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import sys
import time
from typing import Any, List
import numpy as np
import torch
import torch.nn.functional as F
from einops import rearrange
from hydra import main
from loguru import logger
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss
sys.path.append("./")
from config.config import Config
from 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__()
self.bce = BCEWithLogitsLoss(pos_weight=torch.tensor([1.0], device=torch.device("cuda")), 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")
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")
def __call__(self, predicts: List[Tensor], targets: Tensor) -> Tensor:
# Batch_Size x (Anchor + Class) x H x W
tlist = [time.time()]
# 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)
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)
tlist.append(time.time())
logger.info(f"Calculate Loss Run Time {np.diff(np.array(tlist)) * 1e3} ms")
@main(config_path="../config", config_name="config", version_base=None)
def main(cfg):
losser = YOLOLoss(cfg)
targets = torch.load("targets.pt")
predicts = torch.load("predicts.pt")
losser(predicts, targets)
if __name__ == "__main__":
import sys
sys.path.append("./")
from tools.log_helper import custom_logger
custom_logger()
main()
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