From d91cff537ecb8167ef16b6f1a14c54ae508a31e6 Mon Sep 17 00:00:00 2001 From: zhangzhenghai Date: Tue, 4 Aug 2020 15:47:19 +0800 Subject: [PATCH] update ops headers --- third_party/fwkacllib/inc/ops/all_ops.h | 2 - third_party/fwkacllib/inc/ops/array_ops.h | 37 --- third_party/fwkacllib/inc/ops/ctc_ops.h | 72 +---- .../fwkacllib/inc/ops/elewise_calculation_ops.h | 118 +------- third_party/fwkacllib/inc/ops/image_ops.h | 11 +- third_party/fwkacllib/inc/ops/math_ops.h | 6 +- .../fwkacllib/inc/ops/matrix_calculation_ops.h | 39 --- third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h | 30 +- third_party/fwkacllib/inc/ops/nn_calculation_ops.h | 112 +++---- third_party/fwkacllib/inc/ops/nn_detect_ops.h | 68 ++--- third_party/fwkacllib/inc/ops/nn_norm_ops.h | 60 +--- third_party/fwkacllib/inc/ops/nn_pooling_ops.h | 322 +-------------------- third_party/fwkacllib/inc/ops/nn_training_ops.h | 2 +- third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h | 8 +- third_party/fwkacllib/inc/ops/reduce_ops.h | 187 +----------- third_party/fwkacllib/inc/ops/rnn.h | 9 +- third_party/fwkacllib/inc/ops/selection_ops.h | 11 +- .../fwkacllib/inc/ops/split_combination_ops.h | 9 +- third_party/fwkacllib/inc/ops/transformation_ops.h | 24 +- 19 files changed, 168 insertions(+), 959 deletions(-) diff --git a/third_party/fwkacllib/inc/ops/all_ops.h b/third_party/fwkacllib/inc/ops/all_ops.h index c30bf32b..031e955c 100644 --- a/third_party/fwkacllib/inc/ops/all_ops.h +++ b/third_party/fwkacllib/inc/ops/all_ops.h @@ -31,9 +31,7 @@ #include "functional_ops.h" #include "get_data_ops.h" #include "hcom_ops.h" -#include "hvd_ops.h" #include "image_ops.h" -#include "internal_ops.h" #include "linalg_ops.h" #include "logging_ops.h" #include "lookup_ops.h" diff --git a/third_party/fwkacllib/inc/ops/array_ops.h b/third_party/fwkacllib/inc/ops/array_ops.h index 7c6f9b2c..0d2a05a3 100644 --- a/third_party/fwkacllib/inc/ops/array_ops.h +++ b/third_party/fwkacllib/inc/ops/array_ops.h @@ -1084,43 +1084,6 @@ REG_OP(TransShape) .ATTR(outShape,ListInt ,{}) .OP_END_FACTORY_REG(TransShape); -/** -*@brief Computes the (possibly normalized) Levenshtein Edit Distance. - -*@par Inputs: -*@li hypothesis_indices: The indices of the hypothesis list SparseTensor.\n -This is an N x R int64 matrix. -*@li hypothesis_shape: The values of the hypothesis list SparseTensor.\n -This is an N-length vector. -*@li hypothesis_shape: The shape of the hypothesis list SparseTensor.\n -This is an R-length vector. -*@li truth_indices: The indices of the truth list SparseTensor.\n -This is an M x R int64 matrix. -*@li truth_shape: The values of the truth list SparseTensor.\n -This is an M-length vector. -*@li truth_shape: The shape of the truth list SparseTensor.\n -This is an R-length vector - -*@par Attributes: -*@li normalize: boolean (if true, edit distances are normalized by length of truth). - -*@par Outputs: -*@li output: A dense float tensor with rank R - 1. - -*@par Third-party framework compatibility -* Compatible with TensorFlow EditDistance operator. -*/ -REG_OP(EditDistance) - .INPUT(hypothesis_indices, TensorType({DT_INT64})) - .INPUT(hypothesis_values, TensorType::BasicType()) - .INPUT(hypothesis_shape, TensorType({DT_INT64})) - .INPUT(truth_indices, TensorType({DT_INT64})) - .INPUT(truth_values, TensorType::BasicType()) - .INPUT(truth_shape, TensorType({DT_INT64})) - .ATTR(normalize, Bool, true) - .OUTPUT(output, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(EditDistance) - } // namespace ge #endif // GE_OP_ARRAY_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/ctc_ops.h b/third_party/fwkacllib/inc/ops/ctc_ops.h index 74b797f3..00485a14 100644 --- a/third_party/fwkacllib/inc/ops/ctc_ops.h +++ b/third_party/fwkacllib/inc/ops/ctc_ops.h @@ -50,6 +50,7 @@ If not specified, defaults to true *@par Third-party framework compatibility * Compatible with TensorFlow CTCLoss operator. */ + REG_OP(CTCLoss) .INPUT(inputs, TensorType({DT_FLOAT, DT_DOUBLE})) .INPUT(labels_indices, TensorType({DT_INT64})) @@ -62,77 +63,6 @@ REG_OP(CTCLoss) .ATTR(ignore_longer_outputs_than_inputs, Bool, false) .OP_END_FACTORY_REG(CTCLoss) -/** -*@brief Performs greedy decoding on the logits given in inputs. - -*@par Inputs: -*@li inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. -*@li sequence_length: A vector containing sequence lengths, size `(batch_size)`. - -*@par Attributes: -*@li merge_repeated: If True, merge repeated classes in output. - -*@par Outputs: -*@li decoded_indices: Indices matrix, size `(total_decoded_outputs x 2)`,\n -of a `SparseTensor`. The rows store: [batch, time]. -*@li decoded_values: Values vector, size: `(total_decoded_outputs)`,\n -of a `SparseTensor`. The vector stores the decoded classes. -*@li decoded_shape: Shape vector, size `(2)`, of the decoded SparseTensor.\n -Values are: `[batch_size, max_decoded_length]`. -*@li log_probability: Matrix, size `(batch_size x 1)`, containing sequence\n -log-probabilities. - -*@par Third-party framework compatibility -* Compatible with TensorFlow CTCGreedyDecoder operator. -*/ -REG_OP(CTCGreedyDecoder) - .INPUT(inputs, TensorType({DT_FLOAT, DT_DOUBLE})) - .INPUT(sequence_length, TensorType({DT_INT32})) - .ATTR(merge_repeated, Bool, false) - .OUTPUT(decoded_indices, TensorType({DT_INT64})) - .OUTPUT(decoded_values, TensorType({DT_INT64})) - .OUTPUT(decoded_shape, TensorType({DT_INT64})) - .OUTPUT(log_probability, TensorType({DT_FLOAT, DT_DOUBLE})) - .OP_END_FACTORY_REG(CTCGreedyDecoder) - -/** -*@brief Performs beam search decoding on the logits given in input. - -*@par Inputs: -*@li inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. -*@li sequence_length: A vector containing sequence lengths, size `(batch_size)`. - -*@par Attributes: -*@li merge_repeated: If True, merge repeated classes in output. - -*@par Outputs: -*@li decoded_indices: A list (length: top_paths) of indices matrices. Matrix j,\n -size `(total_decoded_outputs[j] x 2)`, has indices of a\n -`SparseTensor`. The rows store: [batch, time]. -*@li decoded_values: A list (length: top_paths) of values vectors. Vector j,\n -size `(length total_decoded_outputs[j])`, has the values of a\n -`SparseTensor`. The vector stores the decoded classes for beam j. -*@li decoded_shape: A list (length: top_paths) of shape vector. Vector j,\n -size `(2)`, stores the shape of the decoded `SparseTensor[j]`.\n -Its values are: `[batch_size, max_decoded_length[j]]`. -*@li log_probability: A matrix, shaped: `(batch_size x top_paths)`. The\n -sequence log-probabilities. - -*@par Third-party framework compatibility -* Compatible with TensorFlow CTCBeamSearchDecoder operator. -*/ -REG_OP(CTCBeamSearchDecoder) - .INPUT(inputs, TensorType({DT_FLOAT, DT_DOUBLE})) - .INPUT(sequence_length, TensorType({DT_INT32})) - .REQUIRED_ATTR(beam_width, Int) - .REQUIRED_ATTR(top_paths, Int) - .ATTR(merge_repeated, Bool, true) - .DYNAMIC_OUTPUT(decoded_indices, TensorType({DT_INT64})) - .DYNAMIC_OUTPUT(decoded_values, TensorType({DT_INT64})) - .DYNAMIC_OUTPUT(decoded_shape, TensorType({DT_INT64})) - .OUTPUT(log_probability, TensorType({DT_FLOAT, DT_DOUBLE})) - .OP_END_FACTORY_REG(CTCBeamSearchDecoder) - } // namespace ge #endif //GE_OP_CTC_OPS_H \ No newline at end of file diff --git a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h index 378eee38..04e1cea3 100644 --- a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h @@ -483,9 +483,9 @@ REG_OP(Equal) *x: A Tensor. Must be one of the following types: float16, float32, double, complex64, complex128. *@par Attributes: -*@li base: An optional attribute of type float32, specifying the base gamma. Defaults to "-1.0". -*@li scale: An optional attribute of type float32, specifying the scale alpha. Defaults to "1.0". -*@li shift: An optional attribute of type float32, specifying the shift beta. Defaults to "0.0". +*@li base: An optional attribute of type float32, specifying the base gamma. Defaults to "-1". +*@li scale: An optional attribute of type float32, specifying the scale alpha. Defaults to "1". +*@li shift: An optional attribute of type float32, specifying the shift beta. Defaults to "0". *@par Outputs: *y: A Tensor of the same type as "x". @@ -1016,17 +1016,17 @@ REG_OP(BesselI1e) * y = log_base(shift + scale * x), with "base" > 0. * @par Inputs: -* @li x: A Tensor of type complex64, complex128, float16, float32 or double. +* @li x: A Tensor of type UnaryDataType. * @par Attributes: -* @li base: An optional float32, specifying the base "e". Defaults to "-1.0" +* @li base: An optional float32, specifying the base "e". Defaults to "-1" * @li scale: An optional float32, specifying the scale of input "x". Defaults -* to "1.0" -* @li shift: An optional float32, specifying the shift. Defaults to "0.0" +* to "1" +* @li shift: An optional float32, specifying the shift. Defaults to "0" * @par Outputs: -* y: A Tensor has same type as "x". +* y: A Tensor of type UnaryDataType. * @attention Constraints: * @li "base" is supposed to be greater than 0. Retaining the default @@ -2262,7 +2262,7 @@ REG_OP(ArgMinD) *dtype: The output type, either "int32" or "int64". Defaults to "int64". *@par Outputs: -*y: A multi-dimensional Tensor of type int32 or int64, specifying the index with the largest value. The dimension is one less than that of "x". +*y: A multi-dimensional Tensor of type int32, specifying the index with the largest value. The dimension is one less than that of "x". *@attention Constraints: *@li x: If there are multiple maximum values, the index of the first maximum value is used. @@ -2398,8 +2398,8 @@ REG_OP(ArgMinWithValue) *y: A Tensor. Has the same type and format as "x". *@par Attributes: -*@li N: A required attribute. the number of input x, max size is 32. Type is int. -*@li model: An optional attribute. Type is int. Defaults to "1". +*@li N: A required attribute. the number of input x, max size is 32. +*@li model: An optional attribute. Defaults to "1". * "0": product, "1": sum, "2": max. *@li coeff: A required attribute. Must met all of following rules: * size of "coeff" must be equal to len("x") or is null. @@ -2693,86 +2693,6 @@ REG_OP(AdamApplyOne) .OP_END_FACTORY_REG(AdamApplyOne) /** -*@brief A fusion operator for bert lamb. - -*@par Inputs: -*Eleven inputs, including: -* @li input0: A Tensor. Must be one of the following types: float16, float32. -* @li input1: A Tensor. Must be one of the following types: float16, float32. -* @li input2: A Tensor. Must be one of the following types: float16, float32. -* @li input3: A Tensor. Must be one of the following types: float16, float32. -* @li input4: A Tensor. Must be one of the following types: float16, float32. -* @li mul0_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul1_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul2_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul3_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul4_x: A Tensor. Must be one of the following types: float16, float32. -* @li add2_y: A Tensor. Must be one of the following types: float16, float32. - -*@par Outputs: -*Three outputs, including: -* @li output0: A Tensor. Must be one of the following types: float16, float32. -* @li output1: A Tensor. Must be one of the following types: float16, float32. -* @li output2: A Tensor. Must be one of the following types: float16, float32. - -*/ -REG_OP(AdamApplyOneWithDecayAssign) - .INPUT(input0, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input1, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input2, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input3, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input4, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul0_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul1_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul2_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul3_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul4_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(add2_y, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output0, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output1, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output2, TensorType({DT_FLOAT16,DT_FLOAT})) - .OP_END_FACTORY_REG(AdamApplyOneWithDecayAssign) - -/** -*@brief A fusion operator for bert lamb. - -*@par Inputs: -*Ten inputs, including: -* @li input0: A Tensor. Must be one of the following types: float16, float32. -* @li input1: A Tensor. Must be one of the following types: float16, float32. -* @li input2: A Tensor. Must be one of the following types: float16, float32. -* @li input3: A Tensor. Must be one of the following types: float16, float32. -* @li input4: A Tensor. Must be one of the following types: float16, float32. -* @li mul0_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul1_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul2_x: A Tensor. Must be one of the following types: float16, float32. -* @li mul3_x: A Tensor. Must be one of the following types: float16, float32. -* @li add2_y: A Tensor. Must be one of the following types: float16, float32. - -*@par Outputs: -*Three outputs, including: -* @li output0: A Tensor. Must be one of the following types: float16, float32. -* @li output1: A Tensor. Must be one of the following types: float16, float32. -* @li output2: A Tensor. Must be one of the following types: float16, float32. - -*/ -REG_OP(AdamApplyOneAssign) - .INPUT(input0, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input1, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input2, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input3, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(input4, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul0_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul1_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul2_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(mul3_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(add2_y, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output0, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output1, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(output2, TensorType({DT_FLOAT16,DT_FLOAT})) - .OP_END_FACTORY_REG(AdamApplyOneAssign) - -/** *@brief Confuse select, maximum, greater and sqrt. *@par Inputs: @@ -3122,22 +3042,6 @@ REG_OP(KLDiv) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OP_END_FACTORY_REG(KLDiv) -/** -*@brief copy data from x to y.. - -*@par Inputs: -*One inputs, including: -* @li x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int32, bool. - -*@par Outputs: -*y: A Tensor. Has the same type as "x". - -*@par Third-party framework compatibility -*/ -REG_OP(TensorMove) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8, DT_BOOL})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8, DT_BOOL})) - .OP_END_FACTORY_REG(TensorMove) } // namespace ge diff --git a/third_party/fwkacllib/inc/ops/image_ops.h b/third_party/fwkacllib/inc/ops/image_ops.h index 9b3694f1..f5ddaf5e 100644 --- a/third_party/fwkacllib/inc/ops/image_ops.h +++ b/third_party/fwkacllib/inc/ops/image_ops.h @@ -934,7 +934,6 @@ REG_OP(EncodeJpeg) /** *@brief PNG-encode an image. - *@par Inputs: *Input image must be unit8 or uint16 type. Inputs include: \n *image: is a 3-D uint8 or uint16 Tensor of shape [height, width, channels] \n @@ -1224,6 +1223,16 @@ REG_OP(CombinedNonMaxSuppression) .ATTR(clip_boxes, Bool, true) .OP_END_FACTORY_REG(CombinedNonMaxSuppression) +REG_OP(SpatialTransformerD) + .INPUT(x, TensorType({DT_FLOAT,DT_FLOAT16})) + .OPTIONAL_INPUT(theta, TensorType({DT_FLOAT,DT_FLOAT16})) + .OUTPUT(y, TensorType({DT_FLOAT,DT_FLOAT16})) + .ATTR(output_size, ListInt, {-1, -1}) + .ATTR(default_theta, ListFloat, {}) + .ATTR(align_corners, Bool, false) + .ATTR(use_default_theta, ListBool, {}) + .OP_END_FACTORY_REG(SpatialTransformerD) + } // namespace ge #endif // GE_OP_MAGE_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/math_ops.h b/third_party/fwkacllib/inc/ops/math_ops.h index b0c35c28..5d34804c 100644 --- a/third_party/fwkacllib/inc/ops/math_ops.h +++ b/third_party/fwkacllib/inc/ops/math_ops.h @@ -29,9 +29,9 @@ namespace ge { * x: A Tensor of type float16 or float32. *@par Attributes: -*@li power: Optional. Must be one of the following types: float32. Defaults to 1.0. -*@li scale: Optional. Must be one of the following types: float32. Defaults to 1.0. -*@li shift: Optional. Must be one of the following types: float32. Defaults to 0.0. +*@li power: Optional. Defaults to 1.0. +*@li scale: Optional. Defaults to 1.0. +*@li shift: Optional. Defaults to 0.0. *@par Outputs: * y: A Tensor. Has the same type and shape as "x". diff --git a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h index 7cfd762f..29cf0df3 100644 --- a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h @@ -699,45 +699,6 @@ REG_OP(FullyConnection) .OP_END_FACTORY_REG(FullyConnection) /** -*@brief Also known as a "fully-connected-compress" layer, computes an inner product with a set of learned weights, and (optionally) adds biases. - -*@par Inputs: -* Four inputs, including: -*@li x: A Tensor of type uint8, int8. -*@li w: A weight matrix of type int8, int8. -*@li w: A compress index matrix of type int8, int8. -*@li b: A Tensor of type float16, int32, int32. -*@li offset_w: A Tensor of type int8.i - -*@par Attributes: -*@li num_output: Reserved. -*@li transpose: A bool, specifying whether to transpose, either "true" or "false". Defaults to "false". -*@li axis: Reserved. -*@li offset_x: Reserved. - -*@par Outputs: -*y: The result tensor of type int32. - -*@par Third-party framework compatibility -* Compatible with the Caffe operator InnerProduct. - -*@par Quantization supported or not -* Yes -*/ -REG_OP(FullyConnectionCompress) - .INPUT(x, TensorType({DT_UINT8, DT_INT8})) - .INPUT(w, TensorType({DT_INT8})) - .INPUT(comress_index, TensorType({DT_INT8})) - .OPTIONAL_INPUT(b, TensorType({DT_INT32})) - .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_INT32})) - .REQUIRED_ATTR(num_output, Int) - .ATTR(transpose, Bool, false) - .ATTR(axis, Int, 1) - .ATTR(offset_x, Int, 0) - .OP_END_FACTORY_REG(FullyConnectionCompress) - -/** *@brief Computes the confusion matrix from predictions and labels. *@par Inputs: diff --git a/third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h b/third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h index 39aaa993..e8eb4769 100644 --- a/third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h @@ -33,12 +33,12 @@ namespace ge { * @li variance: A Tensor. Must be one of the following types: float32. *@par Attributes: -* @li mode: A Tensor. Must be one of the following types: int. defaults: 1. -* @li epsilon: A Tensor. Must be one of the following types: float32. Defaults to 0.000001. -* @li momentum: A Tensor. Must be one of the following types: float32. Defaults to 0.9. -* @li is_training: A Tensor. Must be one of the following types: bool. Defaults to true. -* @li is_training_fusion: A Tensor. Must be one of the following types: bool. Defaults to true. -* @li moving_average_fraction: A Tensor. Must be one of the following types: float32. Defaults to 0.00300002098. +* @li mode: A Tensor. Must be one of the following types: int. +* @li epsilon: A Tensor. Must be one of the following types: float32. +* @li momentum: A Tensor. Must be one of the following types: float32. +* @li is_training: A Tensor. Must be one of the following types: bool. +* @li is_training_fusion: A Tensor. Must be one of the following types: bool. +* @li moving_average_fraction: A Tensor. Must be one of the following types: float32. *@par Outputs: *Three outputs, including: @@ -83,8 +83,8 @@ REG_OP(FusedBatchNorm) * @li save_inv_variance1: A Tensor. Must be one of the following types: float32. *@par Attributes: -* @li epsilon: A Tensor. Must be one of the following types: float32. Defaults to 0.0. -* @li momentum: A Tensor. Must be one of the following types: float32. Defaults to 0.0. +* @li epsilon: A Tensor. Must be one of the following types: float32. +* @li momentum: A Tensor. Must be one of the following types: float32. *@par Outputs: *Three outputs, including: @@ -361,14 +361,14 @@ REG_OP(BatchNormGradExt2) *@par Inputs: *@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW for 4D or NC1HWC0 for 5D. *@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the mean used for inference. -*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" Specifies the variance used for inference. -*@li momentum: A Tensor,represents the mean and the variance's scale factor +*@li variance: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the variance used for inference. +*@li momentum: A Tensor of type float32 or float16, represents the mean and the variance's scale factor *@li scale: An optional tensor of type float16 or float32, no use *@li offset: An optional tensor of type float16 or float32, no use *@par Attributes: *@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.00001". *@li use_global_stats: mean inference mode , only can be "True". -*@li mode: An optional input, not use +*@li mode: An optional attr, not use *@par Outputs:\n *@li y: A 4D or 5D Tensor of type float16 or float32 for the normalized "x" */ @@ -391,11 +391,11 @@ REG_OP(BNInference) *@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the mean used for inference. *@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" Specifies the variance used for inference. -*@li momentum: An optional float, mean and variance's Scale factor +*@li momentum: A Tensor of type float32 or float16, the mean and the variance's Scale factor *@par Attributes: *@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.00001". *@li use_global_stats: mean inference mode , only can be "True". -*@li mode: An optional attr, not use +*@li mode: An optional inpout, not use *@par Outputs: *@li alpha: A Tensor of type float16 or float32 for the cpu calculate mean *@li beta: A Tensor of type float16 or float32 for the cpu calculate variance @@ -418,8 +418,8 @@ REG_OP(BnHost) *@par Inputs: *@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW for 4D or NC1HWC0 for 5D. -*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the mean used for inference. -*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" Specifies the variance used for inference. +*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the mean used for inference. +*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" Specifies the variance used for inference. *@li scale: An optional tensor of type float16 or float32, no use *@li offset: An optional tensor of type float16 or float32, no use *@par Attributes: diff --git a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h index 5818e14b..3529e9ca 100644 --- a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h @@ -143,29 +143,31 @@ REG_OP(DepthwiseConv2DBackpropFilterD) * @par Inputs: * Three inputs include: \n * @li input_size: 4D shape of input tensor [N, C, H, W] or [N, H, W, C], -* support int32, int64 -* @li filter: 4D filter tensor with shape of [H, W, C, K], support float16. +* support int32 +* @li filter: 4D filter tensor with shape of [H, W, C, K], support float16, +* float32, double * @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C]. -* Must be one of the following types: float16. +* Must be one of the following types: float16, float32, double. * @par Attributes: -* @li strides: A required list or tuple of int32. The stride of the sliding window for +* @li strides: A required list or tuple. The stride of the sliding window for * height and width of input "x" of the convolution. * Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, * stride_width, 1]. -* @li dilations: An optional list or tuple of int32. The dilation factor for each -* dimension of input "x". Defaults to "[1, 1, 1, 1]". +* @li dilations: An optional list or tuple. The dilation factor for each +* dimension of input "x". * If set to k > 1, there will be k-1 skipped cells between each filter element * on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] * or [1, dilation_height, dilation_width, 1]. -* @li pads: A required list or tuple of int32. Padding added to each dimension of the +* @li pads: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. Input data format, either "NHWC" or -* "NCHW". Defaults to "NHWC". +* "NCHW". * @par Outputs: * input_grad: Gradient of the deep convolution relative to the input with shape -* [N, C, H, W] or [N, H, W, C] Must be one of the following types: float16. +* [N, C, H, W] or [N, H, W, C] Must be one of the following types: float16, +* float32, double. * @attention Constraints:\n * The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but @@ -257,8 +259,8 @@ REG_OP(DepthwiseConv2DBackpropInputD) *@par Inputs: *Two required inputs and two optional inputs, including: \n -* @li x: A 4D tensor of type float16 or int8, with shape [N, C, H, W] or [N, H, W, C] -* @li filter: A 4D tensor of type float16 or int8, with shape [H, W, C, K] +* @li x: A 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C] +* @li filter: A 4D tensor of type float16, with shape [H, W, C, K] * @li bias: An optional tensor of type float16 or int32 * @li offset_w: An optional float16 or int8, used for quantized inference @@ -271,8 +273,8 @@ REG_OP(DepthwiseConv2DBackpropInputD) * dimension of input "x". * If set to k > 1, there will be k-1 skipped cells between each filter element * on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] -* or [1, dilation_height, dilation_width, 1]. Defaults to "[1, 1, 1, 1]". -* @li pads: A required list or tuple of int32. Padding added to each dimension of the +* or [1, dilation_height, dilation_width, 1]. +* @li pads: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. Input data format, either "NHWC" or * "NCHW". Defaults to "NHWC". @@ -280,7 +282,7 @@ REG_OP(DepthwiseConv2DBackpropInputD) * Defaults to 0. * @par Outputs: -* y: 4D tensor of type float16 or int32, with shape [N, C, H, W] or [N, H, W, C] +* y: 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C] * @attention Constraints:\n * The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but @@ -460,24 +462,24 @@ REG_OP(Conv2DBackpropInputD) * @li x: A Tensor. Must have the same type as "filter". 4D with shape * [batch, out_channels, out_height, out_width]. Gradients with respect * to the output of the convolution. - * @li filter: A Tensor of type float16, float32, double or int8. + * @li filter: A Tensor of type float16. * 4D with shape [out_channels, in_channel, filter_height, filter_width].\n * Two optional inputs: - * @li bias: An optional tensor of type float16, float32, int32 or int64. - * @li offset_w: An optional 1D tensor for quantized deconvolution. Type is int8. Reserved.\n + * @li bias: An optional tensor of type float16 + * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved.\n *@par Attributes: * Six attributes: * @li strides: A tuple or list of 2 integers. The stride of the sliding window - * for H/W dimension. Defaults to [1, 1, 1, 1]. + * for H/W dimension. * @li pads: A tuple or list of 4 integers. The [top, bottom, left, right] - * padding on the feature map. Defaults to [0, 0, 0, 0]. + * padding on the feature map * @li dilations: A tuple or list of 4 integers. The dilation factor for each * dimension of input. Must be [1, 1, 1, 1]. * @li groups: Number of blocked connections from input channels to - output channels. Defaults to "1". - * @li data_format: An optional string from: "NCHW". Defaults to "NCHW". \n + * output channels. + * @li data_format: An optional string from: "NCHW". Defaults to "NCHW".\n Specify the data format of the input and output data. - * @li offset_x: An optional integer for quantized deconvolution. Defaults to "0". + * @li offset_x: An optional integer for quantized deconvolution. *@par Outputs: * y: A Tensor. Has the same type as "filter". 4D tensor with shape * [batch, channels, height, width]. @@ -575,19 +577,17 @@ REG_OP(Conv2DBackpropFilterD) * * The input and output tensor attributes are listed as follows: * @verbatim - |Tensor | x | filter | bias | offset_w | y + Tensor | x | filter | bias | offset_w | y -----------|---------|---------|---------|----------|-------- - |Data Type | float16 | float16 | float16 | _ | float16 - | |---------|---------|---------|----------|-------- - | | float32 | float32 | float32 | _ | float32 - | |---------|---------|---------|----------|-------- - | | float64 | float64 | float64 | _ | float64 - | |---------|---------|---------|----------|-------- - | | int8 | int8 | int32 | int8 | int32 + Data Type | float16 | float16 | float16 | _ | float16 + |---------|---------|---------|----------|-------- + | float32 | float32 | float32 | _ | float32 + |---------|---------|---------|----------|-------- + | int8 | int8 | int32 | int8 | int32 -----------|---------|---------|---------|----------|-------- - |Format | NCHW | NCHW | ND | ND | NCHW - | | NHWC | NHWC | | | NHWC - | | | HWCN | | | + Format | NCHW | NCHW | ND | ND | NCHW + | NHWC | NHWC | | | NHWC + | | HWCN | | | @endverbatim * It should be noted that the data types must correspond to each other, but the * format does not need to. @@ -602,10 +602,10 @@ REG_OP(Conv2DBackpropFilterD) * for dilated convolution. Has the same dimension order and value as "strides". * @li groups: Number of blocked connections from input channels to output * channels. Input channels and output channels must both be divisible by -* "groups".Type is int32. Must be set to 1. -* @li offset_x: An optional integer for quantized convolution. Type is int32. Defaults to "0". +* "groups". +* @li offset_x: An optional integer for quantized convolution. * @li data_format: An optional string from: "NHWC", "NCHW". Specifying the -* data format of the input and output images. Type is string. Defaults to "NHWC". Reserved. +* data format of the input and output images. Reserved. *@par Outputs: * @li y: A 4D Tensor of output images. @@ -613,23 +613,23 @@ REG_OP(Conv2DBackpropFilterD) *@attention * @li The parameter scope is listed as follows: * @verbatim - |Name | Field | Scope + Name | Field | Scope ------------------|--------------|---------- - |Input Image Size | H dimension | [1, 4096] - | | W dimension | [1, 4096] + Input Image Size | H dimension | [1, 4096] + | W dimension | [1, 4096] ------------------|--------------|---------- - |Filter Size | H dimension | [1, 255] - | | W dimension | [1, 255] + Filter Size | H dimension | [1, 255] + | W dimension | [1, 255] ------------------|--------------|---------- - |Stride Size | H dimension | [1, 63] - | | W dimension | [1, 63] + Stride Size | H dimension | [1, 63] + | W dimension | [1, 63] ------------------|--------------|---------- - |Padding Size | top side | [0, 255] - | | bottom side | [0, 255] - | | left side | [0, 255] - | | right side | [0, 255] + Padding Size | top side | [0, 255] + | bottom side | [0, 255] + | left side | [0, 255] + | right side | [0, 255] ------------------|--------------|---------- - |Dilation Size | H dimension | [1, 255] + Dilation Size | H dimension | [1, 255] | W dimension | [1, 255] @endverbatim @@ -654,11 +654,11 @@ REG_OP(Conv2DBackpropFilterD) *@li Compatible with the Caffe operator 2D "Convolution". */ REG_OP(Conv2D) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) - .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8})) + .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) @@ -710,8 +710,8 @@ REG_OP(Conv3D) .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) + .ATTR(strides, ListInt, {1, 1, 1, 1, 1}) + .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3D) @@ -742,7 +742,7 @@ REG_OP(Conv3DBackpropInput) .INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) + .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3DBackpropInput) @@ -771,7 +771,7 @@ REG_OP(Conv3DBackpropInputD) .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) + .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3DBackpropInputD) diff --git a/third_party/fwkacllib/inc/ops/nn_detect_ops.h b/third_party/fwkacllib/inc/ops/nn_detect_ops.h index ceb92f7a..5dca8a9d 100644 --- a/third_party/fwkacllib/inc/ops/nn_detect_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_detect_ops.h @@ -187,15 +187,14 @@ REG_OP(ROIAlignGrad) *@li features: A 5HD Tensor of type float32 or float16. *@li rois: ROI position. A 2D Tensor of float32 or float16 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". -*@li rois_n: An optional input of type int32, specifying the number of valid ROIs. This parameter is reserved. +*@li rois_n: An optional input, specifying the number of valid ROIs. This parameter is reserved. *@par Attributes: -*@li spatial_scale: A required attribute of type float32, specifying the scaling ratio of "features" to the original image. -*@li pooled_height: A required attribute of type int32, specifying the H dimension. -*@li pooled_width: A required attribute of type int32, specifying the W dimension. -*@li sample_num: An optional attribute of type int32, specifying the horizontal and vertical sampling frequency of each output. If this attribute is set to "0", +*@li spatial_scale: A required attribute of type float, specifying the scaling ratio of "features" to the original image. +*@li pooled_height: A required attribute of type int, specifying the H dimension. +*@li pooled_width: A required attribute of type int, specifying the W dimension. +*@li sample_num: An optional attribute of type int, 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". -*@li roi_end_mode: An optional attribute of type int32. Defaults to "1". *@par Outputs: * output: Outputs the feature sample of each ROI position. The format is 5HD Tensor of type float32 or float16. The axis N is the number of input ROIs. Axes H, W, and C are consistent @@ -363,15 +362,15 @@ REG_OP(PSROIPooling) *@li im_info: An ND tensor of type float16 or float32, specifying the Image information. *@li actual_rois_num: An optional NCHW tensor of type int32, specifying the number of valid boxes per batch. *@par Attributes: -*@li batch_rois: An optional int32, specifying the number of images to be predicted. Defaults to "1". +*@li batch_rois: An optional int32, specifying the number of images to be predicted. *@li num_classes: An required int32, specifying the number of classes to be predicted. The value must be greater than 0. *@li score_threshold: An required float32, specifying the threshold for box filtering. The value range is [0.0, 1.0]. *@li iou_threshold: An required float32, specifying the confidence threshold for box filtering, which is the output "obj" of operator Region. The value range is (0.0, 1.0). *@par Outputs: -*@li box: A tensor of type float16 or float32 for proposal of actual output, with output shape [batch, numBoxes,8]. -* 8 means [x1, y1, x2, y2, score, label, batchID, NULL], the maximum value of numBoxes is 1024. +*@li box: An NCHW tensor of type float16 or float32, describing the information of each output box, including the coordinates, class, and confidence. +Proposal of actual output, with output shape [batch, numBoxes,8], 8 means [x1, y1, x2, y2, score, label, batchID, NULL], the maximum value of numBoxes is 1024. That is, take min (the maximum number of input boxes, 1024) -*@li actual_bbox_num: A tensor of type int32 With shape [bacth, num_classes], specifying the number of output boxes. +*@li actual_bbox_num: An NCHW tensor of type int32 With shape [bacth, num_classes], specifying the number of output boxes. *@attention Constraints:\n *@li totalnum < max_rois_num * batch_rois. @@ -415,9 +414,9 @@ REG_OP(FSRDetectionOutput) *@li confidence_threshold: An optional float32, specify the topk filter threshold. Only consider detections with confidence greater than the threshold *@li kernel_name: An optional string, specifying the operator name. Defaults to "ssd_detection_output". *@par Outputs: -*@li out_boxnum: A tensor of type int32, specifying the number of output boxes. -*@li y: A tensor of type float16 or float32 with shape [batch,keep_top_k, 8], describing the information of each output box. -* In output shape, 8 means (batchID, label(classID), score (class probability), xmin, ymin, xmax, ymax, null) +*@li out_boxnum: An NCHW tensor of type int32, specifying the number of output boxes. +*@li y: An NCHW tensor of type float16 or float32 with shape [batch,keep_top_k, 8], describing the information of each output box, including the coordinates, +* class, and confidence. In output shape, 8 means (batchID, label(classID), score (class probability), xmin, ymin, xmax, ymax, null) * It is a custom operator. It has no corresponding operator in Caffe. */ REG_OP(SSDDetectionOutput) @@ -448,10 +447,10 @@ REG_OP(SSDDetectionOutput) *@li boxes: A required int32, specifying the number of anchor boxes. Defaults to "5" for V2 or "3" for V3. *@li coords: An int32, specifying the number of parameters required for locating an object. The value is fixed at "4", corresponding to (x,y,w,h). *@li classes: An int32, specifying the number of prediction classes. Defaults to "80". The value range is [1, 1024]. -*@li yolo_version: A string, specifying the YOLO version, either "V2" or "V3".Defaults to "V3" -*@li softmax: A bool, specifying whether to perform softmax, valid only when "yolo_version = V2". Defaults to "false". -*@li background: A bool, specifying the operation types of the obj and classes, used in conjunction with "softmax" and valid only when "yolo_version = V2". Defaults to "false". -*@li softmaxtree: A bool, Fixed to False, defined in Lite, but not used. Defaults to "false". +*@li yolo_version: A string, specifying the YOLO version, either "V2" or "V3". +*@li softmax: A bool, specifying whether to perform softmax, valid only when "yolo_version = V2". +*@li background: A bool, specifying the operation types of the obj and classes, used in conjunction with "softmax" and valid only when "yolo_version = V2". +*@li softmaxtree: A bool, Fixed to False, defined in Lite, but not used. *@par Outputs: *@li coord_data: A float16 or float32 with shape [N, boxes*coords, ceilx(height*width*2+32, 32)/2], where "ceil" indicates that a detected box is aligned upwards with the second parameter. Specifies the coordinates of a detected box. @@ -502,10 +501,10 @@ and the actual image height and width. *@li pre_nms_topn: An optional int, specifying the number of boxes for non-maximum suppression (NMS). Defaults to "512". * *@par Outputs: -*@li boxout: A tensor of type float16 or float32 with shape [batch,6,post_nms_topn]. describing the information of each output box, -* In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. -*@li boxoutnum: A tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. It means only the first one of the 8 numbers is valid, -* the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 +*@li boxout: An NCHW tensor of type float16 or float32 with shape [batch,6,post_nms_topn]. describing the information of each output box, including the coordinates, class, +and confidence. In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. +*@li boxoutnum: An NCHW tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. It means only the first one of the 8 numbers is valid, +the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 * *@attention Constraints:\n *@li This operator applies only to the YOLO v2 network. @@ -562,10 +561,10 @@ and the actual image height and width. *@li pre_nms_topn: An optional int, specifying the number of boxes for non-maximum suppression (NMS). Defaults to "512". * *@par Outputs: -*@li boxout: A tensor of type float16 or float32 with shape [batch,6,post_nms_topn]. describing the information of each output box, -* In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. -*@li