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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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+"""Model head modules."""
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+
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+import copy
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+import math
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+
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+import torch
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+import torch.nn as nn
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+from torch.nn.init import constant_, xavier_uniform_
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+
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+from ultralytics.utils.tal import TORCH_1_10, dist2bbox, dist2rbox, make_anchors
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+
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+from .block import DFL, BNContrastiveHead, ContrastiveHead, Proto
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+from .conv import Conv, DWConv
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+from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer
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+from .utils import bias_init_with_prob, linear_init
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+
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+__all__ = "Detect", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder", "v10Detect"
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+
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+
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+class Detect(nn.Module):
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+ """YOLO Detect head for detection models."""
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+
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+ dynamic = False # force grid reconstruction
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+ export = False # export mode
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+ format = None # export format
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+ end2end = False # end2end
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+ max_det = 300 # max_det
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+ shape = None
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+ anchors = torch.empty(0) # init
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+ strides = torch.empty(0) # init
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+ legacy = False # backward compatibility for v3/v5/v8/v9 models
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+
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+ def __init__(self, nc=80, ch=()):
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+ """Initializes the YOLO detection layer with specified number of classes and channels."""
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+ super().__init__()
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+ self.nc = nc # number of classes
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+ self.nl = len(ch) # number of detection layers
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+ self.reg_max = 16 # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
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+ self.no = nc + self.reg_max * 4 # number of outputs per anchor
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+ self.stride = torch.zeros(self.nl) # strides computed during build
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+ c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100)) # channels
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+ self.cv2 = nn.ModuleList(
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+ nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
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+ )
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+ self.cv3 = (
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+ nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
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+ if self.legacy
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+ else nn.ModuleList(
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+ nn.Sequential(
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+ nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
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+ nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
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+ nn.Conv2d(c3, self.nc, 1),
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+ )
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+ for x in ch
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+ )
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+ )
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+ self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
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+
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+ if self.end2end:
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+ self.one2one_cv2 = copy.deepcopy(self.cv2)
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+ self.one2one_cv3 = copy.deepcopy(self.cv3)
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+
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+ def forward(self, x):
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+ """Concatenates and returns predicted bounding boxes and class probabilities."""
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+ if self.end2end:
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+ return self.forward_end2end(x)
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+
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+ for i in range(self.nl):
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+ x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
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+ if self.training: # Training path
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+ return x
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+ y = self._inference(x)
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+ return y if self.export else (y, x)
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+
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+ def forward_end2end(self, x):
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+ """
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+ Performs forward pass of the v10Detect module.
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+
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+ Args:
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+ x (tensor): Input tensor.
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+
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+ Returns:
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+ (dict, tensor): If not in training mode, returns a dictionary containing the outputs of both one2many and one2one detections.
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+ If in training mode, returns a dictionary containing the outputs of one2many and one2one detections separately.
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+ """
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+ x_detach = [xi.detach() for xi in x]
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+ one2one = [
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+ torch.cat((self.one2one_cv2[i](x_detach[i]), self.one2one_cv3[i](x_detach[i])), 1) for i in range(self.nl)
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+ ]
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+ for i in range(self.nl):
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+ x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
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+ if self.training: # Training path
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+ return {"one2many": x, "one2one": one2one}
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+
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+ y = self._inference(one2one)
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+ y = self.postprocess(y.permute(0, 2, 1), self.max_det, self.nc)
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+ return y if self.export else (y, {"one2many": x, "one2one": one2one})
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+
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+ def _inference(self, x):
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+ """Decode predicted bounding boxes and class probabilities based on multiple-level feature maps."""
