def fast_rcnn_inference_single_image( boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image ): """ Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS). Args: Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes per image. Returns: Same as `fast_rcnn_inference`, but for only one image. """ scores = scores[:, :-1] num_bbox_reg_classes = boxes.shape[1] // 4 # Convert to Boxes to use the `clip` function ... boxes = Boxes(boxes.reshape(-1, 4)) boxes.clip(image_shape) boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 # Filter results based on detection scores filter_mask = scores > score_thresh # R x K # R' x 2. First column contains indices of the R predictions; # Second column contains indices of classes. filter_inds = filter_mask.nonzero() if num_bbox_reg_classes == 1: boxes = boxes[filter_inds[:, 0], 0] else: boxes = boxes[filter_mask] scores = scores[filter_mask] # Apply per-class NMS keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) if topk_per_image >= 0: keep = keep[:topk_per_image] boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] result = Instances(image_shape) result.pred_boxes = Boxes(boxes) result.scores = scores result.pred_classes = filter_inds[:, 1] return result, filter_inds[:, 0]
def transform_proposals(dataset_dict, image_shape, transforms, min_box_side_len, proposal_topk): """ Apply transformations to the proposals in dataset_dict, if any. Args: dataset_dict (dict): a dict read from the dataset, possibly contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" image_shape (tuple): height, width transforms (TransformList): min_box_side_len (int): keep proposals with at least this size proposal_topk (int): only keep top-K scoring proposals The input dict is modified in-place, with abovementioned keys removed. A new key "proposals" will be added. Its value is an `Instances` object which contains the transformed proposals in its field "proposal_boxes" and "objectness_logits". """ if "proposal_boxes" in dataset_dict: # Transform proposal boxes boxes = transforms.apply_box( BoxMode.convert( dataset_dict.pop("proposal_boxes"), dataset_dict.pop("proposal_bbox_mode"), BoxMode.XYXY_ABS, )) boxes = Boxes(boxes) objectness_logits = torch.as_tensor( dataset_dict.pop("proposal_objectness_logits").astype("float32")) boxes.clip(image_shape) keep = boxes.nonempty(threshold=min_box_side_len) boxes = boxes[keep] objectness_logits = objectness_logits[keep] proposals = Instances(image_shape) proposals.proposal_boxes = boxes[:proposal_topk] proposals.objectness_logits = objectness_logits[:proposal_topk] dataset_dict["proposals"] = proposals
def find_top_rpn_proposals( proposals, pred_objectness_logits, images, nms_thresh, pre_nms_topk, post_nms_topk, min_box_side_len, training, ): """ For each feature map, select the `pre_nms_topk` highest scoring proposals, apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` highest scoring proposals among all the feature maps if `training` is True, otherwise, returns the highest `post_nms_topk` scoring proposals for each feature map. Args: proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 4). All proposal predictions on the feature maps. pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). images (ImageList): Input images as an :class:`ImageList`. nms_thresh (float): IoU threshold to use for NMS pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. When RPN is run on multiple feature maps (as in FPN) this number is per feature map. post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. When RPN is run on multiple feature maps (as in FPN) this number is total, over all feature maps. min_box_side_len (float): minimum proposal box side length in pixels (absolute units wrt input images). training (bool): True if proposals are to be used in training, otherwise False. This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." comment. Returns: proposals (list[Instances]): list of N Instances. The i-th Instances stores post_nms_topk object proposals for image i. """ image_sizes = images.image_sizes # in (h, w) order num_images = len(image_sizes) device = proposals[0].device # 1. Select top-k anchor for every level and every image topk_scores = [] # #lvl Tensor, each of shape N x topk topk_proposals = [] level_ids = [] # #lvl Tensor, each of shape (topk,) batch_idx = torch.arange(num_images, device=device) for level_id, proposals_i, logits_i in zip(itertools.count(), proposals, pred_objectness_logits): Hi_Wi_A = logits_i.shape[1] num_proposals_i = min(pre_nms_topk, Hi_Wi_A) # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812) # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) logits_i, idx = logits_i.sort(descending=True, dim=1) topk_scores_i = logits_i[batch_idx, :num_proposals_i] topk_idx = idx[batch_idx, :num_proposals_i] # each is N x topk topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 topk_proposals.append(topk_proposals_i) topk_scores.append(topk_scores_i) level_ids.append( torch.full((num_proposals_i, ), level_id, dtype=torch.int64, device=device)) # 2. Concat all levels together topk_scores = cat(topk_scores, dim=1) topk_proposals = cat(topk_proposals, dim=1) level_ids = cat(level_ids, dim=0) # 3. For each image, run a per-level NMS, and choose topk results. results = [] for n, image_size in enumerate(image_sizes): boxes = Boxes(topk_proposals[n]) scores_per_img = topk_scores[n] boxes.clip(image_size) # filter empty boxes keep = boxes.nonempty(threshold=min_box_side_len) lvl = level_ids if keep.sum().item() != len(boxes): boxes, scores_per_img, lvl = boxes[keep], scores_per_img[ keep], level_ids[keep] keep = batched_nms(boxes.tensor, scores_per_img, lvl, nms_thresh) # In Detectron1, there was different behavior during training vs. testing. # (https://github.com/facebookresearch/Detectron/issues/459) # During training, topk is over the proposals from *all* images in the training batch. # During testing, it is over the proposals for each image separately. # As a result, the training behavior becomes batch-dependent, # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. # This bug is addressed in Detectron2 to make the behavior independent of batch size. keep = keep[:post_nms_topk] res = Instances(image_size) res.proposal_boxes = boxes[keep] res.objectness_logits = scores_per_img[keep] results.append(res) return results