How to change the AlexNet into FCNs ?
FCNs is a network that only contain convolution layers and no fc layer at all. It's structure can be shown as the following figures:
This image from the paper : <Fully Convolutional Networks for Semantic Segmentation> CVPR 2015.
It could locate the location of object target perfectly as shown in above images and it doesn't need to resize the resolution of input images, which is the mostly different from traditional CNNs. First, Let's review some related network parameters about AlexNet, related structure can be shown as following:
As we can see from the above figure, the input of images must be resized into a fixed resolution, like 224*224, due to the existance of fc_layer. The specific pipeline could be found in this blog, web link: http://blog.csdn.net/sunbaigui/article/details/39938097
The output of Conv 5 is: 6*6*256, we want to obtain the final results: 1*1*1000 (take the 1k classes for an example). How could we use the middle Conv 6, Conv 7, Conv 8 layers to bridge the two results ? Do we need the pool layers added ? How to set the middle parameters in each layers ? Does it really work ?
Let's do it now. We just add 3 Convolution layers for an example. The function used for change the width*height*channel (actually, it only about the width, due to width == height, and the channel only related to the output of each layer.) is :
(W- F + 2P)/S + 1
where W denotes the width of images from bottom layer, F denotes the size of Convolution filter, P means the padding you want to add, this mainly contribute to the same resolution of input and output, S denotes the stride.
Thus, the following layers needed to add to the prototxt files:
from: 6*6*256 ---> 3*3*4096 ---> 1*1*4096 ---> 1*1*43 (take my experiments for an example.)
####################################################################
## the output of Pool 5 is 6*6*256
####################################################################
layer {
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 2
stride: 2
# group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "conv6"
top: "conv6"
}
layer {
name: "conv7"
type: "Convolution"
bottom: "conv6"
top: "conv7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 3
stride: 2
# group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "conv7"
top: "conv7"
}
layer {
name: "conv8"
type: "Convolution"
bottom: "conv7"
top: "conv8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 43
kernel_size: 1
stride: 1
# group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "conv8"
top: "conv8"
}
Then, make your caffe file and waiting for something amazing happens...
Actually, at first, I always run a wrong result, i.e. 2*2*43 ... It really confused me, the function is wrong ? It does not make scene. Because it really worked at the begining of the Network. Lastly, I found I make a stupid mistake, due to I add the Conv 6 from Conv 5, not Pool 5. Thus, it is really important for us to be careful and more careful.
Ok, the all pipeline has done, and due to my ACER lap-top only have a GTX960M, it warning me out of memory. The results running on the terminal are here :
I0423 09:52:24.421512 2763 caffe.cpp:189] Using GPUs 0
I0423 09:52:24.431041 2763 caffe.cpp:194] GPU 0: GeForce GTX 960M
I0423 09:52:24.565281 2763 solver.cpp:48] Initializing solver from parameters:
test_iter: 7600
test_interval: 2000
base_lr: 0.001
display: 12
max_iter: 450000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 2000
snapshot: 2000
snapshot_prefix: "/media/wangxiao/Acer/caffe_models_/"
solver_mode: GPU
device_id: 0
net: "/home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt"
test_initialization: false
I0423 09:52:24.621829 2763 solver.cpp:91] Creating training net from net file: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt
I0423 09:52:24.622601 2763 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0423 09:52:24.622632 2763 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0423 09:52:24.622828 2763 net.cpp:49] Initializing net from parameters:
name: "AlexNet"
state {
phase: TRAIN
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: false
}
image_data_param {
source: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/train_data/newAdd_attribute_label.txt"
batch_size: 12
root_folder: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/train_data/227_227_images/"
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 2
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "conv6"
top: "conv6"
}
layer {
name: "conv7"
type: "Convolution"
bottom: "conv6"
top: "conv7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 3
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "conv7"
top: "conv7"
}
layer {
name: "conv8"
type: "Convolution"
bottom: "conv7"
top: "conv8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 43
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "conv8"
top: "conv8"
}
layer {
name: "sigmoid"
type: "Sigmoid"
bottom: "conv8"
top: "conv8"
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "conv8"
bottom: "label"
top: "loss"
}
I0423 09:52:24.622962 2763 layer_factory.hpp:77] Creating layer data
I0423 09:52:24.623002 2763 net.cpp:91] Creating Layer data
I0423 09:52:24.623009 2763 net.cpp:399] data -> data
I0423 09:52:24.623034 2763 net.cpp:399] data -> label
I0423 09:52:24.623051 2763 image_data_layer.cpp:40] Opening file /media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/train_data/newAdd_attribute_label.txt
I0423 09:52:25.535037 2763 image_data_layer.cpp:67] A total of 265972 images.
