from typing import Dict, List, Optional, Tuple
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from torchvision.ops import boxes as box_ops, Conv2dNormActivation
from . import _utils as det_utils
# Import AnchorGenerator to keep compatibility.
from .anchor_utils import AnchorGenerator # noqa: 401
from .image_list import ImageList
class RPNHead(nn.Module):
"""
Adds a simple RPN Head with classification and regression heads
Args:
in_channels (int): number of channels of the input feature
num_anchors (int): number of anchors to be predicted
conv_depth (int, optional): number of convolutions
"""
_version = 2
def __init__(self, in_channels: int, num_anchors: int, conv_depth=1) -> None:
super().__init__()
convs = []
for _ in range(conv_depth):
convs.append(Conv2dNormActivation(in_channels, in_channels, kernel_size=3, norm_layer=None))
self.conv = nn.Sequential(*convs)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1)
for layer in self.modules():
if isinstance(layer, nn.Conv2d):
torch.nn.init.normal_(layer.weight, std=0.01) # type: ignore[arg-type]
if layer.bias is not None:
torch.nn.init.constant_(layer.bias, 0) # type: ignore[arg-type]
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
version = local_metadata.get("version", None)
if version is None or version < 2:
for type in ["weight", "bias"]:
old_key = f"{prefix}conv.{type}"
new_key = f"{prefix}conv.0.0.{type}"
if old_key in state_dict:
state_dict[new_key] = state_dict.pop(old_key)
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
def forward(self, x: List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]:
logits = []
bbox_reg = []
for feature in x:
t = self.conv(feature)
logits.append(self.cls_logits(t))
bbox_reg.append(self.bbox_pred(t))
return logits, bbox_reg
def permute_and_flatten(layer: Tensor, N: int, A: int, C: int, H: int, W: int) -> Tensor:
layer = layer.view(N, -1, C, H, W)
layer = layer.permute(0, 3, 4, 1, 2)
layer = layer.reshape(N, -1, C)
return layer
def concat_box_prediction_layers(box_cls: List[Tensor], box_regression: List[Tensor]) -> Tuple[Tensor, Tensor]:
box_cls_flattened = []
box_regression_flattened = []
# 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_regression
for box_cls_per_level, box_regression_per_level in zip(box_cls, box_regression):
N, AxC, H, W = box_cls_per_level.shape
Ax4 = box_regression_per_level.shape[1]
A = Ax4 // 4
C = AxC // A
box_cls_per_level = permute_and_flatten(box_cls_per_level, N, A, C, H, W)
box_cls_flattened.append(box_cls_per_level)
box_regression_per_level = permute_and_flatten(box_regression_per_level, N, A, 4, H, W)
box_regression_flattened.append(box_regression_per_level)
# 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 = torch.cat(box_cls_flattened, dim=1).flatten(0, -2)
box_regression = torch.cat(box_regression_flattened, dim=1).reshape(-1, 4)
return box_cls, box_regression
class RegionProposalNetwork(torch.nn.Module):
"""
Implements Region Proposal Network (RPN).
Args:
anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
maps.
head (nn.Module): module that computes the objectness and regression deltas
fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
considered as positive during training of the RPN.
bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
considered as negative during training of the RPN.
batch_size_per_image (int): number of anchors that are sampled during training of the RPN
for computing the loss
positive_fraction (float): proportion of positive anchors in a mini-batch during training
of the RPN
pre_nms_top_n (Dict[str, int]): number of proposals to keep before applying NMS. It should
contain two fields: training and testing, to allow for different values depending
on training or evaluation
post_nms_top_n (Dict[str, int]): number of proposals to keep after applying NMS. It should
contain two fields: training and testing, to allow for different values depending
on training or evaluation
nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
"""
__annotations__ = {
"box_coder": det_utils.BoxCoder,
"proposal_matcher": det_utils.Matcher,
"fg_bg_sampler": det_utils.BalancedPositiveNegativeSampler,
}
def __init__(
self,
anchor_generator: AnchorGenerator,
head: nn.Module,
# Faster-RCNN Training
fg_iou_thresh: float,
bg_iou_thresh: float,
batch_size_per_image: int,
positive_fraction: float,
# Faster-RCNN Inference
pre_nms_top_n: Dict[str, int],
post_nms_top_n: Dict[str, int],
nms_thresh: float,
score_thresh: float = 0.0,
) -> None:
super().__init__()
self.anchor_generator = anchor_generator
self.head = head
self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
# used during training
self.box_similarity = box_ops.box_iou
self.proposal_matcher = det_utils.Matcher(
fg_iou_thresh,
bg_iou_thresh,
allow_low_quality_matches=True,
)
self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(batch_size_per_image, positive_fraction)
# used during testing
self._pre_nms_top_n = pre_nms_top_n
self._post_nms_top_n = post_nms_top_n
self.nms_thresh = nms_thresh
self.score_thresh = score_thresh
self.min_size = 1e-3
def pre_nms_top_n(self) -> int:
if self.training:
return self._pre_nms_top_n["training"]
return self._pre_nms_top_n["testing"]
def post_nms_top_n(self) -> int:
if self.training:
return self._post_nms_top_n["training"]
return self._post_nms_top_n["testing"]
def assign_targets_to_anchors(
self, anchors: List[Tensor], targets: List[Dict[str, Tensor]]
) -> Tuple[List[Tensor], List[Tensor]]:
labels = []
matched_gt_boxes = []
for anchors_per_image, targets_per_image in zip(anchors, targets):
gt_boxes = targets_per_image["boxes"]
if gt_boxes.numel() == 0:
# Background image (negative example)
device = anchors_per_image.device
matched_gt_boxes_per_image = torch.zeros(anchors_per_image.shape, dtype=torch.float32, device=device)
labels_per_image = torch.zeros((anchors_per_image.shape[0],), dtype=torch.float32, device=device)
else:
match_quality_matrix = self.box_similarity(gt_boxes, anchors_per_image)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
matched_gt_boxes_per_image = gt_boxes[matched_idxs.clamp(min=0)]
labels_per_image = matched_idxs >= 0
labels_per_image = labels_per_image.to(dtype=torch.float32)
# Background (negative examples)
bg_indices = matched_idxs == self.proposal_matcher.BELOW_LOW_THRESHOLD
labels_per_image[bg_indices] = 0.0
# discard indices that are between thresholds
inds_to_discard = matched_idxs == self.proposal_matcher.BETWEEN_THRESHOLDS
labels_per_image[inds_to_discard] = -1.0
labels.append(labels_per_image)
matched_gt_boxes.append(matched_gt_boxes_per_image)
return labels, matched_gt_boxes
def _get_top_n_idx(self, objectness: Tensor, num_anchors_per_level: List[int]) -> Tensor:
r = []
offset = 0
for ob in objectness.split(num_anchors_per_level, 1):
num_anchors = ob.shape[1]
pre_nms_top_n = det_utils._topk_min(ob, self.pre_nms_top_n(), 1)
_, top_n_idx = ob.topk(pre_nms_top_n, dim=1)
r.append(top_n_idx + offset)
offset += num_anchors
return torch.cat(r, dim=1)
def filter_proposals(
self,
proposals: Tensor,
objectness: Tensor,
image_shapes: List[Tuple[int, int]],
num_anchors_per_level: List[int],
) -> Tuple[List[Tensor], List[Tensor]]:
num_images = proposals.shape[0]
device = proposals.device
# do not backprop through objectness
objectness = objectness.detach()
objectness = objectness.reshape(num_images, -1)
levels = [
torch.full((n,), idx, dtype=torch.int64, device=device) for idx, n in enumerate(num_anchors_per_level)
]
levels = torch.cat(levels, 0)
levels = levels.reshape(1, -1).expand_as(objectness)
# select top_n boxes independently per level before applying nms
top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level)
image_range = torch.arange(num_images, device=device)
batch_idx = image_range[:, None]
objectness = objectness[batch_idx, top_n_idx]
levels = levels[batch_idx, top_n_idx]
proposals = proposals[batch_idx, top_n_idx]
objectness_prob = torch.sigmoid(objectness)
final_boxes = []
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness_prob, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
# remove small boxes
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
# remove low scoring boxes
# use >= for Backwards compatibility
keep = torch.where(scores >= self.score_thresh)[0]
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
# non-maximum suppression, independently done per level
keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh)
# keep only topk scoring predictions
keep = keep[: self.post_nms_top_n()]
boxes, scores = boxes[keep], scores[keep]
final_boxes.append(boxes)
final_scores.append(scores)
return final_boxes, final_scores
def compute_loss(
self, objectness: Tensor, pred_bbox_deltas: Tensor, labels: List[Tensor], regression_targets: List[Tensor]
) -> Tuple[Tensor, Tensor]:
"""
Args:
objectness (Tensor)
pred_bbox_deltas (Tensor)
labels (List[Tensor])
regression_targets (List[Tensor])
Returns:
objectness_loss (Tensor)
box_loss (Tensor)
"""
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness = objectness.flatten()
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
box_loss = F.smooth_l1_loss(
pred_bbox_deltas[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1 / 9,
reduction="sum",
) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(objectness[sampled_inds], labels[sampled_inds])
return objectness_loss, box_loss
def forward(
self,
images: ImageList,
features: Dict[str, Tensor],
targets: Optional[List[Dict[str, Tensor]]] = None,
) -> Tuple[List[Tensor], Dict[str, Tensor]]:
"""
Args:
images (ImageList): images for which we want to compute the predictions
features (Dict[str, Tensor]): features computed from the images that are
used for computing the predictions. Each tensor in the list
correspond to different feature levels
targets (List[Dict[str, Tensor]]): ground-truth boxes present in the image (optional).
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[str, 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
# the proposals
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
# print(f'boxes:{boxes.shape},scores:{scores.shape}')
losses = {}
if self.training:
if targets is None:
raise ValueError("targets should not be None")
labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets)
regression_targets = self.box_coder.encode(matched_gt_boxes, anchors)
loss_objectness, loss_rpn_box_reg = self.compute_loss(
objectness, pred_bbox_deltas, labels, regression_targets
)
losses = {
"loss_objectness": loss_objectness,
"loss_rpn_box_reg": loss_rpn_box_reg,
}
return boxes, losses