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- /**
- * Copyright 2019-2020 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- #ifndef GE_OP_NN_POOLING_OPS_H
- #define GE_OP_NN_POOLING_OPS_H
-
- #include "graph/operator_reg.h"
- #include "graph/operator.h"
-
- namespace ge {
-
- /**
- *@brief Performs pooling on the input.
- *@par Inputs:
- *@li x: An NCHW tensor of type float16, float32, int8.
- *@par Attributes:
- *@li mode: An optional int32, specifying the pooling algorithm, either "1" (max pooling) or "0" (avg pooling). Defaults to "0".
- *@li global_pooling: An optional bool. Defaults to "false".
- *@li window: Optional, including: \n
- *window[0]: An optional int32, specifying the window size along in the H dimension. The value range is [1, 32768]. Defaults to "1". \n
- *window[1]: An optional int32, specifying the window size along in the W dimension. The value range is [1, 32768]. Defaults to "1". \n
- *@li stride: Optional, including: \n
- *stride[0]: An optional int32, specifying the stride along in the H dimension. The value range is [1, 63]. Defaults to "1". \n
- *stride[1]: An optional int32, specifying the stride along in the W dimension. The value range is [1, 63]. Defaults to "1". \n
- *@li pad: Optional, including: \n
- *pad[0]: An optional int32, specifying the up padding. Defaults to "0". \n
- *pad[1]: An optional int32, specifying the bottom padding. Defaults to "0". \n
- *pad[2]: An optional int32, specifying the left padding. Defaults to "0". \n
- *pad[3]: An optional int32, specifying the right padding. Defaults to "0". \n
- *@li dilation: Optional, including: \n
- *dilation[0]: An optional int32, specifying the up dilation. Defaults to "1". \n
- *dilation[1]: An optional int32, specifying the bottom dilation. Defaults to "1". \n
- *dilation[2]: An optional int32, specifying the left dilation. Defaults to "1". \n
- *dilation[3]: An optional int32, specifying the right dilation. Defaults to "1". \n
- *@li ceil_mode: An optional int32, either "0" (ceil mode) or "1" (floor mode). Defaults to "0".
- *@par Outputs:
- *y: An NCHW tensor of type float16, float32, int32.
- *@attention Constraints:\n
- *@li window[0] * window[1] < 256;
- *@li 1<=input_h<=4096,1<=input_w<=4096
- *@li If input tensor N is a prime number, it should be less than 65535.
- *@par Third-party framework compatibility
- *@li Compatible with the Caffe operator Pooling.
- *@li Compatible with the TensorFlow operator Pooling.
- */
- REG_OP(Pooling)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_INT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_INT32}))
- .ATTR(mode, Int, 0) // 0:max pooling or 1:avg pooling
- .ATTR(global_pooling, Bool, false)
- .ATTR(window, ListInt, {1,1}) // kernel size
- .ATTR(stride, ListInt, {1,1}) // stride size
- .ATTR(pad, ListInt, {0,0,0,0}) // pad size
- .ATTR(dilation, ListInt, {1,1,1,1})
- .ATTR(ceil_mode, Int, 0)
- .OP_END_FACTORY_REG(Pooling)
-
- /**
- *@brief Performs average pooling on the input.
-
- *@par Inputs:
- *x: A tensor of type float16, float32, double.
-
- *@par Attributes:
- *@li ksize: A required list of 4 ints, specifying the size (N, C, H, and W) of the sliding window, where N = C = 1, and H and W are positive integers within the range [1, 32768].
- *@li strides: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C dimensions are 1. The strides of the H and W dimensions are positive integers within the range [1, 63].
- *@li padding: A required string, specifying the padding algorithm, either "VALID" or "SAME". With "SAME" means that the outputs will have the same spatial dimensions as its inputs. With "VALID" means no padding.
- *@li data_format: An optional string, specifying the data format of "ksize" and "strides", either "NCHW", "NC1HWC0", or "NHWC" (default).
-
- *@par Outputs:
- *y: The average pooled output tensor. Has the same type and format as input "x".
-
- *@attention Constraints:
- *@li This operator applies only to a TensorFlow network.
- *@li Only single input and single output are supported.
- *@li Global pooling is supported.
- *@li "ksize_H" and "ksize_W" are positive integers within the range [1, 32768]. ksize_H * ksize_W < 256
- *@li Due to instruction restrictions, the values of "strides_h" and "strides_w" are positive integers within the range [1, 63].
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator AvgPool.
- */
- REG_OP(AvgPool)
- .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(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(AvgPool)
-
- /**
- *@brief Performs max_pool_ext2 on the input.
-
- *@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 padding: A required string. No default value.
- *@li data_format: An optional string. Defaults to "NC1HWC0".
-
- *@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 "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 MaxPoolV2.
- */
- REG_OP(MaxPoolExt2)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE, DT_INT8,
- DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_QINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE, DT_INT8,
- DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_QINT8}))
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(MaxPoolExt2)
-
- /**
- *@brief Performs max pooling on the input.
-
- *@par Inputs:
- * One input:
- *x: An NC1HWC0 Tensor. Supported type:float16, float32, double, int8, int16,
- * int32, int64, uint8, uint16, qint8
-
- *@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.
- *@li data_format: An optional string. Defaults to "NHWC".
-
- *@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 "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 MaxPool.
- */
- REG_OP(MaxPool)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE, DT_INT8,
- DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
- DT_UINT16, DT_QINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE, DT_INT8,
- DT_INT16, DT_INT32, DT_INT64, DT_UINT8, DT_UINT16, DT_QINT8}))
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(MaxPool)
-
- REG_OP(MaxPool3D)
- .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, {0,0,0})
- .ATTR(ceil_mode, Int, 0)
- .ATTR(data_format, String, "NDHWC")
- .OP_END_FACTORY_REG(MaxPool3D)
-
- /**
- * @brief Computes gradients of the maxpooling function.
-
- * @par Inputs:
- * @li x1: A mutable NC1HWC0 tensor of type RealNumberType.
- * @li x2: A mutable NC1HWC0 tensor of type RealNumberTypex.
- * @li grad: A mutable NC1HWC0 tensor of type RealNumberType.
-
- * @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 padding: A required string, specifying the type of padding algorithm
- * to use.
- * @li data_format: An optional string, Specify the data format of the input and
- * output data. With the default format "NHWC".
-
- * @par Outputs:
- * y: A mutable tensor. Has the same shape and type as "x1".
-
- * @attention Constraints:
- * @li Computing gradients of global pooling is not supported, which means
- * "ksize < x1".
- * @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 MaxPoolGrad.
- */
- REG_OP(MaxPoolGrad)
- .INPUT(x1, TensorType::RealNumberType())
- .INPUT(x2, TensorType::RealNumberType())
- .INPUT(grad, TensorType::RealNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(MaxPoolGrad)
-
- /**
- * @brief Computes second-order gradients of the maxpooling function.
-
- * @par Inputs:
- * @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,
- * specifying the size of the sliding window.
- * @li strides: A required list or tuple,
- * specifying the stride of the sliding window.
- * @li padding: A required string, window sliding mode. Either SAME or VALID.
- * @li data_format: An optional string.
- * Format of the original input, either NCHW or NHWC. Defaults to NHWC.
-
- * @attention Constraints:
- * @li Only the Ascend 910 platform is supported.
- * @li "x1" and "grads" must have the same shape.
- * @li "x2" and "y" must have the same shape. Otherwise, an error is reported.
- * @li "x1", "x2", "grads", and "y" must be 5D tensors.
- * @li ksize[H] and ksize[W] is in the range [1, 255].
- * @li strides[H] and strides[W] is in the range [1, 63].
- * @li Other dimensions of ksize and strides is 1.
-
- * @par Outputs:
- * @li y: Result tensor of type RealNumberType
-
- * @par Third-party framework compatibility
- * @li Compatible with the TensorFlow operator MaxPoolGradGrad.
- */
- REG_OP(MaxPoolGradGrad)
- .INPUT(x1, TensorType::RealNumberType())
- .INPUT(x2, TensorType::RealNumberType())
- .INPUT(grad, TensorType::RealNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(MaxPoolGradGrad)
-
- /**
- *@brief Performs max_pool_ext2 on the input.
-
- *@par Inputs:
- * Two inputs:
- *@li x: An NC1HWC0 Tensor of type float16.
- *@li strides: A required type of int32 values, specifying the stride of the sliding window for each dimension of the input tensor. No default value.
- *@li ksize: A required type of int32 values, specifying the size of the window for each dimension of the input tensor. No default value.
-
-
- *@par Attributes:
- *@li padding: A required string. No default value.
- *@li data_format: An optional string. Defaults to "NC1HWC0".
-
- *@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 "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 MaxPoolV2.
- */
- REG_OP(MaxPoolV2)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .INPUT(ksize, TensorType({DT_INT32}))
- .INPUT(strides, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT16}))
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(MaxPoolV2)
-
- /**
- *@brief Performs max pooling on the input and outputs both max values and
- * indices.
