def assign_boxes_to_levels(box_lists, min_level, max_level, canonical_box_size,
canonical_level):
"""
Map each box in `box_lists` to a feature map level index and return the assignment
vector.
Args:
box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
where N is the number of images in the batch.
min_level (int): Smallest feature map level index. The input is considered index 0,
the output of stage 1 is index 1, and so.
max_level (int): Largest feature map level index.
canonical_box_size (int): A canonical box size in pixels (sqrt(box area)).
canonical_level (int): The feature map level index on which a canonically-sized box
should be placed.
Returns:
A tensor of length M, where M is the total number of boxes aggregated over all
N batch images. The memory layout corresponds to the concatenation of boxes
from all images. Each element is the feature map index, as an offset from
`self.min_level`, for the corresponding box (so value i means the box is at
`self.min_level + i`).
"""
eps = sys.float_info.epsilon
box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists]))
# Eqn.(1) in FPN paper
level_assignments = torch.floor(canonical_level +
torch.log2(box_sizes / canonical_box_size +
eps))
# clamp level to (min, max), in case the box size is too large or too small
# for the available feature maps
level_assignments = torch.clamp(level_assignments,
min=min_level,
max=max_level)
return level_assignments.to(torch.int64) - min_level
def __init__(
self,
box2box_transform,
pred_class_logits,
pred_proposal_deltas,
proposals,
smooth_l1_beta,
moco_logits,
moco_labels,
moco_loss_weight,
cls_loss_weight,
):
self.box2box_transform = box2box_transform
self.pred_class_logits = pred_class_logits
self.pred_proposal_deltas = pred_proposal_deltas
self.smooth_l1_beta = smooth_l1_beta
self.moco_logits = moco_logits
self.moco_labels = moco_labels
self.moco_loss_weight = moco_loss_weight
self.cls_loss_weight = cls_loss_weight
self.num_preds_per_image = [len(p) for p in proposals]
box_type = type(proposals[0].proposal_boxes)
# cat(..., dim=0) concatenates over all images in the batch
self.proposals = box_type.cat([p.proposal_boxes for p in proposals])
assert not self.proposals.tensor.requires_grad, "Proposals should not require gradients!"
self.image_shapes = [x.image_size for x in proposals]
# The following fields should exist only when training.
if proposals[0].has("gt_boxes"):
self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals])
assert proposals[0].has("gt_classes")
self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
def cat(instance_lists: List["Instances"]) -> "Instances":
"""
Args:
instance_lists (list[Instances])
Returns:
Instances
"""
assert all(isinstance(i, Instances) for i in instance_lists)
assert len(instance_lists) > 0
if len(instance_lists) == 1:
return instance_lists[0]
image_size = instance_lists[0].image_size
for i in instance_lists[1:]:
assert i.image_size == image_size
ret = Instances(image_size)
for k in instance_lists[0]._fields.keys():
values = [i.get(k) for i in instance_lists]
v0 = values[0]
if isinstance(v0, torch.Tensor):
values = cat(values, dim=0)
elif isinstance(v0, list):
values = list(itertools.chain(*values))
elif hasattr(type(v0), "cat"):
values = type(v0).cat(values)
else:
raise ValueError(
"Unsupported type {} for concatenation".format(type(v0)))
ret.set(k, values)
return ret
def __init__(
self,
box2box_transform,
pred_class_logits,
pred_proposal_deltas,
proposals,
smooth_l1_beta,
box_cls_feat_con,
criterion,
contrast_loss_weight,
queue,
queue_label,
box_reg_weight,
):
"""
Args:
box_cls_feat_con (Tensor): the projected features
to calculate supervised contrastive loss upon
criterion (SupConLoss <- nn.Module): SupConLoss is implemented in fsdet/modeling/contrastive_loss.py
"""
self.box2box_transform = box2box_transform
self.pred_class_logits = pred_class_logits
self.pred_proposal_deltas = pred_proposal_deltas
self.num_preds_per_image = [len(p) for p in proposals]
self.smooth_l1_beta = smooth_l1_beta
self.box_cls_feat_con = box_cls_feat_con
self.criterion = criterion
self.contrast_loss_weight = contrast_loss_weight
self.box_reg_weight = box_reg_weight
self.queue_ = queue
self.queue_label_ = queue_label
box_type = type(proposals[0].proposal_boxes)
# cat(..., dim=0) concatenates over all images in the batch
self.proposals = box_type.cat([p.proposal_boxes for p in proposals
]) # self.proposals = List[Boxes]
assert not self.proposals.tensor.requires_grad, "Proposals should not require gradients!"
