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9b92c75 | 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 | from __future__ import annotations
from collections.abc import Sequence
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .boxes import box_cxcywh_to_xyxy, generalized_box_iou
from .matching import hungarian_match, hungarian_match_layers
def sigmoid_focal_loss(
logits: Tensor, targets: Tensor, alpha: float = 0.25, gamma: float = 2.0
) -> Tensor:
probabilities = logits.sigmoid()
ce = F.binary_cross_entropy_with_logits(logits, targets, reduction="none")
p_t = probabilities * targets + (1.0 - probabilities) * (1.0 - targets)
loss = ce * (1.0 - p_t).pow(gamma)
if alpha >= 0:
alpha_t = alpha * targets + (1.0 - alpha) * (1.0 - targets)
loss = alpha_t * loss
return loss
class ObjectModelCriterion(nn.Module):
def __init__(self, config: dict) -> None:
super().__init__()
loss_config = config.get("loss", config)
self.cost_class = float(loss_config.get("cost_class", 2.0))
self.cost_bbox = float(loss_config.get("cost_bbox", 5.0))
self.cost_giou = float(loss_config.get("cost_giou", 2.0))
self.weight_class = float(loss_config.get("weight_class", 2.0))
self.weight_bbox = float(loss_config.get("weight_bbox", 5.0))
self.weight_giou = float(loss_config.get("weight_giou", 2.0))
self.weight_dense = float(loss_config.get("weight_dense", 1.0))
self.aux_weight = float(loss_config.get("aux_weight", 1.0))
self.dense_topk = int(loss_config.get("dense_topk", 5))
self.alpha = float(loss_config.get("focal_alpha", 0.25))
self.gamma = float(loss_config.get("focal_gamma", 2.0))
def _dense_targets(
self,
logits: Tensor,
targets: Sequence[dict[str, Tensor]],
level_index: int,
) -> tuple[Tensor, Tensor, Tensor]:
batch, _, height, width = logits.shape
device = logits.device
target_logits = torch.zeros_like(logits)
target_boxes_hwc = torch.zeros(batch, height, width, 4, dtype=torch.float32, device=device)
positive = torch.zeros(batch, height, width, dtype=torch.float32, device=device)
offsets = torch.tensor(
[
(-1, -1),
(0, -1),
(1, -1),
(-1, 0),
(0, 0),
(1, 0),
(-1, 1),
(0, 1),
(1, 1),
],
dtype=torch.int64,
device=device,
)
distances = offsets.square().sum(dim=1)
candidate_count = min(self.dense_topk, len(offsets))
nonempty = [(i, t) for i, t in enumerate(targets) if t["labels"].numel() > 0]
if not nonempty:
return target_logits, target_boxes_hwc.permute(0, 3, 1, 2), positive
all_boxes = torch.cat([t["boxes"] for _, t in nonempty])
all_labels = torch.cat([t["labels"] for _, t in nonempty])
all_batch = torch.cat(
[
torch.full((t["labels"].numel(),), i, dtype=torch.int64, device=device)
for i, t in nonempty
]
)
areas = all_boxes[:, 2] * all_boxes[:, 3]
target_levels = torch.where(areas < 0.02, 0, torch.where(areas < 0.15, 1, 2))
level_mask = target_levels == level_index
if not bool(level_mask.any()):
return target_logits, target_boxes_hwc.permute(0, 3, 1, 2), positive
boxes = all_boxes[level_mask]
labels = all_labels[level_mask]
sel_batch = all_batch[level_mask]
grid = (boxes[:, :2] * boxes.new_tensor([width, height])).long()
grid[:, 0].clamp_(0, width - 1)
grid[:, 1].clamp_(0, height - 1)
x = (grid[:, None, 0] + offsets[None, :, 0]).clamp(0, width - 1)
y = (grid[:, None, 1] + offsets[None, :, 1]).clamp(0, height - 1)
sort_key = distances[None] * ((width + 1) * (height + 1))
sort_key = sort_key + x * (height + 1) + y
order = sort_key.argsort(dim=1, stable=True)[:, :candidate_count]
x = x.gather(1, order)
y = y.gather(1, order)
expanded_labels = labels[:, None].expand_as(x)
expanded_batch = sel_batch[:, None].expand_as(x)
target_logits[expanded_batch, expanded_labels, y, x] = 1.0
flat_cells = (expanded_batch * (height * width) + y * width + x).reshape(-1)
owners = torch.full((batch * height * width,), -1, dtype=torch.int64, device=device)
source_owners = torch.arange(boxes.shape[0], device=device)[:, None].expand_as(x).reshape(-1)