-
- *@par Inputs:
- * One input:
- *x: An NC1HWC0 Tensor. Supported type: float, double, int32,
- * uint8, int16, int8, int64, uint16, half, uint32, uint64.
-
- *@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:
- *y: A Tensor. Has the same type and format as input "x".
- *argmax: 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 "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 MaxPoolWithArgmax.
- */
- REG_OP(MaxPoolWithArgmax)
- .INPUT(x, TensorType::RealNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .OUTPUT(argmax, TensorType::IndexNumberType())
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(Targmax, Int, 7)
- .OP_END_FACTORY_REG(MaxPoolWithArgmax)
-
- /**
- *@brief Performs the backpropagation of MaxPoolWithArgmax.
-
- *@par Inputs:
- * Three inputs, including:
- *@li x: An NC1HWC0 tensor. Supported type: float, double, int32,
- * uint8, int16, int8, int64, uint16, half, uint32, uint64.
- *@li grad: An NC1HWC0 tensor. Supported type: float, double, int32,
- * uint8, int16, int8, int64, uint16, half, uint32, uint64.
- *@li argmx: An NC1HWC0 tensor of type int32 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 padding: A required string. No default value.
-
- *@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 "padding" is either "SAME" or "VALID".
-
-
- *@see max_pool_with_argmax
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator MaxPoolGradWithArgmax.
- */
- REG_OP(MaxPoolGradWithArgmax)
- .INPUT(x, TensorType::RealNumberType())
- .INPUT(grad, TensorType::RealNumberType())
- .INPUT(argmax, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .OP_END_FACTORY_REG(MaxPoolGradWithArgmax)
-
- /**
- * @brief Computes second-order gradients of the maxpooling function.
-
- * @par Inputs:
- * @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 of type RealNumberType
-
- * @attention Constraints:
- * @li Only the cloud platform is supported.
- * @li "x1" and "grads" must have the same shape.
- * @li length of the shape of x, grads, argmax, y must be 5.
- * @li shape of argmax must be (fmap_n, fmap_c1, kernel_h * kernel_w,
- * (shape_max_pool[2] * shape_max_pool[3] + 15) // 16 * 16, 1),
- * or (fmap_n, fmap_c1, kernel_h * kernel_w,
- * (shape_max_pool[2] * shape_max_pool[3] + 31) // 16, 16), else failed.
-
- * @par Third-party framework compatibility
- * @li Compatible with the TensorFlow operator MaxPoolGradGradWithArgmax.
- */
- REG_OP(MaxPoolGradGradWithArgmax)
- .INPUT(x, TensorType::RealNumberType())
- .INPUT(grad, TensorType::RealNumberType())
- .INPUT(argmax, TensorType::IndexNumberType())
- .OUTPUT(y, TensorType::RealNumberType())
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .OP_END_FACTORY_REG(MaxPoolGradGradWithArgmax)
-
- /**
- * @brief Computes avgpoograd function.
-
- * @par Inputs:
- * @li orig_input_shape: An NHWC tensor of type int32.
- * @li input_grad: An NHWC tensor of type float16, float32, or double.
-
- * @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 padding: A required string, specifying the type of
- * the padding algorithm to use.
- * @li data_format: An optional string. Defaults to "NHWC".
-
- * @par Outputs:
- * @out_grad: A mutable tensor with the same shape and type as "orig_input".
-
- * @par Third-party framework compatibility
- * @li Compatible with the TensorFlow operator AvgPoolGrad.
- */
- REG_OP(AvgPoolGrad)
- .INPUT(orig_input_shape, TensorType({DT_INT32}))
- .INPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE}))
- .OUTPUT(out_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE}))
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
- .OP_END_FACTORY_REG(AvgPoolGrad)
-
- /**
- * @brief Computes gradients of average pooling function.
-
- * @par Inputs:
- * @input_grad: An NHWC tensor of type float16, float32, or double.
-
- * @par Attributes:
- * @li orig_input_shape: A required Original input dimensions.
- * @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 padding: A required string, specifying the type of the padding algorithm
- * to use.
- * @li data_format: An optional string. Defaults to "NHWC".
-
- * @par Outputs:
- * @out_grad: A mutable tensor with the same shape and type as "orig_input".
