ultralytics 8.0.197 save P, R, F1 curves to metrics (#5354)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: erminkev1 <83356055+erminkev1@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
7fd5dcbd86
commit
12e3eef844
33 changed files with 337 additions and 195 deletions
|
|
@ -176,13 +176,13 @@ class v8DetectionLoss:
|
|||
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
|
||||
# Targets
|
||||
targets = torch.cat((batch['batch_idx'].view(-1, 1), 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)
|
||||
|
||||
# pboxes
|
||||
# Pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
|
||||
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
|
||||
|
|
@ -191,11 +191,11 @@ class v8DetectionLoss:
|
|||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# Cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
# Bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes /= stride_tensor
|
||||
loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
|
||||
|
|
@ -224,7 +224,7 @@ class v8SegmentationLoss(v8DetectionLoss):
|
|||
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, ..
|
||||
# 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()
|
||||
|
|
@ -233,7 +233,7 @@ class v8SegmentationLoss(v8DetectionLoss):
|
|||
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
|
||||
# Targets
|
||||
try:
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
|
|
@ -247,7 +247,7 @@ class v8SegmentationLoss(v8DetectionLoss):
|
|||
"correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
|
||||
'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
|
||||
|
||||
# pboxes
|
||||
# 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(
|
||||
|
|
@ -256,15 +256,15 @@ class v8SegmentationLoss(v8DetectionLoss):
|
|||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# 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
|
||||
|
||||
if fg_mask.sum():
|
||||
# bbox loss
|
||||
# Bbox loss
|
||||
loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
|
||||
target_scores, target_scores_sum, fg_mask)
|
||||
# masks loss
|
||||
# Masks loss
|
||||
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]
|
||||
|
|
@ -344,13 +344,13 @@ class v8SegmentationLoss(v8DetectionLoss):
|
|||
_, _, mask_h, mask_w = proto.shape
|
||||
loss = 0
|
||||
|
||||
# normalize to 0-1
|
||||
# Normalize to 0-1
|
||||
target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]]
|
||||
|
||||
# areas of target bboxes
|
||||
# Areas of target bboxes
|
||||
marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2)
|
||||
|
||||
# normalize to mask size
|
||||
# Normalize to mask size
|
||||
mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device)
|
||||
|
||||
for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)):
|
||||
|
|
@ -393,7 +393,7 @@ class v8PoseLoss(v8DetectionLoss):
|
|||
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, ..
|
||||
# B, grids, ..
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
|
||||
|
|
@ -402,7 +402,7 @@ class v8PoseLoss(v8DetectionLoss):
|
|||
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
|
||||
# Targets
|
||||
batch_size = pred_scores.shape[0]
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
|
|
@ -410,7 +410,7 @@ class v8PoseLoss(v8DetectionLoss):
|
|||
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
|
||||
# pboxes
|
||||
# Pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3)
|
||||
|
||||
|
|
@ -420,11 +420,11 @@ class v8PoseLoss(v8DetectionLoss):
|
|||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# Cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
# Bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes /= stride_tensor
|
||||
loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue