What is wrong with the following train_val_prototxt? (vgg16 with smaller filter size)

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yxc...@gmail.com

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Mar 10, 2017, 10:52:52 PM3/10/17
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What is wrong with the following train_val_prototxt? It is just vgg16 with smaller filter size. But when i use the original filter size, it works and doesnt give error

The error message is 

F0311 02:01:35.163677 6921 benchmark.cpp:92] Check failed: error == cudaSuccess (74 vs. 0) misaligned address
*** Check failure stack trace: ***
@ 0x7f5f4aa0a5cd google::LogMessage::Fail()
@ 0x7f5f4aa0c433 google::LogMessage::SendToLog()
@ 0x7f5f4aa0a15b google::LogMessage::Flush()
@ 0x7f5f4aa0ce1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f5f4b09108a caffe::Timer::MilliSeconds()
@ 0x7f5f4b0905ea caffe::Timer::Seconds()
@ 0x7f5f4b018aed caffe::Solver<>::Step()
@ 0x7f5f4b0194fa caffe::Solver<>::Solve()
@ 0x40aeb4 train()
@ 0x4075a8 main
@ 0x7f5f491a1830 __libc_start_main
@ 0x407e79 _start
@ (nil) (unknown)
Aborted (core dumped)

name: "VGG_ILSVRC_16_layers"
layers {
name: "data"
type: DATA
include {
phase: TRAIN
}
transform_param {
crop_size: 227
mean_file: "/home/yxchng/learncaffe/input/mean.binaryproto"
mirror: true
}
data_param {
source: "/home/yxchng/learncaffe/input/train_lmdb"
batch_size: 8
backend: LMDB
}
top: "data"
top: "label"
}
layers {
name: "data"
type: DATA
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/home/yxchng/learncaffe/input/mean.binaryproto"
mirror: false
}
data_param {
source: "/home/yxchng/learncaffe/input/validation_lmdb"
batch_size: 8
backend: LMDB
}
top: "data"
top: "label"
}
layers {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: CONVOLUTION
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: RELU
}
layers {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: CONVOLUTION
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: RELU
}
layers {
bottom: "conv1_2"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: CONVOLUTION
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: RELU
}
layers {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: CONVOLUTION
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: RELU
}
layers {
bottom: "conv2_2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: RELU
}
layers {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: RELU
}
layers {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: RELU
}
layers {
bottom: "conv3_3"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: RELU
}
layers {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: RELU
}
layers {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: RELU
}
layers {
bottom: "conv4_3"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: RELU
}
layers {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: RELU
}
layers {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: RELU
}
layers {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: INNER_PRODUCT
inner_product_param {
num_output: 1024
}
}
layers {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: RELU
}
layers {
bottom: "fc6"
top: "fc6"
name: "drop6"
type: DROPOUT
dropout_param {
dropout_ratio: 0.8
}
}
layers {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: INNER_PRODUCT
inner_product_param {
num_output: 1024
}
}
layers {
bottom: "fc7"
top: "fc7"
name: "relu7"
type: RELU
}
layers {
bottom: "fc7"
top: "fc7"
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.8
}
}
layers {
name: "fc8"
bottom: "fc7"
top: "fc8"
type: INNER_PRODUCT
inner_product_param {
num_output: 2
}
}
layers {
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc8"
bottom: "label"
top: "loss"
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}


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