diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 0a768a57..0ab515e6 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -90,15 +90,16 @@ jobs:
- name: Test detection
shell: bash # for Windows compatibility
run: |
- echo "TODO"
+ yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=1 img_size=64
+ yolo task=detect mode=val model=runs/exp/weights/last.pt img_size=64
- name: Test segmentation
shell: bash # for Windows compatibility
# TODO: redo val test without hardcoded weights
run: |
yolo task=segment mode=train model=yolov5n-seg.yaml data=coco128-seg.yaml epochs=1 img_size=64
- yolo task=segment mode=val model=runs/exp/weights/last.pt data=coco128-seg.yaml img_size=64
+ yolo task=segment mode=val model=runs/exp2/weights/last.pt data=coco128-seg.yaml img_size=64
- name: Test classification
shell: bash # for Windows compatibility
run: |
yolo task=classify mode=train model=resnet18 data=mnist160 epochs=1 img_size=32
- yolo task=classify mode=val model=runs/exp2/weights/last.pt data=mnist160
\ No newline at end of file
+ yolo task=classify mode=val model=runs/exp3/weights/last.pt data=mnist160
diff --git a/ultralytics/yolo/utils/metrics.py b/ultralytics/yolo/utils/metrics.py
index 1e05843b..1f80b92c 100644
--- a/ultralytics/yolo/utils/metrics.py
+++ b/ultralytics/yolo/utils/metrics.py
@@ -459,14 +459,14 @@ def ap_per_class_box_and_mask(
"boxes": {
"p": results_boxes[0],
"r": results_boxes[1],
- "ap": results_boxes[3],
"f1": results_boxes[2],
+ "ap": results_boxes[3],
"ap_class": results_boxes[4]},
"masks": {
"p": results_masks[0],
"r": results_masks[1],
- "ap": results_masks[3],
"f1": results_masks[2],
+ "ap": results_masks[3],
"ap_class": results_masks[4]}}
return results
@@ -547,7 +547,7 @@ class Metric:
Args:
results: tuple(p, r, ap, f1, ap_class)
"""
- p, r, all_ap, f1, ap_class_index = results
+ p, r, f1, all_ap, ap_class_index = results
self.p = p
self.r = r
self.all_ap = all_ap
diff --git a/ultralytics/yolo/utils/plotting.py b/ultralytics/yolo/utils/plotting.py
index 2bad2819..cdd2fd21 100644
--- a/ultralytics/yolo/utils/plotting.py
+++ b/ultralytics/yolo/utils/plotting.py
@@ -186,7 +186,15 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
@threaded
-def plot_images_and_masks(images, batch_idx, cls, bboxes, masks, paths, confs=None, fname='images.jpg', names=None):
+def plot_images_and_masks(images,
+ batch_idx,
+ cls,
+ bboxes,
+ masks,
+ confs=None,
+ paths=None,
+ fname='images.jpg',
+ names=None):
# Plot image grid with labels
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
@@ -327,3 +335,99 @@ def output_to_target(output, max_det=300):
targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
targets = torch.cat(targets, 0).numpy()
return targets[:, 0], targets[:, 1], targets[:, 2:6], targets[:, 6]
+
+
+@threaded
+def plot_images(images, batch_idx, cls, bboxes, confs=None, paths=None, fname='images.jpg', names=None):
+ # Plot image grid with labels
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(cls, torch.Tensor):
+ cls = cls.cpu().numpy()
+ if isinstance(bboxes, torch.Tensor):
+ bboxes = bboxes.cpu().numpy()
+ if isinstance(batch_idx, torch.Tensor):
+ batch_idx = batch_idx.cpu().numpy()
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ bs, _, h, w = images.shape # batch size, _, height, width
+ bs = min(bs, max_subplots) # limit plot images
+ ns = np.ceil(bs ** 0.5) # number of subplots (square)
+ if np.max(images[0]) <= 1:
+ images *= 255 # de-normalise (optional)
+
+ # Build Image
+ mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y:y + h, x:x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # Annotate
+ fs = int((h + w) * ns * 0.01) # font size
+ annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+ for i in range(i + 1):
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
+ if paths:
+ annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
+ if len(cls) > 0:
+ idx = batch_idx == i
+
+ boxes = xywh2xyxy(bboxes[idx]).T
+ classes = cls[idx].astype('int')
+ labels = confs is None # labels if no conf column
+ conf = None if labels else confs[idx] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ c = classes[j]
+ color = colors(c)
+ c = names[c] if names else c
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
+ annotator.box_label(box, label, color=color)
+ annotator.im.save(fname) # save
+
+
+def plot_results(file='path/to/results.csv', dir=''):
+ # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
+ ax = ax.ravel()
+ files = list(save_dir.glob('results*.csv'))
+ assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
+ for f in files:
+ try:
+ data = pd.read_csv(f)
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
+ y = data.values[:, j].astype('float')
+ # y[y == 0] = np.nan # don't show zero values
+ ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+ ax[i].set_title(s[j], fontsize=12)
+ # if j in [8, 9, 10]: # share train and val loss y axes
+ # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+ except Exception as e:
+ print(f'Warning: Plotting error for {f}: {e}')
+ ax[1].legend()
+ fig.savefig(save_dir / 'results.png', dpi=200)
+ plt.close()
diff --git a/ultralytics/yolo/v8/__init__.py b/ultralytics/yolo/v8/__init__.py
index a18b41ad..cec773ed 100644
--- a/ultralytics/yolo/v8/__init__.py
+++ b/ultralytics/yolo/v8/__init__.py
@@ -1,7 +1,7 @@
from pathlib import Path
-from ultralytics.yolo.v8 import classify, segment
+from ultralytics.yolo.v8 import classify, detect, segment
ROOT = Path(__file__).parents[0] # yolov8 ROOT
-__all__ = ["classify", "segment"]
+__all__ = ["classify", "segment", "detect"]
diff --git a/ultralytics/yolo/v8/detect/__init__.py b/ultralytics/yolo/v8/detect/__init__.py
new file mode 100644
index 00000000..edce22ae
--- /dev/null
+++ b/ultralytics/yolo/v8/detect/__init__.py
@@ -0,0 +1,2 @@
+from ultralytics.yolo.v8.detect.train import DetectionTrainer, train
+from ultralytics.yolo.v8.detect.val import DetectionValidator, val
diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py
new file mode 100644
index 00000000..22e9e36e
--- /dev/null
+++ b/ultralytics/yolo/v8/detect/train.py
@@ -0,0 +1,209 @@
+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()
diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py
new file mode 100644
index 00000000..63e9e46f
--- /dev/null
+++ b/ultralytics/yolo/v8/detect/val.py
@@ -0,0 +1,218 @@
+import os
+
+import hydra
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from ultralytics.yolo.data import build_dataloader
+from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
+from ultralytics.yolo.engine.validator import BaseValidator
+from ultralytics.yolo.utils import ops
+from ultralytics.yolo.utils.checks import check_file, check_requirements
+from ultralytics.yolo.utils.files import yaml_load
+from ultralytics.yolo.utils.metrics import ConfusionMatrix, Metric, ap_per_class, box_iou, fitness_detection
+from ultralytics.yolo.utils.plotting import output_to_target, plot_images
+from ultralytics.yolo.utils.torch_utils import de_parallel
+
+
+class DetectionValidator(BaseValidator):
+
+ def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
+ super().__init__(dataloader, save_dir, pbar, logger, args)
+ if self.args.save_json:
+ check_requirements(['pycocotools'])
+ self.process = ops.process_mask_upsample # more accurate
+ else:
+ self.process = ops.process_mask # faster
+ self.data_dict = yaml_load(check_file(self.args.data)) if self.args.data else None
+ self.is_coco = False
+ self.class_map = None
+ self.targets = None
+
+ def preprocess(self, batch):
+ batch["img"] = batch["img"].to(self.device, non_blocking=True)
+ batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
+ self.nb, _, self.height, self.width = batch["img"].shape # batch size, channels, height, width
+ self.targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
+ self.targets = self.targets.to(self.device)
+ height, width = batch["img"].shape[2:]
+ self.targets[:, 2:] *= torch.tensor((width, height, width, height), device=self.device) # to pixels
+ self.lb = [self.targets[self.targets[:, 0] == i, 1:]
+ for i in range(self.nb)] if self.args.save_hybrid else [] # for autolabelling
+
+ return batch
+
+ def init_metrics(self, model):
+ if self.training:
+ head = de_parallel(model).model[-1]
+ else:
+ head = de_parallel(model).model.model[-1]
+
+ if self.data:
+ self.is_coco = isinstance(self.data.get('val'),
+ str) and self.data['val'].endswith(f'coco{os.sep}val2017.txt')
+ self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
+ self.nc = head.nc
+ self.names = model.names
+ if isinstance(self.names, (list, tuple)): # old format
+ self.names = dict(enumerate(self.names))
+
+ self.iouv = torch.linspace(0.5, 0.95, 10, device=self.device) # iou vector for mAP@0.5:0.95
+ self.niou = self.iouv.numel()
+ self.seen = 0
+ self.confusion_matrix = ConfusionMatrix(nc=self.nc)
+ self.metrics = Metric()
+ self.loss = torch.zeros(4, device=self.device)
+ self.jdict = []
+ self.stats = []
+
+ def get_desc(self):
+ return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
+
+ def postprocess(self, preds):
+ preds = ops.non_max_suppression(preds,
+ self.args.conf_thres,
+ self.args.iou_thres,
+ labels=self.lb,
+ multi_label=True,
+ agnostic=self.args.single_cls,
+ max_det=self.args.max_det)
+ return preds
+
+ def update_metrics(self, preds, batch):
+ # Metrics
+ for si, (pred) in enumerate(preds):
+ labels = self.targets[self.targets[:, 0] == si, 1:]
+ nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
+ shape = batch["ori_shape"][si]
+ # path = batch["shape"][si][0]
+ correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
+ self.seen += 1
+
+ if npr == 0:
+ if nl:
+ self.stats.append((correct_bboxes, *torch.zeros((2, 0), device=self.device), labels[:, 0]))
+ if self.args.plots:
+ self.confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