- */
- REG_OP(AvgPoolGradD)
- .INPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE}))
- .OUTPUT(out_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE}))
- .REQUIRED_ATTR(orig_input_shape, ListInt)
- .REQUIRED_ATTR(ksize, ListInt)
- .REQUIRED_ATTR(strides, ListInt)
- .REQUIRED_ATTR(padding, String)
- .ATTR(data_format, String, "NHWC")
-
- .OP_END_FACTORY_REG(AvgPoolGradD)
-
- REG_OP(MaxPoolWithArgmaxCCE)
- .INPUT(x, TensorType::ALL())
- .OUTPUT(y, TensorType::ALL())
- .OUTPUT(argmax, TensorType::ALL())
- .ATTR(mode, Int, 0)
- .ATTR(pad_mode, Int, 0)
- .ATTR(window, ListInt, {1,1})
- .ATTR(stride, ListInt, {1,1})
- .ATTR(pad, ListInt, {0,0,0,0})
- .ATTR(ceil_mode, Int, 0)
- .ATTR(data_mode, Int, 1)
- .ATTR(nan_opt, Int, 0)
- .OP_END_FACTORY_REG(MaxPoolWithArgmaxCCE)
-
- REG_OP(MaxPoolGradWithArgmaxCCE)
- .INPUT(x, TensorType::ALL())
- .INPUT(grad,TensorType::ALL())
- .INPUT(arg,TensorType::ALL())
- .OUTPUT(output,TensorType::ALL())
- .ATTR(mode, Int, 0)
- .ATTR(max_pool_grad_output_shape, ListInt, {0,0,0,0})
- .ATTR(pad_mode, Int, 0)
- .ATTR(window, ListInt, {1,1})
- .ATTR(stride, ListInt, {1,1})
- .ATTR(pad, ListInt, {0,0,0,0})
- .ATTR(ceil_mode, Int, 0)
- .ATTR(data_mode, Int, 1)
- .ATTR(nan_opt, Int, 0)
- .OP_END_FACTORY_REG(MaxPoolGradWithArgmaxCCE)
- /**
- *@brief :upsample the layer
-
- *@par Inputs:
- * one input, including:
- *@li x: A tensor of type float16 or float32.
- *@par Attributes:
- *@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:
- *y: A tensor of type float16 or float32.
- */
- REG_OP(Upsample)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
- .ATTR(scale, Float, 1)
- .ATTR(stride_h, Int, 2)
- .ATTR(stride_w, Int, 2)
- .OP_END_FACTORY_REG(Upsample)
-
- /**
- *@brief Computes gradient of the FractionalMaxPool function.
-
- *@par Inputs:
- *Inputs include: \n
- * @li orig_input: A Tensor. Must be one of the following types: float32, float64, int32, int64.
- * @li orig_output: A Tensor. Must have the same type as orig_input.
- * @li out_backprop: A Tensor. Must have the same type as orig_input. \n
- 4-D with shape [batch, height, width, channels].
- * @li row_pooling_sequence: A Tensor of type int64.
- * @li col_pooling_sequence: A Tensor of type int64.
-
- *@par Attributes:
- *overlapping: An optional bool. Defaults to False.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as orig_input.
-
- *@attention Constraints:\n
- *-The implementation for FractionalMaxPoolGrad on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow FractionalMaxPoolGrad operator.
- */
- REG_OP(FractionalMaxPoolGrad)
- .INPUT(orig_input, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .INPUT(orig_output, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .INPUT(out_backprop, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .INPUT(row_pooling_sequence, TensorType({ DT_INT64 }))
- .INPUT(col_pooling_sequence, TensorType({ DT_INT64 }))
- .OUTPUT(y, TensorType({ DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64 }))
- .ATTR(overlapping, Bool, false)
- .OP_END_FACTORY_REG(FractionalMaxPoolGrad)
-
- /**
- *@brief Performs fractional average pooling on the input.
-
- *@par Inputs:
- *Inputs include: \n
- *x: A Tensor. Must be one of the following types: float32, float64, int32, int64. \n
- 4-D with shape [batch, height, width, channels].
-
- *@par Attributes:
- *@li pooling_ratio: A list of floats that has length >= 4.
- *@li pseudo_random: An optional bool. Defaults to False.
- *@li overlapping: An optional bool. Defaults to False. When set to True, it means when pooling.
- *@li deterministic: An optional bool. Defaults to False.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *@li y: A Tensor. Has the same type as x.
- *@li row_pooling_sequence: A Tensor of type int64.
- *@li col_pooling_sequence: A Tensor of type int64.
-
- *@attention Constraints:\n
- *-The implementation for FractionalAvgPool on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow FractionalAvgPool operator.
- */
- REG_OP(FractionalAvgPool)
- .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .OUTPUT(row_pooling_sequence, TensorType({DT_INT64}))
- .OUTPUT(col_pooling_sequence, TensorType({DT_INT64}))
- .ATTR(pooling_ratio, ListFloat, {})
- .ATTR(pseudo_random, Bool, false)
- .ATTR(overlapping, Bool, false)
- .ATTR(deterministic, Bool, false)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(FractionalAvgPool)
-
- /**
- *@brief Performs fractional max pooling on the input.