self.image_shapes = [x.image_size for x in proposals]
# The following fields should exist only when training.
if proposals[0].has("gt_boxes"):
self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals])
assert proposals[0].has("gt_classes")
self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
self.ious = cat([p.iou for p in proposals], dim=0)
def permute_all_cls_and_box_to_N_HWA_K_and_concat(box_cls, box_delta, num_classes=80):
"""
Rearrange the tensor layout from the network output, i.e.:
list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi)
to per-image predictions, i.e.:
Tensor: of shape (N x sum(Hi x Wi x A), K)
"""
# for each feature level, permute the outputs to make them be in the
# same format as the labels. Note that the labels are computed for
# all feature levels concatenated, so we keep the same representation
# for the objectness and the box_delta
box_cls_flattened = [permute_to_N_HWA_K(x, num_classes) for x in box_cls]
box_delta_flattened = [permute_to_N_HWA_K(x, 4) for x in box_delta]
# concatenate on the first dimension (representing the feature levels), to
# take into account the way the labels were generated (with all feature maps
# being concatenated as well)
box_cls = cat(box_cls_flattened, dim=1).view(-1, num_classes)
box_delta = cat(box_delta_flattened, dim=1).view(-1, 4)
return box_cls, box_delta
def cat(boxes_list: List["Boxes"]) -> "Boxes":
"""
Concatenates a list of Boxes into a single Boxes
Arguments:
boxes_list (list[Boxes])
Returns:
Boxes: the concatenated Boxes
"""
assert isinstance(boxes_list, (list, tuple))
assert len(boxes_list) > 0
assert all(isinstance(box, Boxes) for box in boxes_list)
cat_boxes = type(boxes_list[0])(cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def __init__(
self, box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta
):
"""
Args:
box2box_transform (Box2BoxTransform/Box2BoxTransformRotated):
box2box transform instance for proposal-to-detection transformations.
pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class
logits for all R predicted object instances.
Each row corresponds to a predicted object instance.
pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for
class-specific or class-agnostic regression. It stores the predicted deltas that
transform proposals into final box detections.
B is the box dimension (4 or 5).
When B is 4, each row is [dx, dy, dw, dh (, ....)].
When B is 5, each row is [dx, dy, dw, dh, da (, ....)].
proposals (list[Instances]): A list of N Instances, where Instances i stores the
proposals for image i, in the field "proposal_boxes".
When training, each Instances must have ground-truth labels
stored in the field "gt_classes" and "gt_boxes".
smooth_l1_beta (float): The transition point between L1 and L2 loss in
the smooth L1 loss function. When set to 0, the loss becomes L1. When
set to +inf, the loss becomes constant 0.
"""
self.box2box_transform = box2box_transform
self.num_preds_per_image = [len(p) for p in proposals]
self.pred_class_logits = pred_class_logits
self.pred_proposal_deltas = pred_proposal_deltas
self.smooth_l1_beta = smooth_l1_beta
box_type = type(proposals[0].proposal_boxes)
# cat(..., dim=0) concatenates over all images in the batch
self.proposals = box_type.cat([p.proposal_boxes for p in proposals])
assert not self.proposals.tensor.requires_grad, "Proposals should not require gradients!"
self.image_shapes = [x.image_size for x in proposals]
# The following fields should exist only when training.
if proposals[0].has("gt_boxes"):
self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals])
assert proposals[0].has("gt_classes")
self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
def convert_boxes_to_pooler_format(box_lists):
"""
Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops
(see description under Returns).
Args:
box_lists (list[Boxes] | list[RotatedBoxes]):
A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch.
Returns:
When input is list[Boxes]:
A tensor of shape (M, 5), where M is the total number of boxes aggregated over all
N batch images.
The 5 columns are (batch index, x0, y0, x1, y1), where batch index
is the index in [0, N) identifying which batch image the box with corners at
(x0, y0, x1, y1) comes from.
When input is list[RotatedBoxes]:
A tensor of shape (M, 6), where M is the total number of boxes aggregated over all
N batch images.
The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees),
where batch index is the index in [0, N) identifying which batch image the
rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from.
"""
def fmt_box_list(box_tensor, batch_index):
repeated_index = torch.full((len(box_tensor), 1),
batch_index,
dtype=box_tensor.dtype,
device=box_tensor.device)
return cat((repeated_index, box_tensor), dim=1)
pooler_fmt_boxes = cat([
fmt_box_list(box_list.tensor, i)
for i, box_list in enumerate(box_lists)
],
dim=0)
return pooler_fmt_boxes
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
def losses(self):
"""
Return the losses from a set of RPN predictions and their associated ground-truth.
Returns:
dict[loss name -> loss value]: A dict mapping from loss name to loss value.
Loss names are: `loss_rpn_cls` for objectness classification and
`loss_rpn_loc` for proposal localization.
"""
def resample(label):
"""
Randomly sample a subset of positive and negative examples by overwriting
the label vector to the ignore value (-1) for all elements that are not
included in the sample.
"""
pos_idx, neg_idx = subsample_labels(label,
self.batch_size_per_image,
self.positive_fraction, 0)
# Fill with the ignore label (-1), then set positive and negative labels
label.fill_(-1)
label.scatter_(0, pos_idx, 1)
label.scatter_(0, neg_idx, 0)
return label
gt_objectness_logits, gt_anchor_deltas = self._get_ground_truth()
"""
gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the
total number of anchors in image i (i.e., len(anchors[i]))
gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), B),
where B is the box dimension
"""
# Collect all objectness labels and delta targets over feature maps and images
# The final ordering is L, N, H, W, A from slowest to fastest axis.
num_anchors_per_map = [
np.prod(x.shape[1:]) for x in self.pred_objectness_logits
]
num_anchors_per_image = sum(num_anchors_per_map)
# Stack to: (N, num_anchors_per_image)
gt_objectness_logits = torch.stack(
[resample(label) for label in gt_objectness_logits], dim=0)
# Log the number of positive/negative anchors per-image that's used in training
num_pos_anchors = (gt_objectness_logits == 1).sum().item()
num_neg_anchors = (gt_objectness_logits == 0).sum().item()
storage = get_event_storage()
storage.put_scalar("rpn/num_pos_anchors",
num_pos_anchors / self.num_images)
storage.put_scalar("rpn/num_neg_anchors",
num_neg_anchors / self.num_images)
assert gt_objectness_logits.shape[1] == num_anchors_per_image
# Split to tuple of L tensors, each with shape (N, num_anchors_per_map)
gt_objectness_logits = torch.split(gt_objectness_logits,
num_anchors_per_map,
dim=1)
# Concat from all feature maps
gt_objectness_logits = cat([x.flatten() for x in gt_objectness_logits],
dim=0)
# Stack to: (N, num_anchors_per_image, B)
gt_anchor_deltas = torch.stack(gt_anchor_deltas, dim=0)
assert gt_anchor_deltas.shape[1] == num_anchors_per_image
B = gt_anchor_deltas.shape[2] # box dimension (4 or 5)
# Split to tuple of L tensors, each with shape (N, num_anchors_per_image)
gt_anchor_deltas = torch.split(gt_anchor_deltas,
num_anchors_per_map,
dim=1)
# Concat from all feature maps
gt_anchor_deltas = cat([x.reshape(-1, B) for x in gt_anchor_deltas],
dim=0)
