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c6dfc69 | 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 | """SAM2 multi-step mask + IoU + objectness loss (Hydra `_target_`: `MultiStepMultiMasksAndIous`)."""
from collections import defaultdict
from typing import Dict, List
import torch
import torch.distributed
import torch.nn as nn
import torch.nn.functional as F
CORE_LOSS_KEY = "core_loss"
def dice_loss(inputs, targets, num_objects, loss_on_multimask=False):
inputs = inputs.sigmoid()
if loss_on_multimask:
assert inputs.dim() == 4 and targets.dim() == 4
inputs = inputs.flatten(2)
targets = targets.flatten(2)
numerator = 2 * (inputs * targets).sum(-1)
else:
inputs = inputs.flatten(1)
numerator = 2 * (inputs * targets).sum(1)
denominator = inputs.sum(-1) + targets.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
if loss_on_multimask:
return loss / num_objects
return loss.sum() / num_objects
def sigmoid_focal_loss(
inputs,
targets,
num_objects,
alpha: float = 0.25,
gamma: float = 2,
loss_on_multimask=False,
):
prob = inputs.sigmoid()
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
p_t = prob * targets + (1 - prob) * (1 - targets)
loss = ce_loss * ((1 - p_t) ** gamma)
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
loss = alpha_t * loss
if loss_on_multimask:
assert loss.dim() == 4
return loss.flatten(2).mean(-1) / num_objects
return loss.mean(1).sum() / num_objects
def iou_loss(
inputs, targets, pred_ious, num_objects, loss_on_multimask=False, use_l1_loss=False
):
assert inputs.dim() == 4 and targets.dim() == 4
pred_mask = inputs.flatten(2) > 0
gt_mask = targets.flatten(2) > 0
area_i = torch.sum(pred_mask & gt_mask, dim=-1).float()
area_u = torch.sum(pred_mask | gt_mask, dim=-1).float()
actual_ious = area_i / torch.clamp(area_u, min=1.0)
if use_l1_loss:
loss = F.l1_loss(pred_ious, actual_ious, reduction="none")
else:
loss = F.mse_loss(pred_ious, actual_ious, reduction="none")
if loss_on_multimask:
return loss / num_objects
return loss.sum() / num_objects
class MultiStepMultiMasksAndIous(nn.Module):
def __init__(
self,
weight_dict,
focal_alpha=0.25,
focal_gamma=2,
supervise_all_iou=False,
iou_use_l1_loss=False,
pred_obj_scores=False,
focal_gamma_obj_score=0.0,
focal_alpha_obj_score=-1,
gpu_num=1,
):
super().__init__()
self.weight_dict = weight_dict
self.focal_alpha = focal_alpha
self.focal_gamma = focal_gamma
self.world_size = gpu_num
assert "loss_mask" in self.weight_dict
assert "loss_dice" in self.weight_dict
assert "loss_iou" in self.weight_dict
if "loss_class" not in self.weight_dict:
self.weight_dict["loss_class"] = 0.0
self.focal_alpha_obj_score = focal_alpha_obj_score
self.focal_gamma_obj_score = focal_gamma_obj_score
self.supervise_all_iou = supervise_all_iou
self.iou_use_l1_loss = iou_use_l1_loss
self.pred_obj_scores = pred_obj_scores
def forward(self, outs_batch: List[Dict], targets_batch: torch.Tensor):
assert len(outs_batch) == len(targets_batch)
num_objects = torch.tensor(
targets_batch.shape[1], device=targets_batch.device, dtype=torch.float
)
torch.distributed.all_reduce(num_objects)
num_objects = torch.clamp(num_objects / self.world_size, min=1).item()
losses = defaultdict(int)
for outs, targets in zip(outs_batch, targets_batch):
cur_losses = self._forward(outs, targets, num_objects)
for k, v in cur_losses.items():
losses[k] += v
return losses
def _forward(self, outputs: Dict, targets: torch.Tensor, num_objects):
target_masks = targets.unsqueeze(1).float()
assert target_masks.dim() == 4
src_masks_list = outputs["multistep_pred_multimasks_high_res"]
ious_list = outputs["multistep_pred_ious"]
object_score_logits_list = outputs["multistep_object_score_logits"]
assert len(src_masks_list) == len(ious_list)
assert len(object_score_logits_list) == len(ious_list)
losses = {"loss_mask": 0, "loss_dice": 0, "loss_iou": 0, "loss_class": 0}
for src_masks, ious, object_score_logits in zip(
src_masks_list, ious_list, object_score_logits_list
):
self._update_losses(
losses, src_masks, target_masks, ious, num_objects, object_score_logits
)
losses[CORE_LOSS_KEY] = self.reduce_loss(losses)
return losses
def _update_losses(
self, losses, src_masks, target_masks, ious, num_objects, object_score_logits
):
target_masks = target_masks.expand_as(src_masks)
loss_multimask = sigmoid_focal_loss(
src_masks,
target_masks,
num_objects,
alpha=self.focal_alpha,
gamma=self.focal_gamma,
loss_on_multimask=True,
)
loss_multidice = dice_loss(
src_masks, target_masks, num_objects, loss_on_multimask=True
)
if not self.pred_obj_scores:
loss_class = torch.tensor(
0.0, dtype=loss_multimask.dtype, device=loss_multimask.device
)
target_obj = torch.ones(
loss_multimask.shape[0],
1,
dtype=loss_multimask.dtype,
device=loss_multimask.device,
)
else:
target_obj = torch.any((target_masks[:, 0] > 0).flatten(1), dim=-1)[
..., None
].float()
loss_class = sigmoid_focal_loss(
object_score_logits,
target_obj,
num_objects,
alpha=self.focal_alpha_obj_score,
gamma=self.focal_gamma_obj_score,
)
loss_multiiou = iou_loss(
src_masks,
target_masks,
ious,
num_objects,
loss_on_multimask=True,
use_l1_loss=self.iou_use_l1_loss,
)
assert loss_multimask.dim() == 2
assert loss_multidice.dim() == 2
assert loss_multiiou.dim() == 2
if loss_multimask.size(1) > 1:
loss_combo = (
loss_multimask * self.weight_dict["loss_mask"]
+ loss_multidice * self.weight_dict["loss_dice"]
)
best_loss_inds = torch.argmin(loss_combo, dim=-1)
batch_inds = torch.arange(loss_combo.size(0), device=loss_combo.device)
loss_mask = loss_multimask[batch_inds, best_loss_inds].unsqueeze(1)
loss_dice = loss_multidice[batch_inds, best_loss_inds].unsqueeze(1)
if self.supervise_all_iou:
loss_iou = loss_multiiou.mean(dim=-1).unsqueeze(1)
else:
loss_iou = loss_multiiou[batch_inds, best_loss_inds].unsqueeze(1)
else:
loss_mask = loss_multimask
loss_dice = loss_multidice
loss_iou = loss_multiiou
loss_mask = loss_mask * target_obj
loss_dice = loss_dice * target_obj
loss_iou = loss_iou * target_obj
losses["loss_mask"] += loss_mask.sum()
losses["loss_dice"] += loss_dice.sum()
losses["loss_iou"] += loss_iou.sum()
losses["loss_class"] += loss_class
def reduce_loss(self, losses):
reduced_loss = 0.0
for loss_key, weight in self.weight_dict.items():
if loss_key not in losses:
raise ValueError(f"{type(self)} doesn't compute {loss_key}")
if weight != 0:
reduced_loss += losses[loss_key] * weight
return reduced_loss
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