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+ # Inference path
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+ shape = x[0].shape # BCHW
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+ x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
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+ if self.format != "imx" and (self.dynamic or self.shape != shape):
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+ self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
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+ self.shape = shape
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+
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+ if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}: # avoid TF FlexSplitV ops
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+ box = x_cat[:, : self.reg_max * 4]
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+ cls = x_cat[:, self.reg_max * 4 :]
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+ else:
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+ box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
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+
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+ if self.export and self.format in {"tflite", "edgetpu"}:
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+ # Precompute normalization factor to increase numerical stability
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+ # See https://github.com/ultralytics/ultralytics/issues/7371
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+ grid_h = shape[2]
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+ grid_w = shape[3]
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+ grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
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+ norm = self.strides / (self.stride[0] * grid_size)
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+ dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
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+ elif self.export and self.format == "imx":
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+ dbox = self.decode_bboxes(
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+ self.dfl(box) * self.strides, self.anchors.unsqueeze(0) * self.strides, xywh=False
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+ )
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+ return dbox.transpose(1, 2), cls.sigmoid().permute(0, 2, 1)
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+ else:
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+ dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
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+
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+ return torch.cat((dbox, cls.sigmoid()), 1)
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+
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+ def bias_init(self):
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+ """Initialize Detect() biases, WARNING: requires stride availability."""
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+ m = self # self.model[-1] # Detect() module
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+ # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
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+ # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency
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+ for a, b, s in zip(m.cv2, m.cv3, m.stride): # from
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+ a[-1].bias.data[:] = 1.0 # box
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+ b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
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+ if self.end2end:
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+ for a, b, s in zip(m.one2one_cv2, m.one2one_cv3, m.stride): # from
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+ a[-1].bias.data[:] = 1.0 # box
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+ b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
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+
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+ def decode_bboxes(self, bboxes, anchors, xywh=True):
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+ """Decode bounding boxes."""
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+ return dist2bbox(bboxes, anchors, xywh=xywh and (not self.end2end), dim=1)
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+
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+ @staticmethod
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+ def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80):
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+ """
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+ Post-processes YOLO model predictions.
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+
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+ Args:
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+ preds (torch.Tensor): Raw predictions with shape (batch_size, num_anchors, 4 + nc) with last dimension
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+ format [x, y, w, h, class_probs].
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+ max_det (int): Maximum detections per image.
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+ nc (int, optional): Number of classes. Default: 80.
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+
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+ Returns:
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+ (torch.Tensor): Processed predictions with shape (batch_size, min(max_det, num_anchors), 6) and last
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+ dimension format [x, y, w, h, max_class_prob, class_index].
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+ """
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+ batch_size, anchors, _ = preds.shape # i.e. shape(16,8400,84)
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+ boxes, scores = preds.split([4, nc], dim=-1)
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+ index = scores.amax(dim=-1).topk(min(max_det, anchors))[1].unsqueeze(-1)
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+ boxes = boxes.gather(dim=1, index=index.repeat(1, 1, 4))
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+ scores = scores.gather(dim=1, index=index.repeat(1, 1, nc))
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+ scores, index = scores.flatten(1).topk(min(max_det, anchors))
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+ i = torch.arange(batch_size)[..., None] # batch indices
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+ return torch.cat([boxes[i, index // nc], scores[..., None], (index % nc)[..., None].float()], dim=-1)
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+
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+
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+class Segment(Detect):
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+ """YOLO Segment head for segmentation models."""
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+
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+ def __init__(self, nc=80, nm=32, npr=256, ch=()):
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+ """Initialize the YOLO model attributes such as the number of masks, prototypes, and the convolution layers."""
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+ super().__init__(nc, ch)
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+ self.nm = nm # number of masks
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+ self.npr = npr # number of protos
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+ self.proto = Proto(ch[0], self.npr, self.nm) # protos
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+
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+ c4 = max(ch[0] // 4, self.nm)
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+ self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
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+
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+ def forward(self, x):
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+ """Return model outputs and mask coefficients if training, otherwise return outputs and mask coefficients."""
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+ p = self.proto(x[0]) # mask protos
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+ bs = p.shape[0] # batch size
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+
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+ mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2) # mask coefficients
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+ x = Detect.forward(self, x)
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+ if self.training:
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+ return x, mc, p
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+ return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
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+
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+
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+class OBB(Detect):
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+ """YOLO OBB detection head for detection with rotation models."""
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+
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+ def __init__(self, nc=80, ne=1, ch=()):
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+ """Initialize OBB with number of classes `nc` and layer channels `ch`."""
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+ super().__init__(nc, ch)
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+ self.ne = ne # number of extra parameters
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+
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+ c4 = max(ch[0] // 4, self.ne)
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+ self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.ne, 1)) for x in ch)
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+
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+ def forward(self, x):
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+ """Concatenates and returns predicted bounding boxes and class probabilities."""
