Merge pull request #194 from panshaowu/master

avoid the problem that ops.Custom call error on Ascend310
mindspore-lab · Sep 1, 2023 · ed30371 · ed30371
2 parents b3c8864 + e7fd842
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diff --git a/docs/en/installation.md b/docs/en/installation.md
@@ -64,8 +64,8 @@ cd mindyolo/csrc
 sh build.sh
 ```
 
-We also provide fused GPU operators which are built upon MindSpore [ops.Custom](https://www.mindspore.cn/tutorials/experts/en/master/operation/op_custom.html) API. The fused GPU operators are able to improve train speed. The source code is provided in C++ and CUDA and is in the folder `mindyolo/models/losses/fused_op`. Before using it, you shall try compiling the source code to dynamic link libraries with the following commands **(This operation is optional)** :
+We also provide fused GPU operators which are built upon MindSpore [ops.Custom](https://www.mindspore.cn/tutorials/experts/en/master/operation/op_custom.html) API. The fused GPU operators are able to improve train speed. The source code is provided in C++ and CUDA and is in the folder `examples/custom_gpu_op/`. To enable this feature in the GPU training process, you shall modify the method `bbox_iou` in `mindyolo/models/losses/iou_loss.py` by referring to the demo script `examples/custom_gpu_op/iou_loss_fused.py`. Before runing `iou_loss_fused.py`, you shall compile the C++ and CUDA source code to dynamic link libraries with the following commands **(This operation is optional)** :
 
 ```shell
-bash mindyolo/models/losses/fused_op/build.sh
+bash examples/custom_gpu_op/fused_op/build.sh
 ```
diff --git a/docs/zh/installation.md b/docs/zh/installation.md
@@ -63,8 +63,8 @@ cd mindyolo/csrc
 sh build.sh
 ```
 
-我们还提供了基于MindSpore [Custom自定义算子](https://www.mindspore.cn/tutorials/experts/zh-CN/master/operation/op_custom.html) 的GPU融合算子，用于提升训练过程的速度。代码采用C++和CUDA开发，位于`mindyolo/models/losses/fused_op`路径下。使用该特性前，需要使用以下的命令，编译生成GPU融合算子运行所依赖的动态库 **(此操作是可选的)** :
+我们还提供了基于MindSpore [Custom自定义算子](https://www.mindspore.cn/tutorials/experts/zh-CN/master/operation/op_custom.html) 的GPU融合算子，用于提升训练过程的速度。代码采用C++和CUDA开发，位于`examples/custom_gpu_op/`路径下。您可参考示例脚本`examples/custom_gpu_op/iou_loss_fused.py`，修改`mindyolo/models/losses/iou_loss.py`的`bbox_iou`方法，在GPU训练过程中使用该特性。运行`iou_loss_fused.py`前，需要使用以下的命令，编译生成GPU融合算子运行所依赖的动态库 **(此操作是可选的)** :
 
 ```shell
-bash mindyolo/models/losses/fused_op/build.sh
+bash examples/custom_gpu_op/fused_op/build.sh
 ```
diff --git a/mindyolo/models/losses/fused_op/__init__.py → examples/custom_gpu_op/fused_op/__init__.py b/mindyolo/models/losses/fused_op/__init__.py → examples/custom_gpu_op/fused_op/__init__.py
diff --git a/mindyolo/models/losses/fused_op/build.sh → examples/custom_gpu_op/fused_op/build.sh b/mindyolo/models/losses/fused_op/build.sh → examples/custom_gpu_op/fused_op/build.sh
diff --git a/...s/losses/fused_op/elementswise_op_impl.cu → ...m_gpu_op/fused_op/elementswise_op_impl.cu b/...s/losses/fused_op/elementswise_op_impl.cu → ...m_gpu_op/fused_op/elementswise_op_impl.cu
diff --git a/...fused_get_boundding_boxes_coord_kernel.cu → ...fused_get_boundding_boxes_coord_kernel.cu b/...fused_get_boundding_boxes_coord_kernel.cu → ...fused_get_boundding_boxes_coord_kernel.cu
diff --git a/.../fused_op/fused_get_center_dist_kernel.cu → .../fused_op/fused_get_center_dist_kernel.cu b/.../fused_op/fused_get_center_dist_kernel.cu → .../fused_op/fused_get_center_dist_kernel.cu
diff --git a/...p/fused_get_ciou_diagonal_angle_kernel.cu → ...p/fused_get_ciou_diagonal_angle_kernel.cu b/...p/fused_get_ciou_diagonal_angle_kernel.cu → ...p/fused_get_ciou_diagonal_angle_kernel.cu
diff --git a/.../losses/fused_op/fused_get_ciou_kernel.cu → ..._gpu_op/fused_op/fused_get_ciou_kernel.cu b/.../losses/fused_op/fused_get_ciou_kernel.cu → ..._gpu_op/fused_op/fused_get_ciou_kernel.cu
diff --git a/...sed_get_convex_diagonal_squared_kernel.cu → ...sed_get_convex_diagonal_squared_kernel.cu b/...sed_get_convex_diagonal_squared_kernel.cu → ...sed_get_convex_diagonal_squared_kernel.cu
diff --git a/..._op/fused_get_intersection_area_kernel.cu → ..._op/fused_get_intersection_area_kernel.cu b/..._op/fused_get_intersection_area_kernel.cu → ..._op/fused_get_intersection_area_kernel.cu
diff --git a/...s/losses/fused_op/fused_get_iou_kernel.cu → ...m_gpu_op/fused_op/fused_get_iou_kernel.cu b/...s/losses/fused_op/fused_get_iou_kernel.cu → ...m_gpu_op/fused_op/fused_get_iou_kernel.cu
diff --git a/examples/custom_gpu_op/iou_loss_fused.py b/examples/custom_gpu_op/iou_loss_fused.py
@@ -0,0 +1,59 @@
+import numpy as np
+
+import mindspore as ms
+from mindspore import ops, Tensor
+
+from fused_op import fused_get_ciou, fused_get_center_dist, fused_get_iou, \
+    fused_get_convex_diagonal_squared, fused_get_ciou_diagonal_angle, \
+    fused_get_boundding_boxes_coord, fused_get_intersection_area
+
+
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+    """
+    Return intersection-over-union (IoU) of boxes.
+    Arguments:
+        box1 (Tensor[N, 4]) or (Tensor[bs, N, 4])
+        box2 (Tensor[N, 4]) or (Tensor[bs, N, 4])
+        xywh (bool): Whether the box format is (x_center, y_center, w, h) or (x1, y1, x2, y2). Default: True.
+        GIoU (bool): Whether to use GIoU. Default: False.
+        DIoU (bool): Whether to use DIoU. Default: False.
+        CIoU (bool): Whether to use CIoU. Default: False.
+    Returns:
+        iou (Tensor[N,]): the IoU values for every element in boxes1 and boxes2
+    """
+    # Get the coordinates of bounding boxes
+    if xywh:  # transform from xywh to xyxy
+        x1, y1, w1, h1 = ops.split(box1, split_size_or_sections=1, axis=-1)
+        x2, y2, w2, h2 = ops.split(box2, split_size_or_sections=1, axis=-1)
+        b1_x1, b1_x2, b1_y1, b1_y2,b2_x1, b2_x2, b2_y1, b2_y2=fused_get_boundding_boxes_coord(x1, y1, w1, h1,x2, y2, w2, h2)
+    else:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = ops.split(box1, split_size_or_sections=1, axis=-1)
+        b2_x1, b2_y1, b2_x2, b2_y2 = ops.split(box2, split_size_or_sections=1, axis=-1)
+
+    # Intersection area
+    inter = fused_get_intersection_area(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
+
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+    iou, union = fused_get_iou(w1, h1, w2, h2, inter)
+
+    if CIoU or DIoU or GIoU:
+        cw = ops.maximum(b1_x2, b2_x2) - ops.minimum(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = ops.maximum(b1_y2, b2_y2) - ops.minimum(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = fused_get_convex_diagonal_squared(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
+            rho2 = fused_get_center_dist(b1_x1, b1_x2, b1_y1, b1_y2, b2_x1, b2_x2, b2_y1, b2_y2)
+            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = fused_get_ciou_diagonal_angle(w1, h1, w2, h2)
+                _, res = fused_get_ciou(v, iou, rho2, c2)
+                return res
+            return iou - rho2 / c2  # DIoU
+        c_area = cw * ch + eps  # convex area
+        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+    return iou  # IoU
+
+if __name__ =="__main__":
+    ms.set_context(device_target="GPU", mode=ms.GRAPH_MODE)
+    box1 = Tensor(np.random.rand(32, 4).astype(np.float32))
+    box2 = Tensor(np.random.rand(32, 4).astype(np.float32))
+    iou = bbox_iou(box1, box2, xywh=True, CIoU=True)
diff --git a/mindyolo/models/losses/iou_loss.py b/mindyolo/models/losses/iou_loss.py
@@ -5,10 +5,6 @@
 
 from mindyolo.models.layers.utils import box_cxcywh_to_xyxy
 
-from .fused_op import fused_get_ciou, fused_get_center_dist, fused_get_iou, \
-    fused_get_convex_diagonal_squared, fused_get_ciou_diagonal_angle, \
-    fused_get_boundding_boxes_coord, fused_get_intersection_area
-
 PI = Tensor(math.pi, ms.float32)
 EPS = 1e-7
 
@@ -101,7 +97,7 @@ def batch_box_iou(batch_box1, batch_box2, xywh=False):
     )  # iou = inter / (area1 + area2 - inter)
 
 
-def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7, use_fused_op=False):
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
     """
     Return intersection-over-union (IoU) of boxes.
     Arguments:
@@ -111,64 +107,45 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
         GIoU (bool): Whether to use GIoU. Default: False.
         DIoU (bool): Whether to use DIoU. Default: False.
         CIoU (bool): Whether to use CIoU. Default: False.
-        use_fused_op(bool): Whether to use fused operator built upon aot customized operator. Default: False.
     Returns:
         iou (Tensor[N,]): the IoU values for every element in boxes1 and boxes2
     """
+
     # Get the coordinates of bounding boxes
     if xywh:  # transform from xywh to xyxy
         x1, y1, w1, h1 = ops.split(box1, split_size_or_sections=1, axis=-1)
         x2, y2, w2, h2 = ops.split(box2, split_size_or_sections=1, axis=-1)
-        if use_fused_op:
-            b1_x1, b1_x2, b1_y1, b1_y2,b2_x1, b2_x2, b2_y1, b2_y2=fused_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_
-            b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+        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_
+        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
     else:  # x1, y1, x2, y2 = box1
         b1_x1, b1_y1, b1_x2, b1_y2 = ops.split(box1, split_size_or_sections=1, axis=-1)
         b2_x1, b2_y1, b2_x2, b2_y2 = ops.split(box2, split_size_or_sections=1, axis=-1)
 
     # Intersection area
-    if use_fused_op:
-        inter = fused_get_intersection_area(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
-    else:
-        inter = (ops.minimum(b1_x2, b2_x2) - ops.maximum(b1_x1, b2_x1)).clip(0., None) * \
-                (ops.minimum(b1_y2, b2_y2) - ops.maximum(b1_y1, b2_y1)).clip(0., None)
+    inter = (ops.minimum(b1_x2, b2_x2) - ops.maximum(b1_x1, b2_x1)).clip(0., None) * \
+            (ops.minimum(b1_y2, b2_y2) - ops.maximum(b1_y1, b2_y1)).clip(0., None)
 
+    # Union Area
     w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
     w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-    if use_fused_op:
-        iou, union = fused_get_iou(w1, h1, w2, h2, inter)
-    else: 
-        union = w1 * h1 + w2 * h2 - inter + eps  # Union Area
-        iou = inter / union  # IoU
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    # IoU
+    iou = inter / union
 
     if CIoU or DIoU or GIoU:
         cw = ops.maximum(b1_x2, b2_x2) - ops.minimum(b1_x1, b2_x1)  # convex (smallest enclosing box) width
         ch = ops.maximum(b1_y2, b2_y2) - ops.minimum(b1_y1, b2_y1)  # convex height
         if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            if use_fused_op:
-                c2 = fused_get_convex_diagonal_squared(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2)
-            else:
-                c2 = cw**2 + ch**2 + eps  # convex diagonal squared
-            if use_fused_op:
-                rho2 = fused_get_center_dist(b1_x1, b1_x2, b1_y1, b1_y2, b2_x1, b2_x2, 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
+            c2 = cw**2 + ch**2 + eps  # convex diagonal squared
+            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:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
-                if use_fused_op:
-                    v = fused_get_ciou_diagonal_angle(w1, h1, w2, h2)
-                else:
-                    # v = (4 / get_pi(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2)
-                    v = (4 / PI.astype(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2)
-                if use_fused_op:
-                    _, res = fused_get_ciou(v, iou, rho2, c2)
-                else:
-                    alpha = v / (v - iou + (1 + eps))
-                    alpha = ops.stop_gradient(alpha)
-                    res = iou - (rho2 / c2 + v * alpha)  # CIoU
-                return res
+                # v = (4 / get_pi(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2)
+                v = (4 / PI.astype(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2)
+                alpha = v / (v - iou + (1 + eps))
+                alpha = ops.stop_gradient(alpha)
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
             return iou - rho2 / c2  # DIoU
         c_area = cw * ch + eps  # convex area
         return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf

diff --git a/mindyolo/models/losses/yolov3_loss.py b/mindyolo/models/losses/yolov3_loss.py
@@ -17,7 +17,7 @@
 @register_model
 class YOLOv3Loss(nn.Cell):
     def __init__(
-        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, use_fused_op=False, **kwargs
+        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs
     ):
         super(YOLOv3Loss, self).__init__()
         self.hyp_box = box
@@ -63,7 +63,6 @@ def __init__(
         )
 
         self.loss_item_name = ["loss", "lbox", "lobj", "lcls"]  # branch name returned by lossitem for print
-        self.use_fused_op = use_fused_op
 
     def construct(self, p, targets, imgs):
         lcls, lbox, lobj = 0.0, 0.0, 0.0
@@ -94,7 +93,7 @@ def construct(self, p, targets, imgs):
                 pxy = ops.Sigmoid()(pxy) * 2 - 0.5
                 pwh = (ops.Sigmoid()(pwh) * 2) ** 2 * anchors[layer_index]
                 pbox = ops.concat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox, tbox[layer_index], CIoU=True, use_fused_op=self.use_fused_op).squeeze()  # iou(prediction, target)
+                iou = bbox_iou(pbox, tbox[layer_index], CIoU=True).squeeze()  # iou(prediction, target)
                 # iou = iou * tmask
                 # lbox += ((1.0 - iou) * tmask).mean()  # iou loss
                 lbox += (((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None)).astype(iou.dtype)

diff --git a/mindyolo/models/losses/yolov4_loss.py b/mindyolo/models/losses/yolov4_loss.py
@@ -31,7 +31,7 @@ def construct(self, object_mask, predict_confidence, ignore_mask):
 
 @register_model
 class YOLOv4Loss(nn.Cell):
-    def __init__(self, box, obj, cls, label_smoothing, ignore_threshold, iou_threshold, anchors, nc, use_fused_op=False, **kwargs):
+    def __init__(self, box, obj, cls, label_smoothing, ignore_threshold, iou_threshold, anchors, nc, **kwargs):
         super(YOLOv4Loss, self).__init__()
         self.ignore_threshold = ignore_threshold
         self.iou = Iou()
@@ -57,7 +57,6 @@ def __init__(self, box, obj, cls, label_smoothing, ignore_threshold, iou_thresho
 
         self.concat = ops.Concat(axis=-1)
         self.reduce_max = ops.ReduceMax(keep_dims=False)
-        self.use_fused_op = use_fused_op
 
     def construct(self, p, targets, imgs):
         image_shape = imgs.shape
@@ -95,7 +94,7 @@ def construct(self, p, targets, imgs):
 
                 # Regression
                 pbox = ops.concat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox, tbox, GIoU=True, use_fused_op=self.use_fused_op).squeeze()  # iou(prediction, target)
+                iou = bbox_iou(pbox, tbox, GIoU=True).squeeze()  # iou(prediction, target)
                 # iou = iou * tmask
                 # lbox += ((1.0 - iou) * tmask).mean()  # iou loss
                 box_loss_scale = 2 - tbox[:, 2] * tbox[:, 3] / gain[0] / gain[1]
@@ -161,7 +160,7 @@ def build_targets(self, p, targets, imgs):
 
         anchor_shapes = ops.zeros((na, 1, 4), ms.float32)
         anchor_shapes[..., 2:] = ops.ExpandDims()(self.anchors, 1)
-        anch_ious = bbox_iou(gt_box, anchor_shapes, use_fused_op=self.use_fused_op).squeeze()
+        anch_ious = bbox_iou(gt_box, anchor_shapes).squeeze()
 
         j = anch_ious == anch_ious.max(axis=0)
         l = anch_ious > self.iou_threshold

diff --git a/mindyolo/models/losses/yolov5_loss.py b/mindyolo/models/losses/yolov5_loss.py
@@ -15,7 +15,7 @@
 class YOLOv5Loss(nn.Cell):
     # Compute losses
     def __init__(
-        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, use_fused_op=False, **kwargs
+        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs
     ):
         super(YOLOv5Loss, self).__init__()
 
@@ -64,7 +64,6 @@ def __init__(
         )
 
         self.loss_item_name = ["loss", "lbox", "lobj", "lcls"]  # branch name returned by loss for print
-        self.use_fused_op = use_fused_op
 
     def scatter_index_tensor(self, x, index):
         x_tmp = ops.transpose(x.reshape((-1, x.shape[-1])), (1, 0))
@@ -102,7 +101,7 @@ def construct(self, p, targets, imgs):  # predictions, targets
                 pxy = ops.Sigmoid()(pxy) * 2 - 0.5
                 pwh = (ops.Sigmoid()(pwh) * 2) ** 2 * anchors[layer_index]
                 pbox = ops.concat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox, tbox[layer_index], CIoU=True, use_fused_op=self.use_fused_op).squeeze()  # iou(prediction, target)
+                iou = bbox_iou(pbox, tbox[layer_index], CIoU=True).squeeze()  # iou(prediction, target)
                 lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum()  # iou loss
 
                 # Objectness

diff --git a/mindyolo/models/losses/yolov7_loss.py b/mindyolo/models/losses/yolov7_loss.py
@@ -17,7 +17,7 @@
 @register_model
 class YOLOv7Loss(nn.Cell):
     def __init__(
-        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, use_fused_op=False, **kwargs
+        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs
     ):
         super(YOLOv7Loss, self).__init__()
         self.hyp_box = box
@@ -63,7 +63,6 @@ def __init__(
         )
 
         self.loss_item_name = ["loss", "lbox", "lobj", "lcls"]  # branch name returned by lossitem for print
-        self.use_fused_op = use_fused_op
 
     def construct(self, p, targets, imgs):
         lcls, lbox, lobj = 0.0, 0.0, 0.0
@@ -99,7 +98,7 @@ def construct(self, p, targets, imgs):
             pbox = ops.concat((pxy, pwh), 1)  # predicted box
             selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
             selected_tbox[:, :2] -= grid
-            iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True, use_fused_op=self.use_fused_op).view(-1)
+            iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True).view(-1)
             lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None)  # iou loss
 
             # Objectness
@@ -365,7 +364,7 @@ def find_3_positive(self, p, targets):
 @register_model
 class YOLOv7AuxLoss(nn.Cell):
     def __init__(
-        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, use_fused_op, **kwargs
+        self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs
     ):
         super(YOLOv7AuxLoss, self).__init__()
         self.hyp_box = box
@@ -417,7 +416,6 @@ def __init__(
         )
 
         self.loss_item_name = ["loss", "lbox", "lobj", "lcls"]  # branch name returned by loss for print
-        self.use_fused_op = use_fused_op
 
     def construct(self, p, targets, imgs):
         lcls, lbox, lobj = 0.0, 0.0, 0.0
@@ -473,7 +471,7 @@ def construct(self, p, targets, imgs):
             pbox = ops.concat((pxy, pwh), 1)  # predicted box
             selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
             selected_tbox[:, :2] -= grid
-            iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True, use_fused_op=self.use_fused_op).view(-1)
+            iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True).view(-1)
             lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None)  # iou loss
             # 1.2. Objectness
             tobj[b, a, gj, gi] = ((1.0 - self.gr) + self.gr * ops.stop_gradient(iou).clip(0, None)) * tmask  # iou ratio
@@ -496,7 +494,7 @@ def construct(self, p, targets, imgs):
             pbox_aux = ops.concat((pxy_aux, pwh_aux), 1)  # predicted box
             selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains[i]
             selected_tbox_aux[:, :2] -= grid_aux
-            iou_aux = bbox_iou(pbox_aux, selected_tbox_aux, xywh=True, CIoU=True, use_fused_op=self.use_fused_op).view(-1)
+            iou_aux = bbox_iou(pbox_aux, selected_tbox_aux, xywh=True, CIoU=True).view(-1)
             lbox += (
                 0.25 * ((1.0 - iou_aux) * tmask_aux).sum() / tmask_aux.astype(iou_aux.dtype).sum().clip(1, None)
             )  # iou loss