def postprocess(self, outputs, images, image_ids, to_cpu):
frames = []
for instances, image, image_id in zip(outputs, images, image_ids):
height, width = image.shape[:2]
instances = detector_postprocess(instances, height, width)
type_valid = [
self.model_meta.thing_classes[pred_class] in TYPE_MAPPING
for pred_class in instances.pred_classes]
instances = instances[type_valid]
instances.pred_classes = torch.as_tensor([
TYPE_MAPPING[self.model_meta.thing_classes[pred_class]]
for pred_class in instances.pred_classes])
if len(instances) > 0:
nms_mapping = torch.as_tensor([
NMS_MAPPING[pred_class.item()]
for pred_class in instances.pred_classes],
dtype=torch.int, device=self.device)
nms_types = nms_mapping[:, 0]
nms_scores = instances.scores + nms_mapping[:, 1]
keep_indices = batched_nms(
instances.pred_boxes.tensor, nms_scores, nms_types,
self.nms_threshold)
instances = instances[keep_indices]
features = instances.roi_features.mean(dim=(2, 3))
features = features / features.norm(dim=1, keepdim=True)
instances.roi_features = features
if to_cpu:
instances = instances.to('cpu')
frame = Frame(image_id, image, instances)
frames.append(frame)
return frames
def ml_nms(boxlist,
nms_thresh,
max_proposals=-1,
score_field="scores",
label_field="labels"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Args:
boxlist (detectron2.structures.Boxes):
nms_thresh (float):
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maximum suppression
score_field (str):
"""
if nms_thresh <= 0:
return boxlist
boxes = boxlist.pred_boxes.tensor
scores = boxlist.scores
labels = boxlist.pred_classes
keep = batched_nms(boxes, scores, labels, nms_thresh)
if max_proposals > 0:
keep = keep[:max_proposals]
boxlist = boxlist[keep]
return boxlist
def inference_single_image(self, anchors, box_cls, box_delta, image_size):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Arguments:
anchors (list[Boxes]): list of #feature levels. Each entry contains
a Boxes object, which contains all the anchors in that feature level.
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.
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
def _get_class_predictions(self, boxes, scores, image_shape):
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 > self.class_score_thresh_test
# R' x 2. First column contains indices of the R predictions;
# Second column contains indices of classes.
class_inds = filter_mask.nonzero()
if num_bbox_reg_classes == 1:
boxes = boxes[class_inds[:, 0], 0]
else:
boxes = boxes[filter_mask]
scores = scores[filter_mask]
# Apply per-class NMS
keep_class = batched_nms(boxes, scores, class_inds[:, 1],
self.class_nms_thresh_test)
if self.topk_per_image_test >= 0:
keep_class = keep_class[:self.topk_per_image_test]
boxes, scores, class_inds = boxes[keep_class], scores[
keep_class], class_inds[keep_class]
return boxes, scores, class_inds
def test_batched_nms_rotated_0_degree_cuda(self):
N = 2000
num_classes = 50
boxes, scores = self._create_tensors(N)
idxs = torch.randint(0, num_classes, (N, ))
rotated_boxes = torch.zeros(N, 5)
rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}"
for iou in [0.2, 0.5, 0.8]:
backup = boxes.clone()
keep_ref = batched_nms(boxes.cuda(), scores.cuda(), idxs, iou)
self.assertTrue(torch.allclose(boxes, backup),
"boxes modified by batched_nms")
backup = rotated_boxes.clone()
keep = batched_nms_rotated(rotated_boxes.cuda(), scores.cuda(),
idxs, iou)
self.assertTrue(
torch.allclose(rotated_boxes, backup),
"rotated_boxes modified by batched_nms_rotated",
)
self.assertLessEqual(nms_edit_distance(keep, keep_ref), 2,
err_msg.format(iou))
def inference_single_image(
self,
anchors: List[Boxes],
box_cls: List[Tensor],
box_delta: List[Tensor],
image_size: Tuple[int, int],
):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Arguments:
anchors (list[Boxes]): list of #feature levels. Each entry contains
a Boxes object, which contains all the anchors in that feature level.
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.
image_size (tuple(H, W)): a tuple of the image height and width.
Returns:
Same as `inference`, but for only one image.
"""
pred = self._decode_multi_level_predictions(
anchors,
box_cls,
box_delta,
self.test_score_thresh,
self.test_topk_candidates,
image_size,
)
keep = batched_nms( # per-class NMS
pred.pred_boxes.tensor, pred.scores, pred.pred_classes,
self.test_nms_thresh)
return pred[keep[:self.max_detections_per_image]]
def ml_nms(boxlist,
nms_thresh,
max_proposals=-1,
score_field="scores",
label_field="labels"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maximum suppression
score_field (str)
"""
if nms_thresh <= 0:
return boxlist
if boxlist.has('pred_boxes'):
boxes = boxlist.pred_boxes.tensor
labels = boxlist.pred_classes
else:
boxes = boxlist.proposal_boxes.tensor
labels = boxlist.proposal_boxes.tensor.new_zeros(
len(boxlist.proposal_boxes.tensor))
scores = boxlist.scores
keep = batched_nms(boxes, scores, labels, nms_thresh)
if max_proposals > 0:
keep = keep[:max_proposals]
boxlist = boxlist[keep]
return boxlist
def fast_rcnn_inference_single_image(boxes,
scores,
image_shape,
score_thresh,
nms_thresh,
topk_per_image,
light=None):
"""
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
if type(light) == type(boxes):
# print(light)
light = Boxes(light.reshape(-1, 4))
# light.clip(image_shape)
light = light.tensor.view(-1, num_bbox_reg_classes, 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]
if type(light) == type(boxes):
light = light[filter_inds[:, 0], 0]
else:
boxes = boxes[filter_mask]
if type(light) == type(boxes):
light = light[filter_mask]
scores = scores[filter_mask]
# Apply per-class NMS
keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
keep = keep[:topk_per_image]
boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
if type(light) == type(boxes):
light = light[keep]
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.pred_classes = filter_inds[:, 1]
if type(light) == type(boxes):
result.pred_light = Boxes(light)
return result, filter_inds[:, 0]
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.
"""
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(
dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
scores = scores[:, :-1]
Tscores = scores
#print (scores)
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]
#print (scores)
# Apply per-class NMS
uniclass = torch.zeros(len(filter_inds[:, 1].tolist())).cuda()
#keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
keep = batched_nms(boxes, scores, uniclass, nms_thresh)
if topk_per_image >= 0:
keep = keep[:topk_per_image]
boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
#print(filter_inds[:, 0])
#print(torch.ByteTensor([0,1,0,0,1]))
#print(filter_inds[:, 1])
#print(keep)
#print(Tscores[filter_inds[:, 0]])
#print (scores)
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
#result.scores = scores
result.scores = Tscores[filter_inds[:, 0]]
result.pred_classes = filter_inds[:, 1]
return result, filter_inds[:, 0]
def fast_rcnn_inference_single_image(boxes, scores, attr_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.
"""
# Make sure boxes and scores don't contain infinite or Nan
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) \
& torch.isfinite(attr_scores).all(dim=1)
# Get scores from finite boxes and scores
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
attr_scores = attr_scores[valid_mask]
scores = scores[:, :-1] # Remove background class?
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
# If using Attributes class:
# attributes = Attributes(attributes.reshape(-1, 295))
# attributes = attributes.tensor.view(-1, num_bbox_reg_classes, 295)
# 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, attr_scores, filter_inds, = boxes[keep], scores[
keep], attr_scores[keep], filter_inds[keep]
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.attr_scores = attr_scores
result.pred_classes = filter_inds[:, 1]
return result, filter_inds[:, 0]
def inference_single_image(self, conf_pred_per_image, loc_pred_per_image,
default_boxes, image_size):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Args:
conf_pred_per_image (list[Tensor]): list of #feature levels. Each entry contains
tensor of size [Hi x Wi x D, C].
loc_pred_per_image (list[Tensor]): same shape as 'conf_pred_per_image' except
that C becomes 4.
default_boxes (list['Boxes']): a list of 'Boxes' elements.
