extract cnn features

caffe_cnn.py

# Make sure that caffe is on the python path:
caffe_root = '/usr0/bin/caffe/'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe
from images import crop_image
import numpy as np

mean = 'caffedata/ilsvrc_2012_mean.npy'

class CNN(object):

    def __init__(self, deploy, model, mean=mean, batch_size=100, width=224, height=224):

        self.deploy = deploy
        self.model = model
        self.mean = mean

        self.batch_size = batch_size
        self.net, self.transformer = self.get_net()
        self.net.blobs['data'].reshape(self.batch_size, 3, height, width)

        self.width = width
        self.height = height

    def get_net(self):
        caffe.set_mode_gpu()
        net = caffe.Net(self.deploy, self.model, caffe.TEST)

        transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
        transformer.set_transpose('data', (2,0,1))
#        transformer.set_mean('data', np.load(self.mean).mean(1).mean(1))
        transformer.set_mean('data', np.array([103.939, 116.779, 123.68]))
        transformer.set_raw_scale('data', 255)
        transformer.set_channel_swap('data', (2,1,0))

        return net, transformer

    def get_features(self, image_list, layers='fc7', layer_sizes=[4096]):
        iter_until = len(image_list) + self.batch_size
        all_feats = np.zeros([len(image_list)] + layer_sizes)

        batch_count = 0
        for start, end in zip(range(0, iter_until, self.batch_size), \
                              range(self.batch_size, iter_until, self.batch_size)):

            image_batch_file = image_list[start:end]
            image_batch = np.array(map(lambda x: crop_image(x, target_width=self.width, target_height=self.height), image_batch_file))

            caffe_in = np.zeros(np.array(image_batch.shape)[[0,3,1,2]], dtype=np.float32)

            for idx, in_ in enumerate(image_batch):
                caffe_in[idx] = self.transformer.preprocess('data', in_)

            out = self.net.forward_all(blobs=[layers], **{'data':caffe_in})
            feats = out[layers]

            all_feats[start:end] = feats
            batch_count += 1
            print("processing batch #: %d" % batch_count)
        return all_feats