boxoutnum: A tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. It means only the first one of the 8 numbers is valid, -* the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 +*@li boxout: An NCHW tensor of type float16, describing the information of each output box, including the coordinates, class, and confidence. +With shape [batch,6,post_nms_topn], 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. +*@li boxoutnum: An NCHW tensor of type int32, specifying the number of output boxes. +With shape [batch,8,1,1], means only the first one of the 8 numbers is valid, the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 * *@attention Constraints:\n *@li This operator applies only to the YOLO v2 network. @@ -622,11 +621,11 @@ and the actual image height and width. *@li pre_nms_topn: An optional int, specifying the number of boxes for non-maximum suppression (NMS). Defaults to "512". * *@par Outputs: -*@li boxout: A tensor of type float16 or float32 with shape [batch,6,post_nms_topn], describing the information of each output box. -* In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. -*@li boxoutnum: A tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. -* The output shape means only the first one of the 8 numbers is valid, the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 -* +*@li boxout: An NCHW tensor of type float16 or float32 with shape [batch,6,post_nms_topn], describing the information of each output box, including the coordinates, class, and confidence. +In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. +*@li boxoutnum: An NCHW tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. +The output shape means only the first one of the 8 numbers is valid, the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 + *@attention Constraints:\n *@li This operator applies only to the YOLO v3 network. *@li The preceding layer of operator Yolov3DetectionOutput must be three Yolo operators. @@ -689,11 +688,12 @@ and the actual image height and width. *@li pre_nms_topn: An optional int, specifying the number of boxes for non-maximum suppression (NMS). Defaults to "512". * *@par Outputs: -*@li boxout: A tensor of type float16 or float32 with shape [batch,6,post_nms_topn], describing the information of each output box. -* In output shape, 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. -*@li boxoutnum: A tensor of type int32 with shape [batch,8,1,1], specifying the number of output boxes. -* The output shape means only the first one of the 8 numbers is valid, the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 +*@li boxout: An NCHW tensor of type float16, describing the information of each output box, including the coordinates, class, and confidence. +With shape [batch,6,post_nms_topn], 6 means x1, y1, x2, y2, score, label(class). Output by the number of box_out_num. +*@li boxoutnum: An NCHW tensor of type int32, specifying the number of output boxes. +With shape [batch,8,1,1], means only the first one of the 8 numbers is valid, the number of valid boxes in each batch, the maximum number of valid boxes in each batch is 1024 * + *@attention Constraints:\n *@li This operator applies only to the YOLO v3 network. *@li The preceding layer of operator Yolov3DetectionOutput must be three Yolo operators. diff --git a/third_party/fwkacllib/inc/ops/nn_norm_ops.h b/third_party/fwkacllib/inc/ops/nn_norm_ops.h index d18a4fa4..d4db7cf0 100644 --- a/third_party/fwkacllib/inc/ops/nn_norm_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_norm_ops.h @@ -291,8 +291,8 @@ REG_OP(BinaryCrossEntropyGrad) * double. Should be a Variable Tensor. *@par Attributes: -*axes: A list of int. The dimension softmax would be performed on. Defaults -* to "[-1]". +*axes: A list of ints. The dimension softmax would be performed on. Defaults +* to "{-1}". *@par Outputs: *y: A Tensor. Has the same dimensionality and shape as the "x" with values in @@ -632,7 +632,7 @@ REG_OP(DropOutDoMask) * Three inputs, including: *@li x: An ND tensor of type float16 or float32. *@li scale: An ND tensor of type float16 or float32. -*@li bias: An optional ND tensor of type float16 or float32. +*@li bias: An ND tensor of type float16 or float32. *@par Attributes: *@li axis: An optional int32 used to compute the shape of scale and bias input from the online bottoms. Defaults to "1". @@ -679,9 +679,9 @@ REG_OP(Scale) * depth_radius = (local_size - 1) / 2. local_size is the number of channels to sum over (for ACROSS_CHANNELS) * or the side length of the square region to sum over (for WITHIN_CHANNEL). *@li bias: An optional float32. An offset, usually > 0 to avoid dividing by 0. -* Defaults to "1.0". +* Defaults to "1". *@li alpha: An optional float32. A scaling factor, usually positive. -* Defaults to "1.0". +* Defaults to "1". *@li beta: An optional float32. An exponent. Defaults to "0.75" for the caffe framework, Defaults to "0.5" for others. *@li norm_region: An optional string. A mode option. "ACROSS_CHANNELS":0, "WITHIN_CHANNEL":1. Defaults to "ACROSS_CHANNELS". @@ -836,56 +836,6 @@ REG_OP(GroupNorm) .ATTR(num_groups, Int, 2) .OP_END_FACTORY_REG(GroupNorm) -/** -*@brief Performs instance normalization. - -*@par Inputs:\n -* Five inputs, including: (NC1HWC0, supported) -*@li x: A 5D Tensor of type float16 or float32, NC1HWC0. -*@li gamma: A Tensor of type float32. -A 5D Tensor for scaling factor, to scale the normalized x. -*@li beta: A Tensor of type float32. -A 5D Tensor for offset, to shift to the normalized x. -*@li mean: A Tensor of type float32. -A 5D Tensor Specifies the mean used for inference. Reserved. -*@li variance: A Tensor of type float32. -A 5D Tensor Specifies the variance used for inference. Reserved. - -*@par Attributes: -*@li is_training: An optional bool, specifying if the operation is used for \n -training or inference. Defaults to "True". -*@li momentum: An optional float32, \n -the value used for the running_mean and running_var computation. Default: "0.1". -*@li epsilon: An optional float32, specifying the small value added to \n -variance to avoid dividing by zero. Defaults to "0.00001". - -*@par Outputs:\n -* Three outputs, including: (NHWC, NCHW NC1HWC0 supported) -*@li y: A 5D tensor of type float16 or float32 for the normalized "x", \n -*@li batch_mean: A Tensor of type float32. -Specifies the mean of "x". -*@li batch_variance: A Tensor of type float32. -Specifies the variance of "x". - -*@par Third-party framework compatibility -*@li Compatible with the PyTorch operator InstanceNorm. -*/ -REG_OP(InstanceNormV2) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) - .OPTIONAL_INPUT(gamma, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(beta, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(mean, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(variance, TensorType({DT_FLOAT})) - - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) - .OUTPUT(batch_mean, TensorType({DT_FLOAT})) - .OUTPUT(batch_variance, TensorType({DT_FLOAT})) - - .ATTR(is_training, Bool, true) - .ATTR(momentum, Float, 0.1) - .ATTR(epsilon, Float, 0.00001) - .OP_END_FACTORY_REG(InstanceNormV2) - } // namespace ge #endif //GE_OP_NN_NORM_OPS_H diff --git a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h index 693e51d1..5eb11445 100644 --- a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h @@ -102,42 +102,6 @@ REG_OP(AvgPool) .OP_END_FACTORY_REG(AvgPool) /** -*@brief Performs average pooling on the input. - -*@par Inputs: -*x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type float16, float32, double. - -*@par Attributes: -*@li ksize: List of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor. -*@li strides:List of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor. -*@li pads: List of ints, implicit zero paddings on both sides of the input. -*@li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. -*@li count_include_pad: When true, will include the zero-padding in the averaging calculation. -*@li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. -*@li data_format: A string, format of input data. - -*@par Outputs: -*y: The average pooled output tensor. - -*@attention Constraints: -*@li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] - -*@par Third-party framework compatibility -* Compatible with the TensorFlow operator AvgPool3D. -*/ -REG_OP(AvgPool3D) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(ceil_mode, Bool, false) - .ATTR(count_include_pad, Bool, true) - .ATTR(divisor_override, Int, 0) - .ATTR(data_format, String, "NDHWC") - .OP_END_FACTORY_REG(AvgPool3D) - -/** *@brief Performs max_pool_ext2 on the input. *@par Inputs: @@ -220,62 +184,17 @@ REG_OP(MaxPool) .OP_END_FACTORY_REG(MaxPool) REG_OP(MaxPool3D) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) + .INPUT(x, TensorType({DT_FLOAT16})) + .OUTPUT(y, TensorType({DT_FLOAT16})) .