diff --git a/.gitignore b/.gitignore index 63f4cce..ef69c30 100644 --- a/.gitignore +++ b/.gitignore @@ -133,4 +133,18 @@ datasets/ runs/ wandb/ -.DS_Store \ No newline at end of file +.DS_Store + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ diff --git a/tests/check_flops.py b/tests/check_flops.py new file mode 100644 index 0000000..1dd0604 --- /dev/null +++ b/tests/check_flops.py @@ -0,0 +1,67 @@ +import torch + +from ultralytics import YOLO +from ultralytics.nn.modules import Detect, Segment + + +def export_onnx(model, file): + # YOLOv5 ONNX export + import onnx + im = torch.zeros(1, 3, 640, 640) + model.eval() + model(im, profile=True) + for k, m in model.named_modules(): + if isinstance(m, (Detect, Segment)): + m.export = True + + torch.onnx.export( + model, + im, + file, + verbose=False, + opset_version=12, + do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + input_names=['images']) + + # Checks + model_onnx = onnx.load(file) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + # Metadata + d = {'stride': int(max(model.stride)), 'names': model.names} + for k, v in d.items(): + meta = model_onnx.metadata_props.add() + meta.key, meta.value = k, str(v) + onnx.save(model_onnx, file) + + +if __name__ == "__main__": + model = YOLO() + print("yolov8n") + model.new("yolov8n.yaml") + print("yolov8n-seg") + model.new("yolov8n-seg.yaml") + print("yolov8s") + model.new("yolov8s.yaml") + # export_onnx(model.model, "yolov8s.onnx") + print("yolov8s-seg") + model.new("yolov8s-seg.yaml") + # export_onnx(model.model, "yolov8s-seg.onnx") + print("yolov8m") + model.new("yolov8m.yaml") + print("yolov8m-seg") + model.new("yolov8m-seg.yaml") + print("yolov8l") + model.new("yolov8l.yaml") + print("yolov8l-seg") + model.new("yolov8l-seg.yaml") + print("yolov8x") + model.new("yolov8x.yaml") + print("yolov8x-seg") + model.new("yolov8x-seg.yaml") + + # n vs n-seg: 8.9GFLOPs vs 12.8GFLOPs, 3.16M vs 3.6M. ch[0] // 4 (11.9GFLOPs, 3.39M) + # s vs s-seg: 28.8GFLOPs vs 44.4GFLOPs, 11.1M vs 12.9M. ch[0] // 4 (39.5GFLOPs, 11.7M) + # m vs m-seg: 79.3GFLOPs vs 113.8GFLOPs, 25.9M vs 29.5M. ch[0] // 4 (103.GFLOPs, 27.1M) + # l vs l-seg: 165.7GFLOPs vs 226.3GFLOPs, 43.7M vs 49.6M. ch[0] // 4 (207GFLOPs, 45.7M) + # x vs x-seg: 258.5GFLOPs vs 353.0GFLOPs, 68.3M vs 77.5M. ch[0] // 4 (324GFLOPs, 71.4M) diff --git a/ultralytics/nn/modules.py b/ultralytics/nn/modules.py index c72f38b..36cc41f 100644 --- a/ultralytics/nn/modules.py +++ b/ultralytics/nn/modules.py @@ -576,11 +576,11 @@ class Detections: class Proto(nn.Module): - # YOLOv5 mask Proto module for segmentation models + # YOLOv8 mask Proto module for segmentation models def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks super().__init__() self.cv1 = Conv(c1, c_, k=3) - self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True) # nn.Upsample(scale_factor=2, mode='nearest') self.cv2 = Conv(c_, c_, k=3) self.cv3 = Conv(c_, c2) @@ -628,16 +628,16 @@ class Detect(nn.Module): shape = x[0].shape # BCHW for i in range(self.nl): x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1) - box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1) if self.training: - return x, box, cls + return x elif self.dynamic or self.shape != shape: self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5)) self.shape = shape + box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1) dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides y = torch.cat((dbox, cls.sigmoid()), 1) - return y if self.export else (y, (x, box, cls)) + return y if self.export else (y, x) def bias_init(self): # Initialize Detect() biases, WARNING: requires stride availability @@ -651,19 +651,27 @@ class Detect(nn.Module): class Segment(Detect): # YOLOv5 Segment head for segmentation models - def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=()): - super().__init__(nc, anchors, ch) + def __init__(self, nc=80, nm=32, npr=256, ch=()): + super().__init__(nc, ch) self.nm = nm # number of masks self.npr = npr # number of protos - self.no = 5 + nc + self.nm # number of outputs per anchor - self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.proto = Proto(ch[0], self.npr, self.nm) # protos self.detect = Detect.forward + c4 = max(ch[0] // 4, self.nm) + self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch) + def forward(self, x): p = self.proto(x[0]) + + mc = [] # mask coefficient + for i in range(self.nl): + mc.append(self.cv4[i](x[i])) + mc = torch.cat([mi.view(p.shape[0], self.nm, -1) for mi in mc], 2) x = self.detect(self, x) - return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) + if self.training: + return x, mc, p + return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p)) class Classify(nn.Module): diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py index 8749c28..9ac875f 100644 --- a/ultralytics/nn/tasks.py +++ b/ultralytics/nn/tasks.py @@ -101,7 +101,7 @@ class DetectionModel(BaseModel): if isinstance(m, (Detect, Segment)): s = 256 # 2x min stride m.inplace = self.inplace - forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, Detect)) else self.forward(x) + forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward self.stride = m.stride m.bias_init() # only run once @@ -163,8 +163,8 @@ class DetectionModel(BaseModel): class SegmentationModel(DetectionModel): # YOLOv5 segmentation model - def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None): - super().