+ continue
+
+ # Predictions
+ if self.args.single_cls:
+ pred[:, 5] = 0
+ predn = pred.clone()
+ ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape) # native-space pred
+
+ # Evaluate
+ if nl:
+ tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes
+ ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ correct_bboxes = self._process_batch(predn, labelsn, self.iouv)
+ # TODO: maybe remove these `self.` arguments as they already are member variable
+ if self.args.plots:
+ self.confusion_matrix.process_batch(predn, labelsn)
+ self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
+
+ # TODO: Save/log
+ '''
+ if self.args.save_txt:
+ save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
+ if self.args.save_json:
+ pred_masks = scale_image(im[si].shape[1:],
+ pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
+ save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
+ # callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
+ '''
+
+ def get_stats(self):
+ stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*self.stats)] # to numpy
+ if len(stats) and stats[0].any():
+ results = ap_per_class(*stats, plot=self.args.plots, save_dir=self.save_dir, names=self.names)
+ self.metrics.update(results[2:])
+ self.nt_per_class = np.bincount(stats[3].astype(int), minlength=self.nc) # number of targets per class
+ metrics = {"fitness": fitness_detection(np.array(self.metrics.mean_results()).reshape(1, -1))}
+ metrics |= zip(self.metric_keys, self.metrics.mean_results())
+ return metrics
+
+ def print_results(self):
+ pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
+ self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
+ if self.nt_per_class.sum() == 0:
+ self.logger.warning(
+ f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
+
+ # Print results per class
+ if (self.args.verbose or (self.nc < 50 and not self.training)) and self.nc > 1 and len(self.stats):
+ for i, c in enumerate(self.metrics.ap_class_index):
+ self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
+
+ if self.args.plots:
+ self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
+
+ def _process_batch(self, detections, labels, iouv):
+ """
+ Return correct prediction matrix
+ Arguments:
+ detections (array[N, 6]), x1, y1, x2, y2, conf, class
+ labels (array[M, 5]), class, x1, y1, x2, y2
+ Returns:
+ correct (array[N, 10]), for 10 IoU levels
+ """
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+ correct_class = labels[:, 0:1] == detections[:, 5]
+ for i in range(len(iouv)):
+ x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
+ if x[0].shape[0]:
+ matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
+ 1).cpu().numpy() # [label, detect, iou]
+ if x[0].shape[0] > 1:
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+ # matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+ correct[matches[:, 1].astype(int), i] = True
+ return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
+
+ def get_dataloader(self, dataset_path, batch_size):
+ # TODO: manage splits differently
+ # calculate stride - check if model is initialized
+ gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
+ return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
+
+ # TODO: align with train loss metrics
+ @property
+ def metric_keys(self):
+ return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)"]
+
+ def plot_val_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"val_batch{ni}_labels.jpg",
+ names=self.names)
+
+ def plot_predictions(self, batch, preds, ni):
+ images = batch["img"]
+ paths = batch["im_file"]
+ plot_images(images, *output_to_target(preds, max_det=15), paths, self.save_dir / f'val_batch{ni}_pred.jpg',
+ self.names) # pred
+
+
+@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
+def val(cfg):
+ cfg.data = cfg.data or "coco128.yaml"
+ validator = DetectionValidator(args=cfg)
+ validator(model=cfg.model)
+
+
+if __name__ == "__main__":
+ val()
diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py
index 5a4af698..a5481d26 100644
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@@ -250,7 +250,7 @@ class SegmentationTrainer(BaseTrainer):
cls,
bboxes,
masks,
- paths,
+ paths=paths,
fname=self.save_dir / f"train_batch{ni}.jpg")
def plot_metrics(self):
diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py
index 18dead90..3784fd3d 100644
--- a/ultralytics/yolo/v8/segment/val.py
+++ b/ultralytics/yolo/v8/segment/val.py
@@ -252,7 +252,7 @@ class SegmentationValidator(BaseValidator):
if len(self.plot_masks):
plot_masks = torch.cat(self.plot_masks, dim=0)
batch_idx, cls, bboxes, conf = output_to_target(preds[0], max_det=15)
- plot_images_and_masks(images, batch_idx, cls, bboxes, plot_masks, paths, conf,
+ plot_images_and_masks(images, batch_idx, cls, bboxes, plot_masks, conf, paths,
self.save_dir / f'val_batch{ni}_pred.jpg', self.names) # pred
self.plot_masks.clear()