I0423 09:52:25.543112 2763 image_data_layer.cpp:94] output data size: 12,3,227,227
I0423 09:52:25.554397 2763 net.cpp:141] Setting up data
I0423 09:52:25.554425 2763 net.cpp:148] Top shape: 12 3 227 227 (1855044)
I0423 09:52:25.554431 2763 net.cpp:148] Top shape: 12 43 1 1 (516)
I0423 09:52:25.554435 2763 net.cpp:156] Memory required for data: 7422240
I0423 09:52:25.554451 2763 layer_factory.hpp:77] Creating layer conv1
I0423 09:52:25.554476 2763 net.cpp:91] Creating Layer conv1
I0423 09:52:25.554481 2763 net.cpp:425] conv1 <- data
I0423 09:52:25.554492 2763 net.cpp:399] conv1 -> conv1
I0423 09:52:25.556519 2763 net.cpp:141] Setting up conv1
I0423 09:52:25.556534 2763 net.cpp:148] Top shape: 12 96 55 55 (3484800)
I0423 09:52:25.556537 2763 net.cpp:156] Memory required for data: 21361440
I0423 09:52:25.556556 2763 layer_factory.hpp:77] Creating layer relu1
I0423 09:52:25.556565 2763 net.cpp:91] Creating Layer relu1
I0423 09:52:25.556568 2763 net.cpp:425] relu1 <- conv1
I0423 09:52:25.556573 2763 net.cpp:386] relu1 -> conv1 (in-place)
I0423 09:52:25.556583 2763 net.cpp:141] Setting up relu1
I0423 09:52:25.556587 2763 net.cpp:148] Top shape: 12 96 55 55 (3484800)
I0423 09:52:25.556591 2763 net.cpp:156] Memory required for data: 35300640
I0423 09:52:25.556594 2763 layer_factory.hpp:77] Creating layer norm1
I0423 09:52:25.556602 2763 net.cpp:91] Creating Layer norm1
I0423 09:52:25.556604 2763 net.cpp:425] norm1 <- conv1
I0423 09:52:25.556609 2763 net.cpp:399] norm1 -> norm1
I0423 09:52:25.556646 2763 net.cpp:141] Setting up norm1
I0423 09:52:25.556653 2763 net.cpp:148] Top shape: 12 96 55 55 (3484800)
I0423 09:52:25.556689 2763 net.cpp:156] Memory required for data: 49239840
I0423 09:52:25.556692 2763 layer_factory.hpp:77] Creating layer pool1
I0423 09:52:25.556700 2763 net.cpp:91] Creating Layer pool1
I0423 09:52:25.556704 2763 net.cpp:425] pool1 <- norm1
I0423 09:52:25.556710 2763 net.cpp:399] pool1 -> pool1
I0423 09:52:25.556749 2763 net.cpp:141] Setting up pool1
I0423 09:52:25.556766 2763 net.cpp:148] Top shape: 12 96 27 27 (839808)
I0423 09:52:25.556769 2763 net.cpp:156] Memory required for data: 52599072
I0423 09:52:25.556772 2763 layer_factory.hpp:77] Creating layer conv2
I0423 09:52:25.556792 2763 net.cpp:91] Creating Layer conv2
I0423 09:52:25.556795 2763 net.cpp:425] conv2 <- pool1
I0423 09:52:25.556802 2763 net.cpp:399] conv2 -> conv2
I0423 09:52:25.565610 2763 net.cpp:141] Setting up conv2
I0423 09:52:25.565634 2763 net.cpp:148] Top shape: 12 256 27 27 (2239488)
I0423 09:52:25.565637 2763 net.cpp:156] Memory required for data: 61557024
I0423 09:52:25.565651 2763 layer_factory.hpp:77] Creating layer relu2
I0423 09:52:25.565660 2763 net.cpp:91] Creating Layer relu2
I0423 09:52:25.565665 2763 net.cpp:425] relu2 <- conv2
I0423 09:52:25.565672 2763 net.cpp:386] relu2 -> conv2 (in-place)
I0423 09:52:25.565681 2763 net.cpp:141] Setting up relu2
I0423 09:52:25.565686 2763 net.cpp:148] Top shape: 12 256 27 27 (2239488)
I0423 09:52:25.565690 2763 net.cpp:156] Memory required for data: 70514976
I0423 09:52:25.565692 2763 layer_factory.hpp:77] Creating layer norm2
I0423 09:52:25.565699 2763 net.cpp:91] Creating Layer norm2
I0423 09:52:25.565702 2763 net.cpp:425] norm2 <- conv2
I0423 09:52:25.565708 2763 net.cpp:399] norm2 -> norm2
I0423 09:52:25.565742 2763 net.cpp:141] Setting up norm2
I0423 09:52:25.565747 2763 net.cpp:148] Top shape: 12 256 27 27 (2239488)
I0423 09:52:25.565750 2763 net.cpp:156] Memory required for data: 79472928
I0423 09:52:25.565753 2763 layer_factory.hpp:77] Creating layer pool2
I0423 09:52:25.565762 2763 net.cpp:91] Creating Layer pool2
I0423 09:52:25.565764 2763 net.cpp:425] pool2 <- norm2
I0423 09:52:25.565769 2763 net.cpp:399] pool2 -> pool2
I0423 09:52:25.565798 2763 net.cpp:141] Setting up pool2
I0423 09:52:25.565804 2763 net.cpp:148] Top shape: 12 256 13 13 (519168)
I0423 09:52:25.565809 2763 net.cpp:156] Memory required for data: 81549600
I0423 09:52:25.565811 2763 layer_factory.hpp:77] Creating layer conv3
I0423 09:52:25.565821 2763 net.cpp:91] Creating Layer conv3
I0423 09:52:25.565824 2763 net.cpp:425] conv3 <- pool2
I0423 09:52:25.565831 2763 net.cpp:399] conv3 -> conv3
I0423 09:52:25.590066 2763 net.cpp:141] Setting up conv3
I0423 09:52:25.590090 2763 net.cpp:148] Top shape: 12 384 13 13 (778752)
I0423 09:52:25.590092 2763 net.cpp:156] Memory required for data: 84664608
I0423 09:52:25.590116 2763 layer_factory.hpp:77] Creating layer relu3
I0423 09:52:25.590126 2763 net.cpp:91] Creating Layer relu3
I0423 09:52:25.590131 2763 net.cpp:425] relu3 <- conv3
I0423 09:52:25.590137 2763 net.cpp:386] relu3 -> conv3 (in-place)
I0423 09:52:25.590145 2763 net.cpp:141] Setting up relu3
I0423 09:52:25.590149 2763 net.cpp:148] Top shape: 12 384 13 13 (778752)
I0423 09:52:25.590152 2763 net.cpp:156] Memory required for data: 87779616
