caffe框架和theano框架的所需cudnn的版本冲突问题
2016-10-05 18:56
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在成功安装完成caffe和theano后（此时使用的cudnn的版本是v3），后跑了个caffe的minist数据集的实验可以成功实现，接着用Python跑theano的实验，提示nvcc 找不到GPU,，应将gpu（cuda）路径添加到环境变量中，但是添加完环境变量还是不行，提示cuDNN版本太低，应更新到V5。
经过搜集资料才知道，caffe官方版本对cudnn只支持到V4版本，不支持V5，而theano只支持cudnn V5版本，所以出现了caffe和theano的版本冲突问题
先将cudnn 版本重新安装到v5， 让theano跑起来再说，国外有人好像解决了让caffe也支持V5的方法：修改caffe代码，放在下面以便后用吧：
在今年 GTC 上，NVIDIA 推出的 NVIDIA Deep Learning SDK 中，cuDNN 赫然在列。与之比肩的还有
DIGITS, cuBLAS, cuSPARSE, NCCL 等。
从 2014 年推出 第一个版本，到如今 cuDNN 已经连续发布 5 个版本。
目前 cuDNN v5 的最新特性有：
(1) 支持递归神经网络（ LSTM / GRU / RNN )；
(2) cudnnConvolutionForward() 和 cudnnConvolutionBackwardData() 现已增加了 3维 FFT 分块算
法；
(3) cudnnConvolutionForward() 和 cudnnConvolutionBackwardData() 现已支持新算法：
CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD 和 CUDNN_CONVOLUTION_BWD_ALGO_WINOGRAD 。
该算法降低了 3x3 卷积计算复杂度。
(4) 增加了两种新层：
Bilinear Spatial Transformer
Dropout
目前官方的 Caffe 只支持 CUDNN V4。 为了用上 V5，需要手动改代码。
先看下 V5 的这个函数：
/* Function to perform backward activation */
cudnnStatus_t CUDNNWINAPI cudnnActivationBackward(
cudnnHandle_t handle,
cudnnActivationDescriptor_t activationDesc,
const void *alpha,
const cudnnTensorDescriptor_t yDesc,
const void *y,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx );
而 Caffe 中调用代码为：
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
CUDNN_ACTIVATION_RELU,
cudnn::dataType<Dtype>::one,
this->top_desc_, top_data, this->top_desc_, top_diff,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->bottom_desc_, bottom_diff));
差异主要在第二个参数。
/*
* activation mode
*/
typedef enum
{
CUDNN_ACTIVATION_SIGMOID = 0,
CUDNN_ACTIVATION_RELU = 1,
CUDNN_ACTIVATION_TANH = 2,
CUDNN_ACTIVATION_CLIPPED_RELU = 3
} cudnnActivationMode_t;
V4 直接使用了 cudnnActivationMode_t，在 V5 中则换为新的 cudnnActivationDescriptor_t ：
cudnnStatus_t CUDNNWINAPI cudnnCreateActivationDescriptor(
cudnnActivationDescriptor_t *activationDesc);
cudnnStatus_t CUDNNWINAPI cudnnSetActivationDescriptor(
cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t mode,
cudnnNanPropagation_t reluNanOpt,
double reluCeiling );
为了适配，需要将 Caffe 中这部分代码替换为 V5 的形式。涉及的文件：
include/caffe/layers/cudnn_relu_layer.hpp, src/caffe/layers/cudnn_relu_layer.cpp,
src/caffe/layers/cudnn_relu_layer.cu
include/caffe/layers/cudnn_sigmoid_layer.hpp, src/caffe/layers/cudnn_sigmoid_layer.cpp,
src/caffe/layers/cudnn_sigmoid_layer.cu
include/caffe/layers/cudnn_tanh_layer.hpp, src/caffe/layers/cudnn_tanh_layer.cpp,
src/caffe/layers/cudnn_tanh_layer.cu
在 阿里云 HPC 上对 Caffe + CUDNN V5 编译成功后进行 ldd 结果如下：
caffe]# ldd ./build/tools/caffe.bin | grep cudnn
libcudnn.so.5 => /disk1/deeplearning/local_install/lib/libcudnn.so.5
(0x00007f14a2339000)
运行 计时，得到输出：
caffe]# ./build/tools/caffe.bin time -model models/bvlc_reference_caffenet/deploy.prototxt
-gpu 0
I0415 17:57:29.857424 45092 caffe.cpp:308] Use GPU with device ID 0
I0415 17:57:30.240999 45092 net.cpp:49] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TRAIN
}
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 10
dim: 3
dim: 227
dim: 227
}
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
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: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 1000
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}
I0415 17:57:30.242352 45092 layer_factory.hpp:77] Creating layer data
I0415 17:57:30.242380 45092 net.cpp:91] Creating Layer data
I0415 17:57:30.242396 45092 net.cpp:399] data -> data
I0415 17:57:30.253039 45092 net.cpp:141] Setting up data
I0415 17:57:30.253085 45092 net.cpp:148] Top shape: 10 3 227 227 (1545870)
I0415 17:57:30.253098 45092 net.cpp:156] Memory required for data: 6183480
I0415 17:57:30.253118 45092 layer_factory.hpp:77] Creating layer conv1
I0415 17:57:30.253144 45092 net.cpp:91] Creating Layer conv1
I0415 17:57:30.253157 45092 net.cpp:425] conv1 <- data
I0415 17:57:30.253180 45092 net.cpp:399] conv1 -> conv1
I0415 17:57:30.489358 45092 net.cpp:141] Setting up conv1
I0415 17:57:30.489420 45092 net.cpp:148] Top shape: 10 96 55 55 (2904000)
I0415 17:57:30.489434 45092 net.cpp:156] Memory required for data: 17799480
I0415 17:57:30.489470 45092 layer_factory.hpp:77] Creating layer relu1
I0415 17:57:30.489495 45092 net.cpp:91] Creating Layer relu1
I0415 17:57:30.489507 45092 net.cpp:425] relu1 <- conv1
I0415 17:57:30.489526 45092 net.cpp:386] relu1 -> conv1 (in-place)
I0415 17:57:30.489871 45092 net.cpp:141] Setting up relu1
I0415 17:57:30.489892 45092 net.cpp:148] Top shape: 10 96 55 55 (2904000)
I0415 17:57:30.489904 45092 net.cpp:156] Memory required for data: 29415480
I0415 17:57:30.489917 45092 layer_factory.hpp:77] Creating layer pool1
I0415 17:57:30.489933 45092 net.cpp:91] Creating Layer pool1
I0415 17:57:30.489943 45092 net.cpp:425] pool1 <- conv1
I0415 17:57:30.489967 45092 net.cpp:399] pool1 -> pool1
I0415 17:57:30.490033 45092 net.cpp:141] Setting up pool1
I0415 17:57:30.490048 45092 net.cpp:148] Top shape: 10 96 27 27 (699840)
I0415 17:57:30.490059 45092 net.cpp:156] Memory required for data: 32214840
I0415 17:57:30.490070 45092 layer_factory.hpp:77] Creating layer norm1
I0415 17:57:30.490090 45092 net.cpp:91] Creating Layer norm1
I0415 17:57:30.490102 45092 net.cpp:425] norm1 <- pool1
I0415 17:57:30.490113 45092 net.cpp:399] norm1 -> norm1
I0415 17:57:30.490322 45092 net.cpp:141] Setting up norm1
I0415 17:57:30.490339 45092 net.cpp:148] Top shape: 10 96 27 27 (699840)
I0415 17:57:30.490350 45092 net.cpp:156] Memory required for data: 35014200
I0415 17:57:30.490362 45092 layer_factory.hpp:77] Creating layer conv2
I0415 17:57:30.490383 45092 net.cpp:91] Creating Layer conv2
I0415 17:57:30.490393 45092 net.cpp:425] conv2 <- norm1
I0415 17:57:30.490406 45092 net.cpp:399] conv2 -> conv2
I0415 17:57:30.493661 45092 net.cpp:141] Setting up conv2
I0415 17:57:30.493688 45092 net.cpp:148] Top shape: 10 256 27 27 (1866240)
I0415 17:57:30.493701 45092 net.cpp:156] Memory required for data: 42479160
I0415 17:57:30.493717 45092 layer_factory.hpp:77] Creating layer relu2
I0415 17:57:30.493734 45092 net.cpp:91] Creating Layer relu2
I0415 17:57:30.493744 45092 net.cpp:425] relu2 <- conv2
I0415 17:57:30.493757 45092 net.cpp:386] relu2 -> conv2 (in-place)
I0415 17:57:30.493957 45092 net.cpp:141] Setting up relu2
I0415 17:57:30.493981 45092 net.cpp:148] Top shape: 10 256 27 27 (1866240)
I0415 17:57:30.493993 45092 net.cpp:156] Memory required for data: 49944120
I0415 17:57:30.494004 45092 layer_factory.hpp:77] Creating layer pool2
I0415 17:57:30.494019 45092 net.cpp:91] Creating Layer pool2
I0415 17:57:30.494029 45092 net.cpp:425] pool2 <- conv2
I0415 17:57:30.494045 45092 net.cpp:399] pool2 -> pool2
I0415 17:57:30.494102 45092 net.cpp:141] Setting up pool2
I0415 17:57:30.494115 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.494128 45092 net.cpp:156] Memory required for data: 51674680
I0415 17:57:30.494139 45092 layer_factory.hpp:77] Creating layer norm2
I0415 17:57:30.494160 45092 net.cpp:91] Creating Layer norm2
