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171 lines
6.9 KiB
171 lines
6.9 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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from copy import copy
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import torch
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import torch.nn as nn
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from ultralytics.nn.tasks import PoseModel
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from ultralytics.yolo import v8
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from ultralytics.yolo.utils import DEFAULT_CFG
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from ultralytics.yolo.utils.loss import KeypointLoss
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from ultralytics.yolo.utils.metrics import OKS_SIGMA
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from ultralytics.yolo.utils.ops import xyxy2xywh
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from ultralytics.yolo.utils.plotting import plot_images, plot_results
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from ultralytics.yolo.utils.tal import make_anchors
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from ultralytics.yolo.utils.torch_utils import de_parallel
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from ultralytics.yolo.v8.detect.train import Loss
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# BaseTrainer python usage
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class PoseTrainer(v8.detect.DetectionTrainer):
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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if overrides is None:
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overrides = {}
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overrides['task'] = 'pose'
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super().__init__(cfg, overrides)
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def get_model(self, cfg=None, weights=None, verbose=True):
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model = PoseModel(cfg, ch=3, nc=self.data['nc'], data_kpt_shape=self.data['kpt_shape'], verbose=verbose)
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if weights:
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model.load(weights)
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return model
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def set_model_attributes(self):
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super().set_model_attributes()
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self.model.kpt_shape = self.data['kpt_shape']
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def get_validator(self):
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self.loss_names = 'box_loss', 'pose_loss', 'kobj_loss', 'cls_loss', 'dfl_loss'
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return v8.pose.PoseValidator(self.test_loader, save_dir=self.save_dir, 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 = PoseLoss(de_parallel(self.model))
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return self.compute_loss(preds, batch)
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def plot_training_samples(self, batch, ni):
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images = batch['img']
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kpts = batch['keypoints']
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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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batch_idx = batch['batch_idx']
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plot_images(images,
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batch_idx,
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cls,
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bboxes,
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kpts=kpts,
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paths=paths,
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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, pose=True) # save results.png
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# Criterion class for computing training losses
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class PoseLoss(Loss):
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def __init__(self, model): # model must be de-paralleled
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super().__init__(model)
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self.kpt_shape = model.model[-1].kpt_shape
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self.bce_pose = nn.BCEWithLogitsLoss()
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is_pose = self.kpt_shape == [17, 3]
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nkpt = self.kpt_shape[0] # number of keypoints
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sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt
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self.keypoint_loss = KeypointLoss(sigmas=sigmas)
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def __call__(self, preds, batch):
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loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility
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feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
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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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# b, grids, ..
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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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pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
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dtype = pred_scores.dtype
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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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batch_size = pred_scores.shape[0]
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batch_idx = batch['batch_idx'].view(-1, 1)
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targets = torch.cat((batch_idx, 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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pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3)
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_, target_bboxes, target_scores, fg_mask, target_gt_idx = 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_scores_sum = max(target_scores.sum(), 1)
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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[3] = 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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target_bboxes /= stride_tensor
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loss[0], loss[4] = 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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keypoints = batch['keypoints'].to(self.device).float().clone()
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keypoints[..., 0] *= imgsz[1]
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keypoints[..., 1] *= imgsz[0]
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for i in range(batch_size):
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if fg_mask[i].sum():
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idx = target_gt_idx[i][fg_mask[i]]
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gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51)
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gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
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gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
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area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
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pred_kpt = pred_kpts[i][fg_mask[i]]
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kpt_mask = gt_kpt[..., 2] != 0
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loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
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# kpt_score loss
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if pred_kpt.shape[-1] == 3:
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loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
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loss[0] *= self.hyp.box # box gain
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loss[1] *= self.hyp.pose / batch_size # pose gain
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loss[2] *= self.hyp.kobj / batch_size # kobj gain
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loss[3] *= self.hyp.cls # cls gain
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loss[4] *= self.hyp.dfl # dfl gain
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
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def kpts_decode(self, anchor_points, pred_kpts):
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y = pred_kpts.clone()
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y[..., :2] *= 2.0
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y[..., 0] += anchor_points[:, [0]] - 0.5
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y[..., 1] += anchor_points[:, [1]] - 0.5
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return y
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def train(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or 'yolov8n-pose.yaml'
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data = cfg.data or 'coco8-pose.yaml'
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device = cfg.device if cfg.device is not None else ''
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args = dict(model=model, data=data, device=device)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).train(**args)
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else:
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trainer = PoseTrainer(overrides=args)
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trainer.train()
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if __name__ == '__main__':
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train()
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