thigi - 5 months ago 121

Python Question

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 `top`

s:

```
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
}
```