I0415 17:57:30.494170 45092 net.cpp:425] norm2 <- pool2
I0415 17:57:30.494184 45092 net.cpp:399] norm2 -> norm2
I0415 17:57:30.494521 45092 net.cpp:141] Setting up norm2
I0415 17:57:30.494545 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.494557 45092 net.cpp:156] Memory required for data: 53405240
I0415 17:57:30.494568 45092 layer_factory.hpp:77] Creating layer conv3
I0415 17:57:30.494582 45092 net.cpp:91] Creating Layer conv3
I0415 17:57:30.494592 45092 net.cpp:425] conv3 <- norm2
I0415 17:57:30.494607 45092 net.cpp:399] conv3 -> conv3
I0415 17:57:30.498520 45092 net.cpp:141] Setting up conv3
I0415 17:57:30.498548 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.498564 45092 net.cpp:156] Memory required for data: 56001080
I0415 17:57:30.498584 45092 layer_factory.hpp:77] Creating layer relu3
I0415 17:57:30.498600 45092 net.cpp:91] Creating Layer relu3
I0415 17:57:30.498611 45092 net.cpp:425] relu3 <- conv3
I0415 17:57:30.498625 45092 net.cpp:386] relu3 -> conv3 (in-place)
I0415 17:57:30.498832 45092 net.cpp:141] Setting up relu3
I0415 17:57:30.498850 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.498863 45092 net.cpp:156] Memory required for data: 58596920
I0415 17:57:30.498878 45092 layer_factory.hpp:77] Creating layer conv4
I0415 17:57:30.498896 45092 net.cpp:91] Creating Layer conv4
I0415 17:57:30.498906 45092 net.cpp:425] conv4 <- conv3
I0415 17:57:30.498920 45092 net.cpp:399] conv4 -> conv4
I0415 17:57:30.502863 45092 net.cpp:141] Setting up conv4
I0415 17:57:30.502892 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.502905 45092 net.cpp:156] Memory required for data: 61192760
I0415 17:57:30.502924 45092 layer_factory.hpp:77] Creating layer relu4
I0415 17:57:30.502939 45092 net.cpp:91] Creating Layer relu4
I0415 17:57:30.502954 45092 net.cpp:425] relu4 <- conv4
I0415 17:57:30.502974 45092 net.cpp:386] relu4 -> conv4 (in-place)
I0415 17:57:30.503175 45092 net.cpp:141] Setting up relu4
I0415 17:57:30.503192 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.503201 45092 net.cpp:156] Memory required for data: 63788600
I0415 17:57:30.503211 45092 layer_factory.hpp:77] Creating layer conv5
I0415 17:57:30.503227 45092 net.cpp:91] Creating Layer conv5
I0415 17:57:30.503237 45092 net.cpp:425] conv5 <- conv4
I0415 17:57:30.503250 45092 net.cpp:399] conv5 -> conv5
I0415 17:57:30.506659 45092 net.cpp:141] Setting up conv5
I0415 17:57:30.506685 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.506706 45092 net.cpp:156] Memory required for data: 65519160
I0415 17:57:30.506731 45092 layer_factory.hpp:77] Creating layer relu5
I0415 17:57:30.506744 45092 net.cpp:91] Creating Layer relu5
I0415 17:57:30.506757 45092 net.cpp:425] relu5 <- conv5
I0415 17:57:30.506770 45092 net.cpp:386] relu5 -> conv5 (in-place)
I0415 17:57:30.506974 45092 net.cpp:141] Setting up relu5
I0415 17:57:30.506992 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.507005 45092 net.cpp:156] Memory required for data: 67249720
I0415 17:57:30.507015 45092 layer_factory.hpp:77] Creating layer pool5
I0415 17:57:30.507032 45092 net.cpp:91] Creating Layer pool5
I0415 17:57:30.507043 45092 net.cpp:425] pool5 <- conv5
I0415 17:57:30.507057 45092 net.cpp:399] pool5 -> pool5
I0415 17:57:30.507122 45092 net.cpp:141] Setting up pool5
I0415 17:57:30.507135 45092 net.cpp:148] Top shape: 10 256 6 6 (92160)
I0415 17:57:30.507148 45092 net.cpp:156] Memory required for data: 67618360
I0415 17:57:30.507159 45092 layer_factory.hpp:77] Creating layer fc6
I0415 17:57:30.507181 45092 net.cpp:91] Creating Layer fc6
I0415 17:57:30.507192 45092 net.cpp:425] fc6 <- pool5
I0415 17:57:30.507206 45092 net.cpp:399] fc6 -> fc6
I0415 17:57:30.612267 45092 net.cpp:141] Setting up fc6
I0415 17:57:30.612323 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.612334 45092 net.cpp:156] Memory required for data: 67782200
I0415 17:57:30.612360 45092 layer_factory.hpp:77] Creating layer relu6
I0415 17:57:30.612383 45092 net.cpp:91] Creating Layer relu6
I0415 17:57:30.612396 45092 net.cpp:425] relu6 <- fc6
I0415 17:57:30.612413 45092 net.cpp:386] relu6 -> fc6 (in-place)
I0415 17:57:30.612900 45092 net.cpp:141] Setting up relu6
I0415 17:57:30.612920 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.612936 45092 net.cpp:156] Memory required for data: 67946040
I0415 17:57:30.612949 45092 layer_factory.hpp:77] Creating layer drop6
I0415 17:57:30.612979 45092 net.cpp:91] Creating Layer drop6
I0415 17:57:30.612992 45092 net.cpp:425] drop6 <- fc6
I0415 17:57:30.613004 45092 net.cpp:386] drop6 -> fc6 (in-place)
I0415 17:57:30.613055 45092 net.cpp:141] Setting up drop6
I0415 17:57:30.613068 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.613080 45092 net.cpp:156] Memory required for data: 68109880
I0415 17:57:30.613091 45092 layer_factory.hpp:77] Creating layer fc7
I0415 17:57:30.613108 45092 net.cpp:91] Creating Layer fc7
I0415 17:57:30.613118 45092 net.cpp:425] fc7 <- fc6
I0415 17:57:30.613138 45092 net.cpp:399] fc7 -> fc7
I0415 17:57:30.660694 45092 net.cpp:141] Setting up fc7
I0415 17:57:30.660760 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.660773 45092 net.cpp:156] Memory required for data: 68273720
I0415 17:57:30.660797 45092 layer_factory.hpp:77] Creating layer relu7
I0415 17:57:30.660817 45092 net.cpp:91] Creating Layer relu7
I0415 17:57:30.660830 45092 net.cpp:425] relu7 <- fc7
I0415 17:57:30.660851 45092 net.cpp:386] relu7 -> fc7 (in-place)
I0415 17:57:30.661133 45092 net.cpp:141] Setting up relu7
I0415 17:57:30.661188 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.661201 45092 net.cpp:156] Memory required for data: 68437560
I0415 17:57:30.661212 45092 layer_factory.hpp:77] Creating layer drop7
I0415 17:57:30.661227 45092 net.cpp:91] Creating Layer drop7
I0415 17:57:30.661238 45092 net.cpp:425] drop7 <- fc7
I0415 17:57:30.661250 45092 net.cpp:386] drop7 -> fc7 (in-place)
I0415 17:57:30.661293 45092 net.cpp:141] Setting up drop7
I0415 17:57:30.661305 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.661320 45092 net.cpp:156] Memory required for data: 68601400
I0415 17:57:30.661331 45092 layer_factory.hpp:77] Creating layer fc8
I0415 17:57:30.661346 45092 net.cpp:91] Creating Layer fc8
I0415 17:57:30.661358 45092 net.cpp:425] fc8 <- fc7
I0415 17:57:30.661375 45092 net.cpp:399] fc8 -> fc8
I0415 17:57:30.673393 45092 net.cpp:141] Setting up fc8
I0415 17:57:30.673418 45092 net.cpp:148] Top shape: 10 1000 (10000)
I0415 17:57:30.673435 45092 net.cpp:156] Memory required for data: 68641400
I0415 17:57:30.673454 45092 layer_factory.hpp:77] Creating layer prob
I0415 17:57:30.673476 45092 net.cpp:91] Creating Layer prob
I0415 17:57:30.673487 45092 net.cpp:425] prob <- fc8
I0415 17:57:30.673506 45092 net.cpp:399] prob -> prob
I0415 17:57:30.673952 45092 net.cpp:141] Setting up prob
I0415 17:57:30.673980 45092 net.cpp:148] Top shape: 10 1000 (10000)
I0415 17:57:30.673995 45092 net.cpp:156] Memory required for data: 68681400
I0415 17:57:30.674005 45092 net.cpp:219] prob does not need backward computation.