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+ bs = x[0].shape[0] # batch size
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+ angle = torch.cat([self.cv4[i](x[i]).view(bs, self.ne, -1) for i in range(self.nl)], 2) # OBB theta logits
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+ # NOTE: set `angle` as an attribute so that `decode_bboxes` could use it.
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+ angle = (angle.sigmoid() - 0.25) * math.pi # [-pi/4, 3pi/4]
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+ # angle = angle.sigmoid() * math.pi / 2 # [0, pi/2]
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+ if not self.training:
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+ self.angle = angle
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+ x = Detect.forward(self, x)
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+ if self.training:
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+ return x, angle
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+ return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))
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+
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+ def decode_bboxes(self, bboxes, anchors):
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+ """Decode rotated bounding boxes."""
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+ return dist2rbox(bboxes, self.angle, anchors, dim=1)
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+
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+
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+class Pose(Detect):
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+ """YOLO Pose head for keypoints models."""
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+
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+ def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
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+ """Initialize YOLO network with default parameters and Convolutional Layers."""
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+ super().__init__(nc, ch)
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+ self.kpt_shape = kpt_shape # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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+ self.nk = kpt_shape[0] * kpt_shape[1] # number of keypoints total
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+
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+ c4 = max(ch[0] // 4, self.nk)
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+ self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nk, 1)) for x in ch)
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+
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+ def forward(self, x):
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+ """Perform forward pass through YOLO model and return predictions."""
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+ bs = x[0].shape[0] # batch size
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+ kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1) # (bs, 17*3, h*w)
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+ x = Detect.forward(self, x)
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+ if self.training:
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+ return x, kpt
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+ pred_kpt = self.kpts_decode(bs, kpt)
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+ return torch.cat([x, pred_kpt], 1) if self.export else (torch.cat([x[0], pred_kpt], 1), (x[1], kpt))
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+
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+ def kpts_decode(self, bs, kpts):
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+ """Decodes keypoints."""
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+ ndim = self.kpt_shape[1]
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+ if self.export:
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+ if self.format in {
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+ "tflite",
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+ "edgetpu",
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+ }: # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
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+ # Precompute normalization factor to increase numerical stability
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+ y = kpts.view(bs, *self.kpt_shape, -1)
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+ grid_h, grid_w = self.shape[2], self.shape[3]
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+ grid_size = torch.tensor([grid_w, grid_h], device=y.device).reshape(1, 2, 1)
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+ norm = self.strides / (self.stride[0] * grid_size)
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+ a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * norm
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+ else:
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+ # NCNN fix
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+ y = kpts.view(bs, *self.kpt_shape, -1)
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+ a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
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+ if ndim == 3:
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+ a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
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+ return a.view(bs, self.nk, -1)
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+ else:
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+ y = kpts.clone()
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+ if ndim == 3:
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+ y[:, 2::3] = y[:, 2::3].sigmoid() # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug)
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+ y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides
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+ y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides
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+ return y
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+
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+
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+class Classify(nn.Module):
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+ """YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
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+
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+ export = False # export mode
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+
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+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
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+ """Initializes YOLO classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
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+ super().__init__()
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+ c_ = 1280 # efficientnet_b0 size
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+ self.conv = Conv(c1, c_, k, s, p, g)
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+ self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)
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+ self.drop = nn.Dropout(p=0.0, inplace=True)
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+ self.linear = nn.Linear(c_, c2) # to x(b,c2)
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+
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+ def forward(self, x):
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+ """Performs a forward pass of the YOLO model on input image data."""
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+ if isinstance(x, list):
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+ x = torch.cat(x, 1)
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+ x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
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+ if self.training:
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+ return x
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+ y = x.softmax(1) # get final output
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+ return y if self.export else (y, x)
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+
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+
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+class WorldDetect(Detect):
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+ """Head for integrating YOLO detection models with semantic understanding from text embeddings."""
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+
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+ def __init__(self, nc=80, embed=512, with_bn=False, ch=()):
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+ """Initialize YOLO detection layer with nc classes and layer channels ch."""