The Boxes contains default boxes of one image on the specific feature level.
image_size (tuple(H, W)): a tuple of the image height and width.
Returns:
Same as `inference`, but for only one image.
"""
# predict confidence
conf_pred = torch.cat(conf_pred_per_image, dim=0) # [R, C]
conf_pred = conf_pred.softmax(dim=1)
# predict boxes
loc_pred = torch.cat(loc_pred_per_image, dim=0) # [R, 4]
default_boxes = Boxes.cat(default_boxes) # [R, 4]
boxes_pred = self.box2box_transform.apply_deltas(
loc_pred, default_boxes.tensor)
num_boxes, num_classes = conf_pred.shape
boxes_pred = boxes_pred.view(num_boxes, 1,
4).expand(num_boxes, num_classes,
4) # [R, C, 4]
labels = torch.arange(num_classes, device=self.device) # [0, ..., C]
labels = labels.view(1, num_classes).expand_as(conf_pred) # [R, C]
# remove predictions with the background label
boxes_pred = boxes_pred[:, :-1]
conf_pred = conf_pred[:, :-1]
labels = labels[:, :-1]
# batch everything, by making every class prediction be a separate instance
boxes_pred = boxes_pred.reshape(-1, 4)
conf_pred = conf_pred.reshape(-1)
labels = labels.reshape(-1)
# remove low scoring boxes
indices = torch.nonzero(conf_pred > self.score_threshold).squeeze(1)
boxes_pred, conf_pred, labels = boxes_pred[indices], conf_pred[
indices], labels[indices]
keep = batched_nms(boxes_pred, conf_pred, labels, self.nms_threshold)
keep = keep[:self.max_detections_per_image]
result = Instances(image_size)
result.pred_boxes = Boxes(boxes_pred[keep])
result.scores = conf_pred[keep]
result.pred_classes = labels[keep]
return result
def fast_rcnn_inference_single_image(
boxes,
scores,
image_shape: Tuple[int, int],
score_thresh: float,
nms_thresh: float,
topk_per_image: int,
):
"""
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.
"""
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
scores = scores[:, :-1]
if len(category_disabler.global_cat_mask) > 0:
print('<<<<<< category disabler activated >>>>>>')
scores *= torch.tensor(category_disabler.global_cat_mask).cuda()
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
# 1. Filter results based on detection scores. It can make NMS more efficient
# by filtering out low-confidence detections.
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]
# 2. Apply NMS for each class independently.
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 inference_single_image(self, locations, box_cls, box_reg, image_size):
boxes_all = []
scores_all = []
class_idxs_all = []
# Iterate over every feature level
for box_cls_i, box_reg_i, locs_i in zip(box_cls, box_reg, locations):
# (HxW, C)
box_cls_i = box_cls_i.sigmoid_()
keep_idxs = box_cls_i > self.pre_nms_thresh
box_cls_i = box_cls_i[keep_idxs]
keep_idxs_nonzero_i = keep_idxs.nonzero()
box_loc_i = keep_idxs_nonzero_i[:, 0]
class_i = keep_idxs_nonzero_i[:, 1]
box_reg_i = box_reg_i[box_loc_i]
locs_i = locs_i[box_loc_i]
per_pre_nms_top_n = keep_idxs.sum().clamp(max=self.pre_nms_top_n)
if keep_idxs.sum().item() > per_pre_nms_top_n.item():
box_cls_i, topk_idxs = box_cls_i.topk(per_pre_nms_top_n,
sorted=False)
class_i = class_i[topk_idxs]
box_reg_i = box_reg_i[topk_idxs]
locs_i = locs_i[topk_idxs]
# predict boxes
predicted_boxes = torch.stack([
locs_i[:, 0] - box_reg_i[:, 0],
locs_i[:, 1] - box_reg_i[:, 1],
locs_i[:, 0] + box_reg_i[:, 2],
locs_i[:, 1] + box_reg_i[:, 3],
],
dim=1)
box_cls_i = torch.sqrt(box_cls_i)
boxes_all.append(predicted_boxes)
scores_all.append(box_cls_i)
class_idxs_all.append(class_i)
boxes_all, scores_all, class_idxs_all = [
cat(x) for x in [boxes_all, scores_all, class_idxs_all]
]
# Apply per-class nms for each image
keep = batched_nms(boxes_all, scores_all, class_idxs_all,
self.nms_thresh)
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
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.