I0423 09:52:25.590155 2763 layer_factory.hpp:77] Creating layer conv4
I0423 09:52:25.590167 2763 net.cpp:91] Creating Layer conv4
I0423 09:52:25.590169 2763 net.cpp:425] conv4 <- conv3
I0423 09:52:25.590176 2763 net.cpp:399] conv4 -> conv4
I0423 09:52:25.608953 2763 net.cpp:141] Setting up conv4
I0423 09:52:25.608975 2763 net.cpp:148] Top shape: 12 384 13 13 (778752)
I0423 09:52:25.608979 2763 net.cpp:156] Memory required for data: 90894624
I0423 09:52:25.608989 2763 layer_factory.hpp:77] Creating layer relu4
I0423 09:52:25.609007 2763 net.cpp:91] Creating Layer relu4
I0423 09:52:25.609011 2763 net.cpp:425] relu4 <- conv4
I0423 09:52:25.609019 2763 net.cpp:386] relu4 -> conv4 (in-place)
I0423 09:52:25.609027 2763 net.cpp:141] Setting up relu4
I0423 09:52:25.609031 2763 net.cpp:148] Top shape: 12 384 13 13 (778752)
I0423 09:52:25.609047 2763 net.cpp:156] Memory required for data: 94009632
I0423 09:52:25.609050 2763 layer_factory.hpp:77] Creating layer conv5
I0423 09:52:25.609061 2763 net.cpp:91] Creating Layer conv5
I0423 09:52:25.609066 2763 net.cpp:425] conv5 <- conv4
I0423 09:52:25.609071 2763 net.cpp:399] conv5 -> conv5
I0423 09:52:25.621208 2763 net.cpp:141] Setting up conv5
I0423 09:52:25.621229 2763 net.cpp:148] Top shape: 12 256 13 13 (519168)
I0423 09:52:25.621233 2763 net.cpp:156] Memory required for data: 96086304
I0423 09:52:25.621258 2763 layer_factory.hpp:77] Creating layer relu5
I0423 09:52:25.621268 2763 net.cpp:91] Creating Layer relu5
I0423 09:52:25.621273 2763 net.cpp:425] relu5 <- conv5
I0423 09:52:25.621279 2763 net.cpp:386] relu5 -> conv5 (in-place)
I0423 09:52:25.621286 2763 net.cpp:141] Setting up relu5
I0423 09:52:25.621290 2763 net.cpp:148] Top shape: 12 256 13 13 (519168)
I0423 09:52:25.621294 2763 net.cpp:156] Memory required for data: 98162976
I0423 09:52:25.621297 2763 layer_factory.hpp:77] Creating layer pool5
I0423 09:52:25.621304 2763 net.cpp:91] Creating Layer pool5
I0423 09:52:25.621306 2763 net.cpp:425] pool5 <- conv5
I0423 09:52:25.621314 2763 net.cpp:399] pool5 -> pool5
I0423 09:52:25.621347 2763 net.cpp:141] Setting up pool5
I0423 09:52:25.621354 2763 net.cpp:148] Top shape: 12 256 6 6 (110592)
I0423 09:52:25.621357 2763 net.cpp:156] Memory required for data: 98605344
I0423 09:52:25.621361 2763 layer_factory.hpp:77] Creating layer conv6
I0423 09:52:25.621373 2763 net.cpp:91] Creating Layer conv6
I0423 09:52:25.621377 2763 net.cpp:425] conv6 <- pool5
I0423 09:52:25.621384 2763 net.cpp:399] conv6 -> conv6
I0423 09:52:25.731640 2763 net.cpp:141] Setting up conv6
I0423 09:52:25.731675 2763 net.cpp:148] Top shape: 12 4096 3 3 (442368)
I0423 09:52:25.731679 2763 net.cpp:156] Memory required for data: 100374816
I0423 09:52:25.731688 2763 layer_factory.hpp:77] Creating layer relu6
I0423 09:52:25.731709 2763 net.cpp:91] Creating Layer relu6
I0423 09:52:25.731714 2763 net.cpp:425] relu6 <- conv6
I0423 09:52:25.731721 2763 net.cpp:386] relu6 -> conv6 (in-place)
I0423 09:52:25.731731 2763 net.cpp:141] Setting up relu6
I0423 09:52:25.731735 2763 net.cpp:148] Top shape: 12 4096 3 3 (442368)
I0423 09:52:25.731739 2763 net.cpp:156] Memory required for data: 102144288
I0423 09:52:25.731741 2763 layer_factory.hpp:77] Creating layer conv7
I0423 09:52:25.731752 2763 net.cpp:91] Creating Layer conv7
I0423 09:52:25.731757 2763 net.cpp:425] conv7 <- conv6
I0423 09:52:25.731765 2763 net.cpp:399] conv7 -> conv7
I0423 09:52:29.661667 2763 net.cpp:141] Setting up conv7
I0423 09:52:29.661705 2763 net.cpp:148] Top shape: 12 4096 1 1 (49152)
I0423 09:52:29.661710 2763 net.cpp:156] Memory required for data: 102340896
I0423 09:52:29.661720 2763 layer_factory.hpp:77] Creating layer relu7
I0423 09:52:29.661741 2763 net.cpp:91] Creating Layer relu7
I0423 09:52:29.661746 2763 net.cpp:425] relu7 <- conv7
I0423 09:52:29.661752 2763 net.cpp:386] relu7 -> conv7 (in-place)
I0423 09:52:29.661761 2763 net.cpp:141] Setting up relu7
I0423 09:52:29.661767 2763 net.cpp:148] Top shape: 12 4096 1 1 (49152)
I0423 09:52:29.661769 2763 net.cpp:156] Memory required for data: 102537504
I0423 09:52:29.661772 2763 layer_factory.hpp:77] Creating layer conv8
I0423 09:52:29.661783 2763 net.cpp:91] Creating Layer conv8
I0423 09:52:29.661788 2763 net.cpp:425] conv8 <- conv7
I0423 09:52:29.661795 2763 net.cpp:399] conv8 -> conv8
I0423 09:52:29.666793 2763 net.cpp:141] Setting up conv8
I0423 09:52:29.666815 2763 net.cpp:148] Top shape: 12 43 1 1 (516)
I0423 09:52:29.666818 2763 net.cpp:156] Memory required for data: 102539568
I0423 09:52:29.666826 2763 layer_factory.hpp:77] Creating layer relu8
I0423 09:52:29.666841 2763 net.cpp:91] Creating Layer relu8
I0423 09:52:29.666844 2763 net.cpp:425] relu8 <- conv8
I0423 09:52:29.666849 2763 net.cpp:386] relu8 -> conv8 (in-place)
I0423 09:52:29.666856 2763 net.cpp:141] Setting up relu8
I0423 09:52:29.666860 2763 net.cpp:148] Top shape: 12 43 1 1 (516)