I0415 17:57:30.674015 45092 net.cpp:219] fc8 does not need backward computation.
I0415 17:57:30.674026 45092 net.cpp:219] drop7 does not need backward computation.
I0415 17:57:30.674036 45092 net.cpp:219] relu7 does not need backward computation.
I0415 17:57:30.674049 45092 net.cpp:219] fc7 does not need backward computation.
I0415 17:57:30.674062 45092 net.cpp:219] drop6 does not need backward computation.
I0415 17:57:30.674072 45092 net.cpp:219] relu6 does not need backward computation.
I0415 17:57:30.674084 45092 net.cpp:219] fc6 does not need backward computation.
I0415 17:57:30.674093 45092 net.cpp:219] pool5 does not need backward computation.
I0415 17:57:30.674103 45092 net.cpp:219] relu5 does not need backward computation.
I0415 17:57:30.674114 45092 net.cpp:219] conv5 does not need backward computation.
I0415 17:57:30.674129 45092 net.cpp:219] relu4 does not need backward computation.
I0415 17:57:30.674140 45092 net.cpp:219] conv4 does not need backward computation.
I0415 17:57:30.674154 45092 net.cpp:219] relu3 does not need backward computation.
I0415 17:57:30.674168 45092 net.cpp:219] conv3 does not need backward computation.
I0415 17:57:30.674180 45092 net.cpp:219] norm2 does not need backward computation.
I0415 17:57:30.674190 45092 net.cpp:219] pool2 does not need backward computation.
I0415 17:57:30.674201 45092 net.cpp:219] relu2 does not need backward computation.
I0415 17:57:30.674218 45092 net.cpp:219] conv2 does not need backward computation.
I0415 17:57:30.674228 45092 net.cpp:219] norm1 does not need backward computation.
I0415 17:57:30.674244 45092 net.cpp:219] pool1 does not need backward computation.
I0415 17:57:30.674258 45092 net.cpp:219] relu1 does not need backward computation.
I0415 17:57:30.674270 45092 net.cpp:219] conv1 does not need backward computation.
I0415 17:57:30.674281 45092 net.cpp:219] data does not need backward computation.
I0415 17:57:30.674288 45092 net.cpp:261] This network produces output prob
I0415 17:57:30.674320 45092 net.cpp:274] Network initialization done.
I0415 17:57:30.674417 45092 caffe.cpp:320] Performing Forward
I0415 17:57:30.710207 45092 caffe.cpp:325] Initial loss: 0
I0415 17:57:30.710258 45092 caffe.cpp:326] Performing Backward
I0415 17:57:30.710271 45092 caffe.cpp:334] *** Benchmark begins ***
I0415 17:57:30.710284 45092 caffe.cpp:335] Testing for 50 iterations.
I0415 17:57:30.728257 45092 caffe.cpp:363] Iteration: 1 forward-backward time: 17.9167 ms.
I0415 17:57:30.742831 45092 caffe.cpp:363] Iteration: 2 forward-backward time: 14.5289 ms.
I0415 17:57:30.757328 45092 caffe.cpp:363] Iteration: 3 forward-backward time: 14.4394 ms.
I0415 17:57:30.771814 45092 caffe.cpp:363] Iteration: 4 forward-backward time: 14.4486 ms.
I0415 17:57:30.786324 45092 caffe.cpp:363] Iteration: 5 forward-backward time: 14.4736 ms.
I0415 17:57:30.800889 45092 caffe.cpp:363] Iteration: 6 forward-backward time: 14.5299 ms.
I0415 17:57:30.815471 45092 caffe.cpp:363] Iteration: 7 forward-backward time: 14.5472 ms.
I0415 17:57:30.830006 45092 caffe.cpp:363] Iteration: 8 forward-backward time: 14.5004 ms.
I0415 17:57:30.844482 45092 caffe.cpp:363] Iteration: 9 forward-backward time: 14.4412 ms.
I0415 17:57:30.858999 45092 caffe.cpp:363] Iteration: 10 forward-backward time: 14.4823 ms.
I0415 17:57:30.873497 45092 caffe.cpp:363] Iteration: 11 forward-backward time: 14.463 ms.
I0415 17:57:30.887987 45092 caffe.cpp:363] Iteration: 12 forward-backward time: 14.4556 ms.
I0415 17:57:30.902302 45092 caffe.cpp:363] Iteration: 13 forward-backward time: 14.2781 ms.
I0415 17:57:30.915726 45092 caffe.cpp:363] Iteration: 14 forward-backward time: 13.3916 ms.
I0415 17:57:30.929179 45092 caffe.cpp:363] Iteration: 15 forward-backward time: 13.4184 ms.
I0415 17:57:30.942584 45092 caffe.cpp:363] Iteration: 16 forward-backward time: 13.3694 ms.
I0415 17:57:30.956038 45092 caffe.cpp:363] Iteration: 17 forward-backward time: 13.4208 ms.
I0415 17:57:30.969511 45092 caffe.cpp:363] Iteration: 18 forward-backward time: 13.4377 ms.
I0415 17:57:30.982931 45092 caffe.cpp:363] Iteration: 19 forward-backward time: 13.3858 ms.
I0415 17:57:30.996414 45092 caffe.cpp:363] Iteration: 20 forward-backward time: 13.4489 ms.
I0415 17:57:31.009891 45092 caffe.cpp:363] Iteration: 21 forward-backward time: 13.4417 ms.
I0415 17:57:31.023388 45092 caffe.cpp:363] Iteration: 22 forward-backward time: 13.4623 ms.
I0415 17:57:31.036834 45092 caffe.cpp:363] Iteration: 23 forward-backward time: 13.4093 ms.
I0415 17:57:31.050341 45092 caffe.cpp:363] Iteration: 24 forward-backward time: 13.4735 ms.
I0415 17:57:31.064051 45092 caffe.cpp:363] Iteration: 25 forward-backward time: 13.6753 ms.
I0415 17:57:31.077127 45092 caffe.cpp:363] Iteration: 26 forward-backward time: 13.0419 ms.
I0415 17:57:31.090260 45092 caffe.cpp:363] Iteration: 27 forward-backward time: 13.0997 ms.
I0415 17:57:31.103308 45092 caffe.cpp:363] Iteration: 28 forward-backward time: 13.0143 ms.
I0415 17:57:31.116411 45092 caffe.cpp:363] Iteration: 29 forward-backward time: 13.0691 ms.
I0415 17:57:31.131355 45092 caffe.cpp:363] Iteration: 30 forward-backward time: 14.9067 ms.
I0415 17:57:31.144529 45092 caffe.cpp:363] Iteration: 31 forward-backward time: 13.1344 ms.
