我必须融合thermal和visible图像，以便更好地分类和改进结果。为此，我使用卷积神经网络模型ResNet。我想分别为resnet和thermal图像培训visible到convolution blocks然后我想融合（连接）两个网络的输出，并且在连接之后，我会将此结果作为输入发送到ResNet的fully connected layers。但我不知道如何采取两个单独的输入并进一步处理它们？另外，我不知道如何连接它们？这是我单独一个数据集的代码，工作正常。我如何修改两个数据集，然后融合？
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import copy
from keras.layers import Input, Dense, Conv2D, MaxPooling2D,
AveragePooling2D, ZeroPadding2D, Flatten, Activation, add
from keras.optimizers import SGD
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import initializers
from keras.engine import Layer, InputSpec
from keras import backend as K
from load_data import visible_load_train_data, visible_load_test_data,
lwir_load_train_data, lwir_load_test_data
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils import np_utils
from sklearn.metrics import log_loss
from keras.applications.inception_v3 import preprocess_input
import csv
import matplotlib.pyplot as plt
import sys
sys.setrecursionlimit(3000)
class Scale(Layer):
'''Custom Layer for ResNet used for BatchNormalization.
Learns a set of weights and biases used for scaling the input data.
the output consists simply in an element-wise multiplication of the input
and a sum of a set of constants:
out = in * gamma + beta,
where 'gamma' and 'beta' are the weights and biases larned.
# Arguments
axis: integer, axis along which to normalize in mode 0. For instance,
if your input tensor has shape (samples, channels, rows, cols),
set axis to 1 to normalize per feature map (channels axis).
momentum: momentum in the computation of the
exponential average of the mean and standard deviation
of the data, for feature-wise normalization.
weights: Initialization weights.
List of 2 Numpy arrays, with shapes:
`[(input_shape,), (input_shape,)]`
beta_init: name of initialization function for shift parameter
(see [initializers](../initializers.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
gamma_init: name of initialization function for scale parameter (see
[initializers](../initializers.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
'''
def __init__(self, weights=None, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs):
self.momentum = momentum
self.axis = axis
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.initial_weights = weights
super(Scale, self).__init__(**kwargs)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
shape = (int(input_shape[self.axis]),)
self.gamma = K.variable(self.gamma_init(shape), name='%s_gamma'%self.name)
self.beta = K.variable(self.beta_init(shape), name='%s_beta'%self.name)
self.trainable_weights = [self.gamma, self.beta]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
def call(self, x, mask=None):
input_shape = self.input_spec[0].shape
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis]
out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape)
return out
def get_config(self):
config = {"momentum": self.momentum, "axis": self.axis}
base_config = super(Scale, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def identity_block(input_tensor, kernel_size, filters, stage, block):
'''The identity_block is the block that has no conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
'''
eps = 1.1e-5
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
scale_name_base = 'scale' + str(stage) + block + '_branch'
x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=False)(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
x = Activation('relu', name=conv_name_base + '2a_relu')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
x = Conv2D(nb_filter2, (kernel_size, kernel_size), name=conv_name_base + '2b', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
x = Activation('relu', name=conv_name_base + '2b_relu')(x)
x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)
x = add([x, input_tensor], name='res' + str(stage) + block)
x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
'''conv_block is the block that has a conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
And the shortcut should have subsample=(2,2) as well
'''
eps = 1.1e-5
nb_filter1, nb_filter2, nb_filter3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
scale_name_base = 'scale' + str(stage) + block + '_branch'
x_l = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=False)(input_tensor)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
x = Activation('relu', name=conv_name_base + '2a_relu')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
x = Conv2D(nb_filter2, (kernel_size, kernel_size),
name=conv_name_base + '2b', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
x = Activation('relu', name=conv_name_base + '2b_relu')(x)
x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x)
x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)
shortcut = Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', use_bias=False)(input_tensor)
shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1')(shortcut)
shortcut = Scale(axis=bn_axis, name=scale_name_base + '1')(shortcut)
x = add([x, shortcut], name='res' + str(stage) + block)
x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
return x
def resnet152_model(img_rows, img_cols, color_type=1, num_classes=None):
'''Instantiate the ResNet152 architecture,
# Arguments
weights_path: path to pretrained weight file
# Returns
A Keras model instance.
'''
eps = 1.1e-5
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type), name='data')
else:
bn_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols), name='data')
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x)
x = Scale(axis=bn_axis, name='scale_conv1')(x)
x = Activation('relu', name='conv1_relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
for i in range(1,8):
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i))
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
for i in range(1,36):
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i))
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)
x_fc = Flatten()(x_fc)
x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)
model = Model(img_input, x_fc)
if K.image_dim_ordering() == 'th':
# Use pre-trained weights for Theano backend
print('NoooooooooooooooooNoooooooooooooooooNooooooooooooooooo')
weights_path = 'imagenet_models/resnet152_weights_th.h5'
else:
# Use pre-trained weights for Tensorflow backend
print('YesssssssssssssssssssssYesssssssssssssssssssssYesssssssssssssssssssss')
convert_all_kernels_in_model(model)
weights_path = 'imagenet_models/resnet152_weights_tf.h5'
model.load_weights(weights_path, by_name=True)
# Truncate and replace softmax layer for transfer learning
# Cannot use model.layers.pop() since model is not of Sequential() type
# The method below works since pre-trained weights are stored in layers but not in the model
x_newfc = AveragePooling2D((7, 7), name='avg_pool')(x)
x_newfc = Flatten()(x_newfc)
x_newfc = Dense(num_classes, activation='softmax', name='fc8')(x_newfc)
model = Model(img_input, x_newfc)
# Learning rate is changed to 0.001
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
return model
if __name__ == '__main__':
img_rows, img_cols = 224, 224 # Resolution of inputs
# img_rows, img_cols = 256, 256 # Resolution of inputs
channel = 3
num_classes = 2
batch_size = 15
nb_epoch = 10
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
X_train, Y_train = lwir_load_train_data()
Y_train = np_utils.to_categorical(Y_train, num_classes)
X_test, Y_test= lwir_load_test_data()
Y_test = np_utils.to_categorical(Y_test, num_classes)
# Load our model
model = resnet152_model(img_rows, img_cols, channel, num_classes)
# Start Fine-tuning
history=model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_test, Y_test),
)
# Make predictions
predictions_valid = model.predict(X_test, batch_size=batch_size, verbose=1)
print(predictions_valid)
for i in range(0,len(Y_test)):
print(i,np.argmax(Y_test[i]),np.argmax(predictions_valid[i]))
# Cross-entropy loss score
score = log_loss(Y_test, predictions_valid)
acc=((len(Y_test)-sum(abs(np.array(np.argmax(Y_test,axis=1))- np.array(np.argmax(predictions_valid,axis=1)))))/len(Y_test))*100
print('acc',acc)
print('score',score)
list = [predictions_valid, np.argmax(predictions_valid,axis=1),np.argmax(Y_test,axis=1)]
with open('prediction_lwir_resnet.csv', 'a+', newline='', encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerows(zip(*list))
# summarize history for accuracy
print(history.history.keys())
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig('lwir_resnet_accuracy.png')
plt.show()