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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"
- /**
- *@brief Performs pooling on the input.
- *@par Inputs:
- *@li x: An NCHW tensor of type float16.
- *@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 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.
- *@attention Constraints:\n
- *@li window[0] * window[1] < 256;
- */
- namespace ge {
- REG_OP(Pooling)
- .INPUT(x, TensorType({DT_FLOAT16}))
- .OUTPUT(y, TensorType({DT_FLOAT16}))
- .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(ceil_mode, Int, 0)
- .OP_END_FACTORY_REG(Pooling)
-
- /**
- *@brief Performs average pooling on the input.
-
- *@par Inputs:
- *x: A tensor of type float16.
-
- *@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.
-
- *@attention Constraints:\n
- *@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].
- */
- 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)
-
- 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)
-
- 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)
-
- /**
- * @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 tuple or list, specifying the size of the window for each
- * dimension of the input tensor.
- * @li strides: A tuple or list, specifying the stride of the sliding window for
- * each dimension of the input tensor.
- * @li padding: A string, specifying the type of padding algorithm to use.
-
- * @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 "ksiez" is in the range [1, 255]. "strides" is in the range [1, 63]
- */
- 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)
- .OP_END_FACTORY_REG(MaxPoolGrad)
-
- /**
- * @brief Computes second-order gradients of the maxpooling function.
-
- * @par Inputs:
- * @li x1: Original forward input tensor of type float16
- * @li x2: Original forward output tensor of type float16
- * @li grad: Gradient tensor of type float16
-
- * @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: window sliding mode. Either SAME or VALID.
- * @li data_format: Format of the original input, either NCHW or NHWC. Defaults
- * to NHWC.
-
- * @attention Constraints:
- * @li Only the cloud 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.
-
- * @par Outputs:
- * @li y: Result tensor of type float16
- */
- 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".
- */
- 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)
-
- 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)
-
- 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 float16
- * @li grad:Gradient tensor of type float16
- * @li argmax:An tensor of type uint16
- * @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: window sliding mode. Either SAME or VALID.
- * @par Outputs:
- * @li y:Result tensor of type float16
-
- * @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.
- */
- 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 tuple or list, specifying the size of the window for each
- * dimension of the input tensor.
- * @li strides: A tuple or list, specifying the stride of the sliding window for\n
- * each dimension of the input tensor.
- * @li padding: A string, specifying the type of the padding algorithm to use.
- * @li data_format: A string. Defaults to "NHWC".
-
- * @par Outputs:
- * @out_grad: A mutable tensor with the same shape and type as "orig_input".
- */
- 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: Original input dimensions.
- * @li ksize: A tuple or list, specifying the size of the window for each
- * dimension of the input tensor.
- * @li strides: A tuple or list, specifying the stride of the sliding window for\n
- * each dimension of the input tensor.
- * @li padding: A string, specifying the type of the padding algorithm to use.
- * @li data_format: A 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)
-
- REG_OP(Upsample)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8}))
- .ATTR(scale, Float, 1)
- .ATTR(stride, Int, 2)
- .OP_END_FACTORY_REG(Upsample)
- } // namespace ge
-
- #endif // GE_OP_NN_POOLING_OPS_H
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