owners.scatter_reduce_(0, flat_cells, source_owners, reduce="amax", include_self=True)
occupied = owners >= 0
positive.view(-1)[occupied] = 1.0
target_boxes_hwc.view(-1, 4)[occupied] = boxes[owners[occupied]]
return target_logits, target_boxes_hwc.permute(0, 3, 1, 2), positive
def _set_loss(
self, outputs: dict[str, Tensor], targets: Sequence[dict[str, Tensor]], matches=None
) -> dict[str, Tensor]:
logits = outputs["pred_logits"]
boxes = outputs["pred_boxes"]
if matches is None:
matches = hungarian_match(
outputs,
targets,
self.cost_class,
self.cost_bbox,
self.cost_giou,
)
device = logits.device
target_classes = torch.zeros_like(logits)
normalizer = max(sum(len(target["labels"]) for target in targets), 1)
nonempty = [
(batch_index, prediction_indices, target_indices)
for batch_index, (prediction_indices, target_indices) in enumerate(matches)
if prediction_indices.numel() > 0
]
if nonempty:
batch_ids = torch.cat(
[torch.full_like(pred_idx, batch_index) for batch_index, pred_idx, _ in nonempty]
)
pred_idx_t = torch.cat([pred_idx for _, pred_idx, _ in nonempty])
tgt_idx_t = torch.cat([tgt_idx for _, _, tgt_idx in nonempty])
counts = torch.tensor([len(target["labels"]) for target in targets], device=device)
offsets = torch.cat([counts.new_zeros(1), counts.cumsum(0)[:-1]])
global_target_idx = tgt_idx_t + offsets[batch_ids]
all_target_boxes = torch.cat([target["boxes"] for target in targets])
all_target_labels = torch.cat([target["labels"] for target in targets])
labels = all_target_labels[global_target_idx]
target_classes[batch_ids, pred_idx_t, labels] = 1.0
predicted = boxes[batch_ids, pred_idx_t]
expected = all_target_boxes[global_target_idx]
else:
predicted = None
expected = None
class_loss = sigmoid_focal_loss(logits, target_classes, self.alpha, self.gamma).sum()
class_loss = class_loss / normalizer
if predicted is not None:
bbox_loss = F.l1_loss(predicted, expected, reduction="sum") / normalizer
giou = generalized_box_iou(box_cxcywh_to_xyxy(predicted), box_cxcywh_to_xyxy(expected))
giou_loss = (1.0 - giou.diag()).sum() / normalizer
else:
bbox_loss = boxes.sum() * 0.0
giou_loss = boxes.sum() * 0.0
return {
"loss_class": class_loss * self.weight_class,
"loss_bbox": bbox_loss * self.weight_bbox,
"loss_giou": giou_loss * self.weight_giou,
}
def _dense_loss(
self, outputs: list[dict[str, Tensor]], targets: Sequence[dict[str, Tensor]]
) -> Tensor:
total = outputs[0]["logits"].sum() * 0.0
normalizer = max(sum(len(target["labels"]) for target in targets), 1)
for level_index, level_output in enumerate(outputs):
logits = level_output["logits"]
boxes = level_output["distances"].sigmoid()
target_logits, target_boxes, positive = self._dense_targets(
logits, targets, level_index
)
cls_loss = sigmoid_focal_loss(logits, target_logits, self.alpha, self.gamma)
cls_loss = cls_loss.sum() / normalizer
positive_mask = positive[:, None].expand_as(boxes)
box_loss = (F.l1_loss(boxes, target_boxes, reduction="none") * positive_mask).sum()
total = total + cls_loss + box_loss / normalizer
return total / len(outputs)
def forward(
self, outputs: dict[str, Tensor], targets: Sequence[dict[str, Tensor]]
) -> dict[str, Tensor]:
layer_outputs = [outputs, *outputs.get("aux_outputs", [])]
layer_matches = hungarian_match_layers(
layer_outputs,
targets,
self.cost_class,
self.cost_bbox,
self.cost_giou,
)
primary = self._set_loss(outputs, targets, layer_matches[0])
total = sum(primary.values())
for auxiliary, matches in zip(
outputs.get("aux_outputs", []), layer_matches[1:], strict=True
):
auxiliary_losses = self._set_loss(auxiliary, targets, matches)
total = total + self.aux_weight * sum(auxiliary_losses.values()) / max(
len(outputs["aux_outputs"]), 1
)
if "dense_outputs" in outputs:
dense = self._dense_loss(outputs["dense_outputs"], targets)
primary["loss_dense"] = dense * self.weight_dense
total = total + primary["loss_dense"]
primary["loss_total"] = total
return primary
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