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