Python config.cfg方法代码示例

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  """A simplified version compared to fast/er RCNN
     For details please see the technical report
  """
  if type(cfg_key) == bytes:
      cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  # Get the scores and bounding boxes
  scores = rpn_cls_prob[:, :, :, num_anchors:]
  rpn_bbox_pred = rpn_bbox_pred.view((-1, 4))
  scores = scores.contiguous().view(-1, 1)
  proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
  proposals = clip_boxes(proposals, im_info[:2])

  # Pick the top region proposals
  scores, order = scores.view(-1).sort(descending=True)
  if pre_nms_topN > 0:
    order = order[:pre_nms_topN]
    scores = scores[:pre_nms_topN].view(-1, 1)
  proposals = proposals[order.data, :]

  # Non-maximal suppression
  keep = nms(torch.cat((proposals, scores), 1).data, nms_thresh)

  # Pick th top region proposals after NMS
  if post_nms_topN > 0:
    keep = keep[:post_nms_topN]
  proposals = proposals[keep, :]
  scores = scores[keep,]

  # Only support single image as input
  batch_inds = Variable(proposals.data.new(proposals.size(0), 1).zero_())
  blob = torch.cat((batch_inds, proposals), 1)

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def parse_args():
  """
  Parse input arguments
  """
  parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
  parser.add_argument('--cfg', dest='cfg_file',
            help='optional config file', default=None, type=str)
  parser.add_argument('--model', dest='model',
            help='model to test',
            default=None, type=str)
  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('--num_dets', dest='max_per_image',
            help='max number of detections per image',
            default=100, type=int)
  parser.add_argument('--tag', dest='tag',
                        help='tag of the model',
                        default='', type=str)
  parser.add_argument('--net', dest='net',
                      help='vgg16, res50, res101, res152, mobile',
                      default='res50', 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 model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
    if type(cfg_key) == bytes:
        cfg_key = cfg_key.decode('utf-8')
    pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
    post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
    nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
    min_size = cfg[cfg_key].ANCHOR_MIN_SIZE

    # Get the scores and bounding boxes
    scores = rpn_cls_prob[:, :, :, num_anchors:]
    scores = tf.reshape(scores, shape=(-1,))
    rpn_bbox_pred = tf.reshape(rpn_bbox_pred, shape=(-1, 4))

    proposals = bbox_transform_inv_tf(anchors, rpn_bbox_pred)
    proposals = clip_boxes_tf(proposals, im_info[:2])

    # removed predicted boxes with either height or width < threshold
    # (NOTE: convert min_size to input image scale stored in im_info[2])
    # keep = _filter_boxes_tf(proposals, min_size * im_info[2])
    # proposals = tf.gather(proposals, keep)
    # scores = tf.gather(scores, keep)

    # Non-maximal suppression
    indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topN, iou_threshold=nms_thresh)

    boxes = tf.gather(proposals, indices)
    boxes = tf.to_float(boxes)
    scores = tf.gather(scores, indices)
    scores = tf.reshape(scores, shape=(-1, 1))

    # Only support single image as input
    batch_inds = tf.zeros((tf.shape(indices)[0], 1), dtype=tf.float32)
    blob = tf.concat([batch_inds, boxes], 1)

    return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  """A simplified version compared to fast/er RCNN
     For details please see the technical report
  """
  if type(cfg_key) == bytes:
      cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  im_info = im_info[0]
  # Get the scores and bounding boxes
  scores = rpn_cls_prob[:, :, :, num_anchors:]
  rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
  scores = scores.reshape((-1, 1))
  proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
  proposals = clip_boxes(proposals, im_info[:2])

  # Pick the top region proposals
  order = scores.ravel().argsort()[::-1]
  if pre_nms_topN > 0:
    order = order[:pre_nms_topN]
  proposals = proposals[order, :]
  scores = scores[order]

  # Non-maximal suppression
  keep = nms(np.hstack((proposals, scores)), nms_thresh)

  # Pick th top region proposals after NMS
  if post_nms_topN > 0:
    keep = keep[:post_nms_topN]
  proposals = proposals[keep, :]
  scores = scores[keep]

  # Only support single image as input
  batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
  blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  """A simplified version compared to fast/er RCNN
     For details please see the technical report
  """
  if type(cfg_key) == bytes:
      cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  # Get the scores and bounding boxes
  scores = rpn_cls_prob[:, :, :, num_anchors:]
  rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
  scores = scores.reshape((-1, 1))
  proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
  proposals = clip_boxes(proposals, im_info[:2])

  # Pick the top region proposals
  order = scores.ravel().argsort()[::-1]
  if pre_nms_topN > 0:
    order = order[:pre_nms_topN]
  proposals = proposals[order, :]
  scores = scores[order]

  # Non-maximal suppression
  keep = nms(np.hstack((proposals, scores)), nms_thresh)

  # Pick th top region proposals after NMS
  if post_nms_topN > 0:
    keep = keep[:post_nms_topN]
  proposals = proposals[keep, :]
  scores = scores[keep]

  # Only support single image as input
  batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
  blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  if type(cfg_key) == bytes:
    cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  # Get the scores and bounding boxes
  scores = rpn_cls_prob[:, :, :, num_anchors:]
  scores = tf.reshape(scores, shape=(-1,))
  rpn_bbox_pred = tf.reshape(rpn_bbox_pred, shape=(-1, 4))

  proposals = bbox_transform_inv_tf(anchors, rpn_bbox_pred)
  proposals = clip_boxes_tf(proposals, im_info[:2])