__init__(cfg, ch, nc) + def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, verbose=True): + super().__init__(cfg, ch, nc, verbose) class ClassificationModel(BaseModel): @@ -300,7 +300,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3) elif m in {Detect, Segment}: args.append([ch[x] for x in f]) if m is Segment: - args[3] = make_divisible(args[3] * gw, 8) + args[2] = make_divisible(args[2] * gw, 8) else: c2 = ch[f] diff --git a/ultralytics/yolo/__init__.py b/ultralytics/yolo/__init__.py index e69de29..86ddd1b 100644 --- a/ultralytics/yolo/__init__.py +++ b/ultralytics/yolo/__init__.py @@ -0,0 +1 @@ +from . import v8 diff --git a/ultralytics/yolo/utils/configs/default.yaml b/ultralytics/yolo/utils/configs/default.yaml index d362027..b3f97bc 100644 --- a/ultralytics/yolo/utils/configs/default.yaml +++ b/ultralytics/yolo/utils/configs/default.yaml @@ -3,7 +3,7 @@ # Task and Mode task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case -mode: "train" # choice=['train', 'val', 'infer'] +mode: "train" # choice=['train', 'val', 'predict'] # Train settings ------------------------------------------------------------------------------------------------------- model: null # i.e. yolov5s.pt, yolo.yaml diff --git a/ultralytics/yolo/utils/tal.py b/ultralytics/yolo/utils/tal.py index 35e9f28..32c621d 100644 --- a/ultralytics/yolo/utils/tal.py +++ b/ultralytics/yolo/utils/tal.py @@ -86,7 +86,8 @@ class TaskAlignedAssigner(nn.Module): if self.n_max_boxes == 0: device = gt_bboxes.device return (torch.full_like(pd_scores[..., 0], self.bg_idx).to(device), torch.zeros_like(pd_bboxes).to(device), - torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device)) + torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device), + torch.zeros_like(pd_scores[..., 0]).to(device)) mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt) @@ -103,7 +104,7 @@ class TaskAlignedAssigner(nn.Module): norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1) target_scores = target_scores * norm_align_metric - return target_labels, target_bboxes, target_scores, fg_mask.bool() + return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt): # get anchor_align metric, (b, max_num_obj, h*w) @@ -146,9 +147,6 @@ class TaskAlignedAssigner(nn.Module): # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w) is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2) # filter invalid bboxes - # assigned topk should be unique, this is for dealing with empty labels - # since empty labels will generate index `0` through `F.one_hot` - # NOTE: but what if the topk_idxs include `0`? is_in_topk = torch.where(is_in_topk > 1, 0, is_in_topk) return is_in_topk.to(metrics.dtype) diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py index 40d7b3f..1b7f867 100644 --- a/ultralytics/yolo/v8/detect/train.py +++ b/ultralytics/yolo/v8/detect/train.py @@ -9,11 +9,10 @@ from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer from ultralytics.yolo.utils import colorstr from ultralytics.yolo.utils.loss import BboxLoss -from ultralytics.yolo.utils.metrics import smooth_BCE from ultralytics.yolo.utils.ops import xywh2xyxy 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, strip_optimizer +from ultralytics.yolo.utils.torch_utils import de_parallel # BaseTrainer python usage @@ -78,7 +77,8 @@ class DetectionTrainer(BaseTrainer): return dict(zip(keys, loss_items)) if loss_items is not None else keys def progress_string(self): - return ('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') + return ('\n' + '%11s' * + (4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') def plot_training_samples(self, batch, ni): plot_images(images=batch["img"], @@ -100,15 +100,13 @@ class Loss: device = next(model.parameters()).device # get model device h = model.args # hyperparameters - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets - 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.nl = m.nl # number of layers + self.no = m.no + self.reg_max = m.reg_max self.device = device self.use_dfl = m.reg_max > 1 @@ -141,12 +139,15 @@ class Loss: def __call__(self, preds, batch): loss = torch.zeros(3, device=self.device) # box, cls, dfl - feats, pred_distri, pred_scores = preds if len(preds) == 3 else preds[1] + feats = preds[1] if isinstance(preds, tuple) else preds + 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) + pred_scores = pred_scores.permute(0, 2, 1).contiguous() pred_distri = pred_distri.permute(0, 2, 1).contiguous() dtype = pred_scores.dtype - batch_size, grid_size = pred_scores.shape[:2] + batch_size = pred_scores.shape[0] 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) @@ -159,7 +160,7 @@ class Loss: # pboxes pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) - target_labels, target_bboxes, target_scores, fg_mask = self.assigner( + _, target_bboxes, target_scores, fg_mask, _ = 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) diff --git a/ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml b/ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml new file mode 100644 index 0000000..b35838f --- /dev/null +++ b/ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.00 # model depth multiple +width_multiple: 1.00 # layer channel multiple + +# YOLOv8.0l backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 3, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C2f, [128, True]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C2f, [256, True]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 6, C2f, [512, True]], + [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 + [-1, 3, C2f, [512, True]], + [-1, 1, SPPF, [512, 5]], # 9 + ] + +# YOLOv8.0l head +head: + [[-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C2f, [512]], # 13 + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C2f, [256]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P4 + [-1, 3, C2f, [512]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 9], 1, Concat, [1]], # cat head P5 + [-1, 3, C2f, [512]], # 23 (P5/32-large) + + [[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml b/ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml new file mode 100644 index 0000000..09084c0 --- /dev/null +++ b/ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple + +# YOLOv8.0m backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 3, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C2f, [128, True]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C2f, [256, True]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 6, C2f, [512, True]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C2f, [768, True]], + [-1, 1, SPPF, [768, 5]], # 9 + ] + +# YOLOv8.0m head +head: + [[-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C2f, [512]], # 13 + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C2f, [256]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P4 + [-1, 3, C2f, [512]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 9], 1, Concat, [1]], # cat head P5 + [-1, 3, C2f, [768]], # 23 (P5/32-large) + + [[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/ultralytics/yolo/v8/models/seg/yolov8n-seg.yaml b/ultralytics/yolo/v8/models/seg/yolov8n-seg.yaml index 18f877e..b8e4025 100644 --- a/ultralytics/yolo/v8/models/seg/yolov8n-seg.yaml +++ b/ultralytics/yolo/v8/models/seg/yolov8n-seg.yaml @@ -4,9 +4,8 @@ nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple -anchors: [[16,19], [55,65], [178,192]] -# YOLOv8n v0.0 backbone +# YOLOv8.0n backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 3, 2]], # 0-P1/2 @@ -21,7 +20,7 @@ backbone: [-1, 1, SPPF, [1024, 5]], # 9 ] -# YOLOv8n v0.0 head +# YOLOv8.0n head head: [[-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 diff --git a/ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml b/ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml new file mode 100644 index 0000000..9cdbb9f --- /dev/null +++ b/ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple + +# YOLOv8.0s backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 3, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C2f, [128, True]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C2f, [256, True]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 6, C2f, [512, True]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C2f, [1024, True]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv8.0s head +head: + [[-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C2f, [512]], # 13 + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C2f, [256]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P4 + [-1, 3, C2f, [512]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 9], 1, Concat, [1]], # cat head P5 + [-1, 3, C2f, [1024]], # 23 (P5/32-large) + + [[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml b/ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml new file mode 100644 index 0000000..42f3e47 --- /dev/null +++ b/ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.00 # model depth multiple +width_multiple: 1.25 # layer channel multiple + +# YOLOv8.0x backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 3, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C2f, [128, True]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C2f, [256, True]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 6, C2f, [512, True]], + [-1, 1, Conv, [512, 3, 2]], # 7-P5/32 + [-1, 3, C2f, [512, True]], + [-1, 1, SPPF, [512, 5]], # 9 + ] + +# YOLOv8.0x head +head: + [[-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C2f, [512]], # 13 + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C2f, [256]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P4 + [-1, 3, C2f, [512]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 9], 1, Concat, [1]], # cat head P5 + [-1, 3, C2f, [512]], # 23 (P5/32-large) + + [[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/ultralytics/yolo/v8/segment/predict.py