I0423 09:52:29.666877 2763 net.cpp:156] Memory required for data: 102541632
I0423 09:52:29.666882 2763 layer_factory.hpp:77] Creating layer sigmoid
I0423 09:52:29.666888 2763 net.cpp:91] Creating Layer sigmoid
I0423 09:52:29.666892 2763 net.cpp:425] sigmoid <- conv8
I0423 09:52:29.666895 2763 net.cpp:386] sigmoid -> conv8 (in-place)
I0423 09:52:29.666901 2763 net.cpp:141] Setting up sigmoid
I0423 09:52:29.666905 2763 net.cpp:148] Top shape: 12 43 1 1 (516)
I0423 09:52:29.666908 2763 net.cpp:156] Memory required for data: 102543696
I0423 09:52:29.666911 2763 layer_factory.hpp:77] Creating layer loss
I0423 09:52:29.666918 2763 net.cpp:91] Creating Layer loss
I0423 09:52:29.666920 2763 net.cpp:425] loss <- conv8
I0423 09:52:29.666924 2763 net.cpp:425] loss <- label
I0423 09:52:29.666931 2763 net.cpp:399] loss -> loss
I0423 09:52:29.666975 2763 net.cpp:141] Setting up loss
I0423 09:52:29.666990 2763 net.cpp:148] Top shape: (1)
I0423 09:52:29.666992 2763 net.cpp:151] with loss weight 1
I0423 09:52:29.667017 2763 net.cpp:156] Memory required for data: 102543700
I0423 09:52:29.667031 2763 net.cpp:217] loss needs backward computation.
I0423 09:52:29.667034 2763 net.cpp:217] sigmoid needs backward computation.
I0423 09:52:29.667038 2763 net.cpp:217] relu8 needs backward computation.
I0423 09:52:29.667040 2763 net.cpp:217] conv8 needs backward computation.
I0423 09:52:29.667043 2763 net.cpp:217] relu7 needs backward computation.
I0423 09:52:29.667047 2763 net.cpp:217] conv7 needs backward computation.
I0423 09:52:29.667050 2763 net.cpp:217] relu6 needs backward computation.
I0423 09:52:29.667053 2763 net.cpp:217] conv6 needs backward computation.
I0423 09:52:29.667057 2763 net.cpp:217] pool5 needs backward computation.
I0423 09:52:29.667060 2763 net.cpp:217] relu5 needs backward computation.
I0423 09:52:29.667063 2763 net.cpp:217] conv5 needs backward computation.
I0423 09:52:29.667068 2763 net.cpp:217] relu4 needs backward computation.
I0423 09:52:29.667070 2763 net.cpp:217] conv4 needs backward computation.
I0423 09:52:29.667073 2763 net.cpp:217] relu3 needs backward computation.
I0423 09:52:29.667076 2763 net.cpp:217] conv3 needs backward computation.
I0423 09:52:29.667080 2763 net.cpp:217] pool2 needs backward computation.
I0423 09:52:29.667084 2763 net.cpp:217] norm2 needs backward computation.
I0423 09:52:29.667088 2763 net.cpp:217] relu2 needs backward computation.
I0423 09:52:29.667091 2763 net.cpp:217] conv2 needs backward computation.
I0423 09:52:29.667094 2763 net.cpp:217] pool1 needs backward computation.
I0423 09:52:29.667098 2763 net.cpp:217] norm1 needs backward computation.
I0423 09:52:29.667101 2763 net.cpp:217] relu1 needs backward computation.
I0423 09:52:29.667104 2763 net.cpp:217] conv1 needs backward computation.
I0423 09:52:29.667109 2763 net.cpp:219] data does not need backward computation.
I0423 09:52:29.667111 2763 net.cpp:261] This network produces output loss
I0423 09:52:29.667127 2763 net.cpp:274] Network initialization done.
I0423 09:52:29.667804 2763 solver.cpp:181] Creating test net (#0) specified by net file: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt
I0423 09:52:29.667937 2763 net.cpp:313] The NetState phase (1) differed from the phase (0) specified by a rule in layer data
I0423 09:52:29.668148 2763 net.cpp:49] Initializing net from parameters:
name: "AlexNet"
state {
phase: TEST
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
}
image_data_param {
source: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/test_data/newAdd_attribute_label_test.txt"
batch_size: 1
root_folder: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/test_data/227_227_test_images/"
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 2
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "conv6"
top: "conv6"
}
layer {
name: "conv7"
type: "Convolution"
bottom: "conv6"
top: "conv7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 3
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "conv7"
top: "conv7"
}
layer {
name: "conv8"
type: "Convolution"
bottom: "conv7"
top: "conv8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 43
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "conv8"
top: "conv8"
}
layer {
name: "sigmoid"
type: "Sigmoid"
bottom: "conv8"
top: "conv8"
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "conv8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "conv8"
bottom: "label"
top: "loss"
}
I0423 09:52:29.668323 2763 layer_factory.hpp:77] Creating layer data
I0423 09:52:29.668349 2763 net.cpp:91] Creating Layer data
I0423 09:52:29.668355 2763 net.cpp:399] data -> data
I0423 09:52:29.668373 2763 net.cpp:399] data -> label
I0423 09:52:29.668382 2763 image_data_layer.cpp:40] Opening file /media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/test_data/newAdd_attribute_label_test.txt
I0423 09:52:29.696005 2763 image_data_layer.cpp:67] A total of 7600 images.
