import subprocess
import time
from pathlib import Path
import hydra
import torch
import torch.nn as nn
import torch.nn.functional as F
from ultralytics.yolo import v8
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
from ultralytics.yolo.utils.anchors import check_anchors
from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE
from ultralytics.yolo.utils.modeling.tasks import SegmentationModel
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy
from ultralytics.yolo.utils.torch_utils import de_parallel
# BaseTrainer python usage
class SegmentationTrainer(BaseTrainer):
def get_dataloader(self, dataset_path, batch_size, rank=0):
# TODO: manage splits differently
# calculate stride - check if model is initialized
gs = max(int(self.model.stride.max() if self.model else 0), 32)
loader = build_dataloader(
img_path=dataset_path,
img_size=self.args.img_size,
batch_size=batch_size,
single_cls=self.args.single_cls,
cache=self.args.cache,
image_weights=self.args.image_weights,
stride=gs,
rect=self.args.rect,
rank=rank,
workers=self.args.workers,
shuffle=self.args.shuffle,
use_segments=True,
)[0]
return loader
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
return batch
def load_model(self, model_cfg, weights, data):
model = SegmentationModel(model_cfg if model_cfg else weights["model"].yaml,
ch=3,
nc=data["nc"],
anchors=self.args.get("anchors"))
check_anchors(model, self.args.anchor_t, self.args.img_size)
if weights:
model.load(weights)
return model
def set_model_attributes(self):
nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps)
self.args.box *= 3 / nl # scale to layers
self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
self.args.obj *= (self.args.img_size / 640) ** 2 * 3 / nl # scale to image size and layers
self.model.nc = self.data["nc"] # attach number of classes to model
self.model.args = self.args # attach hyperparameters to model
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
self.model.names = self.data["names"]
def get_validator(self):
return v8.segment.SegmentationValidator(self.test_loader, self.device, logger=self.console)
def criterion(self, preds, batch):
head = de_parallel(self.model).model[-1]
sort_obj_iou = False
autobalance = False
# init losses
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.cls_pw], device=self.device))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.obj_pw], device=self.device))
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
cp, cn = smooth_BCE(eps=self.args.label_smoothing) # positive, negative BCE targets
# Focal loss
g = self.args.fl_gamma
if self.args.fl_gamma > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
balance = {3: [4.0, 1.0, 0.4]}.get(head.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
ssi = list(head.stride).index(16) if autobalance else 0 # stride 16 index
BCEcls, BCEobj, gr, autobalance = BCEcls, BCEobj, 1.0, autobalance
def single_mask_loss(gt_mask, pred, proto, xyxy, area):
# Mask loss for one image
pred_mask = (pred @ proto.view(head.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")
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
def build_targets(p, targets):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
nonlocal head
na, nt = head.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
gain = torch.ones(8, device=self.device) # normalized to gridspace gain
ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1,
nt) # same as .repeat_interleave(nt)
if self.args.overlap_mask:
batch = p[0].shape[0]
ti = []
for i in range(batch):
num = (targets[:, 0] == i).sum() # find number of targets of each image
ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
ti = torch.cat(ti, 1) # (na, nt)
else:
ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices
g = 0.5 # bias
off = torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=self.device).float() * g # offsets
for i in range(head.nl):
anchors, shape = head.anchors[i], p[i].shape
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain # shape(3,n,7)
if nt:
# Matches
r = t[..., 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1 / r).max(2)[0] < self.args.anchor_t # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1 < g) & (gxy > 1)).T
l, m = ((gxi % 1 < g) & (gxi > 1)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
(a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class
gij = (gxy - offsets).long()
gi, gj = gij.T # grid indices
# Append
indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
tidxs.append(tidx)
xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
return tcls, tbox, indices, anch, tidxs, xywhn
if self.model.training:
p, proto, = preds
else:
p, proto, train_out = preds
p = train_out
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
masks = batch["masks"]
targets, masks = targets.to(self.device), masks.to(self.device).float()
bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
lcls = torch.zeros(1, device=self.device)
lbox = torch.zeros(1, device=self.device)
lobj = torch.zeros(1, device=self.device)
lseg = torch.zeros(1, device=self.device)
tcls, tbox, indices, anchors, tidxs, xywhn = build_targets(p, targets)
# Losses
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
n = b.shape[0] # number of targets
if n:
pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, head.nc, nm), 1) # subset of predictions
# Box regression
pxy = pxy.sigmoid() * 2 - 0.5
pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box
iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
lbox += (1.0 - iou).mean() # iou loss
# Objectness
iou = iou.detach().clamp(0).type(tobj.dtype)
if sort_obj_iou:
j = iou.argsort()
b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
if gr < 1:
iou = (1.0 - gr) + gr * iou
tobj[b, a, gj, gi] = iou # iou ratio
# Classification
if head.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(pcls, cn, device=self.device) # targets
t[range(n), tcls[i]] = cp
lcls += BCEcls(pcls, t) # BCE
# Mask regression
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
for bi in b.unique():
j = b == bi # matching index
if True:
mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
else:
mask_gti = masks[tidxs[i]][j]
lseg += single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
obji = BCEobj(pi[..., 4], tobj)
lobj += obji * balance[i] # obj loss
if autobalance:
balance[i] = balance[i] * 0.9999 + 0.0001 / obji.detach().item()
if autobalance:
balance = [x / balance[ssi] for x in balance]
lbox *= self.args.box
lobj *= self.args.obj
lcls *= self.args.cls
lseg *= self.args.box / bs
loss = lbox + lobj + lcls + lseg
return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
def progress_string(self):
return ('\n' + '%11s' * 7) % \
('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Size')
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = v8.ROOT / "models/yolov5n-seg.yaml"
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
trainer = SegmentationTrainer(cfg)
trainer.train()
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/segment/train.py cfg=yolov5n-seg.yaml data=coco128-segments epochs=100 img_size=640
TODO:
Direct cli support, i.e, yolov8 classify_train args.epochs 10
"""
train()