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(dilation, ListInt, {0,0,0}) .ATTR(ceil_mode, Int, 0) .ATTR(data_format, String, "NDHWC") .OP_END_FACTORY_REG(MaxPool3D) - -/** -* @brief Computes second-order gradients of the maxpooling3d function. - -* @par Inputs: -* @li orig_x: Original forward input tensor(NDC1HWC0) of type float16 -* @li orig_y: Original forward output tensor(NDC1HWC0) of type float16 -* @li grads: Gradient tensor(NDC1HWC0) of type float16 -* @li assist: Assist tensor(NDC1HWC0) of type float16 - -* @par Attributes: -* @li ksize: A required list or tuple, -* specifying the size of the sliding window. -* @li strides: A required list or tuple, -* specifying the stride of the sliding window. -* @li pads: A required list or tuple -* @li padding: A required string, window sliding mode. Either SAME or VALID. -* @li data_format: An optional string. -* Format of the original input, either NCDHW or NDHWC. Defaults to NDHWC. - -* @attention Constraints: -* @li Only the Ascend 910 platform is supported. -* @li "orig_x" and "grads" must have the same shape. -* @li "orig_y" and "y" must have the same shape. Otherwise, an error is reported. -* @li "orig_x", "orig_y", "grads", and "y" must be NDC1HWC0 tensors. - -* @par Outputs: -* @li y: Result tensor of type float16 - -* @par Third-party framework compatibility -* @li Compatible with the TensorFlow operator MaxPool3DGradGrad. -*/ - -REG_OP(MaxPool3DGradGrad) - .INPUT(orig_x, TensorType::RealNumberType()) - .INPUT(orig_y, TensorType::RealNumberType()) - .INPUT(grads, TensorType::RealNumberType()) - .OUTPUT(y, TensorType::RealNumberType()) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(data_format, String, "NDHWC") - .OP_END_FACTORY_REG(MaxPool3DGradGrad) - - /** * @brief Computes gradients of the maxpooling function. @@ -320,10 +239,9 @@ REG_OP(MaxPoolGrad) * @brief Computes second-order gradients of the maxpooling function. * @par Inputs: -* @li x1: Original forward input tensor. Supported type:float, double, int32, - * uint8, int16, int8, int64, uint16, half, uint32, uint64. -* @li x2: Has the same type and format as input "x1". -* @li grad:Has the same type and format as input "x1". +* @li x1: Original forward input tensor of type RealNumberType +* @li x2: Original forward output tensor of type RealNumberType +* @li grad: Gradient tensor of type RealNumberType * @par Attributes: * @li ksize: A required list or tuple, @@ -344,7 +262,7 @@ REG_OP(MaxPoolGrad) * @li Other dimensions of ksize and strides is 1. * @par Outputs: -* @li y: Has the same type and format as input "x1". +* @li y: Result tensor of type RealNumberType * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator MaxPoolGradGrad. @@ -480,55 +398,18 @@ REG_OP(MaxPoolGradWithArgmax) .OP_END_FACTORY_REG(MaxPoolGradWithArgmax) /** -*@brief Performs transform mask to argmax. - -*@par Inputs: -* Two input: -*x: An NC1HWC0 Tensor of type float16. -*mask: An NC1HWC0 Tensor of type uint16. - -*@par Attributes: -*@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for each dimension of the input tensor. No default value. -*@li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for each dimension of the input tensor. No default value. -*@li padding: A required string. No default value. - -*@par Outputs: -*argmax: An NC1HWC0 Tensor of type int32. - -*@attention Constraints: -*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255. -*@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1, strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1. -*@li "padding" is either "SAME" or "VALID". - -*@par Third-party framework compatibility -* Compatible with the TensorFlow operator Mask2Argmax. -*/ -REG_OP(Mask2Argmax) - .INPUT(x, TensorType::RealNumberType()) - .INPUT(mask, TensorType::IndexNumberType()) - .OUTPUT(argmax, TensorType::IndexNumberType()) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(padding, String) - .REQUIRED_ATTR(originshape, ListInt) - .OP_END_FACTORY_REG(Mask2Argmax) - -/** * @brief Computes second-order gradients of the maxpooling function. * @par Inputs: -* @li x: Original forward input tensor. Supported type: float, double, int32, - * uint8, int16, int8, int64, uint16, half, uint32, uint64. -* @li grad: Gradient tensor. Supported type: float, double, int32, - * uint8, int16, int8, int64, uint16, half, uint32, uint64. -* @li argmax: An tensor of type int32 or int64. +* @li x: Original forward input tensor of type RealNumberType +* @li grad: Gradient tensor of type RealNumberType +* @li argmax: An tensor of type IndexNumberType * @par Attributes: * @li ksize: A required list, specifying the size of the sliding window. * @li strides: A required list, specifying the stride of the sliding window. * @li padding: A required string, window sliding mode. Either SAME or VALID. * @par Outputs: -* @li y:Result tensor. Supported type: float, double, int32, - * uint8, int16, int8, int64, uint16, half, uint32, uint64 +* @li y:Result tensor of type RealNumberType * @attention Constraints: * @li Only the cloud platform is supported. @@ -650,7 +531,7 @@ REG_OP(MaxPoolGradWithArgmaxCCE) * one input, including: *@li x: A tensor of type float16 or float32. *@par Attributes: -*@li scale: A optional float32, scale factor of x. Defaults to "1.0". +*@li scale: A optional float, scale factor of x. Defaults to "1.0". *@li stride_h: An optional int32, broadcast the axis of h. Defaults to "2". *@li stride_w: An optional int32, broadcast the axis of w. Defaults to "2". *@par Outputs: @@ -868,186 +749,7 @@ REG_OP(DataFormatVecPermute) .ATTR(dst_format, String, "NCHW") .OP_END_FACTORY_REG(DataFormatVecPermute) -/** -* @brief Computes gradients of the MaxPool3D function. -* @par Inputs: -* @li orig_x: A mutable NDC1HWC0 tensor of type float16. -* @li orig_y: A mutable NDC1HWC0 tensor of type float16. -* @li grads: A mutable NDC1HWC0 tensor of type float16. - -* @par Attributes: -* @li ksize: A required tuple or list, specifying the size of the window for -* each dimension of the input tensor. -* @li strides: A required tuple or list, specifying the stride of the sliding -* window for each dimension of the input tensor. -* @li pads: A list of 6 ints. Supports only padding along the D, -* H and W dimensions in sequence of head, tail, top, bottom, left and right. -* to use. -* @li data_format: An optional string, Specify the data format of the input and -* output data. With the default format "NDHWC". - -* @par Outputs: -* y: A mutable tensor. Has the same shape as "orig_x", but type is float32. - -* @par Third-party framework compatibility -* Compatible with the TensorFlow operator MaxPool3DGrad. -*/ -REG_OP(MaxPool3DGrad) - .INPUT(orig_x, TensorType::RealNumberType()) - .INPUT(orig_y, TensorType::RealNumberType()) - .INPUT(grads, TensorType::RealNumberType()) - .OUTPUT(y, TensorType::RealNumberType()) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(data_format, String, "NDHWC") - .OP_END_FACTORY_REG(MaxPool3DGrad) - -/** -*@brief Performs AvgPool1D on the input. - -*@par Inputs: -*x: A Tensor. Must be one of the following types: int8, uint8, int16, int32, int64, float16, float32, float64. - -*@par Attributes: -*@li ksize: An required int, specifying the size of the window. -*@li strides: An required int. -*@li pads: A required tuple or list. -*@li ceil_mode: An optional bool. Defaults to False. -*@li count_include_pad: An optional bool. Defaults to False. - -*@par Outputs: -*y: A Tensor. Has the same type as x. - -*@par Third-party framework compatibility -*@li compatible with pytorch AvgPool1D operator. -*/ -REG_OP(AvgPool1D) - .INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(ksize, Int) - .REQUIRED_ATTR(strides, Int) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(ceil_mode, Bool, false) - .ATTR(count_include_pad, Bool, false) - .OP_END_FACTORY_REG(AvgPool1D) - -/** -*@brief Performs AvgPool1D on the input. - -*@par Inputs: -*x: A Tensor. Must be one of the following types: int8, uint8, int16, int32, int64, float16, float32, float64. - -*@par Attributes: -*@li ksize: An required int, specifying the size of the window. -*@li strides: An required int. -*@li pads: A required tuple or list. -*@li ceil_mode: An optional bool. Defaults to False. -*@li count_include_pad: An optional bool. Defaults to False. - -*@par Outputs: -*y: A Tensor. Has the same type as x. - -*@par Third-party framework compatibility -*@li compatible with pytorch AvgPool1D operator. -*/ -REG_OP(AvgPool1DD) - .INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .INPUT(assist_matrix, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(ksize, Int) - .REQUIRED_ATTR(strides, Int) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(ceil_mode, Bool, false) - .ATTR(count_include_pad, Bool, false) - .OP_END_FACTORY_REG(AvgPool1DD) -/** -*@brief Performs max pooling on the input and outputs both max values and indices. - -*@par Inputs: -* One input: -*x: An NC1HWC0 Tensor of type float16. -*@par Attributes: -*@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for -* each dimension of the input tensor. No default value. -*@li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for -* each dimension of the input tensor. No default value. -*@li pads: A required string. No default value. -*@li dtype: A optional int. default value is 3. -*@li dilation: A optional list of int8, int16, int32, or int64 values. -*@li ceil_mode: A optional bool. default value is false. - -*@par Outputs: -*y: A Tensor. Has the same type and format as input "x". -*argmax: A Tensor. type:uint16, format:NC1HWC0. -*@attention Constraints: -*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255. -*@li "strides is a list that has length 4: strides[0] = 1 or strides[3] = 1, strides[1] <= 63, strides[0] >= 1, -* strides[2] <= 63, strides[2] >= 1. -*@li "dilation" is a list that has length 4. -*@li "ceil_mode" is a bool, default is false. - -*@par Third-party framework compatibility -* Compatible with the TensorFlow operator MaxPoolWithArgmax. -*/ -REG_OP(MaxPoolWithArgmaxV2) - .INPUT(x, TensorType({DT_FLOAT16})) - .OUTPUT(y, TensorType({DT_FLOAT16})) - .OUTPUT(argmax, TensorType({DT_UINT16})) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(dtype, Int, 3) - .ATTR(dilation, ListInt, {1, 1, 1, 1}) - .ATTR(ceil_mode, Bool, false) - .OP_END_FACTORY_REG(MaxPoolWithArgmaxV2) - -/** -*@brief Performs the backpropagation of MaxPoolWithArgmaxV2. - -*@par Inputs: -* Three inputs, including: -*@li x: An NC1HWC0 tensor of type float16. -*@li grad: An NC1HWC0 tensor of type float16. -*@li argmx: An NC1HWC0 tensor of type uint16 or int64. - -*@par Attributes: -*@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for - * each dimension of the input tensor. No default value. -*@li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for - * each dimension of the input tensor. No default value. -*@li pads: A required string. No default value. -*@li dtype: A optional int. default value is 3. -*@li dilation: A optional list of int8, int16, int32, or int64 values. -*@li ceil_mode: A optional bool. default value is false. - -*@par Outputs: -*y: A Tensor. Has the same type and format as input "x". - -*@attention Constraints: -*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255. -*@li "strides" is a list that has length 4: strides[0] = 1 or strides[3] = 1 -*@li "dilation" is a list that has length 4. -*@li "ceil_mode" is a bool, default is false. - -*@see max_pool_grad_with_argmaxv2 -*@par Third-party framework compatibility -* Compatible with the TensorFlow operator MaxPoolGradWithArgmaxV2. -*/ - -REG_OP(MaxPoolGradWithArgmaxV2) - .INPUT(x, TensorType({DT_FLOAT16})) - .INPUT(grad, TensorType({DT_FLOAT16})) - .INPUT(argmax, TensorType({DT_UINT16})) - .OUTPUT(y, TensorType({DT_FLOAT16})) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(dtype, Int, 3) - .ATTR(dilation, ListInt, {1,1,1,1}) - .ATTR(ceil_mode, Bool, false) - .OP_END_FACTORY_REG(MaxPoolGradWithArgmaxV2) } // namespace ge #endif // GE_OP_NN_POOLING_OPS_H diff --git a/third_party/fwkacllib/inc/ops/nn_training_ops.h b/third_party/fwkacllib/inc/ops/nn_training_ops.h index 368054f5..1c9aa516 100644 --- a/third_party/fwkacllib/inc/ops/nn_training_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_training_ops.h @@ -1508,7 +1508,7 @@ REG_OP(ApplyProximalAdagradD) *@par Attributes: *use_locking: An optional bool. Defaults to "False".\n * If "True", updating of the var and accum tensors will be protected by a lock; \n -* If "False", the behavior is undefined, but may exhibit less contention. +* If "False", the behavior is undefined, but may exhibit less contention. *@par Outputs: *var: A mutable Tensor. Has the same type as "var". diff --git a/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h b/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h index a01073cf..1405fdb7 100644 --- a/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h +++ b/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h @@ -83,7 +83,7 @@ REG_OP(TanhGrad) *@par Inputs: *One input: -*x: A Tensor. Must be one of the following types: float16, float32, complex64, complex128, double. +*x: A Tensor. Must be one of the following types: float16, float32, complex64, complex128, int32, int64 *@par Outputs: *y: A Tensor. Has the same type as "x". @@ -184,7 +184,7 @@ REG_OP(Relu6Grad) * @brief Compute sigmoid of "x" element-wise. * @par Inputs: -* A Tensor of type complex64, complex128, float16, float32 or double. +* A Tensor of type UnaryDataType. * @par Outputs: * A Tensor. Has the same type as "x". @@ -220,7 +220,7 @@ REG_OP(SigmoidGrad) *if x>0, x+log(1+exp(-x)); otherwise log(1+exp(x)). *@par Inputs: -*x: A Tensor of type double, float16 or float32. +*x: A Tensor of type float16 or float32. *@par Outputs: *y: A tensor. Has the same type and format as input "x". @@ -442,7 +442,7 @@ REG_OP(PReluGrad) *x: A float16, float32 or double, for the input data type. *@par Attributes: -*alpha: A float32. Defines at which negative value the ELU saturates. Defaults to "1.0". +*alpha: A float. Defines at which negative value the ELU saturates. Defaults to "1.0". *@par Outputs: *y: A float16, float32 or double, for the normalized result. diff --git a/third_party/fwkacllib/inc/ops/reduce_ops.h b/third_party/fwkacllib/inc/ops/reduce_ops.h index a8aed058..8819d2d5 100644 --- a/third_party/fwkacllib/inc/ops/reduce_ops.h +++ b/third_party/fwkacllib/inc/ops/reduce_ops.h @@ -673,7 +673,7 @@ REG_OP(ReduceAnyD) *@par Attributes: *@li operation: An optional int32 from 1(SUM), 2(ASUM), 3(SUMSQ), and 4(MEAN), -*specifying the reduction algorithm. Defaults to "1". +*specifying the reduction algorithm. Defaults to 1. *@li axis: An optional int32, specifying the first axis to reduce. Defaults to "0". *The value range is [-N, N-1], where N is the input tensor rank. *@li coeff: An optional float32, specifying the scale coefficient. Defaults to "1.0". @@ -745,190 +745,7 @@ REG_OP(EuclideanNormD) .ATTR(keep_dims, Bool, false) .OP_END_FACTORY_REG(EuclideanNormD) - - -/** -*@brief Performs instance normalization for inference. - -*@par Inputs:\n -* Five inputs, including: (NC1HWC0 supported) -*@li x: A Tensor of type float16 or float32. -*@li gamma: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling gamma. -*@li beta: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling beta. -*@li mean: A [N, C1, 1, 1, C0] ensor of type float32, for the mean. -*@li variance: A [N, C1, 1, 1, C0] Tensor of type float32, for the variance. - -*@par Attributes: -*epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. -Defaults to "0.00001". - -*@par Outputs:\n -*y: A Tensor of type float16 or float32 for the normalized "x". -*batch_mean: A Tensor of type float32 for the result mean. -*batch_ variance: A Tensor of type float32 for the result variance. - -*@attention Constraints: -*For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. -*/ -REG_OP(INInferV2) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .OPTIONAL_INPUT(gamma, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(beta, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(mean, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(variance, TensorType({DT_FLOAT})) - .ATTR(epsilon, Float, 0.00001) - .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(batch_mean, TensorType({DT_FLOAT})) - .OUTPUT(batch_variance, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(INInferV2) - -/** -*@brief Performs reduced instance normalization. - -*@par Inputs:\n -*x: A Tensor of type float16 or float32, with format NC1HWC0. - -*@par Outputs: -*@li sum: A Tensor of type float32 for SUM reduced "x". -*@li square_sum: A Tensor of type float32 for SUMSQ reduced "x". - -*@attention Constraints:\n -* This operator is a InstanceNorm fusion operator for updating the moving averages for training. \n -* This operator is used in conjunction with INTrainingUpdateV2. -*/ -REG_OP(INTrainingReduceV2) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(sum, TensorType({DT_FLOAT})) - .OUTPUT(square_sum, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(INTrainingReduceV2) - - -/** -*@brief Performs update instance normalization. - -*@par Inputs:\n -* Seven inputs, including: (NC1HWC0supported) -*@li x: A Tensor of type float16 or float32. -*@li sum: A T [N, C1, 1, 1, C0] ensor of type float32 for the output of operator INTrainingReduceV2. -*@li square_sum: A [N, C1, 1, 1, C0] Tensor of type float32 for the output of operator INTrainingReduceV2. -*@li gamma: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling gamma. -*@li beta: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling beta. -*@li mean: A [N, C1, 1, 1, C0] Tensor of type float32, for the updated mean. -*@li variance: A [N, C1, 1, 1, C0] Tensor of type float32, for the updated variance. - -*@par Attributes: -*@li momentum: A required float32, specifying the momentum to update mean and var. -*@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero. - -*@par Outputs:\n -* Three outputs, including: (NC1HWC0 supported) -*@li y: A Tensor of type float16 or float32, for normalized "x". -*@li batch_mean: A Tensor of type float32, for the updated mean. -*@li batch_variance: A Tensor of type float32, for the updated variance. - -*@attention Constraints: -*@li This operator is a InstanceNorm fusion operator for updating the moving averages for training. \n -* This operator is used in conjunction with INTrainingReduceV2. -*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. -*/ -REG_OP(INTrainingUpdateV2) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(sum, TensorType({DT_FLOAT})) - .INPUT(square_sum, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(gamma, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(beta, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(mean, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(variance, TensorType({DT_FLOAT})) - .ATTR(momentum, Float, 0.1) - .ATTR(epsilon, Float, 0.00001) - .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(batch_mean, TensorType({DT_FLOAT})) - .OUTPUT(batch_variance, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(INTrainingUpdateV2) - - -/** -*@brief Performs reduced group normalization. - -*@par Inputs:\n -*x: A Tensor of type float16 or float32, with format NCHW NHWC. - -*@par Outputs: -*@li sum: A Tensor of type float32 for SUM reduced "x". -*@li square_sum: A Tensor of type float32 for SUMSQ reduced "x". - - -*@par Attributes: -*@li num_groups: Int, specifying the num of groups. required, same to GNTrainingUpdate. - -*@attention Constraints:\n -* This operator is a GroupNorm fusion operator for updating the moving averages for training. \n -* This operator is used in conjunction with GNTrainingUpdate. -*/ -REG_OP(GNTrainingReduce) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(sum, TensorType({DT_FLOAT})) - .OUTPUT(square_sum, TensorType({DT_FLOAT})) - .ATTR(num_groups, Int, 2) - .OP_END_FACTORY_REG(GNTrainingReduce) - - -/** -*@brief Performs update group normalization. - -*@par Inputs:\n -* Eight inputs, including: (NCHW NHWC supported) -*@li x: A Tensor of type float16 or float32. -*@li sum: A 5D Tensor of type float32, -shape is [N, G, D, 1, 1] for NCHW, [N, 1, 1, G, D] for NHWC -for the output of operator GNTrainingReduce. -*@li square_sum: A 5D Tensor of type float32, -shape is [N, G, D, 1, 1] for NCHW, [N, 1, 1, G, D] for NHWC -for the output of operator GNTrainingReduce. -*@li scale: A 5D Tensor of type float32, -shape is [1, G, D, 1, 1] for NCHW, [1, 1, 1, G, D] for NHWC -is for the scaling gamma. -*@li offset: A 5D Tensor of type float32, -shape is [1, G, D, 1, 1] for NCHW, [1, 1, 1, G, D] for NHWC -for the scaling beta. -*@li mean: A 5D Tensor of type float32, -shape is [N, G, D, 1, 1] for NCHW, [N, 1, 1, G, D] for NHWC -for the updated mean. -*@li variance: A 5D Tensor of type float32, -shape is [N, G, D, 1, 1] for NCHW, [N, 1, 1, G, D] for NHWC -for the updated variance. - - -*@par Attributes: -*@li epsilon: A float32, specifying the small value added to variance to avoid dividing by zero. -*@li num_groups: Int, specifying the num of groups. required, same to GNTrainingReduce - -*@par Outputs:\n -* Three outputs, including: (NC1HWC0 supported) -*@li y: A Tensor of type float16 or float32, for normalized "x". -*@li batch_mean: A Tensor of type float32, for the updated mean. -*@li batch_variance: A Tensor of type float32, for the updated variance. - -*@attention Constraints: -*@li This operator is a InstanceNorm fusion operator for updating the moving averages for training. \n -* This operator is used in conjunction with GNTrainingUpdate. -*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. -*/ -REG_OP(GNTrainingUpdate) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(sum, TensorType({DT_FLOAT})) - .INPUT(square_sum, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(scale, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(offset, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(mean, TensorType({DT_FLOAT})) - .OPTIONAL_INPUT(variance, TensorType({DT_FLOAT})) - .ATTR(num_groups, Int, 2) - .ATTR(epsilon, Float, 0.0001) - .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT})) - .OUTPUT(batch_mean, TensorType({DT_FLOAT})) - .OUTPUT(batch_variance, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(GNTrainingUpdate) - } //namespace ge + #endif /* GE_OP_REDUCE_OPS_H */ diff --git a/third_party/fwkacllib/inc/ops/rnn.h b/third_party/fwkacllib/inc/ops/rnn.h index b72d9a79..c4d64b0a 100644 --- a/third_party/fwkacllib/inc/ops/rnn.h +++ b/third_party/fwkacllib/inc/ops/rnn.h @@ -67,13 +67,6 @@ REG_OP(BasicLSTMCell) .ATTR(activation, String, "tanh") .OP_END_FACTORY_REG(BasicLSTMCell) -REG_OP(DynamicLSTM) - .INPUT(x, TensorType({DT_FLOAT32})) - .INPUT(w, TensorType({DT_FLOAT32})) - .INPUT(b, TensorType({DT_FLOAT32})) - .OUTPUT(output_h, TensorType({DT_FLOAT32})) - .OP_END_FACTORY_REG(DynamicLSTM) - /** *@brief: Basic LSTM Cell backward calculation.Calculate the gradient of input and hidden state. *@par Inputs: @@ -94,7 +87,7 @@ REG_OP(BasicLSTMCellInputGrad) .INPUT(dgate, TensorType({DT_FLOAT16})) .INPUT(w, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(dropout_mask, TensorType({DT_UINT8})) - .OUTPUT(dxt, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(dxt, TensorType({DT_FLOAT16})) .OUTPUT(dht, TensorType({DT_FLOAT16, DT_FLOAT32})) .ATTR(keep_prob, Float, 1.0) .OP_END_FACTORY_REG(BasicLSTMCellInputGrad) diff --git a/third_party/fwkacllib/inc/ops/selection_ops.h b/third_party/fwkacllib/inc/ops/selection_ops.h index bbe203cd..aafcece0 100644 --- a/third_party/fwkacllib/inc/ops/selection_ops.h +++ b/third_party/fwkacllib/inc/ops/selection_ops.h @@ -89,8 +89,7 @@ REG_OP(RangeD) *@par Inputs: *Two inputs, including: -* @li x: A Tensor. -* Must be one of the following types: float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. +* @li x: A Tensor of type TensorType::BasicType(). * @li multiples: A 1D Tensor of type int32 or int64. * The length must be the same as the number of dimensions in "input" @@ -497,7 +496,7 @@ REG_OP(UnsortedSegmentSumD) *@par Inputs: * Two inputs, including:\n *@li x: An ND Tensor (up to 8D). \n -*Must be one of the following types: int8, uint8, int16, uint16, int32, int64, bool, float16, float32, double, complex64, complex128, string. +*Must be one of the following types: int8, uint8, int16, uint16, int32, int64, bool, float32, double *@li axis: A 1D Tensor.\n *Must be one of the following types: int32, int64 @@ -1560,14 +1559,14 @@ REG_OP(ProposalD) * If reverse=false: (N, H, W, C)->(N, H/stride, W/stride, C*(stride*stride)) *@par Inputs: -*x: An (N, H, W, C) tensor. Type is float16, float32, int8, uint8, int16, uint16, int32, uint32, int64 or uint64.. +*x: An (N, H, W, C) tensor. All types except double are supported. *@par Attributes: *@li stride: An optional int32, specifying the plane or channel scaling factor. Defaults to "2". *@li reverse: An optional bool, specifying the conversion mode. If "true", depth to space conversion is performed. If "false", space to depth conversion is performed. Defaults to "false". *@par Outputs: -*y: An (N, H, W, C) tensor. Has same type as "x". +*y: An (N, H, W, C) tensor. All types except double are supported. *@attention Constraints: *@li If reverse=true: C/(stride*stride) yields an integer result. If reverse=false: W/stride and H/stride yield integer results. @@ -1594,7 +1593,7 @@ REG_OP(PassThrough) * @li x: A required Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32,int64, uint64. * @li size: A required Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. *@par Attributes: -*@li axis: A required int32, specifying the first dimension to crop. Defaults to "2". +*@li axis: A required int32, specifying the first dimension to crop. *@li offset: A required array, specifying the shift for all/each dimension to align the cropped bottom with the reference bottom. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. *@par Outputs: *y: A required Tensor. Has the same type and shape as "size". diff --git a/third_party/fwkacllib/inc/ops/split_combination_ops.h b/third_party/fwkacllib/inc/ops/split_combination_ops.h index 7e4428d0..700d34b7 100644 --- a/third_party/fwkacllib/inc/ops/split_combination_ops.h +++ b/third_party/fwkacllib/inc/ops/split_combination_ops.h @@ -25,11 +25,11 @@ namespace ge { *@par Inputs: * Two inputs, including: *@li x: An ND Tensor. -*Must be one of the types:float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. +*Must be one of the following types: float16, float32, int32, int8, int16, int64, uint8, uint16, uint32, uint64 *@li split_dim: Must be the following type:int32. Specifies the dimension along which to split. *@par Attributes: -*num_split: A required int32. Specifies the number of output tensors. No default value. +*num_split: A required int8, int16, int32, or int64. Specifies the number of output tensors. No default value. *@par Outputs: *y: Dynamic output.A list of output tensors. Has the same type and format as "x". @@ -186,7 +186,6 @@ REG_OP(ParallelConcat) *@par Attributes: *concat_dim: A required int8, int16, int32, or int64. Specifies the dimension along which to concatenate. No default value. -*N: An attribute int8, int16, int32, or int64. Specifies the number of elements in "x". Defaults to "1". *@par Outputs: *y: A Tensor. Has the same type and format as "x". @@ -268,9 +267,7 @@ REG_OP(ConcatD) *@par Inputs: * Two inputs, including: *@li x: Dynamic input.An NC1HWC0 or ND Tensor. -*Must be one of the following types: float16, float32, double, int32, -* uint8, int16, int8, complex64, int64, qint8, quint8, qint32, uint16, -* complex128, uint32, uint64, qint16, quint16. +*Must be one of the following types: float16, float32, int32, int8, int16, int64, uint8, uint16, uint32, uint64 *@li concat_dim: An int32, or int64. Specifies the dimension along which to concatenate. *@par Attributes: diff --git a/third_party/fwkacllib/inc/ops/transformation_ops.h b/third_party/fwkacllib/inc/ops/transformation_ops.h index 7b8a94f8..69951da9 100644 --- a/third_party/fwkacllib/inc/ops/transformation_ops.h +++ b/third_party/fwkacllib/inc/ops/transformation_ops.h @@ -94,13 +94,6 @@ REG_OP(Transpose) .OUTPUT(y, TensorType::BasicType()) .OP_END_FACTORY_REG(Transpose) -REG_OP(TransData) - .INPUT(src, TensorType::BasicType()) - .OUTPUT(dst, TensorType::BasicType()) - .REQUIRED_ATTR(src_format, String) - .REQUIRED_ATTR(dst_format, String) - .OP_END_FACTORY_REG(TransData) - /** *@brief Permutes the dimensions according to order.\n The returned tensor's dimension i will correspond to the input dimension order[i]. @@ -109,7 +102,7 @@ REG_OP(TransData) *x: A Tensor. Must be one of the following types: float16, float32. *@par Attributes: -*order: A permutation of the dimensions of "x".Type is int32.support any axis transformation.Defaults to "{0}" +*order: A permutation of the dimensions of "x".support any axis transformation *@par Outputs: *y: A Tensor. Has the same type as "x". @@ -298,7 +291,7 @@ REG_OP(DepthToSpace) *@brief Permutes data into spatial data blocks and then prunes them. *@par Inputs: -*@li x: A 4D Tensor with format NHWC. +*@li x: A 4D Tensor with format NC1HWC0. *@li crops: A 1D list or tuple of int32 or int64. *Must be one of the following types: float16, float32 @@ -307,7 +300,7 @@ REG_OP(DepthToSpace) *block_size: A required int8, int16, int32, or int64. No default value. *@par Outputs: -*y: A 4D Tensor with format NHWC, +*y: A 4D Tensor with format NC1HWC0, * of type float16 or float32. @@ -372,7 +365,7 @@ REG_OP(BatchToSpaceD) *@par Inputs: * Two inputs, including: -*@li x: An NHWC Tensor. Must be one of the following types: +*@li x: An NC1HWC0 Tensor. Must be one of the following types: * float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, * int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. *@li paddings: A 2D tensor of type int, specifying the input. @@ -396,7 +389,7 @@ REG_OP(SpaceToBatch) *@brief Outputs a copy of the input tensor where values from the "height" and "width" dimensions are padded and rearranged to the "batch" dimension. *@par Inputs: -*x: An NHWC Tensor. Must be one of the following types: float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. +*x: An NC1HWC0 Tensor. Must be one of the following types: float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. *@par Attributes: @@ -605,13 +598,6 @@ REG_OP(Compress) .OUTPUT(compress_index, TensorType({DT_INT8})) .REQUIRED_ATTR(compress_parameters, ListInt) .OP_END_FACTORY_REG(Compress) - -REG_OP(CompressFcOp) - .INPUT(weight, TensorType({DT_INT8})) - .OUTPUT(weight_compress, TensorType({DT_INT8})) - .OUTPUT(compress_index, TensorType({DT_INT8})) - .REQUIRED_ATTR(compress_parameters, ListInt) - .OP_END_FACTORY_REG(CompressFcOp) } // namespace ge #endif // GE_OP_TRANSFORMATION_OPS_H