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+ super().__init__(nc, ch)
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+ c3 = max(ch[0], min(self.nc, 100))
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+ self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, embed, 1)) for x in ch)
|
|
|
+ self.cv4 = nn.ModuleList(BNContrastiveHead(embed) if with_bn else ContrastiveHead() for _ in ch)
|
|
|
+
|
|
|
+ def forward(self, x, text):
|
|
|
+ """Concatenates and returns predicted bounding boxes and class probabilities."""
|
|
|
+ for i in range(self.nl):
|
|
|
+ x[i] = torch.cat((self.cv2[i](x[i]), self.cv4[i](self.cv3[i](x[i]), text)), 1)
|
|
|
+ if self.training:
|
|
|
+ return x
|
|
|
+
|
|
|
+ # Inference path
|
|
|
+ shape = x[0].shape # BCHW
|
|
|
+ x_cat = torch.cat([xi.view(shape[0], self.nc + self.reg_max * 4, -1) for xi in x], 2)
|
|
|
+ if 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
|
|
|
+
|
|
|
+ if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}: # avoid TF FlexSplitV ops
|
|
|
+ box = x_cat[:, : self.reg_max * 4]
|
|
|
+ cls = x_cat[:, self.reg_max * 4 :]
|
|
|
+ else:
|
|
|
+ box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
|
|
|
+
|
|
|
+ if self.export and self.format in {"tflite", "edgetpu"}:
|
|
|
+ # Precompute normalization factor to increase numerical stability
|
|
|
+ # See https://github.com/ultralytics/ultralytics/issues/7371
|
|
|
+ grid_h = shape[2]
|
|
|
+ grid_w = shape[3]
|
|
|
+ grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
|
|
|
+ norm = self.strides / (self.stride[0] * grid_size)
|
|
|
+ dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
|
|
|
+ else:
|
|
|
+ dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
|
|
|
+
|
|
|
+ y = torch.cat((dbox, cls.sigmoid()), 1)
|
|
|
+ return y if self.export else (y, x)
|
|
|
+
|
|
|
+ def bias_init(self):
|
|
|
+ """Initialize Detect() biases, WARNING: requires stride availability."""
|
|
|
+ m = self # self.model[-1] # Detect() module
|
|
|
+ # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
|
|
|
+ # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency
|
|
|
+ for a, b, s in zip(m.cv2, m.cv3, m.stride): # from
|
|
|
+ a[-1].bias.data[:] = 1.0 # box
|
|
|
+ # b[-1].bias.data[:] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
|
|
|
+
|
|
|
+
|
|
|
+class RTDETRDecoder(nn.Module):
|
|
|
+ """
|
|
|
+ Real-Time Deformable Transformer Decoder (RTDETRDecoder) module for object detection.
|
|
|
+
|
|
|
+ This decoder module utilizes Transformer architecture along with deformable convolutions to predict bounding boxes
|
|
|
+ and class labels for objects in an image. It integrates features from multiple layers and runs through a series of
|
|
|
+ Transformer decoder layers to output the final predictions.
|
|
|
+ """
|
|
|
+
|
|
|
+ export = False # export mode
|
|
|
+
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ nc=80,
|
|
|
+ ch=(512, 1024, 2048),
|
|
|
+ hd=256, # hidden dim
|
|
|
+ nq=300, # num queries
|
|
|
+ ndp=4, # num decoder points
|
|
|
+ nh=8, # num head
|
|
|
+ ndl=6, # num decoder layers
|
|
|
+ d_ffn=1024, # dim of feedforward
|
|
|
+ dropout=0.0,
|
|
|
+ act=nn.ReLU(),
|
|
|
+ eval_idx=-1,
|
|
|
+ # Training args
|
|
|
+ nd=100, # num denoising
|
|
|
+ label_noise_ratio=0.5,
|
|
|
+ box_noise_scale=1.0,
|
|
|
+ learnt_init_query=False,
|
|
|
+ ):
|
|
|
+ """
|
|
|
+ Initializes the RTDETRDecoder module with the given parameters.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ nc (int): Number of classes. Default is 80.
|
|
|
+ ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).
|
|
|
+ hd (int): Dimension of hidden layers. Default is 256.
|
|
|
+ nq (int): Number of query points. Default is 300.
|
|
|
+ ndp (int): Number of decoder points. Default is 4.
