Caffe: Regression with CNN

[shubham-pachori]

Hi!

I am working on some problem statement in Computer vision. I am new to Caffe. Is there anyway in which I could perform regression with CNN such that the size of my input and label (output) is height_width_no. of channels ? And is there any way to import training data and training labels separately in lmdb format in Caffe?

[adnan15110]

Hi!

First, create two separate lmdb files (data_lmdb and label_lmdb). Here is an example for regression problem for image:

import numpy as np
import lmdb,os,sys,math

caffe_root = 'path to caffe'
sys.path.insert(0, caffe_root + 'python')

import caffe

class CreateLmdb:
    def __init__(self):
        # configure below variables
        # location to the folder where the raw image file located
        self.dataset_path = os.path.join("---path to stored image")
        # location to the folder where the train.txt, test.txt and val.txt located
        self.txt_file_path = os.path.join("--path to train.txt and others--")

        dataset_types = ['train','val','test']

        for d_type in dataset_types:
            self.create_lmdb_file(d_type)

    def create_images_labels_list(self, dataset_type):
        images = []
        labels = []
        f = open(os.path.join(self.txt_file_path, dataset_type+'.txt'))
        for line in f.readlines():
            image,label = line.strip().split(' ')
            image_location = os.path.join(self.dataset_path,dataset_type,image)
            images.append(image_location)
            labels.append(float(label))
        return images, labels

    def create_lmdb_file(self, dataset_type):
        print 'Writing LMDB data ...'

        lmdb_data_name = dataset_type + '_data_lmdb'
        lmdb_label_name = dataset_type + '_score_lmdb'

        images,labels = self.create_images_labels_list(dataset_type)
        print 'Writing labels ...'

        # Size of buffer: 1000 elements to reduce memory consumption
        for idx in range(int(math.ceil(len(labels)/1000.0))):
            in_db_label = lmdb.open(lmdb_label_name, map_size=int(1e12))
            with in_db_label.begin(write=True) as in_txn:
                for label_idx, label_ in enumerate(labels[(1000*idx):(1000*(idx+1))]):
                    im_dat = caffe.io.array_to_datum(np.array(label_).astype(float).reshape(1,1,1))
                    in_txn.put('{:0>10d}'.format(1000*idx + label_idx), im_dat.SerializeToString())
                    string_ = str(1000*idx+label_idx+1) + ' / ' + str(len(labels))
                    sys.stdout.write("\r%s" % string_)
                    sys.stdout.flush()
            in_db_label.close()

        print '\nfinished'

        print 'Writing image data'
        for idx in range(int(math.ceil(len(images)/1000.0))):
            in_db_data = lmdb.open(lmdb_data_name, map_size=int(1e12))
            with in_db_data.begin(write=True) as in_txn:
                for in_idx, in_ in enumerate(images[(1000*idx):(1000*(idx+1))]):
                    im = caffe.io.load_image(in_)
                    im_dat = caffe.io.array_to_datum(im.astype(float).transpose((2, 0, 1)))
                    in_txn.put('{:0>10d}'.format(1000*idx + in_idx), im_dat.SerializeToString())

                    string_ = str(1000*idx+in_idx+1) + ' / ' + str(len(images))
                    sys.stdout.write("\r%s" % string_)
                    sys.stdout.flush()
            in_db_data.close()
        print '\nfinished'


if __name__ == '__main__':
    create_lmdb = CreateLmdb() 

Then define you .prototxt file as follows:

layer {
  name: "image"
  type: "Data"
  top: "image"
  include {
    phase: TRAIN
  }
  data_param {
    source: "data_lmdb"
    backend: LMDB
  }
}

layer {
  name: "label"
  type: "Data"
  top: "label"
  data_param {
    source: "labels_lmdb"
    batch_size:1
    backend: LMDB
  }
}

# attach the image layer as bottom to next layer

attach the label to the euclidean loss or hingeloss layer as follows:

layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom:  --last layer that calculates the output--
  bottom: "label"
  top: "loss"
}

** most of the codes are taken from several issues in caffe.

[longjon]

Closing as this looks like a usage issue/request for help.

This tracker is reserved for specific Caffe development issues and bugs; please ask usage questions on the caffe-users list.

For more information, see our contributing guide.

Thanks!