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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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30
ultralytics/utils/tal.py
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@ -7,7 +7,7 @@ from .checks import check_version
from .metrics import bbox_iou, probiou
from .ops import xywhr2xyxyxyxy
TORCH_1_10 = check_version(torch.__version__, '1.10.0')
TORCH_1_10 = check_version(torch.__version__, "1.10.0")
class TaskAlignedAssigner(nn.Module):
@ -61,12 +61,17 @@ 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[..., 0]).to(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[..., 0]).to(device),
)
mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points,
mask_gt)
mask_pos, align_metric, overlaps = self.get_pos_mask(
pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt
)
target_gt_idx, fg_mask, mask_pos = self.select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
@ -148,7 +153,7 @@ class TaskAlignedAssigner(nn.Module):
ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
for k in range(self.topk):
# Expand topk_idxs for each value of k and add 1 at the specified positions
count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
count_tensor.scatter_add_(-1, topk_idxs[:, :, k : k + 1], ones)
# count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
# Filter invalid bboxes
count_tensor.masked_fill_(count_tensor > 1, 0)
@ -192,9 +197,11 @@ class TaskAlignedAssigner(nn.Module):
target_labels.clamp_(0)
# 10x faster than F.one_hot()
target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
dtype=torch.int64,
device=target_labels.device) # (b, h*w, 80)
target_scores = torch.zeros(
(target_labels.shape[0], target_labels.shape[1], self.num_classes),
dtype=torch.int64,
device=target_labels.device,
) # (b, h*w, 80)
target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes) # (b, h*w, 80)
@ -252,7 +259,6 @@ class TaskAlignedAssigner(nn.Module):
class RotatedTaskAlignedAssigner(TaskAlignedAssigner):
def iou_calculation(self, gt_bboxes, pd_bboxes):
"""Iou calculation for rotated bounding boxes."""
return probiou(gt_bboxes, pd_bboxes).squeeze(-1).clamp_(0)
@ -295,7 +301,7 @@ def make_anchors(feats, strides, grid_cell_offset=0.5):
_, _, h, w = feats[i].shape
sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset # shift x
sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset # shift y
sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
sy, sx = torch.meshgrid(sy, sx, indexing="ij") if TORCH_1_10 else torch.meshgrid(sy, sx)
anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
return torch.cat(anchor_points), torch.cat(stride_tensor)