Segmentation support & other enchancements (#40)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia
GitHub
parent
c617ee1c79
commit
f56c9bcc26
17 changed files with 1320 additions and 47 deletions
269
ultralytics/yolo/v8/segment/train.py
Normal file
269
ultralytics/yolo/v8/segment/train.py
Normal file
|
|
@ -0,0 +1,269 @@
|
|||
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.downloads import download
|
||||
from ultralytics.yolo.utils.files import WorkingDirectory
|
||||
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 LOCAL_RANK, de_parallel, torch_distributed_zero_first
|
||||
|
||||
|
||||
# BaseTrainer python usage
|
||||
class SegmentationTrainer(BaseTrainer):
|
||||
|
||||
def get_dataset(self, dataset):
|
||||
# temporary solution. Replace with new ultralytics.yolo.ClassificationDataset module
|
||||
data = Path("datasets") / dataset
|
||||
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(Path.cwd()):
|
||||
data_dir = data if data.is_dir() else (Path.cwd() / data)
|
||||
if not data_dir.is_dir():
|
||||
self.console.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
|
||||
t = time.time()
|
||||
if str(data) == 'imagenet':
|
||||
subprocess.run(f"bash {v8.ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
|
||||
else:
|
||||
url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
|
||||
download(url, dir=data_dir.parent)
|
||||
# TODO: add colorstr
|
||||
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {'bold', data_dir}\n"
|
||||
self.console.info(s)
|
||||
train_set = data_dir.parent / "coco128-seg"
|
||||
test_set = train_set
|
||||
return train_set, test_set
|
||||
|
||||
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_cfg(self, cfg):
|
||||
return SegmentationModel(cfg, nc=80)
|
||||
|
||||
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.cfg = v8.ROOT / "models/yolov5n-seg.yaml"
|
||||
cfg.data = cfg.data or "coco128-segments" # 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()
|
||||
Loading…
Add table
Add a link
Reference in a new issue