update include headers

4 years ago · f87dd9d016
parent d9d99c3cf5
commit f87dd9d016
5 changed files with 233 additions and 112 deletions
--- a/third_party/fwkacllib/inc/ops/linalg_ops.h
+++ b/third_party/fwkacllib/inc/ops/linalg_ops.h
@ -82,6 +82,25 @@ REG_OP(Cholesky)
        DT_FLOAT16, DT_COMPLEX64, DT_COMPLEX128}))
    .OP_END_FACTORY_REG(Cholesky)

+/**
+*@brief Computes the outer product of two 1D vectors . \n
+
+*@par Inputs:
+*The input x and vec2 has to be a 1D vector.Inputs include:
+*@li x:A Tensor. Must be one of the following types: float16, float32. 
+Shape is [N] . \n
+*@li vec2:A Tensor. Must have the same type as x. Shape is [M] . \n
+
+*@par Outputs:
+*y:A Tensor. Has the same type as x . \n
+*/
+
+REG_OP(Ger)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(vec2, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OP_END_FACTORY_REG(Ger)
+
 /**
 *@brief Computes the sign and the log of the absolute value of the determinant
 of one or more square matrices . \n
--- a/third_party/fwkacllib/inc/ops/nn_detect_ops.h
+++ b/third_party/fwkacllib/inc/ops/nn_detect_ops.h
@ -1592,7 +1592,6 @@ selected indices from the boxes tensor, where M <= max_output_size. \n
 *Compatible with onnx NonMaxSuppression operator.
 */

-
 REG_OP(NonMaxSuppressionV7)
    .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
    .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
@ -1649,6 +1648,84 @@ REG_OP(RoiExtractor)
    .ATTR(aligned, Bool, true)
    .OP_END_FACTORY_REG(RoiExtractor)

+/**
+*@brief Performs Position Sensitive PS ROI Pooling . \n
+
+*@par Inputs:
+* Two inputs, including:
+*@li x: An NC1HWC0 tensor of type float16 or float32, describing the feature
+* map, dimension C1 must be equal to
+* (int(output_dim+15)/C0))*group_size*group_size.
+*@li rois: A tensor of type float16 or float32, with shape
+* [batch, 5, rois_num], describing the ROIs, each ROI consists of five
+* elements: "batch_id", "x1", "y1", "x2", and "y2", which "batch_id" indicates
+* the index of the input feature map, "x1", "y1", "x2", or "y2" must be
+* greater than or equal to "0.0" . \n
+
+*@par Attributes:
+*@li output_dim: A required int32, specifying the number of output channels,
+* must be greater than 0.
+*@li group_size: A required int32, specifying the number of groups to encode
+* position-sensitive score maps, must be within the range (0, 128).
+*@li spatial_scale: A required float32, scaling factor for mapping the input
+* coordinates to the ROI coordinates . \n
+
+*@par Outputs:
+*y: An NC1HWC0 tensor of type float16 or float32, describing the result
+* feature map . \n
+
+*@attention Constraints:
+* HC1HWC0: channel must be Group_size squared, rois_num is a multiple of 16
+*/
+REG_OP(PSROIPoolingV2)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(rois, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .REQUIRED_ATTR(spatial_scale, Float)
+    .REQUIRED_ATTR(output_dim, Int)
+    .REQUIRED_ATTR(group_size, Int)
+    .OP_END_FACTORY_REG(PSROIPoolingV2)
+
+/**
+*@brief Performs Position Sensitive PS ROI Pooling Grad . \n
+
+*@par Inputs:
+* Two inputs, including:
+*@li x: An NC1HWC0 tensor of type float16 or float32, describing the result
+* feature map . \n
+*@li rois: A tensor of type float16 or float32, with shape
+* [batch, 5, rois_num], describing the ROIs, each ROI consists of five
+* elements: "batch_id", "x1", "y1", "x2", and "y2", which "batch_id" indicates
+* the index of the input feature map, "x1", "y1", "x2", or "y2" must be
+* greater than or equal to "0.0" . \n
+
+*@par Attributes:
+*@li output_dim: A required int32, specifying the number of output channels,
+* must be greater than 0.
+*@li group_size: A required int32, specifying the number of groups to encode
+* position-sensitive score maps, must be within the range (0, 128).
+*@li spatial_scale: A required float32, scaling factor for mapping the input
+* coordinates to the ROI coordinates . \n
+*@li input_size: A required listInt, mapping the gradinput size: (H, W)
+
+*@par Outputs:
+*y: An NC1HWC0 tensor of type float16 or float32, describing the feature
+* map, dimension C1 must be equal to
+* (int(output_dim+15)/C0))*group_size*group_size.
+
+*@attention Constraints:
+* HC1HWC0: channel must be Group_size squared, rois_num is a multiple of 16
+*/
+REG_OP(PSROIPoolingGradV2D)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(rois, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .REQUIRED_ATTR(spatial_scale, Float)
+    .REQUIRED_ATTR(output_dim, Int)
+    .REQUIRED_ATTR(group_size, Int)
+    .REQUIRED_ATTR(input_size, ListInt)
+    .OP_END_FACTORY_REG(PSROIPoolingGradV2D)
+
 }  // namespace ge

 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_
--- a/third_party/fwkacllib/inc/ops/nn_norm_ops.h
+++ b/third_party/fwkacllib/inc/ops/nn_norm_ops.h
@ -525,6 +525,31 @@ REG_OP(LayerNorm)
    .ATTR(epsilon, Float, 0.0000001)
    .OP_END_FACTORY_REG(LayerNorm)

+/**
+*@brief Returns a tensor where each sub-tensor of input along dimension 
+*       dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm. \n
+
+*@par Inputs:
+*One input, including:
+* @li x: A Tensor. Must be one of the following types: float16, float32 . \n
+
+*@par Attributes:
+* @li p: Specify L_p norm, the type is float. 
+* @li dim: The processed dim, the type is int.
+* @li maxnorm: Threshold for comparison, the type is float.  \n
+
+*@par Outputs:
+*One outputs, including:
+* @li y: shape and dtype of output, should be same shape and type as input.
+*/
+REG_OP(Renorm)
+    .INPUT(x, TensorType::BasicType())
+    .OUTPUT(y, TensorType::BasicType())
+    .REQUIRED_ATTR(p, Float)
+    .REQUIRED_ATTR(dim, Int)
+    .REQUIRED_ATTR(maxnorm, Float)
+    .OP_END_FACTORY_REG(Renorm)
+
 /**
 *@brief LayerNormGrad operator interface implementation
 *  calculating: dy, x, variance, mean, gamma
--- a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h
+++ b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h
@ -397,8 +397,8 @@ No default value.
 specifying the stride of the sliding window for each dimension of
 the input tensor. No default value.
 *@li padding: A required string type of float16.
-*@li pads: A list type of int32. Default value {0, 0, 0}.
-*@li dilation: A list type of int32. Default value {1, 1, 1}.
+*@li pads: A list type of int32. Default value {0,0,0,0,0,0}.
+*@li dilation: A list type of int32. Default value {1,1,1,1,1,1}.
 *@li ceil_mode: A ceil mode number of int32 . Default value 0.
 *@li data_format: An optional string. Defaults to "NDHWC" . \n

@ -421,8 +421,8 @@ REG_OP(MaxPool3D)
    .REQUIRED_ATTR(ksize, ListInt)
    .REQUIRED_ATTR(strides, ListInt)
    .REQUIRED_ATTR(padding, String)
-    .ATTR(pads, ListInt, {0,0,0})
-    .ATTR(dilation, ListInt, {1,1,1})
+    .ATTR(pads, ListInt, {0,0,0,0,0,0})
+    .ATTR(dilation, ListInt, {1,1,1,1,1,1})
    .ATTR(ceil_mode, Int, 0)
    .ATTR(data_format, String, "NDHWC")
    .OP_END_FACTORY_REG(MaxPool3D)
--- a/third_party/fwkacllib/inc/ops/pad_ops.h
+++ b/third_party/fwkacllib/inc/ops/pad_ops.h
@ -418,7 +418,7 @@ REG_OP(EmbeddingRankId)
 */
 REG_OP(FillV2)
    .INPUT(dims, TensorType({DT_INT16, DT_INT32, DT_INT64}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64}))
    .ATTR(value, Float, 0)
    .OP_END_FACTORY_REG(FillV2)

@ -437,7 +437,7 @@ REG_OP(FillV2)
 * Compatible with the ONNX operator ConstantOfShape.
 */
 REG_OP(FillV2D)
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64}))
    .ATTR(value, Float, 0)
    .REQUIRED_ATTR(dims, ListInt)
    .OP_END_FACTORY_REG(FillV2D)