File size: 13,213 Bytes
938e515 |
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 |
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import List, Optional, Tuple
import torch
from fvcore.nn import sigmoid_focal_loss_jit
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec, batched_nms
from detectron2.structures import Boxes, ImageList, Instances, pairwise_point_box_distance
from detectron2.utils.events import get_event_storage
from ..anchor_generator import DefaultAnchorGenerator
from ..backbone import Backbone
from ..box_regression import Box2BoxTransformLinear, _dense_box_regression_loss
from .dense_detector import DenseDetector
from .retinanet import RetinaNetHead
__all__ = ["FCOS"]
logger = logging.getLogger(__name__)
class FCOS(DenseDetector):
"""
Implement FCOS in :paper:`fcos`.
"""
def __init__(
self,
*,
backbone: Backbone,
head: nn.Module,
head_in_features: Optional[List[str]] = None,
box2box_transform=None,
num_classes,
center_sampling_radius: float = 1.5,
focal_loss_alpha=0.25,
focal_loss_gamma=2.0,
test_score_thresh=0.2,
test_topk_candidates=1000,
test_nms_thresh=0.6,
max_detections_per_image=100,
pixel_mean,
pixel_std,
):
"""
Args:
center_sampling_radius: radius of the "center" of a groundtruth box,
within which all anchor points are labeled positive.
Other arguments mean the same as in :class:`RetinaNet`.
"""
super().__init__(
backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std
)
self.num_classes = num_classes
# FCOS uses one anchor point per location.
# We represent the anchor point by a box whose size equals the anchor stride.
feature_shapes = backbone.output_shape()
fpn_strides = [feature_shapes[k].stride for k in self.head_in_features]
self.anchor_generator = DefaultAnchorGenerator(
sizes=[[k] for k in fpn_strides], aspect_ratios=[1.0], strides=fpn_strides
)
# FCOS parameterizes box regression by a linear transform,
# where predictions are normalized by anchor stride (equal to anchor size).
if box2box_transform is None:
box2box_transform = Box2BoxTransformLinear(normalize_by_size=True)
self.box2box_transform = box2box_transform
self.center_sampling_radius = float(center_sampling_radius)
# Loss parameters:
self.focal_loss_alpha = focal_loss_alpha
self.focal_loss_gamma = focal_loss_gamma
# Inference parameters:
self.test_score_thresh = test_score_thresh
self.test_topk_candidates = test_topk_candidates
self.test_nms_thresh = test_nms_thresh
self.max_detections_per_image = max_detections_per_image
def forward_training(self, images, features, predictions, gt_instances):
# Transpose the Hi*Wi*A dimension to the middle:
pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions(
predictions, [self.num_classes, 4, 1]
)
anchors = self.anchor_generator(features)
gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
return self.losses(
anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
)
@torch.no_grad()
def _match_anchors(self, gt_boxes: Boxes, anchors: List[Boxes]):
"""
Match ground-truth boxes to a set of multi-level anchors.
Args:
gt_boxes: Ground-truth boxes from instances of an image.
anchors: List of anchors for each feature map (of different scales).
Returns:
torch.Tensor
A tensor of shape `(M, R)`, given `M` ground-truth boxes and total
`R` anchor points from all feature levels, indicating the quality
of match between m-th box and r-th anchor. Higher value indicates
better match.
"""
# Naming convention: (M = ground-truth boxes, R = anchor points)
# Anchor points are represented as square boxes of size = stride.
num_anchors_per_level = [len(x) for x in anchors]
anchors = Boxes.cat(anchors) # (R, 4)
anchor_centers = anchors.get_centers() # (R, 2)
anchor_sizes = anchors.tensor[:, 2] - anchors.tensor[:, 0] # (R, )
lower_bound = anchor_sizes * 4
lower_bound[: num_anchors_per_level[0]] = 0
upper_bound = anchor_sizes * 8
upper_bound[-num_anchors_per_level[-1] :] = float("inf")
gt_centers = gt_boxes.get_centers()
# FCOS with center sampling: anchor point must be close enough to
# ground-truth box center.
center_dists = (anchor_centers[None, :, :] - gt_centers[:, None, :]).abs_()
sampling_regions = self.center_sampling_radius * anchor_sizes[None, :]
match_quality_matrix = center_dists.max(dim=2).values < sampling_regions
pairwise_dist = pairwise_point_box_distance(anchor_centers, gt_boxes)
pairwise_dist = pairwise_dist.permute(1, 0, 2) # (M, R, 4)
# The original FCOS anchor matching rule: anchor point must be inside GT.
match_quality_matrix &= pairwise_dist.min(dim=2).values > 0
# Multilevel anchor matching in FCOS: each anchor is only responsible
# for certain scale range.
pairwise_dist = pairwise_dist.max(dim=2).values
match_quality_matrix &= (pairwise_dist > lower_bound[None, :]) & (
pairwise_dist < upper_bound[None, :]
)
# Match the GT box with minimum area, if there are multiple GT matches.
gt_areas = gt_boxes.area() # (M, )
match_quality_matrix = match_quality_matrix.to(torch.float32)
match_quality_matrix *= 1e8 - gt_areas[:, None]
return match_quality_matrix # (M, R)
@torch.no_grad()
def label_anchors(self, anchors: List[Boxes], gt_instances: List[Instances]):
"""
Same interface as :meth:`RetinaNet.label_anchors`, but implemented with FCOS
anchor matching rule.
Unlike RetinaNet, there are no ignored anchors.
