File size: 6,302 Bytes
f2370d7 cbbfcfe dcceddd cbbfcfe dc55a8e cbbfcfe b5fa3f1 cbbfcfe 710e371 cbbfcfe 033231b cbbfcfe dcceddd cbbfcfe b5fa3f1 dcceddd cbbfcfe 253e9b1 cbbfcfe 253e9b1 cbbfcfe c9338ee b5fa3f1 c9338ee b5fa3f1 c9338ee f2370d7 c9338ee dcceddd c9338ee f2370d7 584d5bd c9338ee 584d5bd |
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 |
from typing import Any, Dict, 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.utils.bounding_box_utils import (
AnchorBoxConverter,
BoxMatcher,
calculate_iou,
generate_anchors,
)
from yolo.utils.module_utils import divide_into_chunks
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.class_num
self.image_size = list(cfg.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.float32, device=device)
self.scale_up = torch.tensor(self.image_size * 2, device=device)
self.anchors, self.scaler = generate_anchors(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.task.loss.matcher, self.class_num, self.anchors)
self.box_converter = AnchorBoxConverter(cfg, device)
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
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.box_converter(predicts)
# For each predicted targets, assign a best suitable ground truth box.
align_targets, valid_masks = self.matcher(targets, predicts)
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)
return loss_iou, loss_dfl, loss_cls
class DualLoss:
def __init__(self, cfg: Config) -> None:
self.loss = YOLOLoss(cfg)
self.aux_rate = cfg.task.loss.aux
self.iou_rate = cfg.task.loss.objective["BoxLoss"]
self.dfl_rate = cfg.task.loss.objective["DFLoss"]
self.cls_rate = cfg.task.loss.objective["BCELoss"]
def __call__(self, predicts: List[Tensor], targets: Tensor) -> Tuple[Tensor, Dict[str, Tensor]]:
targets[:, :, 1:] = targets[:, :, 1:] * self.loss.scale_up
# TODO: Need Refactor this region, make it flexible!
predicts = divide_into_chunks(predicts[0], 2)
aux_iou, aux_dfl, aux_cls = self.loss(predicts[0], targets)
main_iou, main_dfl, main_cls = self.loss(predicts[1], targets)
loss_dict = {
"BoxLoss": self.iou_rate * (aux_iou * self.aux_rate + main_iou),
"DFLoss": self.dfl_rate * (aux_dfl * self.aux_rate + main_dfl),
"BCELoss": self.cls_rate * (aux_cls * self.aux_rate + main_cls),
}
loss_sum = sum(list(loss_dict.values())) / len(loss_dict)
return loss_sum, loss_dict
def get_loss_function(cfg: Config) -> YOLOLoss:
loss_function = DualLoss(cfg)
logger.info("✅ Success load loss function")
return loss_function
|