|
|
|
+ nh (int): Number of heads in multi-head attention. Default is 8.
|
|
|
+ ndl (int): Number of decoder layers. Default is 6.
|
|
|
+ d_ffn (int): Dimension of the feed-forward networks. Default is 1024.
|
|
|
+ dropout (float): Dropout rate. Default is 0.
|
|
|
+ act (nn.Module): Activation function. Default is nn.ReLU.
|
|
|
+ eval_idx (int): Evaluation index. Default is -1.
|
|
|
+ nd (int): Number of denoising. Default is 100.
|
|
|
+ label_noise_ratio (float): Label noise ratio. Default is 0.5.
|
|
|
+ box_noise_scale (float): Box noise scale. Default is 1.0.
|
|
|
+ learnt_init_query (bool): Whether to learn initial query embeddings. Default is False.
|
|
|
+ """
|
|
|
+ super().__init__()
|
|
|
+ self.hidden_dim = hd
|
|
|
+ self.nhead = nh
|
|
|
+ self.nl = len(ch) # num level
|
|
|
+ self.nc = nc
|
|
|
+ self.num_queries = nq
|
|
|
+ self.num_decoder_layers = ndl
|
|
|
+
|
|
|
+ # Backbone feature projection
|
|
|
+ self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
|
|
|
+ # NOTE: simplified version but it's not consistent with .pt weights.
|
|
|
+ # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)
|
|
|
+
|
|
|
+ # Transformer module
|
|
|
+ decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
|
|
|
+ self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)
|
|
|
+
|
|
|
+ # Denoising part
|
|
|
+ self.denoising_class_embed = nn.Embedding(nc, hd)
|
|
|
+ self.num_denoising = nd
|
|
|
+ self.label_noise_ratio = label_noise_ratio
|
|
|
+ self.box_noise_scale = box_noise_scale
|
|
|
+
|
|
|
+ # Decoder embedding
|
|
|
+ self.learnt_init_query = learnt_init_query
|
|
|
+ if learnt_init_query:
|
|
|
+ self.tgt_embed = nn.Embedding(nq, hd)
|
|
|
+ self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)
|
|
|
+
|
|
|
+ # Encoder head
|
|
|
+ self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
|
|
|
+ self.enc_score_head = nn.Linear(hd, nc)
|
|
|
+ self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)
|
|
|
+
|
|
|
+ # Decoder head
|
|
|
+ self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
|
|
|
+ self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])
|
|
|
+
|
|
|
+ self._reset_parameters()
|
|
|
+
|
|
|
+ def forward(self, x, batch=None):
|
|
|
+ """Runs the forward pass of the module, returning bounding box and classification scores for the input."""
|
|
|
+ from ultralytics.models.utils.ops import get_cdn_group
|
|
|
+
|
|
|
+ # Input projection and embedding
|
|
|
+ feats, shapes = self._get_encoder_input(x)
|
|
|
+
|
|
|
+ # Prepare denoising training
|
|
|
+ dn_embed, dn_bbox, attn_mask, dn_meta = get_cdn_group(
|
|
|
+ batch,
|
|
|
+ self.nc,
|
|
|
+ self.num_queries,
|
|
|
+ self.denoising_class_embed.weight,
|
|
|
+ self.num_denoising,
|
|
|
+ self.label_noise_ratio,
|
|
|
+ self.box_noise_scale,
|
|
|
+ self.training,
|
|
|
+ )
|
|
|
+
|
|
|
+ embed, refer_bbox, enc_bboxes, enc_scores = self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)
|
|
|
+
|
|
|
+ # Decoder
|
|
|
+ dec_bboxes, dec_scores = self.decoder(
|
|
|
+ embed,
|
|
|
+ refer_bbox,
|
|
|
+ feats,
|
|
|
+ shapes,
|
|
|
+ self.dec_bbox_head,
|
|
|
+ self.dec_score_head,
|
|
|
+ self.query_pos_head,
|
|
|
+ attn_mask=attn_mask,
|
|
|
+ )
|
|
|
+ x = dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta
|
|
|
+ if self.training:
|
|
|
+ return x
|
|
|
+ # (bs, 300, 4+nc)
|
|
|
+ y = torch.cat((dec_bboxes.squeeze(0), dec_scores.squeeze(0).sigmoid()), -1)
|
|
|
+ return y if self.export else (y, x)
|
|
|
+
|
|
|
+ def _generate_anchors(self, shapes, grid_size=0.05, dtype=torch.float32, device="cpu", eps=1e-2):
|
|
|
+ """Generates anchor bounding boxes for given shapes with specific grid size and validates them."""
|
|
|
+ anchors = []
|
|
|
+ for i, (h, w) in enumerate(shapes):
|
|
|
+ sy = torch.arange(end=h, dtype=dtype, device=device)
|
|
|
+ sx = torch.arange(end=w, dtype=dtype, device=device)
|
|
|
+ grid_y, grid_x = torch.meshgrid(sy, sx, indexing="ij") if TORCH_1_10 else torch.meshgrid(sy, sx)
|
|
|
+ grid_xy = torch.stack([grid_x, grid_y], -1) # (h, w, 2)
|
|
|
+
|
|
|
+ valid_WH = torch.tensor([w, h], dtype=dtype, device=device)
|
|
|
+ grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH # (1, h, w, 2)
|
|
|
+ wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0**i)
|
|
|
+ anchors.append(torch.cat([grid_xy, wh], -1).view(-1, h * w, 4)) # (1, h*w, 4)
|
|
|
+
|
|
|
+ anchors = torch.cat(anchors, 1) # (1, h*w*nl, 4)
|
|
|
+ valid_mask = ((anchors > eps) & (anchors < 1 - eps)).all(-1, keepdim=True) # 1, h*w*nl, 1
|
|
|
+ anchors = torch.log(anchors / (1 - anchors))
|
|
|
+ anchors = anchors.masked_fill(~valid_mask, float("inf"))
|
|
|
+ return anchors, valid_mask
|
|
|
+
|
|
|
+ def _get_encoder_input(self, x):
|
|
|
+ """Processes and returns encoder inputs by getting projection features from input and concatenating them."""
|
|
|
+ # Get projection features
|
|
|
+ x = [self.input_proj[i](feat) for i, feat in enumerate(x)]
|
|
|
+ # Get encoder inputs
|
|
|
+ feats = []
|
|
|
+ shapes = []
|
|
|
+ for feat in x:
|
|
|
+ h, w = feat.shape[2:]
|
|
|
+ # [b, c, h, w] -> [b, h*w, c]
|
|
|
+ feats.append(feat.flatten(2).permute(0, 2, 1))
|
|
|
+ # [nl, 2]
|
|
|
+ shapes.append([h, w])
|
|
|
+
|
|
|
+ # [b, h*w, c]
|
|
|
+ feats = torch.cat(feats, 1)
|
|
|
+ return feats, shapes
|
|
|
+
|
|
|
+ def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
|
|
|
+ """Generates and prepares the input required for the decoder from the provided features and shapes."""
|
|
|
+ bs = feats.shape[0]
|
|
|
+ # Prepare input for decoder
|
|
|
+ anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
|
|
|
+ features = self.enc_output(valid_mask * feats) # bs, h*w, 256
|
|
|
+
|
|
|
+ enc_outputs_scores = self.enc_score_head(features) # (bs, h*w, nc)
|
|
|
+
|
|
|
+ # Query selection
|
|
|
+ # (bs, num_queries)
|
|
|
+ topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)
|
|
|
+ # (bs, num_queries)
|
|
|
+ batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)
|
|
|
+
|
|
|
+ # (bs, num_queries, 256)
|
|
|
+ top_k_features = features[batch_ind, topk_ind].view(bs, self.num_queries, -1)
|
|
|
+ # (bs, num_queries, 4)
|
|
|
+ top_k_anchors = anchors[:, topk_ind].view(bs, self.num_queries, -1)
|
|
|
+
|
|
|
+ # Dynamic anchors + static content
|
|
|
+ refer_bbox = self.enc_bbox_head(top_k_features) + top_k_anchors
|
|
|
+
|
|
|
+ enc_bboxes = refer_bbox.sigmoid()
|
|
|
+ if dn_bbox is not None:
|
|
|
+ refer_bbox = torch.cat([dn_bbox, refer_bbox], 1)
|
|
|
+ enc_scores = enc_outputs_scores[batch_ind, topk_ind].view(bs, self.num_queries, -1)
|
|
|
+
|
|
|
+ embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1) if self.learnt_init_query else top_k_features
|
|
|
+ if self.training:
|
|
|
+ refer_bbox = refer_bbox.detach()
|
|
|
+ if not self.learnt_init_query:
|
|
|
+ embeddings = embeddings.detach()
|
|
|
+ if dn_embed is not None:
|
|
|
+ embeddings = torch.cat([dn_embed, embeddings], 1)
|
|
|
+
|
|
|
+ return embeddings, refer_bbox, enc_bboxes, enc_scores
|
|
|
+
|
|
|
+ # TODO
|
|
|
+ def _reset_parameters(self):
|
|
|
+ """Initializes or resets the parameters of the model's various components with predefined weights and biases."""
