Caffe：检查失败：shape [i]> = 0（-1对0）错误-Java 学习之路

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我正在尝试设计一个采用IMAGE（224x224x3）和3个参数（X，Y，R）的网络来学习这种关系 .

我的输入是HDF5数据集 . 我收到以下错误：

“创建层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对0）“

我创建了一个HDF5数据集，用于输入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： -

有人可以帮忙吗？

2 回答

0

你得到了输入图像的形状：而不是 H x W x 3 ，caffe期望它是 3 x W x H .
有关在hdf5文件中为caffe排列数组的更多详细信息，请参阅this answer .

PS，
您的 label 数组中不需要Singleton维度 .

回复于 2024-04-29T03:48:37+08:00
0

我收到了同样的错误消息 . 但似乎我的情况与提问者不同 .

我遇到了这个错误，因为我在网络中添加了太多的卷积和池化层，并且图像最终缩小到1x1，在这种情况下，如果我继续添加卷积层，则无法执行卷积，并且出现此错误 .

如果其他人遇到我的问题，我在这里发布 .

回复于 2024-04-29T03:48:37+08:00

Caffe：检查失败：shape [i]> = 0（-1对0）错误

2 回答

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