I0415 17:57:31.157615 45092 caffe.cpp:363] Iteration: 32 forward-backward time: 13.0509 ms.
I0415 17:57:31.170734 45092 caffe.cpp:363] Iteration: 33 forward-backward time: 13.0845 ms.
I0415 17:57:31.183795 45092 caffe.cpp:363] Iteration: 34 forward-backward time: 13.0256 ms.
I0415 17:57:31.196889 45092 caffe.cpp:363] Iteration: 35 forward-backward time: 13.0594 ms.
I0415 17:57:31.210055 45092 caffe.cpp:363] Iteration: 36 forward-backward time: 13.1316 ms.
I0415 17:57:31.223122 45092 caffe.cpp:363] Iteration: 37 forward-backward time: 13.0324 ms.
I0415 17:57:31.236204 45092 caffe.cpp:363] Iteration: 38 forward-backward time: 13.0479 ms.
I0415 17:57:31.249371 45092 caffe.cpp:363] Iteration: 39 forward-backward time: 13.1312 ms.
I0415 17:57:31.262531 45092 caffe.cpp:363] Iteration: 40 forward-backward time: 13.1264 ms.
I0415 17:57:31.275607 45092 caffe.cpp:363] Iteration: 41 forward-backward time: 13.0423 ms.
I0415 17:57:31.288681 45092 caffe.cpp:363] Iteration: 42 forward-backward time: 13.0402 ms.
I0415 17:57:31.301772 45092 caffe.cpp:363] Iteration: 43 forward-backward time: 13.0568 ms.
I0415 17:57:31.314856 45092 caffe.cpp:363] Iteration: 44 forward-backward time: 13.048 ms.
I0415 17:57:31.327955 45092 caffe.cpp:363] Iteration: 45 forward-backward time: 13.0655 ms.
I0415 17:57:31.341047 45092 caffe.cpp:363] Iteration: 46 forward-backward time: 13.0522 ms.
I0415 17:57:31.354151 45092 caffe.cpp:363] Iteration: 47 forward-backward time: 13.0704 ms.
I0415 17:57:31.367300 45092 caffe.cpp:363] Iteration: 48 forward-backward time: 13.0992 ms.
I0415 17:57:31.380395 45092 caffe.cpp:363] Iteration: 49 forward-backward time: 13.0612 ms.
I0415 17:57:31.393465 45092 caffe.cpp:363] Iteration: 50 forward-backward time: 13.0353 ms.
I0415 17:57:31.393489 45092 caffe.cpp:366] Average time per layer:
I0415 17:57:31.393506 45092 caffe.cpp:369] data
forward: 0.00180352 ms.
I0415 17:57:31.393535 45092 caffe.cpp:372] data
backward: 0.0016416 ms.
I0415 17:57:31.393554 45092 caffe.cpp:369] conv1
forward: 0.70582 ms.
I0415 17:57:31.393573 45092 caffe.cpp:372] conv1
backward: 0.706209 ms.
I0415 17:57:31.393594 45092 caffe.cpp:369] relu1
forward: 0.114414 ms.
I0415 17:57:31.393611 45092 caffe.cpp:372] relu1
backward: 0.0016096 ms.
I0415 17:57:31.393631 45092 caffe.cpp:369] pool1
forward: 0.12752 ms.
I0415 17:57:31.393651 45092 caffe.cpp:372] pool1
backward: 0.00160192 ms.
I0415 17:57:31.393666 45092 caffe.cpp:369] norm1
forward: 0.0773696 ms.
I0415 17:57:31.393679 45092 caffe.cpp:372] norm1
backward: 0.206209 ms.
I0415 17:57:31.393697 45092 caffe.cpp:369] conv2
forward: 1.12211 ms.
I0415 17:57:31.393710 45092 caffe.cpp:372] conv2
backward: 1.12579 ms.
I0415 17:57:31.393728 45092 caffe.cpp:369] relu2
forward: 0.0755533 ms.
I0415 17:57:31.393745 45092 caffe.cpp:372] relu2
backward: 0.00160576 ms.
I0415 17:57:31.393760 45092 caffe.cpp:369] pool2
forward: 0.0891341 ms.
I0415 17:57:31.393779 45092 caffe.cpp:372] pool2
backward: 0.00160128 ms.
I0415 17:57:31.393795 45092 caffe.cpp:369] norm2
forward: 0.161537 ms.
I0415 17:57:31.393815 45092 caffe.cpp:372] norm2
backward: 0.488591 ms.
I0415 17:57:31.393831 45092 caffe.cpp:369] conv3
forward: 0.571384 ms.
I0415 17:57:31.393851 45092 caffe.cpp:372] conv3
backward: 0.856935 ms.
I0415 17:57:31.393868 45092 caffe.cpp:369] relu3
forward: 0.0197875 ms.
I0415 17:57:31.393884 45092 caffe.cpp:372] relu3
backward: 0.0016064 ms.
I0415 17:57:31.393905 45092 caffe.cpp:369] conv4
forward: 0.436511 ms.
I0415 17:57:31.393929 45092 caffe.cpp:372] conv4
backward: 0.696554 ms.
I0415 17:57:31.393942 45092 caffe.cpp:369] relu4
forward: 0.0239539 ms.
I0415 17:57:31.393965 45092 caffe.cpp:372] relu4
backward: 0.0015872 ms.
I0415 17:57:31.393985 45092 caffe.cpp:369] conv5
forward: 0.30266 ms.
I0415 17:57:31.394006 45092 caffe.cpp:372] conv5
backward: 0.519668 ms.
I0415 17:57:31.394028 45092 caffe.cpp:369] relu5
forward: 0.0189939 ms.
I0415 17:57:31.394052 45092 caffe.cpp:372] relu5
backward: 0.00227264 ms.
I0415 17:57:31.394070 45092 caffe.cpp:369] pool5
forward: 0.0326426 ms.
I0415 17:57:31.394089 45092 caffe.cpp:372] pool5
backward: 0.00213184 ms.
I0415 17:57:31.394106 45092 caffe.cpp:369] fc6
forward: 1.60053 ms.
I0415 17:57:31.394127 45092 caffe.cpp:372] fc6
backward: 1.36114 ms.
I0415 17:57:31.394145 45092 caffe.cpp:369] relu6
forward: 0.0175546 ms.
I0415 17:57:31.394165 45092 caffe.cpp:372] relu6
backward: 0.00160768 ms.
I0415 17:57:31.394186 45092 caffe.cpp:369] drop6
forward: 0.0205677 ms.
I0415 17:57:31.394201 45092 caffe.cpp:372] drop6
backward: 0.00160832 ms.
I0415 17:57:31.394217 45092 caffe.cpp:369] fc7
forward: 0.724168 ms.
I0415 17:57:31.394232 45092 caffe.cpp:372] fc7
backward: 0.605802 ms.
I0415 17:57:31.394246 45092 caffe.cpp:369] relu7
forward: 0.0171334 ms.
I0415 17:57:31.394264 45092 caffe.cpp:372] relu7
backward: 0.0016 ms.
I0415 17:57:31.394284 45092 caffe.cpp:369] drop7
forward: 0.018528 ms.
I0415 17:57:31.394301 45092 caffe.cpp:372] drop7
backward: 0.002224 ms.
I0415 17:57:31.394318 45092 caffe.cpp:369] fc8
forward: 0.219565 ms.
I0415 17:57:31.394333 45092 caffe.cpp:372] fc8
backward: 0.185244 ms.
I0415 17:57:31.394348 45092 caffe.cpp:369] prob
forward: 0.0198214 ms.
I0415 17:57:31.394366 45092 caffe.cpp:372] prob
backward: 0.00157376 ms.
I0415 17:57:31.394407 45092 caffe.cpp:377] Average Forward pass: 6.67847 ms.
I0415 17:57:31.394419 45092 caffe.cpp:379] Average Backward pass: 6.93449 ms.
I0415 17:57:31.394433 45092 caffe.cpp:381] Average Forward-Backward: 13.6816 ms.
I0415 17:57:31.394448 45092 caffe.cpp:383] Total Time: 684.081 ms.