I0423 09:52:29.697830 2763 image_data_layer.cpp:94] output data size: 1,3,227,227
I0423 09:52:29.699980 2763 net.cpp:141] Setting up data
I0423 09:52:29.700013 2763 net.cpp:148] Top shape: 1 3 227 227 (154587)
I0423 09:52:29.700019 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:29.700022 2763 net.cpp:156] Memory required for data: 618520
I0423 09:52:29.700028 2763 layer_factory.hpp:77] Creating layer label_data_1_split
I0423 09:52:29.700040 2763 net.cpp:91] Creating Layer label_data_1_split
I0423 09:52:29.700048 2763 net.cpp:425] label_data_1_split <- label
I0423 09:52:29.700060 2763 net.cpp:399] label_data_1_split -> label_data_1_split_0
I0423 09:52:29.700075 2763 net.cpp:399] label_data_1_split -> label_data_1_split_1
I0423 09:52:29.700141 2763 net.cpp:141] Setting up label_data_1_split
I0423 09:52:29.700151 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:29.700160 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:29.700176 2763 net.cpp:156] Memory required for data: 618864
I0423 09:52:29.700181 2763 layer_factory.hpp:77] Creating layer conv1
I0423 09:52:29.700196 2763 net.cpp:91] Creating Layer conv1
I0423 09:52:29.700199 2763 net.cpp:425] conv1 <- data
I0423 09:52:29.700206 2763 net.cpp:399] conv1 -> conv1
I0423 09:52:29.701347 2763 net.cpp:141] Setting up conv1
I0423 09:52:29.701369 2763 net.cpp:148] Top shape: 1 96 55 55 (290400)
I0423 09:52:29.701372 2763 net.cpp:156] Memory required for data: 1780464
I0423 09:52:29.701383 2763 layer_factory.hpp:77] Creating layer relu1
I0423 09:52:29.701390 2763 net.cpp:91] Creating Layer relu1
I0423 09:52:29.701395 2763 net.cpp:425] relu1 <- conv1
I0423 09:52:29.701400 2763 net.cpp:386] relu1 -> conv1 (in-place)
I0423 09:52:29.701406 2763 net.cpp:141] Setting up relu1
I0423 09:52:29.701412 2763 net.cpp:148] Top shape: 1 96 55 55 (290400)
I0423 09:52:29.701416 2763 net.cpp:156] Memory required for data: 2942064
I0423 09:52:29.701418 2763 layer_factory.hpp:77] Creating layer norm1
I0423 09:52:29.701426 2763 net.cpp:91] Creating Layer norm1
I0423 09:52:29.701429 2763 net.cpp:425] norm1 <- conv1
I0423 09:52:29.701434 2763 net.cpp:399] norm1 -> norm1
I0423 09:52:29.701464 2763 net.cpp:141] Setting up norm1
I0423 09:52:29.701479 2763 net.cpp:148] Top shape: 1 96 55 55 (290400)
I0423 09:52:29.701483 2763 net.cpp:156] Memory required for data: 4103664
I0423 09:52:29.701486 2763 layer_factory.hpp:77] Creating layer pool1
I0423 09:52:29.701503 2763 net.cpp:91] Creating Layer pool1
I0423 09:52:29.701505 2763 net.cpp:425] pool1 <- norm1
I0423 09:52:29.701510 2763 net.cpp:399] pool1 -> pool1
I0423 09:52:29.701537 2763 net.cpp:141] Setting up pool1
I0423 09:52:29.701544 2763 net.cpp:148] Top shape: 1 96 27 27 (69984)
I0423 09:52:29.701545 2763 net.cpp:156] Memory required for data: 4383600
I0423 09:52:29.701550 2763 layer_factory.hpp:77] Creating layer conv2
I0423 09:52:29.701557 2763 net.cpp:91] Creating Layer conv2
I0423 09:52:29.701561 2763 net.cpp:425] conv2 <- pool1
I0423 09:52:29.701566 2763 net.cpp:399] conv2 -> conv2
I0423 09:52:29.709951 2763 net.cpp:141] Setting up conv2
I0423 09:52:29.709987 2763 net.cpp:148] Top shape: 1 256 27 27 (186624)
I0423 09:52:29.709992 2763 net.cpp:156] Memory required for data: 5130096
I0423 09:52:29.710005 2763 layer_factory.hpp:77] Creating layer relu2
I0423 09:52:29.710014 2763 net.cpp:91] Creating Layer relu2
I0423 09:52:29.710018 2763 net.cpp:425] relu2 <- conv2
I0423 09:52:29.710026 2763 net.cpp:386] relu2 -> conv2 (in-place)
I0423 09:52:29.710033 2763 net.cpp:141] Setting up relu2
I0423 09:52:29.710039 2763 net.cpp:148] Top shape: 1 256 27 27 (186624)
I0423 09:52:29.710042 2763 net.cpp:156] Memory required for data: 5876592
I0423 09:52:29.710046 2763 layer_factory.hpp:77] Creating layer norm2
I0423 09:52:29.710057 2763 net.cpp:91] Creating Layer norm2
I0423 09:52:29.710060 2763 net.cpp:425] norm2 <- conv2
I0423 09:52:29.710067 2763 net.cpp:399] norm2 -> norm2
I0423 09:52:29.710100 2763 net.cpp:141] Setting up norm2
I0423 09:52:29.710108 2763 net.cpp:148] Top shape: 1 256 27 27 (186624)
I0423 09:52:29.710110 2763 net.cpp:156] Memory required for data: 6623088
I0423 09:52:29.710114 2763 layer_factory.hpp:77] Creating layer pool2
I0423 09:52:29.710120 2763 net.cpp:91] Creating Layer pool2
I0423 09:52:29.710124 2763 net.cpp:425] pool2 <- norm2
I0423 09:52:29.710129 2763 net.cpp:399] pool2 -> pool2
I0423 09:52:29.710155 2763 net.cpp:141] Setting up pool2
