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867 lines
40 KiB
867 lines
40 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef GE_OP_NN_CALCULATION_OPS_H
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#define GE_OP_NN_CALCULATION_OPS_H
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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* @brief Computes the gradients of depthwise convolution with respect to
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* the filter.
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* @par Inputs:
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* Three inputs include: \n
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* @li input: 4D origin shape of input tensor [N, C, H, W] or [N, H, W, C],
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* support float16, float32, double
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* @li filter_size: A 4D tensor of type int32, with shape [H, W, C, K]
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* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C].
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* Must be one of the following types: float16, float32, double.
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* @par Attributes:
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* @li strides: A required list or tuple. The stride of the sliding window
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* for height and width of input "x" of the convolution.
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* Must be with shape [1, 1, stride_height, stride_width] or
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* [1, stride_height, stride_width, 1].
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* @li dilations: An optional list or tuple. The dilation factor for each
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* dimension of input "x".
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* If set to k > 1, there will be k-1 skipped cells between each filter element
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* on that dimension. Must be with shape [1, 1, dilation_height, dilation_width]
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* or [1, dilation_height, dilation_width, 1].
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* @li pads: A required list or tuple. Padding added to each dimension of the
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* input.
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* @li data_format: An optional string. Input data format, either "NHWC" or
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* "NCHW".
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* @par Outputs:
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* filter_grad: Gradient of the deep convolution relative to the filter with
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* shape [H, W, C, K]. Must be one of the following types: float16, float32,
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* double.
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* @attention Constraints:\n
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* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
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* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
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* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape
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* [C1, Hf, Wf, K, Co, C0],
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* where K is fixed at 1, and Co and C0 are 16.\n
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* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the
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* data is 5D with shape [N, C1, Ho, Wo, C0],
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* where C is the same as that of the feature map and C0 is 16.\n
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* Limited by Tiling and L1 / L0 buffer memory: 512 * ceil(Wo, 16) + (480 *
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* stride_h + 32 * filter_h) * ceil(Wi, 16) <= l1_size and Hf*Wf <= l0b_size/512.
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* @par Third-party framework compatibility
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* @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropFilter.
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* @li Compatible with the Caffe operator DepthwiseConv2DBackpropFilter.
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*/
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REG_OP(DepthwiseConv2DBackpropFilter)
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.INPUT(input, TensorType({float16}))
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.INPUT(filter_size, TensorType({DT_INT32, DT_INT64}))
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.INPUT(out_backprop, TensorType({float16}))
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.OUTPUT(filter_grad, TensorType({float32}))
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.REQUIRED_ATTR(strides, ListInt)
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.ATTR(dilations, ListInt, {1, 1, 1, 1})
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.REQUIRED_ATTR(pads, ListInt)
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.ATTR(data_format, String, "NHWC")
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.OP_END_FACTORY_REG(DepthwiseConv2DBackpropFilter)
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/**
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* @brief Computes the gradients of depthwise convolution with respect to
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* the filter.
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* @par Inputs:
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* Two inputs include: \n
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* @li input: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of type float16
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* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C],
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* of type float16
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* @par Attributes:
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* @li filter_size: A required list or tuple. Shape of filter.
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* @li strides: A required list or tuple. The stride of the sliding window for
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* height and width of input "x" of the convolution.
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* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height,
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* stride_width, 1].
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* @li dilations: An optional list or tuple. The dilation factor for each
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* dimension of input "x".
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* If set to k > 1, there will be k-1 skipped cells between each filter element
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* on that dimension. Must be with shape [1, 1, dilation_height, dilation_width]
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* or [1, dilation_height, dilation_width, 1].
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* @li pads: A required list or tuple. Padding added to each dimension of the
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* input.
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* @li data_format: An optional string. Input data format, either "NHWC" or
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* "NCHW".
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* @par Outputs:
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* filter_grad: Gradient of the deep convolution relative to the filter with
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* shape [H, W, C, K]. Must be of type float32.
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* @attention Constraints:\n
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* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
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* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
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* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape
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* [C1, Hf, Wf, K, Co, C0],
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* where K is fixed at 1, and Co and C0 are 16.\n
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* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the
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* data is 5D with shape [N, C1, Ho, Wo, C0],
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* where C is the same as that of the feature map and C0 is 16.\n
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* Limited by Tiling and L1 / L0 buffer memory: 512 * ceil(Wo, 16) + (480 *
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* stride_h + 32 * filter_h) * ceil(Wi, 16) <= l1_size and Hf*Wf <= l0b_size/512.