I0415 17:57:31.394464 45092 caffe.cpp:384] *** Benchmark ends ***
经过搜集资料才知道，caffe官方版本对cudnn只支持到V4版本，不支持V5，而theano只支持cudnn V5版本，所以出现了caffe和theano的版本冲突问题
先将cudnn 版本重新安装到v5， 让theano跑起来再说，国外有人好像解决了让caffe也支持V5的方法：修改caffe代码，放在下面以便后用吧：
在今年 GTC 上，NVIDIA 推出的 NVIDIA Deep Learning SDK 中，cuDNN 赫然在列。与之比肩的还有
DIGITS, cuBLAS, cuSPARSE, NCCL 等。
从 2014 年推出 第一个版本，到如今 cuDNN 已经连续发布 5 个版本。
目前 cuDNN v5 的最新特性有：
(1) 支持递归神经网络（ LSTM / GRU / RNN )；
(2) cudnnConvolutionForward() 和 cudnnConvolutionBackwardData() 现已增加了 3维 FFT 分块算
法；
(3) cudnnConvolutionForward() 和 cudnnConvolutionBackwardData() 现已支持新算法：
CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD 和 CUDNN_CONVOLUTION_BWD_ALGO_WINOGRAD 。
该算法降低了 3x3 卷积计算复杂度。
(4) 增加了两种新层：
Bilinear Spatial Transformer
Dropout
目前官方的 Caffe 只支持 CUDNN V4。 为了用上 V5，需要手动改代码。
先看下 V5 的这个函数：
/* Function to perform backward activation */
cudnnStatus_t CUDNNWINAPI cudnnActivationBackward(
cudnnHandle_t handle,
cudnnActivationDescriptor_t activationDesc,
const void *alpha,
const cudnnTensorDescriptor_t yDesc,
const void *y,
const cudnnTensorDescriptor_t dyDesc,
const void *dy,
const cudnnTensorDescriptor_t xDesc,
const void *x,
const void *beta,
const cudnnTensorDescriptor_t dxDesc,
void *dx );
而 Caffe 中调用代码为：
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
CUDNN_ACTIVATION_RELU,
cudnn::dataType<Dtype>::one,
this->top_desc_, top_data, this->top_desc_, top_diff,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->bottom_desc_, bottom_diff));
差异主要在第二个参数。
/*
* activation mode
*/
typedef enum
{
CUDNN_ACTIVATION_SIGMOID = 0,
CUDNN_ACTIVATION_RELU = 1,
CUDNN_ACTIVATION_TANH = 2,
CUDNN_ACTIVATION_CLIPPED_RELU = 3
} cudnnActivationMode_t;
V4 直接使用了 cudnnActivationMode_t，在 V5 中则换为新的 cudnnActivationDescriptor_t ：
cudnnStatus_t CUDNNWINAPI cudnnCreateActivationDescriptor(
cudnnActivationDescriptor_t *activationDesc);
cudnnStatus_t CUDNNWINAPI cudnnSetActivationDescriptor(
cudnnActivationDescriptor_t activationDesc,
cudnnActivationMode_t mode,
cudnnNanPropagation_t reluNanOpt,
double reluCeiling );
为了适配，需要将 Caffe 中这部分代码替换为 V5 的形式。涉及的文件：
include/caffe/layers/cudnn_relu_layer.hpp, src/caffe/layers/cudnn_relu_layer.cpp,
src/caffe/layers/cudnn_relu_layer.cu
include/caffe/layers/cudnn_sigmoid_layer.hpp, src/caffe/layers/cudnn_sigmoid_layer.cpp,
src/caffe/layers/cudnn_sigmoid_layer.cu
include/caffe/layers/cudnn_tanh_layer.hpp, src/caffe/layers/cudnn_tanh_layer.cpp,
src/caffe/layers/cudnn_tanh_layer.cu
在 阿里云 HPC 上对 Caffe + CUDNN V5 编译成功后进行 ldd 结果如下：
caffe]# ldd ./build/tools/caffe.bin | grep cudnn
libcudnn.so.5 => /disk1/deeplearning/local_install/lib/libcudnn.so.5
(0x00007f14a2339000)
运行 计时，得到输出：
caffe]# ./build/tools/caffe.bin time -model models/bvlc_reference_caffenet/deploy.prototxt
-gpu 0
I0415 17:57:29.857424 45092 caffe.cpp:308] Use GPU with device ID 0
I0415 17:57:30.240999 45092 net.cpp:49] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TRAIN
}
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 10
dim: 3
dim: 227
dim: 227
}
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
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: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 1000
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}
I0415 17:57:30.242352 45092 layer_factory.hpp:77] Creating layer data
I0415 17:57:30.242380 45092 net.cpp:91] Creating Layer data
I0415 17:57:30.242396 45092 net.cpp:399] data -> data
I0415 17:57:30.253039 45092 net.cpp:141] Setting up data
I0415 17:57:30.253085 45092 net.cpp:148] Top shape: 10 3 227 227 (1545870)
I0415 17:57:30.253098 45092 net.cpp:156] Memory required for data: 6183480
I0415 17:57:30.253118 45092 layer_factory.hpp:77] Creating layer conv1
I0415 17:57:30.253144 45092 net.cpp:91] Creating Layer conv1
I0415 17:57:30.253157 45092 net.cpp:425] conv1 <- data
I0415 17:57:30.253180 45092 net.cpp:399] conv1 -> conv1
I0415 17:57:30.489358 45092 net.cpp:141] Setting up conv1
I0415 17:57:30.489420 45092 net.cpp:148] Top shape: 10 96 55 55 (2904000)
I0415 17:57:30.489434 45092 net.cpp:156] Memory required for data: 17799480
I0415 17:57:30.489470 45092 layer_factory.hpp:77] Creating layer relu1
I0415 17:57:30.489495 45092 net.cpp:91] Creating Layer relu1
I0415 17:57:30.489507 45092 net.cpp:425] relu1 <- conv1
I0415 17:57:30.489526 45092 net.cpp:386] relu1 -> conv1 (in-place)
I0415 17:57:30.489871 45092 net.cpp:141] Setting up relu1
I0415 17:57:30.489892 45092 net.cpp:148] Top shape: 10 96 55 55 (2904000)
I0415 17:57:30.489904 45092 net.cpp:156] Memory required for data: 29415480
I0415 17:57:30.489917 45092 layer_factory.hpp:77] Creating layer pool1
I0415 17:57:30.489933 45092 net.cpp:91] Creating Layer pool1
I0415 17:57:30.489943 45092 net.cpp:425] pool1 <- conv1
I0415 17:57:30.489967 45092 net.cpp:399] pool1 -> pool1
I0415 17:57:30.490033 45092 net.cpp:141] Setting up pool1
I0415 17:57:30.490048 45092 net.cpp:148] Top shape: 10 96 27 27 (699840)
I0415 17:57:30.490059 45092 net.cpp:156] Memory required for data: 32214840
I0415 17:57:30.490070 45092 layer_factory.hpp:77] Creating layer norm1
I0415 17:57:30.490090 45092 net.cpp:91] Creating Layer norm1
I0415 17:57:30.490102 45092 net.cpp:425] norm1 <- pool1
I0415 17:57:30.490113 45092 net.cpp:399] norm1 -> norm1
I0415 17:57:30.490322 45092 net.cpp:141] Setting up norm1
I0415 17:57:30.490339 45092 net.cpp:148] Top shape: 10 96 27 27 (699840)
I0415 17:57:30.490350 45092 net.cpp:156] Memory required for data: 35014200
I0415 17:57:30.490362 45092 layer_factory.hpp:77] Creating layer conv2
I0415 17:57:30.490383 45092 net.cpp:91] Creating Layer conv2
I0415 17:57:30.490393 45092 net.cpp:425] conv2 <- norm1
I0415 17:57:30.490406 45092 net.cpp:399] conv2 -> conv2
I0415 17:57:30.493661 45092 net.cpp:141] Setting up conv2
I0415 17:57:30.493688 45092 net.cpp:148] Top shape: 10 256 27 27 (1866240)
I0415 17:57:30.493701 45092 net.cpp:156] Memory required for data: 42479160
I0415 17:57:30.493717 45092 layer_factory.hpp:77] Creating layer relu2
I0415 17:57:30.493734 45092 net.cpp:91] Creating Layer relu2
I0415 17:57:30.493744 45092 net.cpp:425] relu2 <- conv2
I0415 17:57:30.493757 45092 net.cpp:386] relu2 -> conv2 (in-place)
I0415 17:57:30.493957 45092 net.cpp:141] Setting up relu2
I0415 17:57:30.493981 45092 net.cpp:148] Top shape: 10 256 27 27 (1866240)
I0415 17:57:30.493993 45092 net.cpp:156] Memory required for data: 49944120
I0415 17:57:30.494004 45092 layer_factory.hpp:77] Creating layer pool2
I0415 17:57:30.494019 45092 net.cpp:91] Creating Layer pool2
I0415 17:57:30.494029 45092 net.cpp:425] pool2 <- conv2
I0415 17:57:30.494045 45092 net.cpp:399] pool2 -> pool2
I0415 17:57:30.494102 45092 net.cpp:141] Setting up pool2
I0415 17:57:30.494115 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.494128 45092 net.cpp:156] Memory required for data: 51674680
I0415 17:57:30.494139 45092 layer_factory.hpp:77] Creating layer norm2
I0415 17:57:30.494160 45092 net.cpp:91] Creating Layer norm2
I0415 17:57:30.494170 45092 net.cpp:425] norm2 <- pool2
