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170 lines
7.0 KiB
170 lines
7.0 KiB
# Ultralytics YOLO 🚀, AGPL-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, _callbacks=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, _callbacks) |
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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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