File size: 15,838 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 |
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
score_thresh (float): only return proposals with an objectness score greater than score_thresh
"""
__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)
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
|