import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.ops import sigmoid_focal_loss
from utils.general import xywh2xyxy, xyxy2xywh
from utils.metrics import bbox_iou
from utils.segment.tal.anchor_generator import dist2bbox, make_anchors, bbox2dist
from utils.segment.tal.assigner import TaskAlignedAssigner
from utils.torch_utils import de_parallel
from utils.segment.general import crop_mask
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
# return positive, negative label smoothing BCE targets
return 1.0 - 0.5 * eps, 0.5 * eps
class VarifocalLoss(nn.Module):
# Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367
def __init__(self):
super().__init__()
def forward(self, pred_score, gt_score, label, alpha=0.75, gamma=2.0):
weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
with torch.cuda.amp.autocast(enabled=False):
loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(),
reduction="none") * weight).sum()
return loss
class FocalLoss(nn.Module):
# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
super().__init__()
self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
self.gamma = gamma
self.alpha = alpha
self.reduction = loss_fcn.reduction
self.loss_fcn.reduction = "none" # required to apply FL to each element
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
# p_t = torch.exp(-loss)
# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
pred_prob = torch.sigmoid(pred) # prob from logits
p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
modulating_factor = (1.0 - p_t) ** self.gamma
loss *= alpha_factor * modulating_factor
if self.reduction == "mean":
return loss.mean()
elif self.reduction == "sum":
return loss.sum()
else: # 'none'
return loss
class BboxLoss(nn.Module):
def __init__(self, reg_max, use_dfl=False):
super().__init__()
self.reg_max = reg_max
self.use_dfl = use_dfl
def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
# iou loss
bbox_mask = fg_mask.unsqueeze(-1).repeat([1, 1, 4]) # (b, h*w, 4)
pred_bboxes_pos = torch.masked_select(pred_bboxes, bbox_mask).view(-1, 4)
target_bboxes_pos = torch.masked_select(target_bboxes, bbox_mask).view(-1, 4)
bbox_weight = torch.masked_select(target_scores.sum(-1), fg_mask).unsqueeze(-1)
iou = bbox_iou(pred_bboxes_pos, target_bboxes_pos, xywh=False, CIoU=True)
loss_iou = 1.0 - iou
loss_iou *= bbox_weight
loss_iou = loss_iou.sum() / target_scores_sum
# dfl loss
if self.use_dfl:
dist_mask = fg_mask.unsqueeze(-1).repeat([1, 1, (self.reg_max + 1) * 4])
pred_dist_pos = torch.masked_select(pred_dist, dist_mask).view(-1, 4, self.reg_max + 1)
target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max)
target_ltrb_pos = torch.masked_select(target_ltrb, bbox_mask).view(-1, 4)
loss_dfl = self._df_loss(pred_dist_pos, target_ltrb_pos) * bbox_weight
loss_dfl = loss_dfl.sum() / target_scores_sum
else:
loss_dfl = torch.tensor(0.0).to(pred_dist.device)
return loss_iou, loss_dfl, iou
def _df_loss(self, pred_dist, target):
target_left = target.to(torch.long)
target_right = target_left + 1
weight_left = target_right.to(torch.float) - target
weight_right = 1 - weight_left
loss_left = F.cross_entropy(pred_dist.view(-1, self.reg_max + 1), target_left.view(-1), reduction="none").view(
target_left.shape) * weight_left
loss_right = F.cross_entropy(pred_dist.view(-1, self.reg_max + 1), target_right.view(-1),
reduction="none").view(target_left.shape) * weight_right
return (loss_left + loss_right).mean(-1, keepdim=True)
class ComputeLoss:
# Compute losses
def __init__(self, model, use_dfl=True, overlap=True):
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device), reduction='none')
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
# Focal loss
g = h["fl_gamma"] # focal loss gamma
if g > 0:
BCEcls = FocalLoss(BCEcls, g)
m = de_parallel(model).model[-1] # Detect() module
self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
self.BCEcls = BCEcls
self.hyp = h
self.stride = m.stride # model strides
self.nc = m.nc # number of classes
self.nl = m.nl # number of layers
self.no = m.no
self.nm = m.nm
self.overlap = overlap
self.reg_max = m.reg_max
self.device = device
self.assigner = TaskAlignedAssigner(topk=int(os.getenv('YOLOM', 10)),
num_classes=self.nc,
alpha=float(os.getenv('YOLOA', 0.5)),
beta=float(os.getenv('YOLOB', 6.0)))
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=use_dfl).to(device)
self.proj = torch.arange(m.reg_max).float().to(device) # / 120.0
self.use_dfl = use_dfl
def preprocess(self, targets, batch_size, scale_tensor):
if targets.shape[0] == 0:
out = torch.zeros(batch_size, 0, 5, device=self.device)
else:
i = targets[:, 0] # image index
_, counts = i.unique(return_counts=True)
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size):
matches = i == j
n = matches.sum()
if n:
out[j, :n] = targets[matches, 1:]
out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
return out
def bbox_decode(self, anchor_points, pred_dist):
if self.use_dfl:
b, a, c = pred_dist.shape # batch, anchors, channels
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
return dist2bbox(pred_dist, anchor_points, xywh=False)
def __call__(self, p, targets, masks, img=None, epoch=0):
loss = torch.zeros(4, device=self.device) # box, cls, dfl
feats, pred_masks, proto = p if len(p) == 3 else p[1]
batch_size, _, mask_h, mask_w = proto.shape
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
(self.reg_max * 4, self.nc), 1)
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
pred_masks = pred_masks.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
batch_size, grid_size = pred_scores.shape[:2]
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
# targets
try:
batch_idx = targets[:, 0].view(-1, 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
except RuntimeError as e:
raise TypeError('ERROR.') from e
# pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
target_labels, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
pred_scores.detach().sigmoid(),
(pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
anchor_points * stride_tensor,
gt_labels,
gt_bboxes,
mask_gt)
target_scores_sum = target_scores.sum()
# cls loss
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
loss[2] = self.BCEcls(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
# bbox loss
if fg_mask.sum():
loss[0], loss[3], _ = self.bbox_loss(pred_distri,
pred_bboxes,
anchor_points,
target_bboxes / stride_tensor,
target_scores,
target_scores_sum,
fg_mask)
# masks loss
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
for i in range(batch_size):
if fg_mask[i].sum():
mask_idx = target_gt_idx[i][fg_mask[i]]
if self.overlap:
gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
else:
gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy,
marea) # seg loss
loss[0] *= 7.5 # box gain
loss[1] *= 2.5 / batch_size
loss[2] *= 0.5 # cls gain
loss[3] *= 1.5 # dfl gain
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
# Mask loss for one image
pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80)
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
#loss = sigmoid_focal_loss(pred_mask, gt_mask, alpha = .25, gamma = 2., reduction = 'none')
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
#p_m = torch.flatten(pred_mask.sigmoid())
#p_m = torch.flatten(pred_mask.softmax(dim = 1))
#g_m = torch.flatten(gt_mask)
#i_m = torch.sum(torch.mul(p_m, g_m))
#u_m = torch.sum(torch.add(p_m, g_m))
#d_c = (2. * i_m + 1.) / (u_m + 1.)
#d_l = (1. - d_c)
#return d_l