"""
gt_labels, matched_gt_boxes = [], []
for inst in gt_instances:
if len(inst) > 0:
match_quality_matrix = self._match_anchors(inst.gt_boxes, anchors)
# Find matched ground-truth box per anchor. Un-matched anchors are
# assigned -1. This is equivalent to using an anchor matcher as used
# in R-CNN/RetinaNet: `Matcher(thresholds=[1e-5], labels=[0, 1])`
match_quality, matched_idxs = match_quality_matrix.max(dim=0)
matched_idxs[match_quality < 1e-5] = -1
matched_gt_boxes_i = inst.gt_boxes.tensor[matched_idxs.clip(min=0)]
gt_labels_i = inst.gt_classes[matched_idxs.clip(min=0)]
# Anchors with matched_idxs = -1 are labeled background.
gt_labels_i[matched_idxs < 0] = self.num_classes
else:
matched_gt_boxes_i = torch.zeros_like(Boxes.cat(anchors).tensor)
gt_labels_i = torch.full(
(len(matched_gt_boxes_i),),
fill_value=self.num_classes,
dtype=torch.long,
device=matched_gt_boxes_i.device,
)
gt_labels.append(gt_labels_i)
matched_gt_boxes.append(matched_gt_boxes_i)
return gt_labels, matched_gt_boxes
def losses(
self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
):
"""
This method is almost identical to :meth:`RetinaNet.losses`, with an extra
"loss_centerness" in the returned dict.
"""
num_images = len(gt_labels)
gt_labels = torch.stack(gt_labels) # (M, R)
pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
num_pos_anchors = pos_mask.sum().item()
get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 300)
# classification and regression loss
gt_labels_target = F.one_hot(gt_labels, num_classes=self.num_classes + 1)[
:, :, :-1
] # no loss for the last (background) class
loss_cls = sigmoid_focal_loss_jit(
torch.cat(pred_logits, dim=1),
gt_labels_target.to(pred_logits[0].dtype),
alpha=self.focal_loss_alpha,
gamma=self.focal_loss_gamma,
reduction="sum",
)
loss_box_reg = _dense_box_regression_loss(
anchors,
self.box2box_transform,
pred_anchor_deltas,
gt_boxes,
pos_mask,
box_reg_loss_type="giou",
)
ctrness_targets = self.compute_ctrness_targets(anchors, gt_boxes) # (M, R)
pred_centerness = torch.cat(pred_centerness, dim=1).squeeze(dim=2) # (M, R)
ctrness_loss = F.binary_cross_entropy_with_logits(
pred_centerness[pos_mask], ctrness_targets[pos_mask], reduction="sum"
)
return {
"loss_fcos_cls": loss_cls / normalizer,
"loss_fcos_loc": loss_box_reg / normalizer,
"loss_fcos_ctr": ctrness_loss / normalizer,
}
def compute_ctrness_targets(self, anchors: List[Boxes], gt_boxes: List[torch.Tensor]):
anchors = Boxes.cat(anchors).tensor # Rx4
reg_targets = [self.box2box_transform.get_deltas(anchors, m) for m in gt_boxes]
reg_targets = torch.stack(reg_targets, dim=0) # NxRx4
if len(reg_targets) == 0:
return reg_targets.new_zeros(len(reg_targets))
left_right = reg_targets[:, :, [0, 2]]
top_bottom = reg_targets[:, :, [1, 3]]
ctrness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]
)
return torch.sqrt(ctrness)
def forward_inference(
self,
images: ImageList,
features: List[torch.Tensor],
predictions: List[List[torch.Tensor]],
):
pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions(
predictions, [self.num_classes, 4, 1]
)
anchors = self.anchor_generator(features)
results: List[Instances] = []
for img_idx, image_size in enumerate(images.image_sizes):
scores_per_image = [
# Multiply and sqrt centerness & classification scores
# (See eqn. 4 in https://arxiv.org/abs/2006.09214)
torch.sqrt(x[img_idx].sigmoid_() * y[img_idx].sigmoid_())
for x, y in zip(pred_logits, pred_centerness)
]
deltas_per_image = [x[img_idx] for x in pred_anchor_deltas]
results_per_image = self.inference_single_image(
anchors, scores_per_image, deltas_per_image, image_size
)
results.append(results_per_image)
return results
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]]
class FCOSHead(RetinaNetHead):
"""
The head used in :paper:`fcos`. It adds an additional centerness
prediction branch on top of :class:`RetinaNetHead`.
"""
def __init__(self, *, input_shape: List[ShapeSpec], conv_dims: List[int], **kwargs):
super().__init__(input_shape=input_shape, conv_dims=conv_dims, num_anchors=1, **kwargs)
# Unlike original FCOS, we do not add an additional learnable scale layer
# because it's found to have no benefits after normalizing regression targets by stride.
self._num_features = len(input_shape)
self.ctrness = nn.Conv2d(conv_dims[-1], 1, kernel_size=3, stride=1, padding=1)
torch.nn.init.normal_(self.ctrness.weight, std=0.01)
torch.nn.init.constant_(self.ctrness.bias, 0)
def forward(self, features):
assert len(features) == self._num_features
logits = []
bbox_reg = []
ctrness = []
for feature in features:
logits.append(self.cls_score(self.cls_subnet(feature)))
bbox_feature = self.bbox_subnet(feature)
bbox_reg.append(self.bbox_pred(bbox_feature))
ctrness.append(self.ctrness(bbox_feature))
return logits, bbox_reg, ctrness
|