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* @par Third-party framework compatibility
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* @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropFilter.
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* @li Compatible with the Caffe operator DepthwiseConv2DBackpropFilter.
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*/
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REG_OP(DepthwiseConv2DBackpropFilterD)
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.INPUT(input, TensorType({float16}))
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.INPUT(out_backprop, TensorType({float16}))
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.OUTPUT(filter_grad, TensorType({float32}))
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.REQUIRED_ATTR(filter_size, ListInt)
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.REQUIRED_ATTR(strides, ListInt)
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.ATTR(dilations, ListInt, {1, 1, 1, 1})
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.REQUIRED_ATTR(pads, ListInt)
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.ATTR(data_format, String, "NHWC")
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.OP_END_FACTORY_REG(DepthwiseConv2DBackpropFilterD)
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/**
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* @brief Computes the gradients of depthwise convolution with respect to the
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* input.
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* @par Inputs:
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* Three inputs include: \n
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* @li input_size: 4D shape of input tensor [N, C, H, W] or [N, H, W, C],
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* support int32
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* @li filter: 4D filter tensor with shape of [H, W, C, K], support float16,
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* float32, double
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* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C].
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* Must be one of the following types: float16, float32, double.
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* @par Attributes:
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* @li strides: A required list or tuple. The stride of the sliding window for
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* height and width of input "x" of the convolution.
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* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height,
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* stride_width, 1].
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* @li dilations: An optional list or tuple. The dilation factor for each
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* dimension of input "x".
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* If set to k > 1, there will be k-1 skipped cells between each filter element
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* on that dimension. Must be with shape [1, 1, dilation_height, dilation_width]
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* or [1, dilation_height, dilation_width, 1].
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* @li pads: A required list or tuple. Padding added to each dimension of the
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* input.
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* @li data_format: An optional string. Input data format, either "NHWC" or
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* "NCHW".
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* @par Outputs:
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* input_grad: Gradient of the deep convolution relative to the input with shape
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* [N, C, H, W] or [N, H, W, C] Must be one of the following types: float16,
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* float32, double.
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* @attention Constraints:\n
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* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
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* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
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* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape
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* [C1, Hf, Wf, K, Co, C0],
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* where K is fixed at 1, and Co and C0 are 16.\n
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* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the
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* data is 5D with shape [N, C1, Ho, Wo, C0],
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* where C is the same as that of the feature map and C0 is 16.\n
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* Limited by Tiling: max_h_in_l1 >= C0, where max_h_in_l1 = (l1_size - Hf *
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* Wf * C0 * C0 * 2) / (2 * Wo *C0).\n
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* @par Third-party framework compatibility
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* @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropInput.
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* @li Compatible with the Caffe operator DepthwiseConv2DBackpropInput.
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*/
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REG_OP(DepthwiseConv2DBackpropInput)
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.INPUT(input_size, TensorType({DT_INT32, DT_INT64}))
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.INPUT(filter, TensorType({DT_FLOAT16}))
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.INPUT(out_backprop, TensorType({DT_FLOAT16}))
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.OUTPUT(input_grad, TensorType({DT_FLOAT16}))
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.REQUIRED_ATTR(strides, ListInt)
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.ATTR(dilations, ListInt, {1, 1, 1, 1})
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.REQUIRED_ATTR(pads, ListInt)
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.ATTR(data_format, String, "NHWC")
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.OP_END_FACTORY_REG(DepthwiseConv2DBackpropInput)
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/**
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* @brief Computes the gradients of depthwise convolution with respect to the
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* input.
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* @par Inputs:
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* Two inputs include: \n
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* @li filter: A 4D tensor of type float16, with shape [H, W, C, K]
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* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of
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* type float16
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* @par Attributes:
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* @li input_size: A required list or tuple. The origin shape of input.
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* @li strides: A required list or tuple. The stride of the sliding window for
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* height and width of input "x" of the convolution.
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* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height,
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* stride_width, 1].
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* @li dilations: An optional list or tuple. The dilation factor for each
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* dimension of input "x".