I0423 09:52:29.710171 2763 net.cpp:148] Top shape: 1 256 13 13 (43264)
I0423 09:52:29.710175 2763 net.cpp:156] Memory required for data: 6796144
I0423 09:52:29.710187 2763 layer_factory.hpp:77] Creating layer conv3
I0423 09:52:29.710197 2763 net.cpp:91] Creating Layer conv3
I0423 09:52:29.710201 2763 net.cpp:425] conv3 <- pool2
I0423 09:52:29.710207 2763 net.cpp:399] conv3 -> conv3
I0423 09:52:29.733366 2763 net.cpp:141] Setting up conv3
I0423 09:52:29.733403 2763 net.cpp:148] Top shape: 1 384 13 13 (64896)
I0423 09:52:29.733407 2763 net.cpp:156] Memory required for data: 7055728
I0423 09:52:29.733420 2763 layer_factory.hpp:77] Creating layer relu3
I0423 09:52:29.733439 2763 net.cpp:91] Creating Layer relu3
I0423 09:52:29.733444 2763 net.cpp:425] relu3 <- conv3
I0423 09:52:29.733453 2763 net.cpp:386] relu3 -> conv3 (in-place)
I0423 09:52:29.733461 2763 net.cpp:141] Setting up relu3
I0423 09:52:29.733466 2763 net.cpp:148] Top shape: 1 384 13 13 (64896)
I0423 09:52:29.733469 2763 net.cpp:156] Memory required for data: 7315312
I0423 09:52:29.733472 2763 layer_factory.hpp:77] Creating layer conv4
I0423 09:52:29.733484 2763 net.cpp:91] Creating Layer conv4
I0423 09:52:29.733489 2763 net.cpp:425] conv4 <- conv3
I0423 09:52:29.733494 2763 net.cpp:399] conv4 -> conv4
I0423 09:52:29.750310 2763 net.cpp:141] Setting up conv4
I0423 09:52:29.750344 2763 net.cpp:148] Top shape: 1 384 13 13 (64896)
I0423 09:52:29.750349 2763 net.cpp:156] Memory required for data: 7574896
I0423 09:52:29.750357 2763 layer_factory.hpp:77] Creating layer relu4
I0423 09:52:29.750366 2763 net.cpp:91] Creating Layer relu4
I0423 09:52:29.750370 2763 net.cpp:425] relu4 <- conv4
I0423 09:52:29.750376 2763 net.cpp:386] relu4 -> conv4 (in-place)
I0423 09:52:29.750393 2763 net.cpp:141] Setting up relu4
I0423 09:52:29.750397 2763 net.cpp:148] Top shape: 1 384 13 13 (64896)
I0423 09:52:29.750401 2763 net.cpp:156] Memory required for data: 7834480
I0423 09:52:29.750403 2763 layer_factory.hpp:77] Creating layer conv5
I0423 09:52:29.750414 2763 net.cpp:91] Creating Layer conv5
I0423 09:52:29.750418 2763 net.cpp:425] conv5 <- conv4
I0423 09:52:29.750423 2763 net.cpp:399] conv5 -> conv5
I0423 09:52:29.762544 2763 net.cpp:141] Setting up conv5
I0423 09:52:29.762580 2763 net.cpp:148] Top shape: 1 256 13 13 (43264)
I0423 09:52:29.762584 2763 net.cpp:156] Memory required for data: 8007536
I0423 09:52:29.762598 2763 layer_factory.hpp:77] Creating layer relu5
I0423 09:52:29.762609 2763 net.cpp:91] Creating Layer relu5
I0423 09:52:29.762614 2763 net.cpp:425] relu5 <- conv5
I0423 09:52:29.762619 2763 net.cpp:386] relu5 -> conv5 (in-place)
I0423 09:52:29.762629 2763 net.cpp:141] Setting up relu5
I0423 09:52:29.762646 2763 net.cpp:148] Top shape: 1 256 13 13 (43264)
I0423 09:52:29.762650 2763 net.cpp:156] Memory required for data: 8180592
I0423 09:52:29.762653 2763 layer_factory.hpp:77] Creating layer pool5
I0423 09:52:29.762662 2763 net.cpp:91] Creating Layer pool5
I0423 09:52:29.762665 2763 net.cpp:425] pool5 <- conv5
I0423 09:52:29.762671 2763 net.cpp:399] pool5 -> pool5
I0423 09:52:29.762707 2763 net.cpp:141] Setting up pool5
I0423 09:52:29.762724 2763 net.cpp:148] Top shape: 1 256 6 6 (9216)
I0423 09:52:29.762727 2763 net.cpp:156] Memory required for data: 8217456
I0423 09:52:29.762740 2763 layer_factory.hpp:77] Creating layer conv6
I0423 09:52:29.762753 2763 net.cpp:91] Creating Layer conv6
I0423 09:52:29.762755 2763 net.cpp:425] conv6 <- pool5
I0423 09:52:29.762761 2763 net.cpp:399] conv6 -> conv6
I0423 09:52:29.868270 2763 net.cpp:141] Setting up conv6
I0423 09:52:29.868306 2763 net.cpp:148] Top shape: 1 4096 3 3 (36864)
I0423 09:52:29.868311 2763 net.cpp:156] Memory required for data: 8364912
I0423 09:52:29.868320 2763 layer_factory.hpp:77] Creating layer relu6
I0423 09:52:29.868330 2763 net.cpp:91] Creating Layer relu6
I0423 09:52:29.868335 2763 net.cpp:425] relu6 <- conv6
I0423 09:52:29.868342 2763 net.cpp:386] relu6 -> conv6 (in-place)
I0423 09:52:29.868350 2763 net.cpp:141] Setting up relu6
I0423 09:52:29.868355 2763 net.cpp:148] Top shape: 1 4096 3 3 (36864)
I0423 09:52:29.868358 2763 net.cpp:156] Memory required for data: 8512368
I0423 09:52:29.868361 2763 layer_factory.hpp:77] Creating layer conv7
I0423 09:52:29.868372 2763 net.cpp:91] Creating Layer conv7
I0423 09:52:29.868376 2763 net.cpp:425] conv7 <- conv6
I0423 09:52:29.868381 2763 net.cpp:399] conv7 -> conv7
I0423 09:52:33.773138 2763 net.cpp:141] Setting up conv7
