Caffe: Check failed: shape[i] >= 0 (-1 vs. 0) 错误
Caffe: Check failed: shape[i] >= 0 (-1 vs. 0) error
我正在尝试设计一个采用图像 (224x224x3) 和 3 个参数 (X、Y、R) 学习关系的网络。
我的输入是一个 HDF5 数据集。我收到以下错误：
"正在创建图层 conv1
I0612 17:17:38.315083 9991 net.cpp:406] conv1 <- 数据
I0612 17:17:38.315107 9991 net.cpp:380] conv1 -> conv1
F0612 17:17:38.352540 9991 blob.cpp:32] 检查失败：shape[i] >= 0 (-1 vs. 0) "
我创建了一个 HDF5 数据集用于输入到 caffe。我的create_dataset代码如下：-
import h5py, os
import caffe
import numpy as np
SIZE = 224
with open( 'val.txt', 'r' ) as T :
lines = T.readlines()
count_files = 0
split_after = 199
count = -1
# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
data = np.zeros( (split_after,SIZE, SIZE,3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
for i,l in enumerate(lines):
count += 1
sp = l.split(' ')
img = caffe.io.load_image( sp[0] )
data[count] = img
label[count][0] = float(sp[1])
label[count][1] = float(sp[2])
label[count][2] = float(sp[3])
#print y1[count]
if (count+1) == split_after:
with h5py.File('val_' + str(count_files) + '.h5','w') as H:
H.create_dataset( 'data', data=data ) # note the name X given to the dataset!
H.create_dataset( 'label', data=label )
data = np.zeros( (split_after, SIZE, SIZE, 3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
with open('val1.txt','a') as L:
L.write( 'val_' + str(count_files) + '.h5') # list all h5 files you are going to use
count_files += 1
count = 0
我正在 HDF5 数据集中创建数据 (224,224,3) 字段和标签 (3,1)。
现在我的caffe模型如下：
name: "CaffeNet"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/test1.txt"
batch_size: 256
shuffle: true
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/val1.txt"
batch_size: 16
}
}
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: "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"
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: 1
}
}
}
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"
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: 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: 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: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
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"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
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: "fc8ft"
type: "InnerProduct"
bottom: "fc7"
top: "fc8ft"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc8ft"
bottom: "label"
top: "loss"
}
详细错误如下：-
"正在创建图层 conv1
I0612 17:17:38.315083 9991 net.cpp:406] conv1 <- 数据
I0612 17:17:38.315107 9991 net.cpp:380] conv1 -> conv1
F0612 17:17:38.352540 9991 blob.cpp:32] 检查失败：shape[i] >= 0 (-1 vs. 0) "
有人能帮忙吗？
你得到了输入图像的倒转形状：。
而不是 HxWx3，caffe 期望它是 3xWxH.
有关在 hdf5 文件中为 caffe 排列数组的更多详细信息，请参阅 。
PS,
您的 label 数组中不需要单例维度。
我收到了同样的错误信息。不过我的情况好像和提问者不一样
我遇到这个错误是因为我在我的网络中添加了太多的卷积层和池化层，最终图像缩小到1x1，在这种情况下如果我继续添加卷积层，就无法进行卷积，这出现错误。
万一其他人遇到我的问题，我post在这里。
我正在尝试设计一个采用图像 (224x224x3) 和 3 个参数 (X、Y、R) 学习关系的网络。
我的输入是一个 HDF5 数据集。我收到以下错误：
"正在创建图层 conv1 I0612 17:17:38.315083 9991 net.cpp:406] conv1 <- 数据 I0612 17:17:38.315107 9991 net.cpp:380] conv1 -> conv1 F0612 17:17:38.352540 9991 blob.cpp:32] 检查失败：shape[i] >= 0 (-1 vs. 0) "
我创建了一个 HDF5 数据集用于输入到 caffe。我的create_dataset代码如下：-
import h5py, os
import caffe
import numpy as np
SIZE = 224
with open( 'val.txt', 'r' ) as T :
lines = T.readlines()
count_files = 0
split_after = 199
count = -1
# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
data = np.zeros( (split_after,SIZE, SIZE,3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
for i,l in enumerate(lines):
count += 1
sp = l.split(' ')
img = caffe.io.load_image( sp[0] )
data[count] = img
label[count][0] = float(sp[1])
label[count][1] = float(sp[2])
label[count][2] = float(sp[3])
#print y1[count]
if (count+1) == split_after:
with h5py.File('val_' + str(count_files) + '.h5','w') as H:
H.create_dataset( 'data', data=data ) # note the name X given to the dataset!
H.create_dataset( 'label', data=label )
data = np.zeros( (split_after, SIZE, SIZE, 3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
with open('val1.txt','a') as L:
L.write( 'val_' + str(count_files) + '.h5') # list all h5 files you are going to use
count_files += 1
count = 0
我正在 HDF5 数据集中创建数据 (224,224,3) 字段和标签 (3,1)。
现在我的caffe模型如下：
name: "CaffeNet"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/test1.txt"
batch_size: 256
shuffle: true
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/val1.txt"
batch_size: 16
}
}
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: "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"
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: 1
}
}
}
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"
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: 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: 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: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
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"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
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: "fc8ft"
type: "InnerProduct"
bottom: "fc7"
top: "fc8ft"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc8ft"
bottom: "label"
top: "loss"
}
详细错误如下：-
"正在创建图层 conv1 I0612 17:17:38.315083 9991 net.cpp:406] conv1 <- 数据 I0612 17:17:38.315107 9991 net.cpp:380] conv1 -> conv1 F0612 17:17:38.352540 9991 blob.cpp:32] 检查失败：shape[i] >= 0 (-1 vs. 0) "
有人能帮忙吗？
你得到了输入图像的倒转形状：。
而不是 HxWx3，caffe 期望它是 3xWxH.
有关在 hdf5 文件中为 caffe 排列数组的更多详细信息，请参阅
PS,
您的 label 数组中不需要单例维度。
我收到了同样的错误信息。不过我的情况好像和提问者不一样
我遇到这个错误是因为我在我的网络中添加了太多的卷积层和池化层，最终图像缩小到1x1，在这种情况下如果我继续添加卷积层，就无法进行卷积，这出现错误。
万一其他人遇到我的问题，我post在这里。