thigi thigi - 1 month ago 50
Python Question

Caffe two class multi label classification with hdf5

I have the following structure in a .txt file:


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



where x and y are integers.

What I want to do now is: Create a hdf5 file to use in Caffe (
'train.prototxt'
)

My Python code looks like this:

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


In fact I want to estimate angles. That means I have two classes one for vertical angles one for horizontal angles. The first class ranges from 0-10 degrees the second from 10-20 and so on (for both horizontal and vertical angles).

How would the .prototxt look like? Here are my last layers

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

You also need to modify the input layer: now you have three tops:

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

Now, the top of your fc7 serves as a "high level descriptor" of your data, from which you wish to predict y1 and y2. Thus, after layer fc7 you should have:

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
}

And:

layer {
  type: "InnerProduct"
  name: "class_y2" 
  bottom: "fc7"
  top: "class_y2"
  #... params num_output: 36 
}
layer {
  type: "SoftmaxWithLoss" # to be replaced with "Softmax" in deploy
  name: "loss_y2"
  bottom: "class_y2"
  bottom: "y2"
  top: "loss_y2"
  # optionally, loss_weight
}