-
- *@par Inputs:
- *Inputs include: \n
- *x: A Tensor. Must be one of the following types: float32, float64, int32, int64. \n
- 4-D with shape [batch, height, width, channels].
-
- *@par Attributes:
- *@li pooling_ratio: A list of floats that has length >= 4. Pooling ratio for each dimension of value.
- *@li pseudo_random: An optional bool. Defaults to False.
- *@li overlapping: An optional bool. Defaults to False.
- *@li deterministic: An optional bool. Defaults to False.
- *@li seed: An optional int. Defaults to 0.
- *@li seed2: An optional int. Defaults to 0.
-
- *@par Outputs:
- *@li y: A Tensor. Has the same type as x.
- *@li row_pooling_sequence: A Tensor of type int64.
- *@li col_pooling_sequence: A Tensor of type int64.
-
- *@attention Constraints:\n
- *-The implementation for FractionalMaxPool on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow FractionalMaxPool operator.
- */
- REG_OP(FractionalMaxPool)
- .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .OUTPUT(row_pooling_sequence, TensorType({DT_INT64}))
- .OUTPUT(col_pooling_sequence, TensorType({DT_INT64}))
- .ATTR(pooling_ratio, ListFloat, {})
- .ATTR(pseudo_random, Bool, false)
- .ATTR(overlapping, Bool, false)
- .ATTR(deterministic, Bool, false)
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .OP_END_FACTORY_REG(FractionalMaxPool)
-
- /**
- *@brief Finds values of the n-th order statistic for the last dimension.
-
- *@par Inputs:
- *Inputs include: \n
- * @li x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, \n
- int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
- * @li n: A Tensor of type int32. 0-D.
-
- *@par Attributes:
- *reverse: An optional bool. Defaults to False.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as x.
-
- *@attention Constraints:\n
- *-The implementation for NthElement on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow NthElement operator.
- */
- REG_OP(NthElement)
- .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
- DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_DOUBLE}))
- .INPUT(n, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
- DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_DOUBLE}))
- .ATTR(reverse, Bool, false)
- .OP_END_FACTORY_REG(NthElement)
-
- /**
- *@brief Computes gradient of the FractionalAvgPool function.
-
- *@par Inputs:
- *Inputs include: \n
- * @li orig_input_tensor_shape: A Tensor of type int64.
- * @li out_backprop: A Tensor. Must be one of the following types: float32, float64, \n
- int32, int64. 4-D with shape [batch, height, width, channels].
- * @li row_pooling_sequence: A Tensor of type int64.
- * @li col_pooling_sequence: A Tensor of type int64.
-
- *@par Attributes:
- *overlapping: An optional bool. Defaults to False.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as out_backprop.
-
- *@attention Constraints:\n
- *-The implementation for FractionalAvgPoolGrad on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow FractionalAvgPoolGrad operator.
- */
- REG_OP(FractionalAvgPoolGrad)
- .INPUT(orig_input_tensor_shape, TensorType({DT_INT64}))
- .INPUT(out_backprop, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .INPUT(row_pooling_sequence, TensorType({DT_INT64}))
- .INPUT(col_pooling_sequence, TensorType({DT_INT64}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .ATTR(overlapping, Bool, false)
- .OP_END_FACTORY_REG(FractionalAvgPoolGrad)
-
- /**
- *@brief Returns the permuted vector/tensor in the destination data format given the.
-
- *@par Inputs:
- *Inputs include: \n
- *x: A Tensor. Must be one of the following types: int32, int64. Vector of size 4 \n
- or Tensor of shape (4, 2) in source data format.
-
- *@par Attributes:
- *@li src_format: An optional string. Defaults to "NHWC". source data format.
- *@li dst_format: An optional string. Defaults to "NCHW". destination data format.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as x.
-
- *@attention Constraints:\n
- *-The implementation for DataFormatVecPermute on Ascend uses AICPU, with bad performance.\n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow DataFormatVecPermute operator.
- */
- REG_OP(DataFormatVecPermute)
- .INPUT(x, TensorType({ DT_INT32, DT_INT64 }))
- .OUTPUT(y, TensorType({ DT_INT32, DT_INT64 }))
- .ATTR(src_format, String, "NHWC")
- .ATTR(dst_format, String, "NCHW")
- .OP_END_FACTORY_REG(DataFormatVecPermute)
-
-
- } // namespace ge
-
- #endif // GE_OP_NN_POOLING_OPS_H
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