# Collect all objectness logits and delta predictions over feature maps
# and images to arrive at the same shape as the labels and targets
# The final ordering is L, N, H, W, A from slowest to fastest axis.
pred_objectness_logits = cat(
[
# Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N*Hi*Wi*A, )
x.permute(0, 2, 3, 1).flatten()
for x in self.pred_objectness_logits
],
dim=0,
)
pred_anchor_deltas = cat(
[
# Reshape: (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B)
# -> (N*Hi*Wi*A, B)
x.view(x.shape[0], -1, B, x.shape[-2], x.shape[-1]).permute(
0, 3, 4, 1, 2).reshape(-1, B)
for x in self.pred_anchor_deltas
],
dim=0,
)
objectness_loss, localization_loss = rpn_losses(
gt_objectness_logits,
gt_anchor_deltas,
pred_objectness_logits,
pred_anchor_deltas,
self.smooth_l1_beta,
)
normalizer = 1.0 / (self.batch_size_per_image * self.num_images)
loss_cls = objectness_loss * normalizer # cls: classification loss
loss_loc = localization_loss * normalizer # loc: localization loss
losses = {"loss_rpn_cls": loss_cls, "loss_rpn_loc": loss_loc}
return losses
def fmt_box_list(box_tensor, batch_index):
repeated_index = torch.full((len(box_tensor), 1),
batch_index,
dtype=box_tensor.dtype,
device=box_tensor.device)
return cat((repeated_index, box_tensor), dim=1)
def inference_single_image(self, box_cls, box_delta, anchors, image_size):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Arguments:
box_cls (list[Tensor]): list of #feature levels. Each entry contains
tensor of size (H x W x A, K)
box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
anchors (list[Boxes]): list of #feature levels. Each entry contains
a Boxes object, which contains all the anchors for that
image in that feature level.
image_size (tuple(H, W)): a tuple of the image height and width.
Returns:
Same as `inference`, but for only one image.
"""
boxes_all = []
scores_all = []
class_idxs_all = []
# Iterate over every feature level
for box_cls_i, box_reg_i, anchors_i in zip(box_cls, box_delta, anchors):
# (HxWxAxK,)
box_cls_i = box_cls_i.flatten().sigmoid_()
# Keep top k top scoring indices only.
num_topk = min(self.topk_candidates, box_reg_i.size(0))
# torch.sort is actually faster than .topk (at least on GPUs)
predicted_prob, topk_idxs = box_cls_i.sort(descending=True)
predicted_prob = predicted_prob[:num_topk]
topk_idxs = topk_idxs[:num_topk]
# filter out the proposals with low confidence score
keep_idxs = predicted_prob > self.score_threshold
predicted_prob = predicted_prob[keep_idxs]
topk_idxs = topk_idxs[keep_idxs]
anchor_idxs = topk_idxs // self.num_classes
classes_idxs = topk_idxs % self.num_classes
box_reg_i = box_reg_i[anchor_idxs]
anchors_i = anchors_i[anchor_idxs]
# predict boxes
predicted_boxes = self.box2box_transform.apply_deltas(box_reg_i, anchors_i.tensor)
boxes_all.append(predicted_boxes)
scores_all.append(predicted_prob)
class_idxs_all.append(classes_idxs)
boxes_all, scores_all, class_idxs_all = [
cat(x) for x in [boxes_all, scores_all, class_idxs_all]
]
keep = batched_nms(boxes_all, scores_all, class_idxs_all, self.nms_threshold)
keep = keep[: self.max_detections_per_image]
result = Instances(image_size)
result.pred_boxes = Boxes(boxes_all[keep])
result.scores = scores_all[keep]
result.pred_classes = class_idxs_all[keep]
return result
