Python config.cfg方法代码示例

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def setup(self, bottom, top):
        # parse the layer parameter string, which must be valid YAML
        layer_params = yaml.load(self.param_str_)

        self._feat_stride = layer_params['feat_stride']
        self._anchors     = generate_anchors(cfg.TRAIN.RPN_BASE_SIZE, cfg.TRAIN.RPN_ASPECTS, cfg.TRAIN.RPN_SCALES)
        self._num_anchors = self._anchors.shape[0]

        if DEBUG:
            print 'feat_stride: {}'.format(self._feat_stride)
            print 'anchors:'
            print self._anchors

        # rois blob: holds R regions of interest, each is a 5-tuple
        # (n, x1, y1, x2, y2) specifying an image batch index n and a
        # rectangle (x1, y1, x2, y2)
        top[0].reshape(1, 5)

        # scores blob: holds scores for R regions of interest
        if len(top) > 1:
            top[1].reshape(1, 1, 1, 1) 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def combined_roidb(imdb_names):
    def get_roidb(imdb_name):
        imdb = get_imdb(imdb_name)
        print 'Loaded dataset `{:s}` for training'.format(imdb.name)
        imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
        print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
        roidb = get_training_roidb(imdb)
        return roidb

    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    if len(roidbs) > 1:
        for r in roidbs[1:]:
            roidb.extend(r)
        imdb = datasets.imdb.imdb(imdb_names)
    else:
        imdb = get_imdb(imdb_names)
    return imdb, roidb 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Generate bbox output from a Fast R-CNN network')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU id(s) to use',
                        default='0', type=str)
    parser.add_argument('--def', dest='prototxt',
                        help='prototxt file defining the network',
                        default=None, type=str)
    parser.add_argument('--out', dest='outfile',
                        help='output filepath',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file', default=None, type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--imgroot', type=str, default='/workspace/images/')
    parser.add_argument('--split', type=str, default='valid')
    parser.add_argument('--caffemodel', type=str, default='./resnet101_faster_rcnn_final_iter_320000.caffemodel')

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Test a DPL network')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
                        default=0, type=int)
    parser.add_argument('--def', dest='prototxt',
                        help='prototxt file defining the network',
                        default=None, type=str)
    parser.add_argument('--net', dest='caffemodel',
                        help='model to test',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file', default=None, type=str)
    parser.add_argument('--wait', dest='wait',
                        help='wait until net file exists',
                        default=True, type=bool)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to test',
                        default='voc_2007_test', type=str)
    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
                        action='store_true')
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--task', dest='task',
                        default=None, type=str)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a DPL network')
    parser.add_argument('--gpu', dest='gpu_id',
                        help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--solver', dest='solver',
                        help='solver prototxt',
                        default=None, type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=80000, type=int)
    parser.add_argument('--weights', dest='pretrained_model',
                        help='initialize with pretrained model weights',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='voc_2007_trainval', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Test an OICR network')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
                        default=0, type=int)
    parser.add_argument('--def', dest='prototxt',
                        help='prototxt file defining the network',
                        default=None, type=str)
    parser.add_argument('--net', dest='caffemodel',
                        help='model to test',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file', default=None, type=str)
    parser.add_argument('--wait', dest='wait',
                        help='wait until net file exists',
                        default=True, type=bool)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to test',
                        default='voc_2007_test', type=str)
    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
                        action='store_true')
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train an OICR network')
    parser.add_argument('--gpu', dest='gpu_id',
                        help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--solver', dest='solver',
                        help='solver prototxt',
                        default=None, type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=70000, type=int)
    parser.add_argument('--weights', dest='pretrained_model',
                        help='initialize with pretrained model weights',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='voc_2007_trainval', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a Face-Magnet network')
    parser.add_argument('--gpus', dest='gpu_id',
                        help='GPUs device id to use [0]',
                        default='0,1', type=str)
    parser.add_argument('--solver', dest='solver',
                        help='solver prototxt',
                        default=None, type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=38000, type=int)
    parser.add_argument('--weights', dest='pretrained_model',
                        help='initialize with pretrained model weights',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='wider', type=str)
    parser.add_argument('--max_size', dest='max_size',
                        help='dataset to max size on',
                        default='10', type=str)
    parser.add_argument('--min_size', dest='max_size',
                        help='dataset to max size on',
                        default='10', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--reload', dest='reload',
                        help='Reloading saved weights. Set it if not initializing with imagenet weights.',
                        action='store_true')
    parser.add_argument('--randomize', dest='randomize',
                        help='Randomize the training.',
                        action='store_true')
    parser.add_argument('--shuffle', dest='shuffle',
                        help='Shuffle the testing order, for parallel testing',
                        action='store_true')

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args 

# 需要导入模块: from fast_rcnn import config [as 别名]
# 或者: from fast_rcnn.config import cfg [as 别名]
def get_detections_from_im(net, im_file, image_id, conf_thresh=0.2):
    """
    :param net:
    :param im_file: full path to an image
    :param image_id:
    :param conf_thresh:
    :return: all information from detection and attr prediction
    """
    im = cv2.imread(im_file)
    scores, boxes, attr_scores, rel_scores = im_detect(net, im)

    # Keep the original boxes, don't worry about the regresssion bbox outputs
    rois = net.blobs['rois'].data.copy()
    # unscale back to raw image space
    blobs, im_scales = _get_blobs(im, None)

    cls_boxes = rois[:, 1:5] / im_scales[0]
    cls_prob = net.blobs['cls_prob'].data
    attr_prob = net.blobs['attr_prob'].data
    pool5 = net.blobs['pool5_flat'].data

    # Keep only the best detections
    max_conf = np.zeros((rois.shape[0]))
    for cls_ind in range(1, cls_prob.shape[1]):
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
        keep = np.array(nms(dets, cfg.TEST.NMS))
        max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep])

    keep_boxes = np.where(max_conf >= conf_thresh)[0]
    if len(keep_boxes) < MIN_BOXES:
        keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
    elif len(keep_boxes) > MAX_BOXES:
        keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]

    objects = np.argmax(cls_prob[keep_boxes][:, 1:], axis=1)
    objects_conf = np.max(cls_prob[keep_boxes][:, 1:], axis=1)
    attrs = np.argmax(attr_prob[keep_boxes][:, 1:], axis=1)
    attrs_conf = np.max(attr_prob[keep_boxes][:, 1:], axis=1)

    return {
        "img_id": image_id,
        "img_h": np.size(im, 0),
        "img_w": np.size(im, 1),
        "objects_id": base64.b64encode(objects),  # int64
        "objects_conf": base64.b64encode(objects_conf),  # float32
        "attrs_id": base64.b64encode(attrs),  # int64
        "attrs_conf": base64.b64encode(attrs_conf),  # float32
        "num_boxes": len(keep_boxes),
        "boxes": base64.b64encode(cls_boxes[keep_boxes]),  # float32
        "features": base64.b64encode(pool5[keep_boxes])  # float32
    }