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add build_targets_optim #99

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add build_targets_optim #99

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6 changes: 4 additions & 2 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,7 @@


def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
(save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, bbox_iou_optim, multi_tensor_optimizer) = (
(save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, bbox_iou_optim, build_targets_optim, multi_tensor_optimizer) = (
Path(opt.save_dir),
opt.epochs,
opt.batch_size,
Expand All @@ -99,6 +99,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
opt.workers,
opt.freeze,
opt.bbox_iou_optim,
opt.build_targets_optim,
opt.multi_tensor_optimizer,
)

Expand Down Expand Up @@ -285,7 +286,7 @@ def f(x):
# scaler = flow.cuda.amp.GradScaler(enabled=amp)

stopper, _ = EarlyStopping(patience=opt.patience), False
compute_loss = ComputeLoss(model, bbox_iou_optim=bbox_iou_optim) # init loss class
compute_loss = ComputeLoss(model, bbox_iou_optim=bbox_iou_optim, build_targets_optim=build_targets_optim) # init loss class
callbacks.run("on_train_start")
LOGGER.info(
f"Image sizes {imgsz} train, {imgsz} val\n"
Expand Down Expand Up @@ -517,6 +518,7 @@ def parse_opt(known=False):
parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
parser.add_argument("--bbox_iou_optim", action="store_true", help="optim bbox_iou function in compute_loss")
parser.add_argument("--build_targets_optim", action="store_true", help="optim build_targets function in compute_loss")
parser.add_argument("--multi_tensor_optimizer", action="store_true", help="apply multi_tensor implement in optimizer")
parser.add_argument(
"--patience",
Expand Down
12 changes: 8 additions & 4 deletions utils/loss.py
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Expand Up @@ -93,7 +93,7 @@ class ComputeLoss:
sort_obj_iou = False

# Compute losses
def __init__(self, model, autobalance=False, bbox_iou_optim=False):
def __init__(self, model, autobalance=False, bbox_iou_optim=False, build_targets_optim=False):
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters

Expand Down Expand Up @@ -125,6 +125,7 @@ def __init__(self, model, autobalance=False, bbox_iou_optim=False):
self.anchors = m.anchors
self.device = device
self.bbox_iou_optim = bbox_iou_optim
self.build_targets_optim = build_targets_optim

def __call__(self, p, targets): # predictions, targets
lcls = flow.zeros(1, device=self.device) # class loss
Expand Down Expand Up @@ -235,9 +236,12 @@ def build_targets(self, p, targets):
# 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 = flow.stack((flow.ones_like(j), j, k, l, m))
if self.build_targets_optim:
j = flow._C.fused_yolov5_get_target_offsets(gxy, gxi, g)
else:
j, k = ((gxy % 1 < g) & (gxy > 1)).T
l, m = ((gxi % 1 < g) & (gxi > 1)).T
j = flow.stack((flow.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (flow.zeros_like(gxy)[None] + off[:, None])[j]
else:
Expand Down
14 changes: 7 additions & 7 deletions utils/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -228,7 +228,7 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, bbox_iou
if xywh: # transform from xywh to xyxy
(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, 1), box2.chunk(4, 1)
if bbox_iou_optim:
b1_x1, b1_x2, b1_y1, b1_y2, b2_x1, b2_x2, b2_y1, b2_y2 = flow._C.fused_get_boundding_boxes_coord(x1, y1, w1, h1, x2, y2, w2, h2)
b1_x1, b1_x2, b1_y1, b1_y2, b2_x1, b2_x2, b2_y1, b2_y2 = flow._C.fused_yolov5_get_boundding_boxes_coord(x1, y1, w1, h1, x2, y2, w2, h2)
else:
w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
Expand All @@ -241,12 +241,12 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, bbox_iou

# Intersection area
if bbox_iou_optim:
inter = flow._C.fused_get_intersection_area(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
inter = flow._C.fused_yolov5_get_intersection_area(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
else:
inter = (flow.min(b1_x2, b2_x2) - flow.max(b1_x1, b2_x1)).clamp(0) * (flow.min(b1_y2, b2_y2) - flow.max(b1_y1, b2_y1)).clamp(0)

if bbox_iou_optim and CIoU:
iou = flow._C.fused_get_iou(w1, h1, w2, h2, inter, eps)
iou = flow._C.fused_yolov5_get_iou(w1, h1, w2, h2, inter, eps)
else:
# Union Area
union = w1 * h1 + w2 * h2 - inter + eps
Expand All @@ -260,18 +260,18 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, bbox_iou
ch = flow.max(b1_y2, b2_y2) - flow.min(b1_y1, b2_y1) # convex height
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
if bbox_iou_optim:
c2 = flow._C.fused_get_convex_diagonal_squared(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, eps)
c2 = flow._C.fused_yolov5_get_convex_diagonal_squared(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, eps)
else:
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
if bbox_iou_optim:
rho2 = flow._C.fused_get_center_dist(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
rho2 = flow._C.fused_yolov5_get_center_dist(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
else:
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2

if CIoU:
if bbox_iou_optim:
v = flow._C.fused_get_ciou_diagonal_angle(w1, h1, w2, h2, eps)
return flow._C.fused_get_ciou_result(v, iou, rho2, c2, eps)[0]
v = flow._C.fused_yolov5_get_ciou_diagonal_angle(w1, h1, w2, h2, eps)
return flow._C.fused_yolov5_get_ciou_result(v, iou, rho2, c2, eps)[0]
else:
# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi ** 2) * flow.pow(flow.atan(w2 / (h2 + eps)) - flow.atan(w1 / (h1 + eps)), 2)
Expand Down