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 trend_rcnn_inference_single_image(boxes, scores, attributes, image_shape,
score_thresh, nms_thresh, topk_per_image,
attr_score_thresh, num_attr_classes,
max_attr_pred):
"""
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]
attributes = attributes[valid_mask]
scores = scores[:, :-1]
num_bbox_reg_classes = boxes.shape[1] // 4
#print("Printing the number of classes in the box: ", num_bbox_reg_classes)
# 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
num_attr_reg_classes = attributes.shape[1] // num_attr_classes
# [ANMOL] this just prints the number of object classes that we have... here its 46
attributes = attributes.view(-1, num_attr_reg_classes, num_attr_classes)
# [ANMOL] reshaped the attributes [proposals, objectclass, attrclass]
# Filter results based on detection scores
filter_mask = scores > score_thresh # R x K
# filter mask shape is same as score shape: [proposals, obj classes]
# R' x 2. First column contains indices of the R predictions;
# Second column contains indices of classes.
filter_inds = filter_mask.nonzero()
# there would be more indices/proposals after this compared as more number of scores might be >
# greater than threshold would be interesting to check how it would work class agnostic attr classification
# might fail there.. In the current example: R=1000, but R'=45806
#print("filter ind shape: ", filter_inds.shape)
if num_bbox_reg_classes == 1:
boxes = boxes[filter_inds[:, 0], 0]
else:
boxes = boxes[filter_mask]
scores = scores[filter_mask]
#before this scores shape was [R,num_classes], after filter mask it will just convert to [R']
if num_attr_reg_classes == 1:
attributes = attributes[filter_inds[:, 0], 0]
else:
attributes = attributes[filter_mask]
#BOTH of these should produce attribute of shape [R', attr_classes]
# 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, attributes = boxes[keep], scores[
keep], filter_inds[keep], attributes[keep]
attributes[attributes < attr_score_thresh] = 0
attr_scores_sorted, attr_indices = torch.sort(attributes,
1,
descending=True)
attr_indices[attr_scores_sorted < attr_score_thresh] = 294
attributes_inds = attr_indices[:, 0:max_attr_pred]
#del attr_indices
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.attr_scores = attributes
result.attr_classes = attributes_inds
result.pred_classes = filter_inds[:, 1]
return result, filter_inds[:, 0]