I0415 17:57:30.494184 45092 net.cpp:399] norm2 -> norm2
I0415 17:57:30.494521 45092 net.cpp:141] Setting up norm2
I0415 17:57:30.494545 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.494557 45092 net.cpp:156] Memory required for data: 53405240
I0415 17:57:30.494568 45092 layer_factory.hpp:77] Creating layer conv3
I0415 17:57:30.494582 45092 net.cpp:91] Creating Layer conv3
I0415 17:57:30.494592 45092 net.cpp:425] conv3 <- norm2
I0415 17:57:30.494607 45092 net.cpp:399] conv3 -> conv3
I0415 17:57:30.498520 45092 net.cpp:141] Setting up conv3
I0415 17:57:30.498548 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.498564 45092 net.cpp:156] Memory required for data: 56001080
I0415 17:57:30.498584 45092 layer_factory.hpp:77] Creating layer relu3
I0415 17:57:30.498600 45092 net.cpp:91] Creating Layer relu3
I0415 17:57:30.498611 45092 net.cpp:425] relu3 <- conv3
I0415 17:57:30.498625 45092 net.cpp:386] relu3 -> conv3 (in-place)
I0415 17:57:30.498832 45092 net.cpp:141] Setting up relu3
I0415 17:57:30.498850 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.498863 45092 net.cpp:156] Memory required for data: 58596920
I0415 17:57:30.498878 45092 layer_factory.hpp:77] Creating layer conv4
I0415 17:57:30.498896 45092 net.cpp:91] Creating Layer conv4
I0415 17:57:30.498906 45092 net.cpp:425] conv4 <- conv3
I0415 17:57:30.498920 45092 net.cpp:399] conv4 -> conv4
I0415 17:57:30.502863 45092 net.cpp:141] Setting up conv4
I0415 17:57:30.502892 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.502905 45092 net.cpp:156] Memory required for data: 61192760
I0415 17:57:30.502924 45092 layer_factory.hpp:77] Creating layer relu4
I0415 17:57:30.502939 45092 net.cpp:91] Creating Layer relu4
I0415 17:57:30.502954 45092 net.cpp:425] relu4 <- conv4
I0415 17:57:30.502974 45092 net.cpp:386] relu4 -> conv4 (in-place)
I0415 17:57:30.503175 45092 net.cpp:141] Setting up relu4
I0415 17:57:30.503192 45092 net.cpp:148] Top shape: 10 384 13 13 (648960)
I0415 17:57:30.503201 45092 net.cpp:156] Memory required for data: 63788600
I0415 17:57:30.503211 45092 layer_factory.hpp:77] Creating layer conv5
I0415 17:57:30.503227 45092 net.cpp:91] Creating Layer conv5
I0415 17:57:30.503237 45092 net.cpp:425] conv5 <- conv4
I0415 17:57:30.503250 45092 net.cpp:399] conv5 -> conv5
I0415 17:57:30.506659 45092 net.cpp:141] Setting up conv5
I0415 17:57:30.506685 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.506706 45092 net.cpp:156] Memory required for data: 65519160
I0415 17:57:30.506731 45092 layer_factory.hpp:77] Creating layer relu5
I0415 17:57:30.506744 45092 net.cpp:91] Creating Layer relu5
I0415 17:57:30.506757 45092 net.cpp:425] relu5 <- conv5
I0415 17:57:30.506770 45092 net.cpp:386] relu5 -> conv5 (in-place)
I0415 17:57:30.506974 45092 net.cpp:141] Setting up relu5
I0415 17:57:30.506992 45092 net.cpp:148] Top shape: 10 256 13 13 (432640)
I0415 17:57:30.507005 45092 net.cpp:156] Memory required for data: 67249720
I0415 17:57:30.507015 45092 layer_factory.hpp:77] Creating layer pool5
I0415 17:57:30.507032 45092 net.cpp:91] Creating Layer pool5
I0415 17:57:30.507043 45092 net.cpp:425] pool5 <- conv5
I0415 17:57:30.507057 45092 net.cpp:399] pool5 -> pool5
I0415 17:57:30.507122 45092 net.cpp:141] Setting up pool5
I0415 17:57:30.507135 45092 net.cpp:148] Top shape: 10 256 6 6 (92160)
I0415 17:57:30.507148 45092 net.cpp:156] Memory required for data: 67618360
I0415 17:57:30.507159 45092 layer_factory.hpp:77] Creating layer fc6
I0415 17:57:30.507181 45092 net.cpp:91] Creating Layer fc6
I0415 17:57:30.507192 45092 net.cpp:425] fc6 <- pool5
I0415 17:57:30.507206 45092 net.cpp:399] fc6 -> fc6
I0415 17:57:30.612267 45092 net.cpp:141] Setting up fc6
I0415 17:57:30.612323 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.612334 45092 net.cpp:156] Memory required for data: 67782200
I0415 17:57:30.612360 45092 layer_factory.hpp:77] Creating layer relu6
I0415 17:57:30.612383 45092 net.cpp:91] Creating Layer relu6
I0415 17:57:30.612396 45092 net.cpp:425] relu6 <- fc6
I0415 17:57:30.612413 45092 net.cpp:386] relu6 -> fc6 (in-place)
I0415 17:57:30.612900 45092 net.cpp:141] Setting up relu6
I0415 17:57:30.612920 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.612936 45092 net.cpp:156] Memory required for data: 67946040
I0415 17:57:30.612949 45092 layer_factory.hpp:77] Creating layer drop6
I0415 17:57:30.612979 45092 net.cpp:91] Creating Layer drop6
I0415 17:57:30.612992 45092 net.cpp:425] drop6 <- fc6
I0415 17:57:30.613004 45092 net.cpp:386] drop6 -> fc6 (in-place)
I0415 17:57:30.613055 45092 net.cpp:141] Setting up drop6
I0415 17:57:30.613068 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.613080 45092 net.cpp:156] Memory required for data: 68109880
I0415 17:57:30.613091 45092 layer_factory.hpp:77] Creating layer fc7
I0415 17:57:30.613108 45092 net.cpp:91] Creating Layer fc7
I0415 17:57:30.613118 45092 net.cpp:425] fc7 <- fc6
I0415 17:57:30.613138 45092 net.cpp:399] fc7 -> fc7
I0415 17:57:30.660694 45092 net.cpp:141] Setting up fc7
I0415 17:57:30.660760 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.660773 45092 net.cpp:156] Memory required for data: 68273720
I0415 17:57:30.660797 45092 layer_factory.hpp:77] Creating layer relu7
I0415 17:57:30.660817 45092 net.cpp:91] Creating Layer relu7
I0415 17:57:30.660830 45092 net.cpp:425] relu7 <- fc7
I0415 17:57:30.660851 45092 net.cpp:386] relu7 -> fc7 (in-place)
I0415 17:57:30.661133 45092 net.cpp:141] Setting up relu7
I0415 17:57:30.661188 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.661201 45092 net.cpp:156] Memory required for data: 68437560
I0415 17:57:30.661212 45092 layer_factory.hpp:77] Creating layer drop7
I0415 17:57:30.661227 45092 net.cpp:91] Creating Layer drop7
I0415 17:57:30.661238 45092 net.cpp:425] drop7 <- fc7
I0415 17:57:30.661250 45092 net.cpp:386] drop7 -> fc7 (in-place)
I0415 17:57:30.661293 45092 net.cpp:141] Setting up drop7
I0415 17:57:30.661305 45092 net.cpp:148] Top shape: 10 4096 (40960)
I0415 17:57:30.661320 45092 net.cpp:156] Memory required for data: 68601400
I0415 17:57:30.661331 45092 layer_factory.hpp:77] Creating layer fc8
I0415 17:57:30.661346 45092 net.cpp:91] Creating Layer fc8
I0415 17:57:30.661358 45092 net.cpp:425] fc8 <- fc7
I0415 17:57:30.661375 45092 net.cpp:399] fc8 -> fc8
I0415 17:57:30.673393 45092 net.cpp:141] Setting up fc8
I0415 17:57:30.673418 45092 net.cpp:148] Top shape: 10 1000 (10000)
I0415 17:57:30.673435 45092 net.cpp:156] Memory required for data: 68641400
I0415 17:57:30.673454 45092 layer_factory.hpp:77] Creating layer prob
I0415 17:57:30.673476 45092 net.cpp:91] Creating Layer prob
I0415 17:57:30.673487 45092 net.cpp:425] prob <- fc8
I0415 17:57:30.673506 45092 net.cpp:399] prob -> prob
I0415 17:57:30.673952 45092 net.cpp:141] Setting up prob
I0415 17:57:30.673980 45092 net.cpp:148] Top shape: 10 1000 (10000)
I0415 17:57:30.673995 45092 net.cpp:156] Memory required for data: 68681400
I0415 17:57:30.674005 45092 net.cpp:219] prob does not need backward computation.