b/ultralytics/yolo/v8/segment/predict.py index bd0384f..55cb9f5 100644 --- a/ultralytics/yolo/v8/segment/predict.py +++ b/ultralytics/yolo/v8/segment/predict.py @@ -12,17 +12,14 @@ class SegmentationPredictor(DetectionPredictor): def postprocess(self, preds, img, orig_img): masks = [] - if len(preds) == 2: # eval - p, proto, = preds - else: # len(3) train - p, proto, _ = preds # TODO: filter by classes - p = ops.non_max_suppression(p, + p = ops.non_max_suppression(preds[0], self.args.conf_thres, self.args.iou_thres, agnostic=self.args.agnostic_nms, max_det=self.args.max_det, nm=32) + proto = preds[1][-1] for i, pred in enumerate(p): shape = orig_img[i].shape if self.webcam else orig_img.shape if not len(pred): diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py index b98548c..4809f6e 100644 --- a/ultralytics/yolo/v8/segment/train.py +++ b/ultralytics/yolo/v8/segment/train.py @@ -6,9 +6,10 @@ import torch.nn.functional as F from ultralytics.nn.tasks import SegmentationModel from ultralytics.yolo import v8 from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG -from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE -from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy +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 from ..detect import DetectionTrainer @@ -31,188 +32,9 @@ class SegmentationTrainer(DetectionTrainer): 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 single_mask_loss(gt_mask, pred, proto, xyxy, area): - # Mask loss for one image - pred_mask = (pred @ proto.view(head.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() - - 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, tidxs, xywhn = [], [], [], [], [], [] - gain = torch.ones(8, device=self.device) # normalized to gridspace gain - ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, - nt) # same as .repeat_interleave(nt) - if self.args.overlap_mask: - batch = p[0].shape[0] - ti = [] - for i in range(batch): - num = (targets[:, 0] == i).sum() # find number of targets of each image - ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) - ti = torch.cat(ti, 1) # (na, nt) - else: - ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) - targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., 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, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors - (a, tidx), (b, c) = at.long().T, 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 - tidxs.append(tidx) - xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized - - return tcls, tbox, indices, anch, tidxs, xywhn - - if len(preds) == 2: # eval - p, proto, = preds - else: # len(3) train - _, proto, p = preds - - targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1) - masks = batch["masks"] - targets, masks = targets.to(self.device), masks.to(self.device).float() - - bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width - lcls = torch.zeros(1, device=self.device) - lbox = torch.zeros(1, device=self.device) - lobj = torch.zeros(1, device=self.device) - lseg = torch.zeros(1, device=self.device) - tcls, tbox, indices, anchors, tidxs, xywhn = 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 - - n = b.shape[0] # number of targets - if n: - pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, head.nc, nm), 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 - - # Mask regression - if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample - masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] - marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized - mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) - for bi in b.unique(): - j = b == bi # matching index - if self.args.overlap_mask: - mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) - else: - mask_gti = masks[tidxs[i]][j] - lseg += single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) - else: - lseg += (proto * 0).sum() - - 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 - lseg *= self.args.box / bs - - loss = lbox + lobj + lcls + lseg - return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() - - def label_loss_items(self, loss_items=None, prefix="train"): - # We should just use named tensors here in future - keys = [f"{prefix}/{x}" for x in self.loss_names] - return dict(zip(keys, loss_items)) if loss_items is not None else keys - - def progress_string(self): - return ('\n' + '%11s' * 8) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') + 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"] @@ -227,6 +49,129 @@ class SegmentationTrainer(DetectionTrainer): 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 "models/yolov8n-seg.yaml" diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py index f91c67b..6d9c848 100644 --- a/ultralytics/yolo/v8/segment/val.py +++ b/ultralytics/yolo/v8/segment/val.py @@ -66,7 +66,7 @@ class SegmentationValidator(DetectionValidator): agnostic=self.args.single_cls, max_det=self.args.max_det, nm=self.nm) - return p, preds[1], preds[2] + return p, preds[1][-1] def update_metrics(self, preds, batch): # Metrics