|
|
|
+ # Class and bbox head init
|
|
|
+ bias_cls = bias_init_with_prob(0.01) / 80 * self.nc
|
|
|
+ # NOTE: the weight initialization in `linear_init` would cause NaN when training with custom datasets.
|
|
|
+ # linear_init(self.enc_score_head)
|
|
|
+ constant_(self.enc_score_head.bias, bias_cls)
|
|
|
+ constant_(self.enc_bbox_head.layers[-1].weight, 0.0)
|
|
|
+ constant_(self.enc_bbox_head.layers[-1].bias, 0.0)
|
|
|
+ for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
|
|
|
+ # linear_init(cls_)
|
|
|
+ constant_(cls_.bias, bias_cls)
|
|
|
+ constant_(reg_.layers[-1].weight, 0.0)
|
|
|
+ constant_(reg_.layers[-1].bias, 0.0)
|
|
|
+
|
|
|
+ linear_init(self.enc_output[0])
|
|
|
+ xavier_uniform_(self.enc_output[0].weight)
|
|
|
+ if self.learnt_init_query:
|
|
|
+ xavier_uniform_(self.tgt_embed.weight)
|
|
|
+ xavier_uniform_(self.query_pos_head.layers[0].weight)
|
|
|
+ xavier_uniform_(self.query_pos_head.layers[1].weight)
|
|
|
+ for layer in self.input_proj:
|
|
|
+ xavier_uniform_(layer[0].weight)
|
|
|
+
|
|
|
+
|
|
|
+class v10Detect(Detect):
|
|
|
+ """
|
|
|
+ v10 Detection head from https://arxiv.org/pdf/2405.14458.
|
|
|
+
|
|
|
+ Args:
|
|
|
+ nc (int): Number of classes.
|
|
|
+ ch (tuple): Tuple of channel sizes.
|
|
|
+
|
|
|
+ Attributes:
|
|
|
+ max_det (int): Maximum number of detections.
|
|
|
+
|
|
|
+ Methods:
|
|
|
+ __init__(self, nc=80, ch=()): Initializes the v10Detect object.
|
|
|
+ forward(self, x): Performs forward pass of the v10Detect module.
|
|
|
+ bias_init(self): Initializes biases of the Detect module.
|
|
|
+
|
|
|
+ """
|
|
|
+
|
|
|
+ end2end = True
|
|
|
+
|
|
|
+ def __init__(self, nc=80, ch=()):
|
|
|
+ """Initializes the v10Detect object with the specified number of classes and input channels."""
|
|
|
+ super().__init__(nc, ch)
|
|
|
+ c3 = max(ch[0], min(self.nc, 100)) # channels
|
|
|
+ # Light cls head
|
|
|
+ self.cv3 = nn.ModuleList(
|
|
|
+ nn.Sequential(
|
|
|
+ nn.Sequential(Conv(x, x, 3, g=x), Conv(x, c3, 1)),
|
|
|
+ nn.Sequential(Conv(c3, c3, 3, g=c3), Conv(c3, c3, 1)),
|
|
|
+ nn.Conv2d(c3, self.nc, 1),
|
|
|
+ )
|
|
|
+ for x in ch
|
|
|
+ )
|
|
|
+ self.one2one_cv3 = copy.deepcopy(self.cv3)
|
田运杰