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* If set to k > 1, there will be k-1 skipped cells between each filter element
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* on that dimension. Must be with shape [1, 1, dilation_height, dilation_width]
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* or [1, dilation_height, dilation_width, 1].
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* @li pads: A required list or tuple. Padding added to each dimension of the
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* input.
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* @li data_format: An optional string. Input data format, either "NHWC" or
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* "NCHW".
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* @par Outputs:
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* input_grad: Gradient of the deep convolution relative to the input with
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* shape [N, C, H, W] or [N, H, W, C]. Must be of type float16.
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* @attention Constraints:\n
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* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
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* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
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* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape
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* [C1, Hf, Wf, K, Co, C0],
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* where K is fixed at 1, and Co and C0 are 16.\n
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* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the
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* data is 5D with shape [N, C1, Ho, Wo, C0],
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* where C is the same as that of the feature map and C0 is 16.\n
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* Limited by Tiling: max_h_in_l1 >= C0, where max_h_in_l1 = (l1_size - Hf *
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* Wf * C0 * C0 * 2) / (2 * Wo *C0).\n
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* @par Third-party framework compatibility
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* @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropInput.
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* @li Compatible with the Caffe operator DepthwiseConv2DBackpropInput.
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*/
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REG_OP(DepthwiseConv2DBackpropInputD)
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.INPUT(filter, TensorType({DT_FLOAT16}))
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.INPUT(out_backprop, TensorType({DT_FLOAT16}))
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.OUTPUT(input_grad, TensorType({DT_FLOAT16}))
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.REQUIRED_ATTR(input_size, ListInt)
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.REQUIRED_ATTR(strides, ListInt)
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.ATTR(dilations, ListInt, {1, 1, 1, 1})
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.REQUIRED_ATTR(pads, ListInt)
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.ATTR(data_format, String, "NHWC")
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.OP_END_FACTORY_REG(DepthwiseConv2DBackpropInputD)
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/**
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*@brief Computes a 2D deep convolution given a 4D input tensor and a filter
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* tensor.
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*@par Inputs:
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*Two required inputs and two optional inputs, including: \n
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* @li x: A 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C]
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* @li filter: A 4D tensor of type float16, with shape [H, W, C, K]
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* @li bias: An optional tensor of type float16 or int32
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* @li offset_w: An optional float16 or int8, used for quantized inference
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* @par Attributes:
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* @li strides: A required list or tuple. The stride of the sliding window for
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* height and width of input "x" of the convolution.
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* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height,
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* stride_width, 1].
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* @li dilations: An optional list or tuple. The dilation factor for each
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* dimension of input "x".
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* If set to k > 1, there will be k-1 skipped cells between each filter element
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* on that dimension. Must be with shape [1, 1, dilation_height, dilation_width]
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* or [1, dilation_height, dilation_width, 1].
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* @li pads: A required list or tuple. Padding added to each dimension of the
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* input.
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* @li data_format: An optional string. Input data format, either "NHWC" or
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* "NCHW". Defaults to "NHWC".
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* @li offset_x: An optional int. Input offset, used for quantized inference.
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* Defaults to 0.
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* @par Outputs:
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* y: 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C]
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* @attention Constraints:\n
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* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
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* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
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* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape
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* [C1, Hf, Wf, K, Co, C0],
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* where K is fixed at 1, and Co and C0 are 16.\n
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* Limited by the size of L1 buffer memory: \n
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* (l1_size - filter_h*filter_w*BLOCK_SIZE*BLOCK_SIZE*data_size) // (Wi *
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* BLOCK_SIZE * data_size) >= (BLOCK_SIZE * strides_h + filter_h - strides_h).\n
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* @par Quantization supported or not
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* Yes
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* @par Third-party framework compatibility
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* @li Compatible with the TensorFlow operator DepthwiseConv2D.
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* @li Compatible with the Caffe operator DepthwiseConv2D.
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*/
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REG_OP(DepthwiseConv2D)
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.INPUT(x, TensorType({DT_FLOAT16, DT_INT8}))
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.INPUT(filter, TensorType({DT_FLOAT16, DT_INT8}))
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.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32}))
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.OPTIONAL_INPUT(offset_w, TensorType({DT_FLOAT16, DT_INT8}))
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.OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32}))
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.REQUIRED_ATTR(strides, ListInt)
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.ATTR(dilations, ListInt, {1, 1, 1, 1})
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.REQUIRED_ATTR(pads, ListInt)
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.ATTR(data_format, String, "NHWC")
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.ATTR(offset_x, Int, 0)
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.OP_END_FACTORY_REG(DepthwiseConv2D)
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REG_OP(Conv2DCCE)
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.INPUT(x, TensorType{DT_FLOAT}) // The input tensor
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.INPUT(w, TensorType({DT_FLOAT, DT_INT8})) // The weight tensor ,If QuantType =1 ,shall use type""tensor(int8)
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.OPTIONAL_INPUT(b, TensorType{DT_FLOAT}) // Optional 1D bias to be added to the convolution, has size of M.
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.OUTPUT(y, TensorType{DT_FLOAT}) // The output tensor
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.ATTR(mode, Int, 1)
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.ATTR(group, Int, 1) // number of groups input channels and output channels are divided into
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.ATTR(num_output, Int, 0) // number of output tensor
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.ATTR(pad, ListInt, {0, 0, 0, 0}) // Padding for the beginning and ending along each axis
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.ATTR(kernel, ListInt, {0, 0})
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.ATTR(stride, ListInt, {1, 1}) // Stride along each axis.
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.ATTR(dilation, ListInt, {1, 1}) // dilation value along each axis of the filter.
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.ATTR(pad_mode, Int, 0) // pad mode, 0:NOTSET, 1:SAME_UPPER, SAME_LOWER or 2:VALID.defaul default value is 0:NOTSET
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.ATTR(algo, Int, 2)
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.OP_END_FACTORY_REG(Conv2DCCE)
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REG_OP(Conv2DBackpropFilterCCE)
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.INPUT(x, TensorType{DT_FLOAT})
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.INPUT(filter_sizes, TensorType{DT_INT8})
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.INPUT(out_backprop, TensorType{DT_FLOAT})
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.OUTPUT(y, TensorType{DT_FLOAT})
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.ATTR(conv_grad_filter_output_shape, ListInt, {0, 0, 0, 0})
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.ATTR(mode, Int, 1)
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.ATTR(group, Int, 1)
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.ATTR(pad, ListInt, {0, 0, 0, 0})
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.ATTR(stride, ListInt, {1, 1})
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.ATTR(dilation, ListInt, {1, 1})
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.ATTR(padding, Int, 0) //pad_mode:same valid
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.ATTR(algo, Int, 0)
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.OP_END_FACTORY_REG(Conv2DBackpropFilterCCE)
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REG_OP(Conv2DBackpropInputCCE)
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.INPUT(input_sizes, TensorType{DT_INT8})
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.INPUT(filter, TensorType{DT_FLOAT})
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.INPUT(out_backprop, TensorType{DT_FLOAT})
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.OUTPUT(output, TensorType{DT_FLOAT})
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.ATTR(conv_grad_input_output_shape, ListInt, {0, 0, 0, 0})
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.ATTR(mode, Int, 1)
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.ATTR(format, Int, 0)
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.ATTR(group, Int, 1)
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.ATTR(pad_mode, Int, 0)
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.ATTR(stride, ListInt, {1, 1})
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.ATTR(dilation, ListInt, {1, 1})
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.ATTR(pad, ListInt, {0, 0, 0, 0})
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.ATTR(algo, Int, 0)
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.OP_END_FACTORY_REG(Conv2DBackpropInputCCE)
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/**
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*@brief Performs the the backward operation for "BiasAdd" on the "bias" tensor.
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* It accumulates all the values from out_backprop into the feature
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* dimension. For NHWC data format, the feature dimension is the last.
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* For NCHW data format, the feature dimension is the third-to-last.
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*@par Inputs:
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*x: A Tensor of type NumberType.
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*@par Attributes:
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*data_format: Data format. Defaults to "NHWC".
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|
|
|
*@par Outputs:
|
|
*y: A Tensor.Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator BiasAddGrad.
|
|
*/
|
|
REG_OP(BiasAddGrad)
|
|
.INPUT(x, TensorType::NumberType())
|
|
.OUTPUT(y, TensorType::NumberType())
|
|
.ATTR(data_format, String, "NHWC")
|
|
.OP_END_FACTORY_REG(BiasAddGrad)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution with respect to the input.
|
|
*@par Inputs:
|
|
* Three inputs:
|
|
* @li input_size: A Tensor of type int32. An integer vector representing the shape of input,
|
|
* where input is a 4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
|
|
* @li filter: A Tensor. Must be one of the following types: float16, float32, float64.
|
|
* 4-D with shape [filter_height, filter_width, in_channels, out_channels]
|
|
* or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]
|
|
* or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Five attributes:
|
|
* @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
|
|
* @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
|
|
* @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1]
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as filter,and has same format as input_size
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv2d_backprop_input
|
|
*/
|
|
REG_OP(Conv2DBackpropInput)
|
|
.INPUT(input_size, TensorType({DT_INT32}))
|
|
.INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.INPUT(out_backprop, 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(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.OP_END_FACTORY_REG(Conv2DBackpropInput)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution with respect to the input.
|
|
*@par Inputs:
|
|
* Two inputs:
|
|
* @li filter: A Tensor. Types is float16.
|
|
* 4-D with shape [filter_height, filter_width, in_channels, out_channels] or [out_channels, filter_height, filter_width, in_channels]
|
|
* or [out_channels, in_channel, filter_height, filter_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]
|
|
* or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Six attributes:
|
|
* @li input_size A Tensor of type int32. An integer vector representing the shape of input,
|
|
* where input is a 4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
|
|
* @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
|
|
* @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
|
|
* @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1]
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as filter,4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv2d_backprop_input
|
|
*/
|
|
REG_OP(Conv2DBackpropInputD)
|
|
.INPUT(filter, TensorType({DT_FLOAT16, DT_INT8}))
|
|
.INPUT(out_backprop, TensorType({DT_FLOAT16, DT_INT8}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32}))
|
|
.REQUIRED_ATTR(input_size, ListInt)
|
|
.REQUIRED_ATTR(strides, ListInt)
|
|
.REQUIRED_ATTR(pads, ListInt)
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.OP_END_FACTORY_REG(Conv2DBackpropInputD)
|
|
|
|
/**
|
|
*@brief Computes the Deconvolution with respect to the input.
|
|
*@par Inputs:
|
|
* Three inputs:
|
|
* @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.
|
|
* 4D with shape [out_channels, in_channel, filter_height, filter_width].\n
|
|
* Two optional inputs:
|
|
* @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.
|
|
* @li pads: A tuple or list of 4 integers. The [top, bottom, left, right]
|
|
* 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.
|
|
* @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.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as "filter". 4D tensor with shape
|
|
* [batch, channels, height, width].
|
|
*/
|
|
REG_OP(Deconvolution)
|
|
.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}))
|
|
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32}))
|
|
.ATTR(strides, ListInt, {1, 1})
|
|
.ATTR(pads, ListInt, {0, 0, 0, 0})
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NCHW")
|
|
.ATTR(offset_x, Int, 0)
|
|
.OP_END_FACTORY_REG(Deconvolution)
|
|
/**
|
|
*@brief Computes the gradients of convolution with respect to the filter
|
|
*@par Inputs:
|
|
* Three inputs:
|
|
* @li x: A Tensor. Must be one of the following types: float16, float32, float64.
|
|
* 4-D with shape [batch, in_height, in_width, in_channels] or [batch, in_channels, in_height, in_width].
|
|
* @li filter_size: A Tensor of type int32. An integer vector representing the tensor shape of filter,
|
|
* where filter is a 4-D tensor [filter_height, filter_width, in_channels, out_channels]
|
|
* or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as x. 4-D with shape [batch, out_height, out_width, out_channels]
|
|
* or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Five attributes:
|
|
* @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
|
|
* @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map.
|
|
* @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1].
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as x
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv2d_backprop_filter
|
|
*/
|
|
REG_OP(Conv2DBackpropFilter)
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.INPUT(filter_size, TensorType({DT_INT32}))
|
|
.INPUT(out_backprop, 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(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.OP_END_FACTORY_REG(Conv2DBackpropFilter)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution with respect to the filter.
|
|
*@par Inputs:
|
|
* Two inputs:
|
|
* @li x: A Tensor. Type is float16.
|
|
* 4-D with shape [batch, in_height, in_width, in_channels] or [batch, in_channels, in_height, in_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as x. 4-D with shape [batch, out_height, out_width, out_channels]
|
|
* or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Six attributes:
|
|
* @li filter_size: A Tensor of type integers. An integer vector representing the tensor shape of filter,
|
|
* where filter is a 4-D tensor [filter_height, filter_width, in_channels, out_channels]
|
|
* or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
|
|
* @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
|
|
* @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
|
|
* @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1].
