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216 lines
9.4 KiB
216 lines
9.4 KiB
from copy import copy
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import hydra
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import torch
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import torch.nn as nn
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from ultralytics.nn.tasks import DetectionModel
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from ultralytics.yolo import v8
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
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from ultralytics.yolo.engine.trainer import BaseTrainer
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from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr
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from ultralytics.yolo.utils.loss import BboxLoss
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from ultralytics.yolo.utils.ops import xywh2xyxy
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from ultralytics.yolo.utils.plotting import plot_images, plot_results
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from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
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from ultralytics.yolo.utils.torch_utils import de_parallel
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# BaseTrainer python usage
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class DetectionTrainer(BaseTrainer):
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def get_dataloader(self, dataset_path, batch_size, mode="train", rank=0):
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# TODO: manage splits differently
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# calculate stride - check if model is initialized
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gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
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return create_dataloader(path=dataset_path,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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stride=gs,
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hyp=dict(self.args),
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augment=mode == "train",
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cache=self.args.cache,
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pad=0 if mode == "train" else 0.5,
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rect=self.args.rect,
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rank=rank,
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workers=self.args.workers,
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close_mosaic=self.args.close_mosaic != 0,
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prefix=colorstr(f'{mode}: '),
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shuffle=mode == "train",
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seed=self.args.seed)[0] if self.args.v5loader else \
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build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
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def preprocess_batch(self, batch):
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batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
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return batch
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def set_model_attributes(self):
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nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps)
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self.args.box *= 3 / nl # scale to layers
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# self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
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self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
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self.model.nc = self.data["nc"] # attach number of classes to model
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self.model.args = self.args # attach hyperparameters to model
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# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
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self.model.names = self.data["names"]
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def get_model(self, cfg=None, weights=None, verbose=True):
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model = DetectionModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose)
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if weights:
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model.load(model)
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return model
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def get_validator(self):
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self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
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return v8.detect.DetectionValidator(self.test_loader,
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save_dir=self.save_dir,
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logger=self.console,
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args=copy(self.args))
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def criterion(self, preds, batch):
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if not hasattr(self, 'compute_loss'):
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self.compute_loss = Loss(de_parallel(self.model))
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return self.compute_loss(preds, batch)
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def label_loss_items(self, loss_items=None, prefix="train"):
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"""
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Returns a loss dict with labelled training loss items tensor
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"""
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# Not needed for classification but necessary for segmentation & detection
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keys = [f"{prefix}/{x}" for x in self.loss_names]
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if loss_items is not None:
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loss_items = [round(float(x), 5) for x in loss_items] # convert tensors to 5 decimal place floats
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return dict(zip(keys, loss_items))
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else:
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return keys
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def progress_string(self):
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return ('\n' + '%11s' *
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(4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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def plot_training_samples(self, batch, ni):
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plot_images(images=batch["img"],
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batch_idx=batch["batch_idx"],
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cls=batch["cls"].squeeze(-1),
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bboxes=batch["bboxes"],
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paths=batch["im_file"],
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fname=self.save_dir / f"train_batch{ni}.jpg")
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def plot_metrics(self):
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plot_results(file=self.csv) # save results.png
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# Criterion class for computing training losses
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class Loss:
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def __init__(self, model): # model must be de-paralleled
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device = next(model.parameters()).device # get model device
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h = model.args # hyperparameters
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m = model.model[-1] # Detect() module
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self.bce = nn.BCEWithLogitsLoss(reduction='none')
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self.hyp = h
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self.stride = m.stride # model strides
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self.nc = m.nc # number of classes
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self.no = m.no
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self.reg_max = m.reg_max
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self.device = device
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self.use_dfl = m.reg_max > 1
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self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
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self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
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self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
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def preprocess(self, targets, batch_size, scale_tensor):
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if targets.shape[0] == 0:
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out = torch.zeros(batch_size, 0, 5, device=self.device)
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else:
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i = targets[:, 0] # image index
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_, counts = i.unique(return_counts=True)
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out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
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for j in range(batch_size):
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matches = i == j
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n = matches.sum()
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if n:
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out[j, :n] = targets[matches, 1:]
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out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
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return out
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def bbox_decode(self, anchor_points, pred_dist):
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if self.use_dfl:
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b, a, c = pred_dist.shape # batch, anchors, channels
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pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# 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)
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return dist2bbox(pred_dist, anchor_points, xywh=False)
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def __call__(self, preds, batch):
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loss = torch.zeros(3, device=self.device) # box, cls, dfl
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feats = preds[1] if isinstance(preds, tuple) else preds
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pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
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(self.reg_max * 4, self.nc), 1)
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pred_scores = pred_scores.permute(0, 2, 1).contiguous()
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pred_distri = pred_distri.permute(0, 2, 1).contiguous()
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dtype = pred_scores.dtype
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batch_size = pred_scores.shape[0]
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imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
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anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
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# targets
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targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
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targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
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gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
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mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
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# pboxes
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
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_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
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pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
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anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
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target_bboxes /= stride_tensor
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target_scores_sum = target_scores.sum()
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# cls loss
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# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
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loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
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# bbox loss
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if fg_mask.sum():
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loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
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target_scores_sum, fg_mask)
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loss[0] *= 7.5 # box gain
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loss[1] *= 0.5 # cls gain
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loss[2] *= 1.5 # dfl gain
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def train(cfg):
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cfg.model = cfg.model or "yolov8n.yaml"
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cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
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# trainer = DetectionTrainer(cfg)
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# trainer.train()
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from ultralytics import YOLO
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model = YOLO(cfg.model)
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model.train(**cfg)
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if __name__ == "__main__":
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"""
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CLI usage:
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python ultralytics/yolo/v8/detect/train.py model=yolov8n.yaml data=coco128 epochs=100 imgsz=640
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TODO:
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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"""
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train()
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