev=0.01),
                     bias_initializer=Constant(value=0.1))(merged_padding)

上面的层只是对形状的引用,因此显然它不适用于SelectiveSearch:

>>> import selectivesearch
>>> selectivesearch.selective_search(merged_conv, scale=500, sigma=0.9, min_size=10)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/somepath/selectivesearch.py", line 262, in selective_search
    assert im_orig.shape[2] == 3, "3ch image is expected"
AssertionError: 3ch image is expected

我想我应该这样做:

from keras import Model
import numpy as np
import cv2
import selectivesearch
img = cv2.imread('someimage.jpg')
img = img.reshape(-1, 1280, 1280, 3)
textmaps = np.ones(-1, 164, 164, 128)  # Just for example
model = Model(inputs=[screenshot, textmaps], outputs=merged_conv)
model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
feature_maps = np.reshape(model.predict([img, textmaps]), (96, 164, 164))
feature_map_1 = feature_maps[0][0]
img_lbl, regions = selectivesearch.selective_search(feature_map_1, scale=500, sigma=0.9, min_size=10)

但是,如果我想添加一个SoftMax分类器,它接受“区域”变量呢?(顺便说一句,我知道选择性搜索除了通道3的输入之外,几乎没有其他问题,但这与问题无关)

问题:

区域建议(使用选择性搜索)是神经网络的一个重要组成部分,如何对其进行修改,使其从卷积层获取特征映射(激活) merged_conv ?

也许我应该创建自己的Keras图层?