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as x
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv2d_backprop_filter
|
|
*/
|
|
REG_OP(Conv2DBackpropFilterD)
|
|
.INPUT(x, TensorType({DT_FLOAT16}))
|
|
.INPUT(out_backprop, TensorType({DT_FLOAT16}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT}))
|
|
.REQUIRED_ATTR(filter_size, ListInt)
|
|
.REQUIRED_ATTR(strides, ListInt)
|
|
.REQUIRED_ATTR(pads, ListInt)
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.OP_END_FACTORY_REG(Conv2DBackpropFilterD)
|
|
|
|
/**
|
|
*@brief Computes a 2D convolution given 4D "x" and "filter" tensors.
|
|
*@par Inputs:
|
|
* @li x: A 4D tensor of input images.
|
|
* @li filter: A 4D tensor of filters.
|
|
* @li bias: An optional 1D tensor.
|
|
* @li offset_w: An optional 1D tensor for quantized convolution. Reserved.
|
|
*
|
|
* The input and output tensor attributes are listed as follows:
|
|
* @verbatim
|
|
Tensor | x | filter | bias | offset_w | y
|
|
-----------|---------|---------|---------|----------|--------
|
|
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 | | |
|
|
@endverbatim
|
|
* It should be noted that the data types must correspond to each other, but the
|
|
* format does not need to.
|
|
|
|
*@par Attributes:
|
|
* @li strides: A list of 4 integers. Specifying the strides of the
|
|
* convolution along the height and width. The dimension order is determined
|
|
* by the data format of "x". By default the N and C dimensions are set to 1.
|
|
* @li pads: A list of 4 integers. Specifying the top, bottom, left and right
|
|
* padding.
|
|
* @li dilations: A list of 4 integers. Specifying the dilation rate to use
|
|
* 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".
|
|
* @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. Reserved.
|
|
|
|
*@par Outputs:
|
|
* @li y: A 4D Tensor of output images.
|
|
|
|
*@attention
|
|
* @li The parameter scope is listed as follows:
|
|
* @verbatim
|
|
Name | Field | Scope
|
|
------------------|--------------|----------
|
|
Input Image Size | H dimension | [1, 4096]
|
|
| W dimension | [1, 4096]
|
|
------------------|--------------|----------
|
|
Filter Size | H dimension | [1, 255]
|
|
| W dimension | [1, 255]
|
|
------------------|--------------|----------
|
|
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]
|
|
------------------|--------------|----------
|
|
Dilation Size | H dimension | [1, 255]
|
|
| W dimension | [1, 255]
|
|
@endverbatim
|
|
|
|
* @li There are restrictions for certain scenarios:
|
|
* @verbatim
|
|
Output | Restrictions
|
|
------------------|----------------------------------------------
|
|
W dimension == 1 | HxW(input) == HxW(filter)
|
|
H dimension == 1 |
|
|
------------------|----------------------------------------------
|
|
W dimension == 1 | Not supported
|
|
H dimension != 1 |
|
|
@endverbatim
|
|
* As shown above, "HxW(input)" indicates the image size after padding and
|
|
* "HxW(filter)" indicates the filter size after dilation.
|
|
|
|
*@par Quantization supported or not
|
|
* Yes
|
|
|
|
*@par Third-party framework compatibility
|
|
*@li Compatible with the TensorFlow operator "conv2d".
|
|
*@li Compatible with the Caffe operator 2D "Convolution".
|
|
*/
|
|
REG_OP(Conv2D)
|
|
.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_INT32}))
|
|
.REQUIRED_ATTR(strides, ListInt)
|
|
.REQUIRED_ATTR(pads, ListInt)
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.ATTR(offset_x, Int, 0)
|
|
.OP_END_FACTORY_REG(Conv2D)
|
|
|
|
REG_OP(Conv2DCompress)
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8}))
|
|
.INPUT(filter_compress, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8}))
|
|
.INPUT(compress_index, TensorType({DT_INT8}))
|
|
.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32}))
|
|
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32}))
|
|
.REQUIRED_ATTR(strides, ListInt)
|
|
.REQUIRED_ATTR(pads, ListInt)
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NHWC")
|
|
.ATTR(offset_x, Int, 0)
|
|
.OP_END_FACTORY_REG(Conv2DCompress)
|
|
|
|
/**
|
|
*@brief Computes a 3D convolution given 5D "x" and "filter" tensors.
