def __init__(self, net_file=None, meta_file=None): """Initializes DecafNet. Input: net_file: the trained network file. meta_file: the meta information for images. """ logging.info('Initializing decafnet...') try: if not net_file: # use the internal decafnet file. net_file = _JEFFNET_FILE if not meta_file: # use the internal meta file. meta_file = _META_FILE cuda_decafnet = pickle.load(open(net_file)) meta = pickle.load(open(meta_file)) except IOError: raise RuntimeError('Cannot find DecafNet files.') # First, translate the network self._net = translator.translate_cuda_network( cuda_decafnet, {'data': (INPUT_DIM, INPUT_DIM, 3)}) # Then, get the labels and image means. self.label_names = meta['label_names'] self._data_mean = translator.img_cudaconv_to_decaf( meta['data_mean'], 256, 3) logging.info('Jeffnet initialized.') return
def __init__(self, net_file=None, meta_file=None): """Initializes DecafNet. Input: net_file: the trained network file. meta_file: the meta information for images. """ logging.info('Initializing decafnet...') try: if not net_file: # use the internal decafnet file. net_file = _JEFFNET_FILE if not meta_file: # use the internal meta file. meta_file = _META_FILE cuda_decafnet = pickle.load(open(net_file)) meta = pickle.load(open(meta_file)) except IOError: raise RuntimeError('Cannot find DecafNet files.') # First, translate the network self._net = translator.translate_cuda_network( cuda_decafnet, {'data': (INPUT_DIM, INPUT_DIM, 3)}) # Then, get the labels and image means. self.label_names = meta['label_names'] self._data_mean = translator.img_cudaconv_to_decaf( meta['data_mean'], 256, 3) logging.info('Jeffnet initialized.') return
def load_jeffnet(self,net_opt): self.jf_opt = net_opt meta = pickle.load(open(net_opt.meta_file)) self.jf_label_names = meta['label_names'] self.jf_data_mean = translator.img_cudaconv_to_decaf(meta['data_mean'], 256, 3)
FLAGS = gflags.FLAGS """ from collections import namedtuple FLAGS = namedtuple("FLAGS", "net_file meta_file") FLAGS.net_file = '../scripts/imagenet.decafnet.epoch90' FLAGS.meta_file = '../scripts/imagenet.decafnet.meta' from decaf.util import translator, transform _JEFFNET_FLIP = True if __name__ == '__main__': net = imagenet.DecafNet(net_file=FLAGS.net_file, meta_file=FLAGS.meta_file) import scipy.io meta = pickle.load(open(FLAGS.meta_file)) data_mean = translator.img_cudaconv_to_decaf(meta['data_mean'], 256, 3) TT = int(sys.argv[1]) if TT==-1: fns = ['./test.jpg'] else: if TT==0: DD= '/home/Stephen/Desktop/Data/Seg/BSR/BSDS500/data/images/train/' if TT==1: DD= '/home/Stephen/Desktop/Data/Seg/BSR/BSDS500/data/images/val/' elif TT==2: DD= '/home/Stephen/Desktop/Data/Seg/BSR/BSDS500/data/images/test/' fns = sorted(glob.glob(DD+'*.jpg')) patch_id = 3 if patch_id==1: