NPU自定义算子

映射关系

NPU自定义算子参数中存在部分映射关系可参考下表。

详细算子接口说明

torch_npu.npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v))

count adam result.

• Parameters：
  • beta1_power (Number) - power of beta1.
  • beta2_power (Number) - power of beta2.
  • lr (Number) - learning rate.
  • beta1 (Number) - exponential decay rate for the 1st moment estimates.
  • beta2 (Number) - exponential decay rate for the 2nd moment estimates.
  • epsilon (Number) - term added to the denominator to improve numerical stability.
  • grad (Tensor) - the gradient.
  • use_locking (bool) - If True use locks for update operations.
  • use_nesterov (bool) -If True, uses the nesterov update.
  • var (Tensor) - variables to be optimized.
  • m (Tensor) - mean value of variables.
  • v (Tensor) - variance of variables.
• constraints：

  None

• Examples：

  None

torch_npu.npu_convolution_transpose(input, weight, bias, padding, output_padding, stride, dilation, groups) -> Tensor

Applies a 2D or 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

• Parameters：
  • input (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) or (minibatch, in_channels, iT, iH, iW)
  • weight (Tensor) - filters of shape(in_channels, out_channels/groups, kH, kW) or (in_channels, out_channels/groups, kT, kH, kW)
  • bias (Tensor, optional) - optional bias of shape(out_channels)
  • padding (ListInt) - (dilation * (kernel_size - 1) - padding) zero-padding will be added to both sides of each dimension in the input
  • output_padding (ListInt) - additional size added to one side of each dimension in the output shape.
  • stride (ListInt) - the stride of the convolving kernel
  • dilation (ListInt) - the spacing between kernel elements
  • groups (Number) - split input into groups, in_channels should be divisible by the number of groups
• constraints：

  None

• Examples：

  None

torch_npu.npu_conv_transpose2d(input, weight, bias, padding, output_padding, stride, dilation, groups) -> Tensor

Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.

• Parameters：
  • input (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW)
  • weight (Tensor) - filters of shape(in_channels, out_channels/groups, kH, kW)
  • bias (Tensor, optional) - optional bias of shape(out_channels)
  • padding (ListInt) - (dilation * (kernel_size - 1) - padding) zero-padding will be added to both sides of each dimension in the input
  • output_padding (ListInt) - additional size added to one side of each dimension in the output shape.
  • stride (ListInt) - the stride of the convolving kernel
  • dilation (ListInt) - the spacing between kernel elements
  • groups (Number) - split input into groups, in_channels should be divisible by the number of groups
• constraints：

  None

• Examples：

  None

torch_npu.npu_convolution(input, weight, bias, stride, padding, dilation, groups) -> Tensor

Applies a 2D or 3D convolution over an input image composed of several input planes.

• Parameters：
  • input (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) or (minibatch, in_channels, iT, iH, iW)
  • weight (Tensor) - filters of shape(out_channels, in_channels/groups, kH, kW) or (out_channels, in_channels/groups, kT, kH, kW)
  • bias (Tensor, optional) - optional bias of shape(out_channels)
  • stride (ListInt) - the stride of the convolving kernel
  • padding (ListInt) - implicit paddings on both sides of the input
  • dilation (ListInt) - the spacing between kernel elements
  • groups (ListInt) - split input into groups, in_channels should be divisible by the number of groups
• constraints：

  None

• Examples：

  None

torch_npu.npu_conv2d(input, weight, bias, stride, padding, dilation, groups) -> Tensor

Applies a 2D convolution over an input image composed of several input planes.

• Parameters：
  • input (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW)
  • weight (Tensor) - filters of shape(out_channels, in_channels/groups, kH, kW)
  • bias (Tensor, optional) - optional bias of shape(out_channels)
  • stride (ListInt) - the stride of the convolving kernel
  • padding (ListInt) - implicit paddings on both sides of the input
  • dilation (ListInt) - the spacing between kernel elements
  • groups (ListInt) - split input into groups, in_channels should be divisible by the number of groups
• constraints：

  None

• Examples：

  None

torch_npu.npu_conv3d(input, weight, bias, stride, padding, dilation, groups) -> Tensor

Applies a 3D convolution over an input image composed of several input planes.

• Parameters：
  • input (Tensor) - input tensor of shape(minibatch, in_channels, iT, iH, iW)
  • weight (Tensor) - filters of shape(out_channels, in_channels/groups, kT, kH, kW)
  • bias (Tensor, optional) - optional bias of shape(out_channels)
  • stride (ListInt) - the stride of the convolving kernel
  • padding (ListInt) - implicit paddings on both sides of the input
  • dilation (ListInt) - the spacing between kernel elements
  • groups (ListInt) - split input into groups, in_channels should be divisible by the number of groups
• constraints：

  None

• Examples：

  None

torch_npu.one_(self) -> Tensor

Fills self tensor with ones.

• Parameters：
• self (Tensor) - input tensor
• constraints：

  None

• Examples：

  >>> x = torch.rand(2, 3).npu()
  >>> xtensor([[0.6072, 0.9726, 0.3475],
          [0.3717, 0.6135, 0.6788]], device='npu:0')
  >>> x.one_()tensor([[1., 1., 1.],
          [1., 1., 1.]], device='npu:0')

torch_npu.npu_sort_v2(self, dim=-1, descending=False, out=None) -> Tensor

Sorts the elements of the input tensor along a given dimension in ascending order by value without indices. If dim is not given, the last dimension of the input is chosen. If descending is True then the elements are sorted in descending order by value.

• Parameters：
  • self (Tensor) - the input tensor
  • dim (int, optional) - the dimension to sort along
  • descending (bool, optional) - controls the sorting order (ascending or descending)
• constraints：

  At present only support the last dim(-1).

• Examples：

  >>> x = torch.randn(3, 4).npu()
  >>> x
  tensor([[-0.0067,  1.7790,  0.5031, -1.7217],
          [ 1.1685, -1.0486, -0.2938,  1.3241],
          [ 0.1880, -2.7447,  1.3976,  0.7380]], device='npu:0')
  >>> sorted_x = torch_npu.npu_sort_v2(x)
  >>> sorted_x
  tensor([[-1.7217, -0.0067,  0.5029,  1.7793],
          [-1.0488, -0.2937,  1.1689,  1.3242],
          [-2.7441,  0.1880,  0.7378,  1.3975]], device='npu:0')

torch_npu.npu_format_cast(self, acl_format) -> Tensor

Change the format of a npu tensor.

• Parameters：
  • self (Tensor) - the input tensor
  • acl_format (int) - the target format to transform
• constraints：

  None

• Examples：

  >>> x = torch.rand(2, 3, 4, 5).npu()
  >>> torch_npu.get_npu_format(x)
  0
  >>> x1 = x.npu_format_cast(29)
  >>> torch_npu.get_npu_format(x1)
  29

torch_npu.npu_format_cast_
torch_npu.npu_format_cast_.src(self, src) -> Tensor

In-place Change the format of self, with the same format as src.

• Parameters：
  • self (Tensor) - the input tensor
  • src (Tensor) - the target format to transform
• constraints：

  None

• Examples：

  >>> x = torch.rand(2, 3, 4, 5).npu()
  >>> torch_npu.get_npu_format(x)
  0
  >>> torch_npu.get_npu_format(x.npu_format_cast_(29))
  29

torch_npu.npu_transpose(self, perm) -> Tensor

Returns a view of the original tensor with its dimensions permuted, and make the result contiguous.

• Parameters：
  • self (Tensor) - the input tensor
  • perm (ListInt) - The desired ordering of dimensions
• constraints：

  None

• Examples：

  >>> x = torch.randn(2, 3, 5).npu()
  >>> x.shape
  torch.Size([2, 3, 5])
  >>> x1 = torch_npu.npu_transpose(x, (2, 0, 1))
  >>> x1.shape
  torch.Size([5, 2, 3])
  >>> x2 = x.npu_transpose(2, 0, 1)
  >>> x2.shape
  torch.Size([5, 2, 3])

torch_npu.npu_broadcast(self, perm) -> Tensor

Returns a new view of the self tensor with singleton dimensions expanded to a larger size, and make the result contiguous.

torch_npu.npu_dtype_cast(input, dtype) -> Tensor

torch_npu.empty_with_format(size, dtype, layout, device, pin_memory, acl_format) -> Tensor

torch_npu.copy_memory_(dst, src, non_blocking=False) -> Tensor

torch_npu.npu_one_hot(input, num_classes=-1, depth=1, on_value=1, off_value=0) -> Tensor

torch_npu.npu_stride_add(x1, x2, offset1, offset2, c1_len) -> Tensor

torch_npu.npu_softmax_cross_entropy_with_logits(features, labels) -> Tensor

Computes softmax cross entropy cost.

• Parameters：
  • features (Tensor) - A Tensor. A "batch_size * num_classes" matrix.
  • labels (Tensor) - A Tensor of the same type as "features". A "batch_size * num_classes" matrix.
• constraints：

  None

• Examples：

  None

torch_npu.npu_ps_roi_pooling(x, rois, spatial_scale, group_size, output_dim) -> Tensor

torch_npu.npu_roi_align(features, rois, spatial_scale, pooled_height, pooled_width, sample_num, roi_end_mode) -> Tensor

Obtains the ROI feature matrix from the feature map. It is a customized FasterRcnn operator.

• Parameters：
  • features (Tensor) - A Tensor in 5HD.
  • rois (Tensor) - ROI position. A 2D Tensor with shape (N, 5). "N" indicates the number of ROIs, the value "5" indicates the indexes of images where the ROIs are located, "x0", "y0", "x1", and "y1".
  • spatial_scale (Number) - A required attribute of type float32, specifying the scaling ratio of "features" to the original image.
  • pooled_height (Number) - A required attribute of type int32, specifying the H dimension.
  • pooled_width (Number) - A required attribute of type int32, specifying the W dimension.
  • sample_num (Number) - An optional attribute of type int32, specifying the horizontal and vertical sampling frequency of each output. If this attribute is set to "0", the sampling frequency is equal to the rounded up value of "rois", which is a floating point number. Defaults to "2".
  • roi_end_mode (Number) - An optional attribute of type int32. Defaults to "1".
• constraints：

  None

• Examples：

  >>> x = torch.FloatTensor([[[[1, 2, 3 , 4, 5, 6],
                              [7, 8, 9, 10, 11, 12],
                              [13, 14, 15, 16, 17, 18],
                              [19, 20, 21, 22, 23, 24],
                              [25, 26, 27, 28, 29, 30],
                              [31, 32, 33, 34, 35, 36]]]]).npu()
  >>> rois = torch.tensor([[0, -2.0, -2.0, 22.0, 22.0]]).npu()
  >>> out = torch_npu.npu_roi_align(x, rois, 0.25, 3, 3, 2, 0)
  >>> out
  tensor([[[[ 4.5000,  6.5000,  8.5000],
            [16.5000, 18.5000, 20.5000],
            [28.5000, 30.5000, 32.5000]]]], device='npu:0')

torch_npu.npu_nms_v4(boxes, scores, max_output_size, iou_threshold, scores_threshold, pad_to_max_output_size=False) -> (Tensor, Tensor)

torch_npu.npu_nms_rotated(dets, scores, iou_threshold, scores_threshold=0, max_output_size=-1, mode=0) -> (Tensor, Tensor)

torch_npu.npu_lstm(x, weight, bias, seq_len, h, c, has_biases, num_layers, dropout, train, bidirectional, batch_first, flag_seq, direction)

DynamicRNN calculation.

• Parameters：
  • x (Tensor) - A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • weight (Tensor) - A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM.
  • bias (Tensor) - A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND.
  • seq_len (Tensor) - A optional Tensor. Only Support float16 in FRACTAL_NZ and int32 in ND.
  • h (Tensor) - A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • c (Tensor) - A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • has_biases (bool) - If the value is true, bias exists.
  • num_layers (Number) - Number of recurrent layers. Only Support single layer currently.
  • dropout (Number) - If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. unsupport currently.
  • train (bool) - An bool identifying is training in the op. Default to true .
  • bidirectional (bool) - If True, becomes a bidirectional LSTM. unsupport currently.
  • batch_first (bool) - If True, then the input and output tensors are provided as (batch, seq, feature). unsupport currently.
  • flag_seq (bool) - If True, then the input is PackSequnce. unsupport currently.
  • direction (bool) - If True, then the direction is "REDIRECTIONAL", otherwise is "UNIDIRECTIONAL".
• Returns:
  • y - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • output_h - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • output_c - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • i - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • j - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • f - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • o - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • tanhct - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
• constraints：

  None

• Examples：

  None

torch_npu.npu_iou(bboxes, gtboxes, mode=0) -> Tensor torch_npu.npu_ptiou(bboxes, gtboxes, mode=0) -> Tensor

torch_npu.npu_pad(input, paddings) -> Tensor

torch_npu.npu_nms_with_mask(input, iou_threshold) -> (Tensor, Tensor, Tensor)

torch_npu.npu_bounding_box_encode(anchor_box, ground_truth_box, means0, means1, means2, means3, stds0, stds1, stds2, stds3) -> Tensor

torch_npu.npu_bounding_box_decode(rois, deltas, means0, means1, means2, means3, stds0, stds1, stds2, stds3, max_shape, wh_ratio_clip) -> Tensor

torch_npu.npu_gru(input, hx, weight_input, weight_hidden, bias_input, bias_hidden, seq_length, has_biases, num_layers, dropout, train, bidirectional, batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)

DynamicGRUV2 calculation.

• Parameters：
  • input (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_NZ.
  • hx (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • weight_input (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_Z.
  • weight_hidden (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_Z.
  • bias_input (Tensor) - Must be one of the following types: float16, float32. The format must be ND.
  • bias_hidden (Tensor) - Must be one of the following types: float16, float32. The format must be ND.
  • seq_length (Tensor) - Must be one of the following types: int32. The format must be ND.
  • has_biases (bool) - Default to true.
  • num_layers (Number)
  • dropout (Number)
  • train (bool) - An bool identifying is training in the op. Default to true.
  • bidirectional (bool) - Default to true.
  • batch_first (bool) - Default to true.
• Returns:
  • y (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • output_h (Tensor) - output_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • update (Tensor) - update:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • reset (Tensor) - reset:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • new (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
  • hidden_new (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
• constraints：

  None

• Examples：

  None

torch_npu.npu_random_choice_with_mask(x, count=256, seed=0, seed2=0) -> (Tensor, Tensor)

torch_npu.npu_batch_nms(self, scores, score_threshold, iou_threshold, max_size_per_class, max_total_size, change_coordinate_frame=False, transpose_box=False) -> (Tensor, Tensor, Tensor, Tensor)

torch_npu.npu_slice(self, offsets, size) -> Tensor

Extracts a slice from a tensor

• Parameters：
  • self (Tensor) - the input tensor.
  • offsets (ListInt) - type int32 or int64.
  • size (ListInt) - type int32 or int64.
• constraints：

  None

• Examples：

  >>> input = torch.tensor([[1,2,3,4,5], [6,7,8,9,10]], dtype=torch.float16).to("npu")
  >>> offsets = [0, 0]>>> size = [2, 2]
  >>> output = torch_npu.npu_slice(input, offsets, size)
  >>> output
  tensor([[1., 2.],
          [6., 7.]], device='npu:0', dtype=torch.float16)

torch_npu.npu_dropoutV2(self, seed, p) -> (Tensor, Tensor, Tensor(a!))

count dropout result with seed

• Parameters：
  • self (Tensor) - The input Tensor.
  • seed (Tensor) - The input Tensor.
  • p (Float) - Dropout probability.
• Returns：
  • y - A tensor with the same shape and type as "x".
  • mask - A tensor with the same shape and type as "x".
  • new_seed - A tensor with the same shape and type as "seed".
• constraints：

  None

• Examples：

  >>> input = torch.tensor([1.,2.,3.,4.]).npu()
  >>> input
  tensor([1., 2., 3., 4.], device='npu:0')
  >>> seed = torch.rand((32,),dtype=torch.float32).npu()
  >>> seed
  tensor([0.4368, 0.7351, 0.8459, 0.4657, 0.6783, 0.8914, 0.8995, 0.4401, 0.4408,
        0.4453, 0.2404, 0.9680, 0.0999, 0.8665, 0.2993, 0.5787, 0.0251, 0.6783,
        0.7411, 0.0670, 0.9430, 0.9165, 0.3983, 0.5849, 0.7722, 0.4659, 0.0486,
        0.2693, 0.6451, 0.2734, 0.3176, 0.0176], device='npu:0')
  >>> prob = 0.3
  >>> output, mask, out_seed = torch_npu.npu_dropoutV2(input, seed, prob)
  >>> output
  tensor([0.4408, 0.4453, 0.2404, 0.9680], device='npu:0')
  >>> mask
  tensor([0., 0., 0., 0.], device='npu:0')
  >>> out_seed
  tensor([0.4408, 0.4453, 0.2404, 0.9680, 0.0999, 0.8665, 0.2993, 0.5787, 0.0251,
          0.6783, 0.7411, 0.0670, 0.9430, 0.9165, 0.3983, 0.5849, 0.7722, 0.4659,
          0.0486, 0.2693, 0.6451, 0.2734, 0.3176, 0.0176, 0.0000, 0.0000, 0.0000,
          0.0000, 0.0000, 0.0000, 0.0000, 0.0000], device='npu:0')

torch_npu._npu_dropout(self, p) -> (Tensor, Tensor)

count dropout result without seed

• Parameters： Similar to torch.dropout, optimize implemention to npu device.
  • self (Tensor) - The input Tensor.
  • p (Float) - Dropout probability.
• constraints：

  None

• Examples：

  >>> input = torch.tensor([1.,2.,3.,4.]).npu()
  >>> input
  tensor([1., 2., 3., 4.], device='npu:0')
  >>> prob = 0.3>>> output, mask = torch_npu._npu_dropout(input, prob)
  >>> output
  tensor([0.0000, 2.8571, 0.0000, 0.0000], device='npu:0')
  >>> mask
  tensor([ 98, 255, 188, 186, 120, 157, 175, 159,  77, 223, 127,  79, 247, 151,
        253, 255], device='npu:0', dtype=torch.uint8)

torch_npu._npu_dropout_inplace(result, p) -> (Tensor(a!), Tensor)

torch_npu.npu_indexing(self, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0) -> Tensor

torch_npu.npu_ifmr(Tensor data, Tensor data_min, Tensor data_max, Tensor cumsum, float min_percentile, float max_percentile, float search_start, float search_end, float search_step, bool with_offset) -> (Tensor, Tensor)

count ifmr result by begin,end,strides array, Input Feature Map Reconstruction

• Parameters：
  • data (Tensor) - A Tensor of feature map.
  • data_min (Tensor) - A Tensor of min value of feature map.
  • data_max (Tensor) - A Tensor of max value of feature map.
  • cumsum (Tensor) - A Tensor of cumsum bin of data.
  • min_percentile (Float) - min init percentile.
  • max_percentile (Float) - max init percentile.
  • search_start (Float) - search start.
  • search_end (Float) - search end.
  • search_step (Float) - step size of searching.
  • with_offset (bool) - whether using offset.
• Returns:
  • scale - optimal scale.
  • offset - optimal offset .
• constraints：

  None

• Examples：

  >>> input = torch.rand((2,2,3,4),dtype=torch.float32).npu()
  >>> input
  tensor([[[[0.4508, 0.6513, 0.4734, 0.1924],
            [0.0402, 0.5502, 0.0694, 0.9032],
            [0.4844, 0.5361, 0.9369, 0.7874]],
          [[0.5157, 0.1863, 0.4574, 0.8033],
            [0.5986, 0.8090, 0.7605, 0.8252],
            [0.4264, 0.8952, 0.2279, 0.9746]]],
          [[[0.0803, 0.7114, 0.8773, 0.2341], 
           [0.6497, 0.0423, 0.8407, 0.9515], 
           [0.1821, 0.5931, 0.7160, 0.4968]],
            [[0.7977, 0.0899, 0.9572, 0.0146],
            [0.2804, 0.8569, 0.2292, 0.1118],
            [0.5747, 0.4064, 0.8370, 0.1611]]]], device='npu:0')  
  >>> min_value = torch.min(input)  
  >>> min_value  
  tensor(0.0146, device='npu:0')
  >>> max_value = torch.max(input)  
  >>> max_value  
  tensor(0.9746, device='npu:0')  
  >>> hist = torch.histc(input.to('cpu'),
                           bins=128,
                           min=min_value.to('cpu'),
                           max=max_value.to('cpu'))  
  >>> hist  
  tensor([1., 0., 0., 2., 0., 0., 0., 1., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0.,
            0., 1., 0., 0., 2., 1., 0., 0., 0., 0., 2., 1., 0., 0., 0., 0., 0., 1.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
            1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 0., 1., 0., 0., 1., 0., 1.,
            0., 0., 1., 0., 0., 2., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 
           0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 2., 0., 0.,
            1., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1.,
            0., 1.])  >>> cdf = torch.cumsum(hist,dim=0).int().npu()  
  >>> cdf  
  tensor([ 1,  1,  1,  3,  3,  3,  3,  4,  5,  5,  6,  6,  7,  7,  7,  7,  7,  7,
            7,  8,  8,  8, 10, 11, 11, 11, 11, 11, 13, 14, 14, 14, 14, 14, 14, 15,
            15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16,
            17, 17, 17, 17, 18, 19, 19, 20, 21, 21, 22, 22, 23, 23, 23, 24, 24, 25, 
           25, 25, 26, 26, 26, 28, 28, 28, 28, 28, 28, 28, 30, 30, 30, 30, 30, 30, 
           30, 30, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 34, 35, 37, 37, 37,
            38, 39, 40, 40, 41, 41, 41, 42, 42, 43, 44, 44, 44, 44, 45, 45, 46, 47,
            47, 48], device='npu:0', dtype=torch.int32)  
  >>> scale, offset = torch_npu.npu_ifmr(input,
                                       min_value, 
                                      max_value,  
                                     cdf, 
                                      min_percentile=0.999999, 
                                      max_percentile=0.999999,
                                       search_start=0.7, 
                                      search_end=1.3, 
                                      search_step=0.01,
                                       with_offset=False)  
  >>> scale  tensor(0.0080, device='npu:0')  
  >>> offset  tensor(0., device='npu:0')

torch_npu.npu_max.dim(self, dim, keepdim=False) -> (Tensor, Tensor)

count max result with dim.

• Parameters： Similar to torch.max, optimize implemention to npu device.
  • self (Tensor) – the input tensor.
  • dim (Number) – the dimension to reduce.
  • keepdim (bool) – whether the output tensor has dim retained or not.
• Returns:
  • values - max values in the input tensor.
  • indices - index of max values in the input tensor.
• constraints：

  None

• Examples：

  >>> input = torch.randn(2, 2, 2, 2, dtype = torch.float32).npu()
  >>> input
  tensor([[[[-1.8135,  0.2078],
            [-0.6678,  0.7846]],
  
          [[ 0.6458, -0.0923],
            [-0.2124, -1.9112]]],
  
          [[[-0.5800, -0.4979], 
           [ 0.2580,  1.1335]],
  
            [[ 0.6669,  0.1876],
            [ 0.1160, -0.1061]]]], device='npu:0')
  >>> outputs, indices = torch_npu.npu_max(input, 2)
  >>> outputs
  tensor([[[-0.6678,  0.7846],
          [ 0.6458, -0.0923]],
  
          [[ 0.2580,  1.1335],
          [ 0.6669,  0.1876]]], device='npu:0')
  >>> indices
  tensor([[[1, 1],
          [0, 0]],
  
          [[1, 1],
          [0, 0]]], device='npu:0', dtype=torch.int32)

torch_npu.npu_min.dim(self, dim, keepdim=False) -> (Tensor, Tensor)

torch_npu.npu_scatter(self, indices, updates, dim) -> Tensor

count scatter result with dim.

• Parameters： Similar to torch.scatter, optimize implemention to npu device.
  • self (Tensor) - the input tensor.
  • indices (Tensor) – the indices of elements to scatter, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged.
  • updates (Tensor) – the source element(s) to scatter.
• dim (Number) – the axis along which to index
• constraints：

  None

• Examples：

  >>> input    = torch.tensor([[1.6279, 0.1226], [0.9041, 1.0980]]).npu()
  >>> input
  tensor([[1.6279, 0.1226],
          [0.9041, 1.0980]], device='npu:0')
  >>> indices  = torch.tensor([0, 1],dtype=torch.int32).npu()
  >>> indices
  tensor([0, 1], device='npu:0', dtype=torch.int32)
  >>> updates  = torch.tensor([-1.1993, -1.5247]).npu()
  >>> updates
  tensor([-1.1993, -1.5247], device='npu:0')
  >>> dim = 0
  >>> output = torch_npu.npu_scatter(input, indices, updates, dim)
  >>> output
  tensor([[-1.1993,  0.1226],
          [ 0.9041, -1.5247]], device='npu:0')

torch_npu.npu_layer_norm_eval(input, normalized_shape, weight=None, bias=None, eps=1e-05) -> Tensor

torch_npu.npu_alloc_float_status(self) -> Tensor

Produces eight numbers with a value of zero

• Parameters：
  • self (Tensor) - Any Tensor
• constraints：

  None

• Examples：

  >>> input    = torch.randn([1,2,3]).npu()
  >>> output = torch_npu.npu_alloc_float_status(input)
  >>> input
  tensor([[[ 2.2324,  0.2478, -0.1056],
          [ 1.1273, -0.2573,  1.0558]]], device='npu:0')
  >>> output
  tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0')

torch_npu.npu_get_float_status(self) -> Tensor

Computes NPU get float status operator function.

• Parameters：
  • self (Tensor) - A Tensor of data memory address. Must be float32 .
• Constraints：

  None

• Examples：

  >>> x = torch.rand(2).npu()
  >>> torch_npu.npu_get_float_status(x)
  tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0')

torch_npu.npu_clear_float_status(self) -> Tensor

Set the value of address 0x40000 to 0 in each core.

• Parameters：
  • self (Tensor) - A tensor of type float32.
• Constraints：

  None

• Examples：

  >>> x = torch.rand(2).npu()
  >>> torch_npu.npu_clear_float_status(x)
  tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0')

torch_npu.npu_confusion_transpose(self, perm, shape, transpose_first) -> Tensor

Confuse reshape and transpose.

• Parameters：
  • self (Tensor) - A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
  • perm (ListInt) - A permutation of the dimensions of "x".
  • shape (ListInt) - The shape of the input.
  • transpose_first (bool) - If True, the transpose is first, otherwise the reshape is first.
• Constraints：

  None

• Examples：

  >>> x = torch.rand(2, 3, 4, 6).npu()
  >>> x.shape
  torch.Size([2, 3, 4, 6])
  >>> y = torch_npu.npu_confusion_transpose(x, (0, 2, 1, 3), (2, 4, 18), True)
  >>> y.shape
  torch.Size([2, 4, 18])
  >>> y2 = torch_npu.npu_confusion_transpose(x, (0, 2, 1), (2, 12, 6), False)
  >>> y2.shape
  torch.Size([2, 6, 12])

torch_npu.npu_bmmV2(self, mat2, output_sizes) -> Tensor

Multiplies matrix "a" by matrix "b", producing "a * b" .

• Parameters：
  • self (Tensor) - A matrix Tensor. Must be one of the following types: float16, float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
  • mat2 (Tensor) - A matrix Tensor. Must be one of the following types: float16, float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
  • output_sizes (ListInt) - Output's shape, used in matmul's backpropagation, default [].
• Constraints：

  None

• Examples：

  >>> mat1 = torch.randn(10, 3, 4).npu()
  >>> mat2 = torch.randn(10, 4, 5).npu()
  >>> res = torch_npu.npu_bmmV2(mat1, mat2, [])
  >>> res.shape
  torch.Size([10, 3, 5])

torch_npu.fast_gelu(self) -> Tensor

Computes the gradient for the fast_gelu of "x" .

• Parameters：
  • self (Tensor) - A Tensor. Must be one of the following types: float16, float32
• Constraints：

  None

• Examples：

  >>> x = torch.rand(2).npu()
  >>> x
  tensor([0.5991, 0.4094], device='npu:0')
  >>> torch_npu.fast_gelu(x)
  tensor([0.4403, 0.2733], device='npu:0')

torch_npu.npu_deformable_conv2d(self, weight, offset, bias, kernel_size, stride, padding, dilation=[1,1,1,1], groups=1, deformable_groups=1, modulated=True) -> (Tensor, Tensor)

torch_npu.npu_mish(self) -> Tensor

Computes hyperbolic tangent of "x" element-wise.

• Parameters：
  • self (Tensor) - A Tensor. Must be one of the following types: float16, float32.
• Constraints：

  None

• Examples：

  >>> x = torch.rand(10, 30, 10).npu()
  >>> y = torch_npu.npu_mish(x)
  >>> y.shape
  torch.Size([10, 30, 10])

torch_npu.npu_anchor_response_flags(self, featmap_size, stride, num_base_anchors) -> Tensor

Generate the responsible flags of anchor in a single feature map.

• Parameters：
  • self (Tensor) - Ground truth box, 2-D Tensor with shape [batch, 4].
  • featmap_size (ListInt) - The size of feature maps, listint.
  • strides (ListInt) - Stride of current level, listint.
  • num_base_anchors (Number) - The number of base anchors.
• Constraints：

  None

• Examples：

  >>> x = torch.rand(100, 4).npu()
  >>> y = torch_npu.npu_anchor_response_flags(x, [60, 60], [2, 2], 9)
  >>> y.shape
  torch.Size([32400])

torch_npu.npu_yolo_boxes_encode(self, gt_bboxes, stride, performance_mode=False) -> Tensor

Generates bounding boxes based on yolo's "anchor" and "ground-truth" boxes. It is a customized mmdetection operator.

• Parameters：
  • self (Tensor) - anchor boxes generated by the yolo training set. A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of ROIs, "N" indicates the number of ROIs, and the value "4" refers to (tx, ty, tw, th).
  • gt_bboxes (Tensor) - target of the transformation, e.g, ground-truth boxes. A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of ROIs, and 4 indicates "dx", "dy", "dw", and "dh".
  • strides (Tensor) - Scale for each box. A 1D Tensor of type int32 shape (N,). "N" indicates the number of ROIs.
• performance_mode (bool) - Select performance mode, "high_precision" or "high_performance". select "high_precision" when input type is float32, the output tensor precision will be smaller than 0.0001, select "high_performance" when input type is float32, the ops will be best performance, but precision will be only smaller than 0.005.
• Constraints：

  input anchor boxes only support maximum N=20480.

• Examples：

  >>> anchor_boxes = torch.rand(2, 4).npu()
  >>> gt_bboxes = torch.rand(2, 4).npu()
  >>> stride = torch.tensor([2, 2], dtype=torch.int32).npu()
  >>> output = torch_npu.npu_yolo_boxes_encode(anchor_boxes, gt_bboxes, stride, False)
  >>> output.shape
  torch.Size([2, 4])

torch_npu.npu_grid_assign_positive(self, overlaps, box_responsible_flags, max_overlaps, argmax_overlaps, gt_max_overlaps, gt_argmax_overlaps, num_gts, pos_iou_thr, min_pos_iou, gt_max_assign_all) -> Tensor

Performs Position Sensitive PS ROI Pooling Grad.

• Parameters：
  • self (Tensor) - Tensor of type float16 or float32, shape (n, )
  • overlaps (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. IOU between gt_bboxes and bboxes. shape(k, n)
  • box_responsible_flags (Tensor) - A Tensor. Support uint8. Flag to indicate whether box is responsible.
  • max_overlaps (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=0).
  • argmax_overlaps (Tensor) - A Tensor. Support int32. overlaps.argmax(axis=0).
  • gt_max_overlaps (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=1).
  • gt_argmax_overlaps (Tensor) - A Tensor. Support int32. overlaps.argmax(axis=1).
  • num_gts (Tensor) - A Tensor. Support int32. real k. shape (1, )
  • pos_iou_thr (Float) - loat. IOU threshold for positive bboxes.
  • min_pos_iou (Float) - float. minimum iou for a bbox to be considered as a positive bbox
  • gt_max_assign_all (bool) - bool. whether to assign all bboxes with the same highest overlap with some gt to that gt.
• Constraints：

  None

• Examples：

  >>> assigned_gt_inds = torch.rand(4).npu()
  >>> overlaps = torch.rand(2,4).npu()
  >>> box_responsible_flags = torch.tensor([1, 1, 1, 0], dtype=torch.uint8).npu()
  >>> max_overlap = torch.rand(4).npu()
  >>> argmax_overlap = torch.tensor([1, 0, 1, 0], dtype=torch.int32).npu()
  >>> gt_max_overlaps = torch.rand(2).npu()
  >>> gt_argmax_overlaps = torch.tensor([1, 0],dtype=torch.int32).npu()
  >>> output = torch_npu.npu_grid_assign_positive(assigned_gt_inds, overlaps, box_responsible_flags, max_overlap, argmax_overlap, gt_max_overlaps, gt_argmax_overlaps, 128, 0.5, 0., True)
  >>> output.shape
  torch.Size([4])

torch_npu.npu_normalize_batch(self, seq_len, normalize_type=0) -> Tensor

Performs batch normalization .

• Parameters：
  • self (Tensor) - A Tensor. Support float32. shape (n, c, d).
  • seq_len (Tensor) - A Tensor. Each batch normalize data num. Support Int32. Shape (n, ).
  • normalize_type (Number) - Str. Support "per_feature" or "all_features".
• constraints：

  None

• Examples：

  >>> a=np.random.uniform(1,10,(2,3,6)).astype(np.float32)
  >>> b=np.random.uniform(3,6,(2)).astype(np.int32)
  >>> x=torch.from_numpy(a).to("npu")
  >>> seqlen=torch.from_numpy(b).to("npu")
  >>> out = torch_npu.npu_normalize_batch(x, seqlen, 0)
  >>> out
  tensor([[[ 1.1496, -0.6685, -0.4812,  1.7611, -0.5187,  0.7571],
          [ 1.1445, -0.4393, -0.7051,  1.0474, -0.2646, -0.1582],
          [ 0.1477,  0.9179, -1.0656, -6.8692, -6.7437,  2.8621]],
  
          [[-0.6880,  0.1337,  1.3623, -0.8081, -1.2291, -0.9410],
          [ 0.3070,  0.5489, -1.4858,  0.6300,  0.6428,  0.0433],
          [-0.5387,  0.8204, -1.1401,  0.8584, -0.3686,  0.8444]]],
        device='npu:0')

torch_npu.npu_masked_fill_range(self, start, end, value, axis=-1) -> Tensor

masked fill tensor along with one axis by range.boxes. It is a customized masked fill range operator .

• Parameters：
  • self (Tensor) - input tensor. A ND Tensor of float32/float16/int32/int8 with shapes 1-D (D,), 2-D(N, D), 3-D(N, C, D).
  • start (Tensor) - masked fill start pos. A 3D Tensor of int32 with shape (num, N).
  • end (Tensor) - masked fill end pos. A 3D Tensor of int32 with shape (num, N).
  • value (Tensor) - masked fill value. A 2D Tensor of float32/float16/int32/int8 with shape (num,).
  • axis (Number) - axis with masked fill of int32. Defaults to -1.
• constraints：

  None

• Examples：

  >>> a=torch.rand(4,4).npu()
  >>> a
  tensor([[0.9419, 0.4919, 0.2874, 0.6560],
          [0.6691, 0.6668, 0.0330, 0.1006],
          [0.3888, 0.7011, 0.7141, 0.7878],
          [0.0366, 0.9738, 0.4689, 0.0979]], device='npu:0')
  >>> start = torch.tensor([[0,1,2]], dtype=torch.int32).npu()
  >>> end = torch.tensor([[1,2,3]], dtype=torch.int32).npu()
  >>> value = torch.tensor([1], dtype=torch.float).npu()
  >>> out = torch_npu.npu_masked_fill_range(a, start, end, value, 1)
  >>> out
  tensor([[1.0000, 0.4919, 0.2874, 0.6560],
          [0.6691, 1.0000, 0.0330, 0.1006],
          [0.3888, 0.7011, 1.0000, 0.7878],
          [0.0366, 0.9738, 0.4689, 0.0979]], device='npu:0')

torch_npu.npu_linear(input, weight, bias=None) -> Tensor

Multiplies matrix "a" by matrix "b", producing "a * b" .

• Parameters：
  • input (Tensor) - A matrix Tensor. 2D. Must be one of the following types: float32, float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ].
  • weight (Tensor) - A matrix Tensor. 2D. Must be one of the following types: float32, float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ].
  • bias (Tensor) - A 1D Tensor. Must be one of the following types: float32, float16, int32. Has format [ND, NHWC].
• constraints：

  None

• Examples：

  >>> x=torch.rand(2,16).npu()
  >>> w=torch.rand(4,16).npu()
  >>> b=torch.rand(4).npu()
  >>> output = torch_npu.npu_linear(x, w, b)
  >>> output
  tensor([[3.6335, 4.3713, 2.4440, 2.0081],
          [5.3273, 6.3089, 3.9601, 3.2410]], device='npu:0')

torch_npu.npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, step_size=None, adam_mode=0, *, out=（var,m,v）)

count adam result.

• Parameters:
  • var (Tensor) - A Tensor. Support float16/float32.
  • m (Tensor) - A Tensor. Datatype and shape are same as exp_avg.
  • v (Tensor) - A Tensor. Datatype and shape are same as exp_avg.
  • lr (Number) - Datatype is same as exp_avg.
  • beta1 (Number) - Datatype is same as exp_avg.
  • beta2 (Number) - Datatype is same as exp_avg.
  • epsilon (Number) - Datatype is same as exp_avg.
  • grad (Tensor) - A Tensor. Datatype and shape are same as exp_avg.
  • max_grad_norm (Number) - Datatype is same as exp_avg.
  • global_grad_norm (Number) - Datatype is same as exp_avg.
  • weight_decay (Number) - Datatype is same as exp_avg.
• Keyword Arguments :
  • out :A Tensor, optional. The output tensor.
• constraints:

  None

• Examples：

  >>> var_in = torch.rand(321538).uniform_(-32., 21.).npu()
  >>> m_in = torch.zeros(321538).npu()
  >>> v_in = torch.zeros(321538).npu()
  >>> grad = torch.rand(321538).uniform_(-0.05, 0.03).npu()
  >>> max_grad_norm = -1.
  >>> beta1 = 0.9
  >>> beta2 = 0.99
  >>> weight_decay = 0.
  >>> lr = 0.
  >>> epsilon = 1e-06
  >>> global_grad_norm = 0.
  >>> var_out, m_out, v_out = torch_npu.npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out=(var_in, m_in, v_in))
  >>> var_out
  tensor([ 14.7733, -30.1218,  -1.3647,  ..., -16.6840,   7.1518,   8.4872],
        device='npu:0')

torch_npu.npu_giou(self, gtboxes, trans=False, is_cross=False, mode=0) -> Tensor

First calculate the minimum closure area of the two boxes, IoU, the proportion of the closed area that does not belong to the two boxes in the closure area, and finally subtract this proportion from IoU to get GIoU .

• Parameters：
  • self (Tensor) - Bounding boxes, a 2D Tensor of type float16 or float32 with shape (N, 4). "N" indicates the number of bounding boxes, and the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h].
  • gtboxes (Tensor) - Ground-truth boxes, a 2D Tensor of type float16 or float32 with shape (M, 4). "M" indicates the number of ground truth boxes, and the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h].
  • trans (bool) - An optional bool, true for 'xywh', false for 'xyxy'.
  • is_cross (bool) - An optional bool, control whether the output shape is [M, N] or [1, N].
  • mode: (Number) - Computation mode, a character string with the value range of [iou, iof] .
• constraints：

  None

• Examples：

  >>> a=np.random.uniform(0,1,(4,10)).astype(np.float16)
  >>> b=np.random.uniform(0,1,(4,10)).astype(np.float16)
  >>> box1=torch.from_numpy(a).to("npu")
  >>> box2=torch.from_numpy(a).to("npu")
  >>> output = torch_npu.npu_giou(box1, box2, trans=True, is_cross=False, mode=0)
  >>> output
  tensor([[1.],
          [1.],
          [1.],
          [1.],
          [1.],
          [1.],
          [1.],
          [1.],
          [1.],
          [1.]], device='npu:0', dtype=torch.float16)

torch_npu.npu_silu(self) -> Tensor

Computes the for the Swish of "x" .

• Parameters：
  • self (Tensor) - A Tensor. Must be one of the following types: float16, float32
• constraints：

  None

• Examples：

  >>> a=torch.rand(2,8).npu()
  >>> output = torch_npu.npu_silu(a)
  >>> output
  tensor([[0.4397, 0.7178, 0.5190, 0.2654, 0.2230, 0.2674, 0.6051, 0.3522],
          [0.4679, 0.1764, 0.6650, 0.3175, 0.0530, 0.4787, 0.5621, 0.4026]],
         device='npu:0')

torch_npu.npu_reshape(self, shape, bool can_refresh=False) -> Tensor

Reshapes a tensor. Only the tensor shape is changed, without changing the data.

• Parameters：
  • self (Tensor) - A Tensor.
  • shape (ListInt) - Defines the shape of the output tensor.
  • can_refresh (bool) - Used to specify whether reshape can be refreshed in place.
• constraints：

  This operator cannot be directly called by the acllopExecute API.

• Examples：

  >>> a=torch.rand(2,8).npu()
  >>> out=torch_npu.npu_reshape(a,(4,4))
  >>> out
  tensor([[0.6657, 0.9857, 0.7614, 0.4368],
          [0.3761, 0.4397, 0.8609, 0.5544],
          [0.7002, 0.3063, 0.9279, 0.5085],
          [0.1009, 0.7133, 0.8118, 0.6193]], device='npu:0')

torch_npu.npu_rotated_overlaps(self, query_boxes, trans=False) -> Tensor

Calculate the overlapping area of the rotated box.

• Parameters：
  • self (Tensor) - data of grad increment, a 3D Tensor of type float32 with shape (B, 5, N).
  • query_boxes (Tensor) - Bounding boxes, a 3D Tensor of type float32 with shape (B, 5, K).
  • trans (bool) - An optional attr, true for 'xyxyt', false for 'xywht'.
• constraints：

  None

• Examples：

  >>> a=np.random.uniform(0,1,(1,3,5)).astype(np.float16)
  >>> b=np.random.uniform(0,1,(1,2,5)).astype(np.float16)
  >>> box1=torch.from_numpy(a).to("npu")
  >>> box2=torch.from_numpy(a).to("npu")
  >>> output = torch_npu.npu_rotated_overlaps(box1, box2, trans=False)
  >>> output
  tensor([[[0.0000, 0.1562, 0.0000],
          [0.1562, 0.3713, 0.0611],
          [0.0000, 0.0611, 0.0000]]], device='npu:0', dtype=torch.float16)

torch_npu.npu_rotated_iou(self, query_boxes, trans=False, mode=0, is_cross=True) -> Tensor

torch_npu.npu_rotated_box_encode(anchor_box, gt_bboxes, weight) -> Tensor

torch_npu.npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor

Rotate Bounding Box Encoding

• Parameters：
  • anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" .
  • deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N).
  • weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0].
• constraints：

  None

• Examples：

  >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu")
      >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu")
      >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu()
      >>> out = torch_npu.npu_rotated_box_decode(anchor_boxes, deltas, weight)
      >>> out
      tensor([[[  1.7861],
              [-10.5781],
              [ 33.0000],
              [ 17.2969],
              [-88.4375]]], device='npu:0', dtype=torch.float16)

torch_npu.npu_ciou(Tensor self, Tensor gtboxes, bool trans=False, bool is_cross=True, int mode=0, bool atan_sub_flag=False) -> Tensor

Applies an NPU based CIOU operation.

A penalty item is added on the basis of DIoU, and CIoU is proposed.

• Notes:

  Util now, ciou backward only support trans==True, is_cross==False, mode==0('iou') current version if you need to back propagation, please ensure your parameter is correct!

• Args：
  • boxes1 (Tensor): Predicted bboxes of format xywh, shape (4, n).
  • boxes2 (Tensor): Corresponding gt bboxes, shape (4, n).
  • trans (Bool): Whether there is an offset
  • is_cross (Bool): Whether there is a cross operation between box1 and box2.
  • mode (int): Select the calculation mode of diou.
  • atan_sub_flag (Bool): whether to pass the second value of the forward to the reverse.
• Returns：

  torch.Tensor: The result of the mask operation

• Examples：

      >>> box1 = torch.randn(4, 32).npu()
      >>> box1.requires_grad = True
      >>> box2 = torch.randn(4, 32).npu()
      >>> box2.requires_grad = True
      >>> ciou = torch_npu.contrib.function.npu_ciou(box1, box2) 
      >>> l = ciou.sum()
      >>> l.backward()

torch_npu.npu_diou(Tensor self, Tensor gtboxes, bool trans=False, bool is_cross=False, int mode=0) -> Tensor

Applies an NPU based DIOU operation.

Taking into account the distance between the targets,the overlap rate of the distance and the range, different targets or boundaries will tend to be stable.

• Notes:

  Util now, diou backward only support trans==True, is_cross==False, mode==0('iou') current version if you need to back propagation, please ensure your parameter is correct!

• Args：
  • boxes1 (Tensor): Predicted bboxes of format xywh, shape (4, n).
  • boxes2 (Tensor): Corresponding gt bboxes, shape (4, n).
  • trans (Bool): Whether there is an offset
  • is_cross (Bool): Whether there is a cross operation between box1 and box2.
  • mode (int): Select the calculation mode of diou.
• Returns：

  torch.Tensor: The result of the mask operation

• Examples：

      >>> box1 = torch.randn(4, 32).npu()
      >>> box1.requires_grad = True
      >>> box2 = torch.randn(4, 32).npu()
      >>> box2.requires_grad = True
      >>> ciou = torch_npu.contrib.function.npu_diou(box1, box2) 
      >>> l = diou.sum()
      >>> l.backward()

torch_npu.npu_sign_bits_pack(Tensor self, int size) -> Tensor

one-bit Adam pack of float into uint8.

• Args：
  • x(Tensor) - A floats Tensor in 1D.
  • size(Number) - A required int. First dimension of output tensor when reshaping.
  • constraints：

    Size needs to be divisible by output of packing floats. If size of x is divisible by 8, size of output is (size of x) / 8; otherwise, size of output is (size of x // 8) + 1, -1 float values will be added to fill divisibility, at little endian positions.

    The AI Processors that support input type float32 and float16:

    • 昇腾310P AI处理器
    • 昇腾910 AI处理器

    昇腾310 AI处理器 only supports input type float16.

• Examples：

      >>>a = torch.tensor([5,4,3,2,0,-1,-2, 4,3,2,1,0,-1,-2],dtype=torch.float32).npu()
      >>>b = torch_npu.sign_bits_pack(a, 2)
      >>>b
      >>>tensor([[159],[15]], device='npu:0')
      >>>(binary form of 159 is ob10011111, corresponds to 4, -2, -1, 0, 2, 3, 4, 5 respectively)

torch_npu.sign_bits_unpack(x, dtype, size) -> Tensor

one-bit Adam unpack of uint8 into float.

• Args：
  • x(Tensor) - A uint8 Tensor in 1D.
  • dtype(Number) - A required int. 1 sets float16 as output, 0 sets float32 as output.
  • size(Number) - A required int. First dimension of output tensor when reshaping.
• constraints：

  Size needs to be divisible by output of unpacking uint8s. Size of output is (size of x) * 8;

• Examples：

      >>>a = torch.tensor([159, 15], dtype=torch.uint8).npu()
      >>>b = torch_npu.sign_bits_unpack(a, 0, 2)
      >>>b
      >>>tensor([[1., 1., 1., 1., 1., -1., -1., 1.],
      >>>[1., 1., 1., 1., -1., -1., -1., -1.]], device='npu:0')
  (binary form of 159 is ob00001111)