  # Non-maximal suppression
  indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topN, iou_threshold=nms_thresh)

  boxes = tf.gather(proposals, indices)
  boxes = tf.to_float(boxes)
  scores = tf.gather(scores, indices)
  scores = tf.reshape(scores, shape=(-1, 1))

  # Only support single image as input
  batch_inds = tf.zeros((tf.shape(indices)[0], 1), dtype=tf.float32)
  blob = tf.concat([batch_inds, boxes], 1)

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  """A simplified version compared to fast/er RCNN
     For details please see the technical report
  """
  if type(cfg_key) == bytes:
      cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  # Get the scores and bounding boxes
  scores = rpn_cls_prob[:, :, :, num_anchors:]
  rpn_bbox_pred = rpn_bbox_pred.view((-1, 4))
  scores = scores.contiguous().view(-1, 1)
  proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
  proposals = clip_boxes(proposals, im_info[:2])

  # Pick the top region proposals
  scores, order = scores.view(-1).sort(descending=True)
  if pre_nms_topN > 0:
    order = order[:pre_nms_topN]
    scores = scores[:pre_nms_topN].view(-1, 1)
  proposals = proposals[order.data, :]

  # Non-maximal suppression
  keep = nms(torch.cat((proposals, scores), 1).data, nms_thresh)

  # Pick th top region proposals after NMS
  if post_nms_topN > 0:
    keep = keep[:post_nms_topN]
  proposals = proposals[keep, :]  # test(300,4)
  scores = scores[keep,]

  # Our RPN implementation only supports a single input image,
  # so all batch inds are 0
  # 即这些roi都属于一个图片，如果后续实现了多个输入图片，这个roi要区分它属于哪一个图片(即哪一个batch)
  batch_inds = Variable(proposals.data.new(proposals.size(0), 1).zero_())
  blob = torch.cat((batch_inds, proposals), 1)

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer_fpn(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
  """A simplified version compared to fast/er RCNN
     For details please see the technical report
  """
  if type(cfg_key) == bytes:
      cfg_key = cfg_key.decode('utf-8')
  pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
  post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
  nms_thresh = cfg[cfg_key].RPN_NMS_THRESH

  proposals_total = []
  scores_total = []
  for idx in range(len(rpn_cls_prob)):
    # Get the scores and bounding boxes
    scores = rpn_cls_prob[idx][:, :, :, num_anchors:]
    rpn_bbox_pred[idx] = rpn_bbox_pred[idx].view((-1, 4))
    scores = scores.contiguous().view(-1, 1)
    proposals = bbox_transform_inv(anchors[idx], rpn_bbox_pred[idx])
    proposals = clip_boxes(proposals, im_info[:2])
    
    # Pick the top region proposals
    scores, order = scores.view(-1).sort(descending=True)
    if pre_nms_topN > 0:
      order = order[:pre_nms_topN]
      scores = scores[:pre_nms_topN].view(-1, 1)
    proposals = proposals[order.data, :]

    proposals_total.append(proposals)
    scores_total.append(scores)

  proposals = torch.cat(proposals_total)
  scores = torch.cat(scores_total)

  # Non-maximal suppression
  keep = nms(torch.cat((proposals, scores), 1).data, nms_thresh)

  # Pick th top region proposals after NMS
  if post_nms_topN > 0:
    keep = keep[:post_nms_topN]
  proposals = proposals[keep, :]
  scores = scores[keep,]

  # Only support single image as input
  batch_inds = Variable(proposals.data.new(proposals.size(0), 1).zero_())
  blob = torch.cat((batch_inds, proposals), 1)

  return blob, scores 

# 需要导入模块: from model import config [as 别名]
# 或者: from model.config import cfg [as 别名]
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
    """A simplified version compared to fast/er RCNN
       For details please see the technical report
    """
    if type(cfg_key) == bytes:
        cfg_key = cfg_key.decode('utf-8')
    pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
    post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
    nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
    min_size = cfg[cfg_key].ANCHOR_MIN_SIZE

    # Get the scores and bounding boxes
    scores = rpn_cls_prob[:, :, :, num_anchors:]
    rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
    scores = scores.reshape((-1, 1))
    proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
    proposals = clip_boxes(proposals, im_info[:2])

    # removed predicted boxes with either height or width < threshold
    # (NOTE: convert min_size to input image scale stored in im_info[2])
    # keep = _filter_boxes(proposals, min_size * im_info[2])
    # proposals = proposals[keep, :]
    # scores = scores[keep]

    # Pick the top region proposals
    order = scores.ravel().argsort()[::-1]
    if pre_nms_topN > 0:
        order = order[:pre_nms_topN]
    proposals = proposals[order, :]
    scores = scores[order]

    # Non-maximal suppression
    keep = nms(np.hstack((proposals, scores)), nms_thresh)

    # Pick th top region proposals after NMS
    if post_nms_topN > 0:
        keep = keep[:post_nms_topN]
    proposals = proposals[keep, :]
    scores = scores[keep]

    # Only support single image as input
    batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
    blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))

    return blob, scores