I0423 09:52:33.773177 2763 net.cpp:148] Top shape: 1 4096 1 1 (4096)
I0423 09:52:33.773182 2763 net.cpp:156] Memory required for data: 8528752
I0423 09:52:33.773192 2763 layer_factory.hpp:77] Creating layer relu7
I0423 09:52:33.773203 2763 net.cpp:91] Creating Layer relu7
I0423 09:52:33.773219 2763 net.cpp:425] relu7 <- conv7
I0423 09:52:33.773232 2763 net.cpp:386] relu7 -> conv7 (in-place)
I0423 09:52:33.773247 2763 net.cpp:141] Setting up relu7
I0423 09:52:33.773257 2763 net.cpp:148] Top shape: 1 4096 1 1 (4096)
I0423 09:52:33.773265 2763 net.cpp:156] Memory required for data: 8545136
I0423 09:52:33.773269 2763 layer_factory.hpp:77] Creating layer conv8
I0423 09:52:33.773283 2763 net.cpp:91] Creating Layer conv8
I0423 09:52:33.773286 2763 net.cpp:425] conv8 <- conv7
I0423 09:52:33.773293 2763 net.cpp:399] conv8 -> conv8
I0423 09:52:33.778169 2763 net.cpp:141] Setting up conv8
I0423 09:52:33.778193 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:33.778198 2763 net.cpp:156] Memory required for data: 8545308
I0423 09:52:33.778203 2763 layer_factory.hpp:77] Creating layer relu8
I0423 09:52:33.778221 2763 net.cpp:91] Creating Layer relu8
I0423 09:52:33.778226 2763 net.cpp:425] relu8 <- conv8
I0423 09:52:33.778233 2763 net.cpp:386] relu8 -> conv8 (in-place)
I0423 09:52:33.778239 2763 net.cpp:141] Setting up relu8
I0423 09:52:33.778244 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:33.778246 2763 net.cpp:156] Memory required for data: 8545480
I0423 09:52:33.778249 2763 layer_factory.hpp:77] Creating layer sigmoid
I0423 09:52:33.778255 2763 net.cpp:91] Creating Layer sigmoid
I0423 09:52:33.778260 2763 net.cpp:425] sigmoid <- conv8
I0423 09:52:33.778265 2763 net.cpp:386] sigmoid -> conv8 (in-place)
I0423 09:52:33.778270 2763 net.cpp:141] Setting up sigmoid
I0423 09:52:33.778275 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:33.778277 2763 net.cpp:156] Memory required for data: 8545652
I0423 09:52:33.778295 2763 layer_factory.hpp:77] Creating layer conv8_sigmoid_0_split
I0423 09:52:33.778301 2763 net.cpp:91] Creating Layer conv8_sigmoid_0_split
I0423 09:52:33.778303 2763 net.cpp:425] conv8_sigmoid_0_split <- conv8
I0423 09:52:33.778318 2763 net.cpp:399] conv8_sigmoid_0_split -> conv8_sigmoid_0_split_0
I0423 09:52:33.778339 2763 net.cpp:399] conv8_sigmoid_0_split -> conv8_sigmoid_0_split_1
I0423 09:52:33.778373 2763 net.cpp:141] Setting up conv8_sigmoid_0_split
I0423 09:52:33.778389 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:33.778393 2763 net.cpp:148] Top shape: 1 43 1 1 (43)
I0423 09:52:33.778408 2763 net.cpp:156] Memory required for data: 8545996
I0423 09:52:33.778411 2763 layer_factory.hpp:77] Creating layer accuracy
I0423 09:52:33.778419 2763 net.cpp:91] Creating Layer accuracy
I0423 09:52:33.778422 2763 net.cpp:425] accuracy <- conv8_sigmoid_0_split_0
I0423 09:52:33.778426 2763 net.cpp:425] accuracy <- label_data_1_split_0
I0423 09:52:33.778432 2763 net.cpp:399] accuracy -> accuracy
I0423 09:52:33.778439 2763 net.cpp:141] Setting up accuracy
I0423 09:52:33.778446 2763 net.cpp:148] Top shape: (1)
I0423 09:52:33.778452 2763 net.cpp:156] Memory required for data: 8546000
I0423 09:52:33.778457 2763 layer_factory.hpp:77] Creating layer loss
I0423 09:52:33.778477 2763 net.cpp:91] Creating Layer loss
I0423 09:52:33.778496 2763 net.cpp:425] loss <- conv8_sigmoid_0_split_1
I0423 09:52:33.778503 2763 net.cpp:425] loss <- label_data_1_split_1
I0423 09:52:33.778513 2763 net.cpp:399] loss -> loss
I0423 09:52:33.778563 2763 net.cpp:141] Setting up loss
I0423 09:52:33.778573 2763 net.cpp:148] Top shape: (1)
I0423 09:52:33.778578 2763 net.cpp:151] with loss weight 1
I0423 09:52:33.778602 2763 net.cpp:156] Memory required for data: 8546004
I0423 09:52:33.778609 2763 net.cpp:217] loss needs backward computation.
I0423 09:52:33.778616 2763 net.cpp:219] accuracy does not need backward computation.
I0423 09:52:33.778621 2763 net.cpp:217] conv8_sigmoid_0_split needs backward computation.
I0423 09:52:33.778625 2763 net.cpp:217] sigmoid needs backward computation.
I0423 09:52:33.778627 2763 net.cpp:217] relu8 needs backward computation.
I0423 09:52:33.778630 2763 net.cpp:217] conv8 needs backward computation.
I0423 09:52:33.778633 2763 net.cpp:217] relu7 needs backward computation.
I0423 09:52:33.778636 2763 net.cpp:217] conv7 needs backward computation.
I0423 09:52:33.778640 2763 net.cpp:217] relu6 needs backward computation.
I0423 09:52:33.778642 2763 net.cpp:217] conv6 needs backward computation.
