File size: 6,869 Bytes
cbbfcfe 1197f7d cbbfcfe dc55a8e cbbfcfe 710e371 cbbfcfe dc55a8e cbbfcfe 38f4931 cbbfcfe |
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 |
import time
from typing import Any, List, Tuple
import torch
import torch.nn.functional as F
from einops import rearrange
from loguru import logger
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss
from yolo.config.config import Config
from yolo.tools.bbox_helper import (
BoxMatcher,
calculate_iou,
make_anchor,
transform_bbox,
)
def get_loss_function(*args, **kwargs):
raise NotImplementedError
class BCELoss(nn.Module):
def __init__(self) -> None:
super().__init__()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.bce = BCEWithLogitsLoss(pos_weight=torch.tensor([1.0], device=device), reduction="none")
def forward(self, predicts_cls: Tensor, targets_cls: Tensor, cls_norm: Tensor) -> Any:
return self.bce(predicts_cls, targets_cls).sum() / cls_norm
class BoxLoss(nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(
self, predicts_bbox: Tensor, targets_bbox: Tensor, valid_masks: Tensor, box_norm: Tensor, cls_norm: Tensor
) -> Any:
valid_bbox = valid_masks[..., None].expand(-1, -1, 4)
picked_predict = predicts_bbox[valid_bbox].view(-1, 4)
picked_targets = targets_bbox[valid_bbox].view(-1, 4)
iou = calculate_iou(picked_predict, picked_targets, "ciou").diag()
loss_iou = 1.0 - iou
loss_iou = (loss_iou * box_norm).sum() / cls_norm
return loss_iou
class DFLoss(nn.Module):
def __init__(self, anchors: Tensor, scaler: Tensor, reg_max: int) -> None:
super().__init__()
self.anchors = anchors
self.scaler = scaler
self.reg_max = reg_max
def forward(
self, predicts_anc: Tensor, targets_bbox: Tensor, valid_masks: Tensor, box_norm: Tensor, cls_norm: Tensor
) -> Any:
valid_bbox = valid_masks[..., None].expand(-1, -1, 4)
bbox_lt, bbox_rb = targets_bbox.chunk(2, -1)
anchors_norm = (self.anchors / self.scaler[:, None])[None]
targets_dist = torch.cat(((anchors_norm - bbox_lt), (bbox_rb - anchors_norm)), -1).clamp(0, self.reg_max - 1.01)
picked_targets = targets_dist[valid_bbox].view(-1)
picked_predict = predicts_anc[valid_bbox].view(-1, self.reg_max)
label_left, label_right = picked_targets.floor(), picked_targets.floor() + 1
weight_left, weight_right = label_right - picked_targets, picked_targets - label_left
loss_left = F.cross_entropy(picked_predict, label_left.to(torch.long), reduction="none")
loss_right = F.cross_entropy(picked_predict, label_right.to(torch.long), reduction="none")
loss_dfl = loss_left * weight_left + loss_right * weight_right
loss_dfl = loss_dfl.view(-1, 4).mean(-1)
loss_dfl = (loss_dfl * box_norm).sum() / cls_norm
return loss_dfl
class YOLOLoss:
def __init__(self, cfg: Config) -> None:
self.reg_max = cfg.model.anchor.reg_max
self.class_num = cfg.hyper.data.class_num
self.image_size = list(cfg.hyper.data.image_size)
self.strides = cfg.model.anchor.strides
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.reverse_reg = torch.arange(self.reg_max, dtype=torch.float16, device=device)
self.scale_up = torch.tensor(self.image_size * 2, device=device)
self.anchors, self.scaler = make_anchor(self.image_size, self.strides, device)
self.cls = BCELoss()
self.dfl = DFLoss(self.anchors, self.scaler, self.reg_max)
self.iou = BoxLoss()
self.matcher = BoxMatcher(cfg.hyper.train.matcher, self.class_num, self.anchors)
def parse_predicts(self, predicts: List[Tensor]) -> Tensor:
"""
args:
[B x AnchorClass x h1 x w1, B x AnchorClass x h2 x w2, B x AnchorClass x h3 x w3] // AnchorClass = 4 * 16 + 80
return:
[B x HW x ClassBbox] // HW = h1*w1 + h2*w2 + h3*w3, ClassBox = 80 + 4 (xyXY)
"""
preds = []
for pred in predicts:
preds.append(rearrange(pred, "B AC h w -> B (h w) AC")) # B x AC x h x w-> B x hw x AC
preds = torch.concat(preds, dim=1) # -> B x (H W) x AC
preds_anc, preds_cls = torch.split(preds, (self.reg_max * 4, self.class_num), dim=-1)
preds_anc = rearrange(preds_anc, "B hw (P R)-> B hw P R", P=4)
pred_LTRB = preds_anc.softmax(dim=-1) @ self.reverse_reg * self.scaler.view(1, -1, 1)
lt, rb = pred_LTRB.chunk(2, dim=-1)
pred_minXY = self.anchors - lt
pred_maxXY = self.anchors + rb
predicts = torch.cat([preds_cls, pred_minXY, pred_maxXY], dim=-1)
return predicts, preds_anc
def parse_targets(self, targets: Tensor, batch_size: int = 16) -> List[Tensor]:
"""
return List:
"""
targets[:, 2:] = transform_bbox(targets[:, 2:], "xycwh -> xyxy") * self.scale_up
bbox_num = targets[:, 0].int().bincount()
batch_targets = torch.zeros(batch_size, bbox_num.max(), 5, device=targets.device)
for instance_idx, bbox_num in enumerate(bbox_num):
instance_targets = targets[targets[:, 0] == instance_idx]
batch_targets[instance_idx, :bbox_num] = instance_targets[:, 1:].detach()
return batch_targets
def separate_anchor(self, anchors):
"""
separate anchor and bbouding box
"""
anchors_cls, anchors_box = torch.split(anchors, (self.class_num, 4), dim=-1)
anchors_box = anchors_box / self.scaler[None, :, None]
return anchors_cls, anchors_box
@torch.autocast("cuda" if torch.cuda.is_available() else "cpu")
def __call__(self, predicts: List[Tensor], targets: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
# Batch_Size x (Anchor + Class) x H x W
# TODO: check datatype, why targets has a little bit error with origin version
predicts, predicts_anc = self.parse_predicts(predicts[0])
targets = self.parse_targets(targets, batch_size=predicts.size(0))
align_targets, valid_masks = self.matcher(targets, predicts)
# calculate loss between with instance and predict
targets_cls, targets_bbox = self.separate_anchor(align_targets)
predicts_cls, predicts_bbox = self.separate_anchor(predicts)
cls_norm = targets_cls.sum()
box_norm = targets_cls.sum(-1)[valid_masks]
## -- CLS -- ##
loss_cls = self.cls(predicts_cls, targets_cls, cls_norm)
## -- IOU -- ##
loss_iou = self.iou(predicts_bbox, targets_bbox, valid_masks, box_norm, cls_norm)
## -- DFL -- ##
loss_dfl = self.dfl(predicts_anc, targets_bbox, valid_masks, box_norm, cls_norm)
logger.info("Loss IoU: {:.5f}, DFL: {:.5f}, CLS: {:.5f}", loss_iou, loss_dfl, loss_cls)
return loss_iou, loss_dfl, loss_cls
|