Detection support (#60)

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ultralytics/yolo/v8/detect/train.py

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+import hydra
+import torch
+import torch.nn as nn
+
+from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
+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
+
+from ..segment import SegmentationTrainer
+from .val import DetectionValidator
+
+
+# BaseTrainer python usage
+class DetectionTrainer(SegmentationTrainer):
+
+    def load_model(self, model_cfg, weights, data):
+        model = DetectionModel(model_cfg or weights["model"].yaml,
+                               ch=3,
+                               nc=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):
+        return 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()
+
+    # TODO: improve from API users perspective
+    def label_loss_items(self, loss_items=None, prefix="train"):
+        # We should just use named tensors here in future
+        keys = [f"{prefix}/lbox", f"{prefix}/lobj", f"{prefix}/lcls"]
+        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', 'box_loss', 'obj_loss', 'cls_loss', '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/segment/train.py cfg=yolov5n-seg.yaml data=coco128-segments epochs=100 img_size=640
+
+    TODO:
+    Direct cli support, i.e, yolov8 classify_train args.epochs 10
+    """
+    train()