"""
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
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
# 1. Filter results based on detection scores. It can make NMS more efficient
# by filtering out low-confidence detections.
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]
# 2. Apply NMS for each class independently.
keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
# DIOU NMS commented for now
# keep = batched_diou_nms(boxes, scores, filter_inds[:, 1], nms_thresh) \
# if global_cfg.MODEL.ROI_BOX_HEAD.NMS_TYPE == "diou_nms" \
# else \
# 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 fast_rcnn_inference_single_image(
boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image, fc_box_features=None,
):
"""
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.
"""
probs = scores.clone().detach()
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]
# Compact all fc layers into a single tensor to work nicely with Instance class for now
if fc_box_features is not None:
fc_box_features = [fc_layer_box_features[filter_inds[:, 0]] for fc_layer_box_features in fc_box_features]
# will need to know number of layers and dimensions to unpack
fc_box_features = torch.cat(fc_box_features, dim=1)
result.fc_box_features = fc_box_features
probs = probs[filter_inds[:, 0]]
result.probs = probs
return result, filter_inds[:, 0]
def softmax_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.
"""
# print("boxes:", boxes.shape, boxes[0, :4])
# print("scores:", scores.shape, torch.sum(scores[0,:]))
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
#remove bg from scores
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]
old_scores = scores[filter_inds[:, 0], :]
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, old_scores, scores, filter_inds = boxes[keep], old_scores[keep], scores[keep], filter_inds[keep]
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.pred_classes = filter_inds[:, 1]
result.softmax_scores = old_scores
return result, filter_inds[:, 0]
def get_pgt_mist(self,
prev_pred_boxes,
prev_pred_scores,
proposals,
top_pro=0.15,
suffix=""):
pgt_scores, pgt_boxes, pgt_classes, pgt_weights = self.get_pgt_top_k(
prev_pred_boxes,
prev_pred_scores,
proposals,
top_k=top_pro,
# thres=0.05,
thres=0.0,
need_instance=False,
need_weight=True,
suffix=suffix,
)
# NMS
pgt_idxs = [torch.zeros_like(pgt_class) for pgt_class in pgt_classes]
keeps = [
batched_nms(pgt_box, pgt_score, pgt_class,
0.2) for pgt_box, pgt_score, pgt_class in zip(
pgt_boxes, pgt_scores, pgt_idxs)
]
pgt_scores = [
pgt_score[keep] for pgt_score, keep in zip(pgt_scores, keeps)
]
pgt_boxes = [pgt_box[keep] for pgt_box, keep in zip(pgt_boxes, keeps)]
pgt_classes = [
pgt_class[keep] for pgt_class, keep in zip(pgt_classes, keeps)
]
pgt_weights = [
pgt_weight[keep] for pgt_weight, keep in zip(pgt_weights, keeps)
]
pgt_boxes = [Boxes(pgt_box) for pgt_box in pgt_boxes]
targets = [
Instances(
proposals[i].image_size,
gt_boxes=pgt_box,
gt_classes=pgt_class,
gt_scores=pgt_score,
gt_weights=pgt_weight,
) for i, (pgt_box, pgt_class, pgt_score, pgt_weight) in enumerate(
zip(pgt_boxes, pgt_classes, pgt_scores, pgt_scores))
]
self._vis_pgt(targets, "pgt_mist", suffix)
return targets
def measure_nms_perf(boxes_shape, scores_shape, levels_shape, threshold):
"""
Args:
"""
assert len(boxes_shape) == 2
assert len(scores_shape) == 1
assert len(levels_shape) == 1
assert boxes_shape[0] == scores_shape[0]
assert boxes_shape[0] == levels_shape[0]
# Preparing Inputs
# (0,1100) range chosen based on boxes observed in runs of detectron.
boxes = torch.FloatTensor(boxes_shape[0], boxes_shape[1]).uniform_(0, 1100)
# creating a random distribution between [-0.8, 0.8)
scores_per_img = 1.6 * torch.rand(scores_shape, dtype=torch.float) - 0.8
if levels_shape[0] > 8000:
# max lvl value = 4. First 2000 entries: 0, next 2000 entries: 1, ... : 3, remaining entries: 4
lvl = torch.tensor(np.array([i / 2000
for i in range(levels_shape[0])]),
dtype=torch.long)
else:
# overdoing simple things
lower_bound = levels_shape[0] // 5
upper_bound = levels_shape[0] // 4
np_lvl = []
count = 0
for lvl in range(4):
tmp_shape = (random.randint(lower_bound, upper_bound))
tmp = np.full(tmp_shape, lvl, dtype=int)
#tmp = np.array([lvl for i in range(random.randint(lower_bound, upper_bound))])
np_lvl.append(tmp)
count += len(tmp)
#np_lvl.append(np.array([4 for _ in range(levels_shape - count)]))
np_lvl.append(np.full(levels_shape[0] - count, 4, dtype=int))
lvl = torch.tensor(np.concatenate(np_lvl), dtype=torch.long)
assert lvl.shape == levels_shape, "ensure lvl shape is correct"
boxes = boxes.cuda()
scores_per_img = scores_per_img.cuda()
lvl = lvl.cuda()
# Forward Pass
# warmup - 2 iters
batched_nms(boxes, scores_per_img, lvl, threshold)
batched_nms(boxes, scores_per_img, lvl, threshold)
torch.cuda.synchronize()
start = time.time()
for _ in range(ITERATIONS):
batched_nms(boxes, scores_per_img, lvl, threshold)
torch.cuda.synchronize()
end = time.time()
fwd_time = (end - start) * 1000 / ITERATIONS
return fwd_time
def test_nms_scriptability(self):
N = 2000
num_classes = 50
boxes, scores = self._create_tensors(N)
idxs = torch.randint(0, num_classes, (N,))
scripted_batched_nms = torch.jit.script(batched_nms)
err_msg = "NMS is incompatible with jit-scripted NMS for IoU={}"
for iou in [0.2, 0.5, 0.8]:
keep_ref = batched_nms(boxes, scores, idxs, iou)
backup = boxes.clone()
scripted_keep = scripted_batched_nms(boxes, scores, idxs, iou)
assert torch.allclose(boxes, backup), "boxes modified by jit-scripted batched_nms"
self.assertTrue(torch.equal(keep_ref, scripted_keep), err_msg.format(iou))
def fast_rcnn_inference_single_image(image_shape,
boxes,
scores,
classes=None,
score_thresh=0.05,
nms_thresh=0.5,
topk_per_image=1000):
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(
dim=1)
replace_cls = classes is not None
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
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]
if replace_cls:
classes = classes[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
if replace_cls:
result.pred_classes = classes[keep]
else:
result.pred_classes = filter_inds[:, 1]
return result, filter_inds[:, 0]
def inference(self, _box_cls, _box_pred, image_sizes):
"""
Arguments:
box_cls (Tensor): tensor of shape (batch_size, K, H, W).
box_pred (Tensor): tensors of shape (batch_size, 4, H, W) .
image_sizes (List[torch.Size]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
box_cls = _box_cls.flatten(2)
box_pred = _box_pred.flatten(2)
assert len(box_cls) == len(image_sizes)
results = []
scores = torch.sigmoid(box_cls)
for i, (scores_per_image, box_pred_per_image, image_size) in enumerate(zip(
scores, box_pred, image_sizes
)):
result = Instances(image_size)
# refer to https://github.com/FateScript/CenterNet-better
topk_score_cat, topk_inds_cat = torch.topk(scores_per_image, k=self.num_boxes)
topk_score, topk_inds = torch.topk(topk_score_cat.reshape(-1), k=self.num_boxes)
topk_clses = topk_inds // self.num_boxes
scores_per_image = topk_score
labels_per_image = topk_clses
topk_box_cat = box_pred_per_image[:, topk_inds_cat.reshape(-1)]
topk_box = topk_box_cat[:, topk_inds]
box_pred_per_image = topk_box.transpose(0, 1)
if self.nms:
keep = batched_nms(box_pred_per_image,
scores_per_image,
labels_per_image,
0.5)
box_pred_per_image = box_pred_per_image[keep]
scores_per_image = scores_per_image[keep]
labels_per_image = labels_per_image[keep]
result.pred_boxes = Boxes(box_pred_per_image)
result.scores = scores_per_image
result.pred_classes = labels_per_image
results.append(result)
return results
def inference_single_image(self, logits, init_boxes, refine_boxes,
image_size):
boxes_all = []
init_boxes_all = []
class_idxs_all = []
scores_all = []
for logit, init_box, refine_box in zip(logits, init_boxes,
refine_boxes):
scores, cls = logit.sigmoid().max(0)
cls = cls.view(-1)
scores = scores.view(-1)
init_box = init_box.view(4, -1).permute(1, 0)
refine_box = refine_box.view(4, -1).permute(1, 0)
predicted_prob, topk_idxs = scores.sort(descending=True)
num_topk = min(self.topk_candidates, cls.size(0))
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]
init_box_topk = init_box[topk_idxs]
refine_box_topk = refine_box[topk_idxs]
cls_topk = cls[topk_idxs]
score_topk = scores[topk_idxs]
boxes_all.append(refine_box_topk)
init_boxes_all.append(init_box_topk)
class_idxs_all.append(cls_topk)
scores_all.append(score_topk)
boxes_all, scores_all, class_idxs_all, init_boxes_all = [
cat(x)
for x in [boxes_all, scores_all, class_idxs_all, init_boxes_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]
result.init_boxes = init_boxes_all[keep]
return result
def postprocess_single_image(self, box_cls, box_delta):
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,
self.anchors):
box_cls_i = box_cls_i.flatten() # (HxWxAxK,)
# 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]
predicted_boxes = self.box2box_transform.apply_deltas(
box_reg_i, anchors_i)
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(self.image_size)
result.pred_boxes = Boxes(boxes_all[keep])
result.scores = scores_all[keep]
result.pred_classes = class_idxs_all[keep]
return result
def inference_single_image(
self,
anchors: List[Boxes],
box_cls: List[torch.Tensor],
box_delta: List[torch.Tensor],
image_size: Tuple[int, int],
):
"""
Identical to :meth:`RetinaNet.inference_single_image.
"""
pred = self._decode_multi_level_predictions(
anchors,
box_cls,
box_delta,
self.test_score_thresh,
self.test_topk_candidates,
image_size,
)
keep = batched_nms(pred.pred_boxes.tensor, pred.scores,
pred.pred_classes, self.test_nms_thresh)
return pred[keep[:self.max_detections_per_image]]
def select_over_all_levels(self, instances, image_sizes):
results = []
for instance in instances:
# multiclass nms
keep = batched_nms(instance.proposal_boxes.tensor, instance.objectness_logits, instance.labels.float(), self.nms_thresh)
instance = instance[keep]
cls_scores = instance.objectness_logits
number_of_detections = len(cls_scores)
# Limit to max_per_image detections **over all classes**
if number_of_detections > self.fpn_post_nms_top_n > 0:
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(),
number_of_detections - self.fpn_post_nms_top_n + 1
)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
instance = instance[keep]
instance.remove("labels")
results.append(instance)
return results
def general_standard_nms_postprocessing(input_im,
outputs,
nms_threshold=0.5,
max_detections_per_image=100):
"""
Args:
input_im (list): an input im list generated from dataset handler.
outputs (list): output list form model specific inference function
nms_threshold (float): non-maximum suppression threshold
max_detections_per_image (int): maximum allowed number of detections per image.
Returns:
result (Instances): final results after nms
"""
predicted_boxes, predicted_boxes_covariance, predicted_prob, classes_idxs, predicted_prob_vectors = outputs
# Perform nms
keep = batched_nms(predicted_boxes, predicted_prob, classes_idxs,
nms_threshold)
keep = keep[:max_detections_per_image]
# Keep highest scoring results
result = Instances(
(input_im[0]['image'].shape[1], input_im[0]['image'].shape[2]))
result.pred_boxes = Boxes(predicted_boxes[keep])
result.scores = predicted_prob[keep]
result.pred_classes = classes_idxs[keep]
result.pred_cls_probs = predicted_prob_vectors[keep]
# Handle case where there is no covariance matrix such as classical
# inference.
if isinstance(predicted_boxes_covariance, torch.Tensor):
result.pred_boxes_covariance = predicted_boxes_covariance[keep]
else:
result.pred_boxes_covariance = torch.zeros(
predicted_boxes[keep].shape + (4, )).to(device)
return result
def test_batched_nms_rotated_0_degree_cpu(self, device="cpu"):
N = 2000
num_classes = 50
boxes, scores = self._create_tensors(N, device=device)
idxs = torch.randint(0, num_classes, (N,))
rotated_boxes = torch.zeros(N, 5, device=device)
rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}"
for iou in [0.2, 0.5, 0.8]:
backup = boxes.clone()
keep_ref = batched_nms(boxes, scores, idxs, iou)
assert torch.allclose(boxes, backup), "boxes modified by batched_nms"
backup = rotated_boxes.clone()
keep = batched_nms_rotated(rotated_boxes, scores, idxs, iou)
assert torch.allclose(
rotated_boxes, backup
), "rotated_boxes modified by batched_nms_rotated"
# Occasionally the gap can be large if there are many IOU on the threshold boundary
self.assertLessEqual(nms_edit_distance(keep, keep_ref), 5, err_msg.format(iou))
def postprocess(self, instances, batched_inputs, image_sizes):
"""
Rescale the output instances to the target size.
"""
# note: private function; subject to changes
processed_results = []
for results_per_image, input_per_image, image_size in zip(
instances, batched_inputs, image_sizes):
boxes = results_per_image.pred_boxes.tensor
scores = results_per_image.scores
class_idxs = results_per_image.pred_classes
# Apply per-class nms for each image
keep = batched_nms(boxes, scores, class_idxs, self.nms_thresh)
keep = keep[:self.max_detections_per_image]
results_per_image = results_per_image[keep]
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
def test_batched_nms_rotated_0_degree_cpu(self):
# torch.manual_seed(0)
N = 2000
num_classes = 50
boxes, scores = self._create_tensors(N)
idxs = torch.randint(0, num_classes, (N, ))
rotated_boxes = torch.zeros(N, 5)
rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}"
for iou in [0.2, 0.5, 0.8]:
backup = boxes.clone()
keep_ref = batched_nms(boxes, scores, idxs, iou)
assert torch.allclose(boxes,
backup), "boxes modified by batched_nms"
backup = rotated_boxes.clone()
keep = batched_nms_rotated(rotated_boxes, scores, idxs, iou)
assert torch.allclose(
rotated_boxes,
backup), "rotated_boxes modified by batched_nms_rotated"
assert torch.equal(keep, keep_ref), err_msg.format(iou)
def merge_branch_instances(instances, num_branch, nms_thrsh, topk_per_image):
"""
Merge detection results from different branches of TridentNet.
Return detection results by applying non-maximum suppression (NMS) on bounding boxes
and keep the unsuppressed boxes and other instances (e.g mask) if any.
Args:
instances (list[Instances]): A list of N * num_branch instances that store detection
results. Contain N images and each image has num_branch instances.
num_branch (int): Number of branches used for merging detection results for each image.
nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1].
topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
all detections.
Returns:
results: (list[Instances]): A list of N instances, one for each image in the batch,
that stores the topk most confidence detections after merging results from multiple
branches.
"""
if num_branch == 1:
return instances
batch_size = len(instances) // num_branch
results = []
for i in range(batch_size):
instance = Instances.cat(
[instances[i + batch_size * j] for j in range(num_branch)])
# Apply per-class NMS
keep = batched_nms(instance.pred_boxes.tensor, instance.scores,
instance.pred_classes, nms_thrsh)
keep = keep[:topk_per_image]
result = instance[keep]
results.append(result)
return results