|
|
*@par Inputs:
|
|
*@li x: A 5D tensor. Must be one of the following types: float16, float32, float64. The format is NCDHW or NDHWC.
|
|
*@li filter: A 5D tensor of the same type as "x". The format is NCDHW, NDHWC or DHWCN.
|
|
*@li bias: An optional 1D tensor of the same type as "x".
|
|
|
|
*@par Attributes:
|
|
*@li strides: A list of 5 ints. Specifies the stride of the sliding window for each dimension of "x". The N and C dimensions must be 1. Has the same format as "x".
|
|
*@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.
|
|
*@li data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". Specify the data format of the input and output data.
|
|
*@li dilations: A list of 5 ints. Specifies the dilation factor for each dimension of "x". The N and C dimensions must be 1. Has the same format as "x".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@attention Constraints:\n
|
|
*The image size after padding is greater than the filter size.\n
|
|
|
|
*@par Third-party framework compatibility
|
|
*@li Compatible with the TensorFlow operator conv3d.
|
|
*@li Compatible with the Caffe operator Convolution.
|
|
*/
|
|
REG_OP(Conv3D)
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.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}))
|
|
.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)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution 3d with respect to the input.
|
|
*@par Inputs:
|
|
* Three inputs:
|
|
* @li input_size: A Tensor of type int32, int64. An integer vector representing the shape of input,
|
|
* where input is a 5-D tensor [batch, depth, height, width, channels] or [batch, channels, depth, height, width].
|
|
* @li filter: A Tensor. Must be one of the following types: float16, float32, float64.
|
|
* @li grads: A Tensor. Must have the same type as filter. 5-D with shape [batch, depth, out_height, out_width, out_channels]
|
|
* or [batch, out_channels, depth, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Four attributes:
|
|
* @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension.
|
|
* @li pads: A tuple/list of 6 integers
|
|
* @li dilations: A tuple/list of 6 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1]
|
|
* @li data_format: An optional string from: "NDHWC", "NCHWD". Defaults to "NDHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as filter,and has same format as input_size
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv3d_backprop_input
|
|
*/
|
|
REG_OP(Conv3DBackpropInput)
|
|
.INPUT(input_size, TensorType({DT_INT32, DT_INT64}))
|
|
.INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
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|
.INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.REQUIRED_ATTR(strides, 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)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution 3d with respect to the input.
|
|
*@par Inputs:
|
|
* Two inputs:
|
|
* @li filter: A Tensor. Types is float16.
|
|
* @li grads: A Tensor. Must have the same type as filter.
|
|
*@par Attributes:
|
|
* Five attributes:
|
|
* @li input_size A Tensor of type int32. An integer vector representing the shape of input,
|
|
* @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension.
|
|
* @li pads: A tuple/list of 4 integers
|
|
* @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1]
|
|
* @li data_format: An optional string from: "NDHWC", "NCHWD". Defaults to "NDHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as filter
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv3d_backprop_input
|
|
*/
|
|
REG_OP(Conv3DBackpropInputD)
|
|
.INPUT(filter, TensorType({DT_FLOAT16}))
|
|
.INPUT(grads, TensorType({DT_FLOAT16}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16}))
|
|
.REQUIRED_ATTR(input_size, ListInt)
|
|
.REQUIRED_ATTR(strides, 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)
|
|
|
|
REG_OP(LSTM)
|
|
.INPUT(x, TensorType({DT_FLOAT16}))
|
|
.INPUT(cont, TensorType({DT_FLOAT32,DT_FLOAT16}))
|
|
.INPUT(w_x, TensorType({DT_FLOAT16}))
|
|
.INPUT(bias, TensorType({DT_FLOAT16,DT_FLOAT32,DT_INT16,DT_INT32}))
|
|
.INPUT(w_h, TensorType({DT_FLOAT16}))
|
|
.OPTIONAL_INPUT(x_static, TensorType({DT_FLOAT16}))
|
|
.OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16,DT_FLOAT32}))
|
|
.OPTIONAL_INPUT(c_0, TensorType({DT_FLOAT16,DT_FLOAT32}))
|
|
.OPTIONAL_INPUT(w_x_static, TensorType({DT_FLOAT16}))
|
|
.OUTPUT(h, TensorType({DT_FLOAT16, DT_FLOAT}))
|
|
.OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT}))
|
|
.OUTPUT(c_t, TensorType({DT_FLOAT16, DT_FLOAT}))
|
|
.ATTR(num_output, Int, 0)
|
|
.ATTR(expose_hidden, Bool, false)
|
|
.OP_END_FACTORY_REG(LSTM)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution3D with respect to the filter
|
|
*@par Inputs:
|
|
* Three inputs:
|
|
* @li x: A Tensor. Must be one of the following types: float16
|
|
* 5-D with shape [batch, in_depth, in_height, in_width, in_channels] or [batch, in_depth, in_channels, in_height, in_width].
|
|
* @li filter_size: A Tensor of type int32. An integer vector representing the tensor shape of filter,
|
|
* where filter is a 5-D tensor [filter_depth, filter_height, filter_width, in_channels, out_channels]
|
|
* or [out_channels, filter_depth, filter_height, filter_width, in_channels] or [out_channels, filter_depth, in_channel, filter_height, filter_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as x. 5-D with shape [batch, out_depth, out_height, out_width, out_channels]
|
|
* or [batch, out_depth, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Three attributes:
|
|
* @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension.
|
|
* @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] pads on feature map.
|
|
* @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1].
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NDHWC", "NDCHW". Defaults to "NDHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as x
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv3d_backprop_filter
|
|
*/
|
|
REG_OP(Conv3DBackpropFilter)
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
|
|
.INPUT(filter_size, TensorType({DT_INT32}))
|
|
.INPUT(out_backprop, 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(dilations, ListInt, {1, 1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NDHWC")
|
|
.OP_END_FACTORY_REG(Conv3DBackpropFilter)
|
|
|
|
/**
|
|
*@brief Computes the gradients of convolution with respect to the filter.
|
|
*@par Inputs:
|
|
* Two inputs:
|
|
* @li x: A Tensor. Type is float16.
|
|
* 5-D with shape [batch, in_depth, in_height, in_width, in_channels] or [batch, in_depth, in_channels, in_height, in_width].
|
|
* @li out_backprop: A Tensor. Must have the same type as x. 5-D with shape [batch, out_depth, out_height, out_width, out_channels]
|
|
* or [batch, out_depth, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
|
|
*@par Attributes:
|
|
* Four attributes:
|
|
* @li filter_size: A Tensor of type integers. An integer vector representing the tensor shape of filter,
|
|
* where filter is a 5-D tensor [filter_depth, filter_height, filter_width, in_channels, out_channels]
|
|
* or [out_channels, filter_depth, filter_height, filter_width, in_channels] or [out_channels, filter_depth, in_channel, filter_height, filter_width].
|
|
* @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension.
|
|
* @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] pads on feature map
|
|
* @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1].
|
|
* @li groups: Number of blocked connections from input channels to output channels.
|
|
* @li data_format: An optional string from: "NDHWC", "NDCHW". Defaults to "NDHWC". Specify the data format of the input and output data.
|
|
*@par Outputs:
|
|
* y: A Tensor. Has the same type as x
|
|
*@par Third-party framework compatibility
|
|
* Compatible with Tensorflow's conv3d_backprop_filter
|
|
*/
|
|
REG_OP(Conv3DBackpropFilterD)
|
|
.INPUT(x, TensorType({DT_FLOAT16}))
|
|
.INPUT(out_backprop, TensorType({DT_FLOAT16}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT}))
|
|
.REQUIRED_ATTR(filter_size, ListInt)
|
|
.REQUIRED_ATTR(strides, ListInt)
|
|
.REQUIRED_ATTR(pads, ListInt)
|
|
.ATTR(dilations, ListInt, {1, 1, 1, 1, 1})
|
|
.ATTR(groups, Int, 1)
|
|
.ATTR(data_format, String, "NDHWC")
|
|
.OP_END_FACTORY_REG(Conv3DBackpropFilterD)
|
|
} // namespace ge
|
|
#endif // GE_OP_NN_CALCULATION_OPS_H
|