I0415 17:57:30.674015 45092 net.cpp:219] fc8 does not need backward computation.
I0415 17:57:30.674026 45092 net.cpp:219] drop7 does not need backward computation.
I0415 17:57:30.674036 45092 net.cpp:219] relu7 does not need backward computation.
I0415 17:57:30.674049 45092 net.cpp:219] fc7 does not need backward computation.
I0415 17:57:30.674062 45092 net.cpp:219] drop6 does not need backward computation.
I0415 17:57:30.674072 45092 net.cpp:219] relu6 does not need backward computation.
I0415 17:57:30.674084 45092 net.cpp:219] fc6 does not need backward computation.
I0415 17:57:30.674093 45092 net.cpp:219] pool5 does not need backward computation.
I0415 17:57:30.674103 45092 net.cpp:219] relu5 does not need backward computation.
I0415 17:57:30.674114 45092 net.cpp:219] conv5 does not need backward computation.
I0415 17:57:30.674129 45092 net.cpp:219] relu4 does not need backward computation.
I0415 17:57:30.674140 45092 net.cpp:219] conv4 does not need backward computation.
I0415 17:57:30.674154 45092 net.cpp:219] relu3 does not need backward computation.
I0415 17:57:30.674168 45092 net.cpp:219] conv3 does not need backward computation.
I0415 17:57:30.674180 45092 net.cpp:219] norm2 does not need backward computation.
I0415 17:57:30.674190 45092 net.cpp:219] pool2 does not need backward computation.
I0415 17:57:30.674201 45092 net.cpp:219] relu2 does not need backward computation.
I0415 17:57:30.674218 45092 net.cpp:219] conv2 does not need backward computation.
I0415 17:57:30.674228 45092 net.cpp:219] norm1 does not need backward computation.
I0415 17:57:30.674244 45092 net.cpp:219] pool1 does not need backward computation.
I0415 17:57:30.674258 45092 net.cpp:219] relu1 does not need backward computation.
I0415 17:57:30.674270 45092 net.cpp:219] conv1 does not need backward computation.
I0415 17:57:30.674281 45092 net.cpp:219] data does not need backward computation.
I0415 17:57:30.674288 45092 net.cpp:261] This network produces output prob
I0415 17:57:30.674320 45092 net.cpp:274] Network initialization done.
I0415 17:57:30.674417 45092 caffe.cpp:320] Performing Forward
I0415 17:57:30.710207 45092 caffe.cpp:325] Initial loss: 0
I0415 17:57:30.710258 45092 caffe.cpp:326] Performing Backward
I0415 17:57:30.710271 45092 caffe.cpp:334] *** Benchmark begins ***
I0415 17:57:30.710284 45092 caffe.cpp:335] Testing for 50 iterations.
I0415 17:57:30.728257 45092 caffe.cpp:363] Iteration: 1 forward-backward time: 17.9167 ms.
I0415 17:57:30.742831 45092 caffe.cpp:363] Iteration: 2 forward-backward time: 14.5289 ms.
I0415 17:57:30.757328 45092 caffe.cpp:363] Iteration: 3 forward-backward time: 14.4394 ms.
I0415 17:57:30.771814 45092 caffe.cpp:363] Iteration: 4 forward-backward time: 14.4486 ms.
I0415 17:57:30.786324 45092 caffe.cpp:363] Iteration: 5 forward-backward time: 14.4736 ms.
I0415 17:57:30.800889 45092 caffe.cpp:363] Iteration: 6 forward-backward time: 14.5299 ms.
I0415 17:57:30.815471 45092 caffe.cpp:363] Iteration: 7 forward-backward time: 14.5472 ms.
I0415 17:57:30.830006 45092 caffe.cpp:363] Iteration: 8 forward-backward time: 14.5004 ms.
I0415 17:57:30.844482 45092 caffe.cpp:363] Iteration: 9 forward-backward time: 14.4412 ms.
I0415 17:57:30.858999 45092 caffe.cpp:363] Iteration: 10 forward-backward time: 14.4823 ms.
I0415 17:57:30.873497 45092 caffe.cpp:363] Iteration: 11 forward-backward time: 14.463 ms.
I0415 17:57:30.887987 45092 caffe.cpp:363] Iteration: 12 forward-backward time: 14.4556 ms.
I0415 17:57:30.902302 45092 caffe.cpp:363] Iteration: 13 forward-backward time: 14.2781 ms.
I0415 17:57:30.915726 45092 caffe.cpp:363] Iteration: 14 forward-backward time: 13.3916 ms.
I0415 17:57:30.929179 45092 caffe.cpp:363] Iteration: 15 forward-backward time: 13.4184 ms.
I0415 17:57:30.942584 45092 caffe.cpp:363] Iteration: 16 forward-backward time: 13.3694 ms.
I0415 17:57:30.956038 45092 caffe.cpp:363] Iteration: 17 forward-backward time: 13.4208 ms.
I0415 17:57:30.969511 45092 caffe.cpp:363] Iteration: 18 forward-backward time: 13.4377 ms.
I0415 17:57:30.982931 45092 caffe.cpp:363] Iteration: 19 forward-backward time: 13.3858 ms.
I0415 17:57:30.996414 45092 caffe.cpp:363] Iteration: 20 forward-backward time: 13.4489 ms.
I0415 17:57:31.009891 45092 caffe.cpp:363] Iteration: 21 forward-backward time: 13.4417 ms.
I0415 17:57:31.023388 45092 caffe.cpp:363] Iteration: 22 forward-backward time: 13.4623 ms.
I0415 17:57:31.036834 45092 caffe.cpp:363] Iteration: 23 forward-backward time: 13.4093 ms.
I0415 17:57:31.050341 45092 caffe.cpp:363] Iteration: 24 forward-backward time: 13.4735 ms.
I0415 17:57:31.064051 45092 caffe.cpp:363] Iteration: 25 forward-backward time: 13.6753 ms.
I0415 17:57:31.077127 45092 caffe.cpp:363] Iteration: 26 forward-backward time: 13.0419 ms.
I0415 17:57:31.090260 45092 caffe.cpp:363] Iteration: 27 forward-backward time: 13.0997 ms.
I0415 17:57:31.103308 45092 caffe.cpp:363] Iteration: 28 forward-backward time: 13.0143 ms.
I0415 17:57:31.116411 45092 caffe.cpp:363] Iteration: 29 forward-backward time: 13.0691 ms.
I0415 17:57:31.131355 45092 caffe.cpp:363] Iteration: 30 forward-backward time: 14.9067 ms.
