Question

我在.txt文件中有以下结构：

/path/to/image x y
/path/to/image x y

其中x和y是整数。

我现在要做的是：创建一个在Caffe中使用的hdf5文件（'train.prototxt'）

我的Python代码如下所示：

import h5py, os
import caffe
import numpy as np

SIZE = 256
with open( 'train.txt', 'r' ) as T :
    lines = T.readlines()


count_files = 0
split_after = 1000
count = -1

# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
X = np.zeros( (split_after, 3, SIZE, SIZE), dtype='f4' )
y1 = np.zeros( (split_after, 1), dtype='f4' )
y2 = np.zeros( (split_after, 1), dtype='f4' )

for i,l in enumerate(lines):
    count += 1
    sp = l.split(' ')
    img = caffe.io.load_image( sp[0] )
    img = caffe.io.resize( img, (3, SIZE, SIZE) )

    X[count] = img
    y1[count] = float(sp[1])
    y2[count] = float(sp[2])

    if (count+1) == split_after:
        with h5py.File('train_' + str(count_files) +  '.h5','w') as H:
            H.create_dataset( 'X', data=X ) # note the name X given to the dataset!
            H.create_dataset( 'y1', data=y1 )
            H.create_dataset( 'y2', data=y2 )

            X = np.zeros( (split_after, 3, SIZE, SIZE), dtype='f4' )
            y1 = np.zeros( (split_after, 1), dtype='f4' )
            y2 = np.zeros( (split_after, 1), dtype='f4' )
        with open('train_h5_list.txt','a') as L:
            L.write( 'train_' + str(count_files) + '.h5') # list all h5 files you are going to use
        count_files += 1
        count = 0

事实上我想估算角度。这意味着我有两个类别，一个用于垂直角度，一个用于水平角度。第一类的范围为0-10度，第二类的范围为10-20，依此类推（水平和垂直角度）。

.prototxt怎么样？这是我的最后一层

layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 36
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "y"
  top: "loss"
}

Answer 1

您还需要修改输入图层：现在您有三个top s：

layer {
  type: "HDF5Data"
  name: "data"
  top: "X"
  top: "y1"
  top: "y2"
  # ... params and phase
}

现在，top的{{1}}充当您数据的“高级描述符”，您希望从中预测fc7和y1。因此，在图层y2之后，您应该：

fc7

和

layer {
  type: "InnerProduct"
  name: "class_y1" 
  bottom: "fc7"
  top: "class_y1"
  #... params num_output: 36 
}
layer {
  type: "SoftmaxWithLoss" # to be replaced with "Softmax" in deploy
  name: "loss_y1"
  bottom: "class_y1"
  bottom: "y1"
  top: "loss_y1"
  # optionally, loss_weight
}

Caffe两类多标签分类与hdf5

1 个答案: