taotaotao
June 2, 2020, 1:13pm
#1
Hey, I am learning Faster-rcnn by reading the source code,
degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
if degenerate_boxes.any():
# print the first degenrate box
bb_idx = degenerate_boxes.any(dim=1).nonzero().view(-1)[0]
degen_bb: List[float] = boxes[bb_idx].tolist()
raise ValueError("All bounding boxes should have positive height and width."
" Found invaid box {} for target at index {}."
.format(degen_bb, target_idx))
features = self.backbone(images.tensors)
if isinstance(features, torch.Tensor):
features = OrderedDict([('0', features)])
proposals, proposal_losses = self.rpn(images, features, targets)
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
losses = {}
losses.update(detector_losses)
losses.update(proposal_losses)
if torch.jit.is_scripting():
This is passed as a parameter to the self.rpn function on the next line.
and In rpn turn the Order dict into an array
If provided, each element in the dict should contain a field `boxes`,
with the locations of the ground-truth boxes.
Returns:
boxes (List[Tensor]): the predicted boxes from the RPN, one Tensor per
image.
losses (Dict[Tensor]): the losses for the model during training. During
testing, it is an empty dict.
"""
# RPN uses all feature maps that are available
features = list(features.values())
objectness, pred_bbox_deltas = self.head(features)
anchors = self.anchor_generator(images, features)
num_images = len(anchors)
num_anchors_per_level_shape_tensors = [o[0].shape for o in objectness]
num_anchors_per_level = [s[0] * s[1] * s[2] for s in num_anchors_per_level_shape_tensors]
objectness, pred_bbox_deltas = \
concat_box_prediction_layers(objectness, pred_bbox_deltas)
# apply pred_bbox_deltas to anchors to obtain the decoded proposals
# note that we detach the deltas because Faster R-CNN do not backprop through
I think here is a reason, can someone help me ?
I assume the OrderedDict is used to guarantee that the passed features are in the right order.features.values() might reorder the values, which would yield a wrong behavior.
From the Python2 docs :
CPython implementation detail: Keys and values are listed in an arbitrary order which is non-random, varies across Python implementations, and depends on the dictionary’s history of insertions and deletions.
Python 3 docs :
Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end.