I0423 09:52:33.778646 2763 net.cpp:217] pool5 needs backward computation.
I0423 09:52:33.778651 2763 net.cpp:217] relu5 needs backward computation.
I0423 09:52:33.778655 2763 net.cpp:217] conv5 needs backward computation.
I0423 09:52:33.778657 2763 net.cpp:217] relu4 needs backward computation.
I0423 09:52:33.778661 2763 net.cpp:217] conv4 needs backward computation.
I0423 09:52:33.778664 2763 net.cpp:217] relu3 needs backward computation.
I0423 09:52:33.778666 2763 net.cpp:217] conv3 needs backward computation.
I0423 09:52:33.778671 2763 net.cpp:217] pool2 needs backward computation.
I0423 09:52:33.778673 2763 net.cpp:217] norm2 needs backward computation.
I0423 09:52:33.778677 2763 net.cpp:217] relu2 needs backward computation.
I0423 09:52:33.778681 2763 net.cpp:217] conv2 needs backward computation.
I0423 09:52:33.778684 2763 net.cpp:217] pool1 needs backward computation.
I0423 09:52:33.778687 2763 net.cpp:217] norm1 needs backward computation.
I0423 09:52:33.778692 2763 net.cpp:217] relu1 needs backward computation.
I0423 09:52:33.778694 2763 net.cpp:217] conv1 needs backward computation.
I0423 09:52:33.778698 2763 net.cpp:219] label_data_1_split does not need backward computation.
I0423 09:52:33.778702 2763 net.cpp:219] data does not need backward computation.
I0423 09:52:33.778705 2763 net.cpp:261] This network produces output accuracy
I0423 09:52:33.778709 2763 net.cpp:261] This network produces output loss
I0423 09:52:33.778728 2763 net.cpp:274] Network initialization done.
I0423 09:52:33.778976 2763 solver.cpp:60] Solver scaffolding done.
I0423 09:52:33.779458 2763 caffe.cpp:133] Finetuning from /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 09:52:34.067591 2763 upgrade_proto.cpp:43] Attempting to upgrade input file specified using deprecated transformation parameters: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 09:52:34.067654 2763 upgrade_proto.cpp:46] Successfully upgraded file specified using deprecated data transformation parameters.
W0423 09:52:34.067659 2763 upgrade_proto.cpp:48] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0423 09:52:34.067752 2763 upgrade_proto.cpp:52] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 09:52:34.193063 2763 upgrade_proto.cpp:60] Successfully upgraded file specified using deprecated V1LayerParameter
I0423 09:52:34.196166 2763 net.cpp:753] Ignoring source layer fc6
I0423 09:52:34.196195 2763 net.cpp:753] Ignoring source layer drop6
I0423 09:52:34.196199 2763 net.cpp:753] Ignoring source layer fc7
I0423 09:52:34.196203 2763 net.cpp:753] Ignoring source layer drop7
I0423 09:52:34.196207 2763 net.cpp:753] Ignoring source layer fc8
I0423 09:52:34.491250 2763 upgrade_proto.cpp:43] Attempting to upgrade input file specified using deprecated transformation parameters: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 09:52:34.491279 2763 upgrade_proto.cpp:46] Successfully upgraded file specified using deprecated data transformation parameters.
W0423 09:52:34.491284 2763 upgrade_proto.cpp:48] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0423 09:52:34.491298 2763 upgrade_proto.cpp:52] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 09:52:34.615309 2763 upgrade_proto.cpp:60] Successfully upgraded file specified using deprecated V1LayerParameter
I0423 09:52:34.617781 2763 net.cpp:753] Ignoring source layer fc6
I0423 09:52:34.617805 2763 net.cpp:753] Ignoring source layer drop6
I0423 09:52:34.617808 2763 net.cpp:753] Ignoring source layer fc7
I0423 09:52:34.617812 2763 net.cpp:753] Ignoring source layer drop7
I0423 09:52:34.617815 2763 net.cpp:753] Ignoring source layer fc8
I0423 09:52:34.619755 2763 caffe.cpp:223] Starting Optimization
I0423 09:52:34.619771 2763 solver.cpp:279] Solving AlexNet
I0423 09:52:34.619776 2763 solver.cpp:280] Learning Rate Policy: step
I0423 09:52:35.070583 2763 solver.cpp:228] Iteration 0, loss = 7.51117
I0423 09:52:35.070628 2763 sgd_solver.cpp:106] Iteration 0, lr = 0.001
F0423 09:52:35.071538 2763 syncedmem.cpp:56] Check failed: error == cudaSuccess (2 vs. 0) out of memory
*** Check failure stack trace: ***
@ 0x7f3d97747daa (unknown)
@ 0x7f3d97747ce4 (unknown)
@ 0x7f3d977476e6 (unknown)
@ 0x7f3d9774a687 (unknown)
@ 0x7f3d97e0fbd1 caffe::SyncedMemory::to_gpu()
@ 0x7f3d97e0ef39 caffe::SyncedMemory::mutable_gpu_data()
@ 0x7f3d97e76c02 caffe::Blob<>::mutable_gpu_data()
@ 0x7f3d97e8857c caffe::SGDSolver<>::ComputeUpdateValue()
@ 0x7f3d97e88f73 caffe::SGDSolver<>::ApplyUpdate()
@ 0x7f3d97e2827c caffe::Solver<>::Step()
@ 0x7f3d97e288c9 caffe::Solver<>::Solve()
@ 0x408abe train()
@ 0x405f8c main
@ 0x7f3d96a55ec5 (unknown)
@ 0x4066c1 (unknown)
@ (nil) (unknown)
View Code
Later, we will concentrate on how to locate the target object and shown us the feature from each Convolution layers.
Waiting and Continuing ...
All right, the terminal shown me this, oh, my god ... Wrong ! Wrong ! Wrong !!!
The loss = nan , fuck, how it possible ???
Due to the base_lr = 0.001, and change into base_lr = 0.000001, the loss become normal.
时间： 2024-09-25 04:03:59