I0415 17:57:31.144529 45092 caffe.cpp:363] Iteration: 31 forward-backward time: 13.1344 ms.
I0415 17:57:31.157615 45092 caffe.cpp:363] Iteration: 32 forward-backward time: 13.0509 ms.
I0415 17:57:31.170734 45092 caffe.cpp:363] Iteration: 33 forward-backward time: 13.0845 ms.
I0415 17:57:31.183795 45092 caffe.cpp:363] Iteration: 34 forward-backward time: 13.0256 ms.
I0415 17:57:31.196889 45092 caffe.cpp:363] Iteration: 35 forward-backward time: 13.0594 ms.
I0415 17:57:31.210055 45092 caffe.cpp:363] Iteration: 36 forward-backward time: 13.1316 ms.
I0415 17:57:31.223122 45092 caffe.cpp:363] Iteration: 37 forward-backward time: 13.0324 ms.
I0415 17:57:31.236204 45092 caffe.cpp:363] Iteration: 38 forward-backward time: 13.0479 ms.
I0415 17:57:31.249371 45092 caffe.cpp:363] Iteration: 39 forward-backward time: 13.1312 ms.
I0415 17:57:31.262531 45092 caffe.cpp:363] Iteration: 40 forward-backward time: 13.1264 ms.
I0415 17:57:31.275607 45092 caffe.cpp:363] Iteration: 41 forward-backward time: 13.0423 ms.
I0415 17:57:31.288681 45092 caffe.cpp:363] Iteration: 42 forward-backward time: 13.0402 ms.
I0415 17:57:31.301772 45092 caffe.cpp:363] Iteration: 43 forward-backward time: 13.0568 ms.
I0415 17:57:31.314856 45092 caffe.cpp:363] Iteration: 44 forward-backward time: 13.048 ms.
I0415 17:57:31.327955 45092 caffe.cpp:363] Iteration: 45 forward-backward time: 13.0655 ms.
I0415 17:57:31.341047 45092 caffe.cpp:363] Iteration: 46 forward-backward time: 13.0522 ms.
I0415 17:57:31.354151 45092 caffe.cpp:363] Iteration: 47 forward-backward time: 13.0704 ms.
I0415 17:57:31.367300 45092 caffe.cpp:363] Iteration: 48 forward-backward time: 13.0992 ms.
I0415 17:57:31.380395 45092 caffe.cpp:363] Iteration: 49 forward-backward time: 13.0612 ms.
I0415 17:57:31.393465 45092 caffe.cpp:363] Iteration: 50 forward-backward time: 13.0353 ms.
I0415 17:57:31.393489 45092 caffe.cpp:366] Average time per layer:
I0415 17:57:31.393506 45092 caffe.cpp:369] data
forward: 0.00180352 ms.
I0415 17:57:31.393535 45092 caffe.cpp:372] data
backward: 0.0016416 ms.
I0415 17:57:31.393554 45092 caffe.cpp:369] conv1
forward: 0.70582 ms.
I0415 17:57:31.393573 45092 caffe.cpp:372] conv1
backward: 0.706209 ms.
I0415 17:57:31.393594 45092 caffe.cpp:369] relu1
forward: 0.114414 ms.
I0415 17:57:31.393611 45092 caffe.cpp:372] relu1
backward: 0.0016096 ms.
I0415 17:57:31.393631 45092 caffe.cpp:369] pool1
forward: 0.12752 ms.
I0415 17:57:31.393651 45092 caffe.cpp:372] pool1
backward: 0.00160192 ms.
I0415 17:57:31.393666 45092 caffe.cpp:369] norm1
forward: 0.0773696 ms.
I0415 17:57:31.393679 45092 caffe.cpp:372] norm1
backward: 0.206209 ms.
I0415 17:57:31.393697 45092 caffe.cpp:369] conv2
forward: 1.12211 ms.
I0415 17:57:31.393710 45092 caffe.cpp:372] conv2
backward: 1.12579 ms.
I0415 17:57:31.393728 45092 caffe.cpp:369] relu2
forward: 0.0755533 ms.
I0415 17:57:31.393745 45092 caffe.cpp:372] relu2
backward: 0.00160576 ms.
I0415 17:57:31.393760 45092 caffe.cpp:369] pool2
forward: 0.0891341 ms.
I0415 17:57:31.393779 45092 caffe.cpp:372] pool2
backward: 0.00160128 ms.
I0415 17:57:31.393795 45092 caffe.cpp:369] norm2
forward: 0.161537 ms.
I0415 17:57:31.393815 45092 caffe.cpp:372] norm2
backward: 0.488591 ms.
I0415 17:57:31.393831 45092 caffe.cpp:369] conv3
forward: 0.571384 ms.
I0415 17:57:31.393851 45092 caffe.cpp:372] conv3
backward: 0.856935 ms.
I0415 17:57:31.393868 45092 caffe.cpp:369] relu3
forward: 0.0197875 ms.
I0415 17:57:31.393884 45092 caffe.cpp:372] relu3
backward: 0.0016064 ms.
I0415 17:57:31.393905 45092 caffe.cpp:369] conv4
forward: 0.436511 ms.
I0415 17:57:31.393929 45092 caffe.cpp:372] conv4
backward: 0.696554 ms.
I0415 17:57:31.393942 45092 caffe.cpp:369] relu4
forward: 0.0239539 ms.
I0415 17:57:31.393965 45092 caffe.cpp:372] relu4
backward: 0.0015872 ms.
I0415 17:57:31.393985 45092 caffe.cpp:369] conv5
forward: 0.30266 ms.
I0415 17:57:31.394006 45092 caffe.cpp:372] conv5
backward: 0.519668 ms.
I0415 17:57:31.394028 45092 caffe.cpp:369] relu5
forward: 0.0189939 ms.
I0415 17:57:31.394052 45092 caffe.cpp:372] relu5
backward: 0.00227264 ms.
I0415 17:57:31.394070 45092 caffe.cpp:369] pool5
forward: 0.0326426 ms.
I0415 17:57:31.394089 45092 caffe.cpp:372] pool5
backward: 0.00213184 ms.
I0415 17:57:31.394106 45092 caffe.cpp:369] fc6
forward: 1.60053 ms.
I0415 17:57:31.394127 45092 caffe.cpp:372] fc6
backward: 1.36114 ms.
I0415 17:57:31.394145 45092 caffe.cpp:369] relu6
forward: 0.0175546 ms.
I0415 17:57:31.394165 45092 caffe.cpp:372] relu6
backward: 0.00160768 ms.
I0415 17:57:31.394186 45092 caffe.cpp:369] drop6
forward: 0.0205677 ms.
I0415 17:57:31.394201 45092 caffe.cpp:372] drop6
backward: 0.00160832 ms.
I0415 17:57:31.394217 45092 caffe.cpp:369] fc7
forward: 0.724168 ms.
I0415 17:57:31.394232 45092 caffe.cpp:372] fc7
backward: 0.605802 ms.
I0415 17:57:31.394246 45092 caffe.cpp:369] relu7
forward: 0.0171334 ms.
I0415 17:57:31.394264 45092 caffe.cpp:372] relu7
backward: 0.0016 ms.
I0415 17:57:31.394284 45092 caffe.cpp:369] drop7
forward: 0.018528 ms.
I0415 17:57:31.394301 45092 caffe.cpp:372] drop7
backward: 0.002224 ms.
I0415 17:57:31.394318 45092 caffe.cpp:369] fc8
forward: 0.219565 ms.
I0415 17:57:31.394333 45092 caffe.cpp:372] fc8
backward: 0.185244 ms.
I0415 17:57:31.394348 45092 caffe.cpp:369] prob
forward: 0.0198214 ms.
I0415 17:57:31.394366 45092 caffe.cpp:372] prob
backward: 0.00157376 ms.
I0415 17:57:31.394407 45092 caffe.cpp:377] Average Forward pass: 6.67847 ms.
I0415 17:57:31.394419 45092 caffe.cpp:379] Average Backward pass: 6.93449 ms.
I0415 17:57:31.394433 45092 caffe.cpp:381] Average Forward-Backward: 13.6816 ms.
I0415 17:57:31.394448 45092 caffe.cpp:383] Total Time: 684.081 ms.
I0415 17:57:31.394464 45092 caffe.cpp:384] *** Benchmark ends ***
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