tpn_train.py 文件源码

def load_nets(args, cur_gpu):
    # initialize solver and feature net,
    # RNN should be initialized before CNN, because CNN cudnn conv layers
    # may assume using all available memory
    caffe.set_mode_gpu()
    caffe.set_device(cur_gpu)
    solver = caffe.SGDSolver(args.solver)
    if args.snapshot:
        print "Restoring history from {}".format(args.snapshot)
        solver.restore(args.snapshot)
    rnn = solver.net
    if args.weights:
        rnn.copy_from(args.weights)
    feature_net = caffe.Net(args.feature_net, args.feature_param, caffe.TEST)

    # apply bbox regression normalization on the net weights
    with open(args.bbox_mean, 'rb') as f:
        bbox_means = cPickle.load(f)
    with open(args.bbox_std, 'rb') as f:
        bbox_stds = cPickle.load(f)
    feature_net.params['bbox_pred_vid'][0].data[...] = \
        feature_net.params['bbox_pred_vid'][0].data * bbox_stds[:, np.newaxis]
    feature_net.params['bbox_pred_vid'][1].data[...] = \
        feature_net.params['bbox_pred_vid'][1].data * bbox_stds + bbox_means
    return solver, feature_net, rnn, bbox_means, bbox_stds