YOLOv8-16bit/ultralytics/yolo/v8/segment/train.py

import hydra
import torch
import torch.nn as nn
import torch.nn.functional as F

from ultralytics.nn.tasks import SegmentationModel
from ultralytics.yolo import v8
from ultralytics.yolo.utils import DEFAULT_CONFIG
from ultralytics.yolo.utils.loss import BboxLoss
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel


# BaseTrainer python usage
class SegmentationTrainer(v8.detect.DetectionTrainer):

    def load_model(self, model_cfg=None, weights=None, verbose=True):
        model = SegmentationModel(model_cfg or getattr(weights, 'yaml', None) or weights['model'].yaml,
                                  ch=3,
                                  nc=self.data["nc"],
                                  verbose=verbose)
        if weights:
            model.load(weights['model'] if isinstance(weights, dict) else weights, verbose)
        return model

    def get_validator(self):
        self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
        return v8.segment.SegmentationValidator(self.test_loader,
                                                save_dir=self.save_dir,
                                                logger=self.console,
                                                args=self.args)

    def criterion(self, preds, batch):
        if not hasattr(self, 'compute_loss'):
            self.compute_loss = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask)
        return self.compute_loss(preds, batch)

    def plot_training_samples(self, batch, ni):
        images = batch["img"]
        masks = batch["masks"]
        cls = batch["cls"].squeeze(-1)
        bboxes = batch["bboxes"]
        paths = batch["im_file"]
        batch_idx = batch["batch_idx"]
        plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg")

    def plot_metrics(self):
        plot_results(file=self.csv, segment=True)  # save results.png


# Criterion class for computing training losses
class SegLoss:

    def __init__(self, model, overlap=True):  # model must be de-paralleled

        device = next(model.parameters()).device  # get model device
        h = model.args  # hyperparameters

        m = model.model[-1]  # Detect() module
        self.bce = nn.BCEWithLogitsLoss(reduction='none')
        self.hyp = h
        self.stride = m.stride  # model strides
        self.nc = m.nc  # number of classes
        self.no = m.no
        self.nm = m.nm  # number of masks
        self.reg_max = m.reg_max
        self.overlap = overlap
        self.device = device

        self.use_dfl = m.reg_max > 1
        self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
        self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
        self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)

    def preprocess(self, targets, batch_size, scale_tensor):
        if targets.shape[0] == 0:
            out = torch.zeros(batch_size, 0, 5, device=self.device)
        else:
            i = targets[:, 0]  # image index
            _, counts = i.unique(return_counts=True)
            out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
            for j in range(batch_size):
                matches = i == j
                n = matches.sum()
                if n:
                    out[j, :n] = targets[matches, 1:]
            out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
        return out

    def bbox_decode(self, anchor_points, pred_dist):
        if self.use_dfl:
            b, a, c = pred_dist.shape  # batch, anchors, channels
            pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
            # pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
            # 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)
        return dist2bbox(pred_dist, anchor_points, xywh=False)

    def __call__(self, preds, batch):
        loss = torch.zeros(4, device=self.device)  # box, cls, dfl
        feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
        batch_size, _, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width
        pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
            (self.reg_max * 4, self.nc), 1)

        # b, grids, ..
        pred_scores = pred_scores.permute(0, 2, 1).contiguous()
        pred_distri = pred_distri.permute(0, 2, 1).contiguous()
        pred_masks = pred_masks.permute(0, 2, 1).contiguous()

        dtype = pred_scores.dtype
        imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
        anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)

        # targets
        batch_idx = batch["batch_idx"].view(-1, 1)
        targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
        gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)

        masks = batch["masks"].to(self.device).float()
        if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
            masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]

        # pboxes
        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)

        _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
            pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
            anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)

        target_scores_sum = target_scores.sum()

        # cls loss
        # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
        loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE

        # bbox loss
        if fg_mask.sum():
            loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
                                              target_scores, target_scores_sum, fg_mask)
            for i in range(batch_size):
                if fg_mask[i].sum():
                    mask_idx = target_gt_idx[i][fg_mask[i]] + 1
                    if self.overlap:
                        gt_mask = torch.where(masks[[i]] == mask_idx.view(-1, 1, 1), 1.0, 0.0)
                    else:
                        gt_mask = masks[batch_idx == i][mask_idx]
                    xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
                    marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
                    mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
                    loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy,
                                                     marea)  # seg loss
        # WARNING: Uncomment lines below in case of Multi-GPU DDP unused gradient errors
        #         else:
        #             loss[1] += proto.sum() * 0
        # else:
        #     loss[1] += proto.sum() * 0

        loss[0] *= 7.5  # box gain
        loss[1] *= 7.5 / batch_size  # seg gain
        loss[2] *= 0.5  # cls gain
        loss[3] *= 1.5  # dfl gain

        return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)

    def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
        # Mask loss for one image
        pred_mask = (pred @ proto.view(self.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()


@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def train(cfg):
    cfg.model = cfg.model or "yolov8n-seg.yaml"
    cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
    trainer = SegmentationTrainer(cfg)
    trainer.train()


if __name__ == "__main__":
    """
    CLI usage:
    python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640

    TODO:
    Direct cli support, i.e, yolov8 classify_train args.epochs 10
    """
    train()