You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
232 lines
9.8 KiB
232 lines
9.8 KiB
import hydra
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
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.metrics import FocalLoss, bbox_iou, smooth_BCE
|
|
from ultralytics.yolo.utils.modeling.tasks import DetectionModel
|
|
from ultralytics.yolo.utils.plotting import plot_images, plot_results
|
|
from ultralytics.yolo.utils.torch_utils import de_parallel
|
|
|
|
|
|
# BaseTrainer python usage
|
|
class DetectionTrainer(BaseTrainer):
|
|
|
|
def get_dataloader(self, dataset_path, batch_size, mode="train", rank=0):
|
|
# TODO: manage splits differently
|
|
# calculate stride - check if model is initialized
|
|
gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
|
|
return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
|
|
|
|
def preprocess_batch(self, batch):
|
|
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
|
|
return batch
|
|
|
|
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.imgsz / 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 load_model(self, model_cfg=None, weights=None):
|
|
model = DetectionModel(model_cfg or weights["model"].yaml,
|
|
ch=3,
|
|
nc=self.data["nc"],
|
|
anchors=self.args.get("anchors"))
|
|
if weights:
|
|
model.load(weights)
|
|
for _, v in model.named_parameters():
|
|
v.requires_grad = True # train all layers
|
|
return model
|
|
|
|
def get_validator(self):
|
|
self.loss_names = 'box_loss', 'obj_loss', 'cls_loss'
|
|
return v8.detect.DetectionValidator(self.test_loader,
|
|
save_dir=self.save_dir,
|
|
logger=self.console,
|
|
args=self.args)
|
|
|
|
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 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 = [], [], [], []
|
|
gain = torch.ones(7, device=self.device) # normalized to gridspace gain
|
|
ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)
|
|
targets = torch.cat((targets.repeat(na, 1, 1), ai[..., 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, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
|
|
a, (b, c) = a.long().view(-1), 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
|
|
|
|
return tcls, tbox, indices, anch
|
|
|
|
if len(preds) == 2: # eval
|
|
_, p = preds
|
|
else: # len(3) train
|
|
p = preds
|
|
|
|
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
|
|
targets = targets.to(self.device)
|
|
|
|
lcls = torch.zeros(1, device=self.device)
|
|
lbox = torch.zeros(1, device=self.device)
|
|
lobj = torch.zeros(1, device=self.device)
|
|
tcls, tbox, indices, anchors = 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
|
|
bs = tobj.shape[0]
|
|
n = b.shape[0] # number of targets
|
|
if n:
|
|
pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, head.nc), 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
|
|
|
|
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
|
|
|
|
loss = lbox + lobj + lcls
|
|
return loss * bs, torch.cat((lbox, lobj, lcls)).detach()
|
|
|
|
def label_loss_items(self, loss_items=None, prefix="train"):
|
|
# We should just use named tensors here in future
|
|
keys = [f"{prefix}/{x}" for x in self.loss_names]
|
|
return dict(zip(keys, loss_items)) if loss_items is not None else keys
|
|
|
|
def progress_string(self):
|
|
return ('\n' + '%11s' * 6) % \
|
|
('Epoch', 'GPU_mem', *self.loss_names, 'Size')
|
|
|
|
def plot_training_samples(self, batch, ni):
|
|
images = batch["img"]
|
|
cls = batch["cls"].squeeze(-1)
|
|
bboxes = batch["bboxes"]
|
|
paths = batch["im_file"]
|
|
batch_idx = batch["batch_idx"]
|
|
plot_images(images, batch_idx, cls, bboxes, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg")
|
|
|
|
def plot_metrics(self):
|
|
plot_results(file=self.csv) # save results.png
|
|
|
|
|
|
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
|
|
def train(cfg):
|
|
cfg.model = cfg.model or "models/yolov5n.yaml"
|
|
cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
|
|
trainer = DetectionTrainer(cfg)
|
|
trainer.train()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
"""
|
|
CLI usage:
|
|
python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 imgsz=640
|
|
|
|
TODO:
|
|
yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=100
|
|
"""
|
|
train()
|