Add CumSum Kerner for CPU
This commit is contained in:
shaoxiangdong
parent
efa7d78b57
commit
accf7c4671
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@ -0,0 +1,248 @@
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/**
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* Copyright 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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#include <thread>
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#include "backend/kernel_compiler/cpu/cumsum_cpu_kernel.h"
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#include "runtime/device/cpu/cpu_device_address.h"
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namespace mindspore {
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namespace kernel {
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void CumSumCPUKernel::InitKernel(const CNodePtr &kernel_node) {
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CheckParam(kernel_node);
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shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
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axis_ = static_cast<int>(AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "axis"));
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dst_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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exclusive_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "exclusive");
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reverse_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "reverse");
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int input_dim_length = SizeToInt(shape_.size());
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if (axis_ >= input_dim_length) {
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MS_LOG(EXCEPTION) << "Axis out of bounds.";
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}
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while (axis_ < 0) {
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axis_ += input_dim_length;
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}
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}
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template <typename T>
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void CumSumCPUKernel::InitWorkspaceSize() {
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input_size_0_ = sizeof(T);
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for (size_t i = 0; i < shape_.size(); i++) {
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input_size_0_ *= shape_[i];
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}
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workspace_size_list_.emplace_back(input_size_0_);
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}
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void CumSumCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
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CPUKernel::InitInputOutputSize(kernel_node);
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if (dtype_ == kNumberTypeFloat32) {
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InitWorkspaceSize<float_t>();
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} else if (dtype_ == kNumberTypeFloat16) {
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InitWorkspaceSize<float16>();
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} else if (dtype_ == kNumberTypeInt32) {
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InitWorkspaceSize<int32_t>();
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} else if (dtype_ == kNumberTypeInt8) {
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InitWorkspaceSize<int8_t>();
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} else if (dtype_ == kNumberTypeUInt8) {
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InitWorkspaceSize<uint8_t>();
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}
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}
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bool CumSumCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> &workspace,
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const std::vector<kernel::AddressPtr> &outputs) {
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Reshape();
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if (dtype_ == kNumberTypeFloat32) {
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LaunchKernel<float_t>(inputs, workspace, outputs);
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} else if (dtype_ == kNumberTypeFloat16) {
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LaunchKernel<float16>(inputs, workspace, outputs);
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} else if (dtype_ == kNumberTypeInt32) {
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LaunchKernel<int32_t>(inputs, workspace, outputs);
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} else if (dtype_ == kNumberTypeInt8) {
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LaunchKernel<int8_t>(inputs, workspace, outputs);
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} else if (dtype_ == kNumberTypeUInt8) {
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LaunchKernel<uint8_t>(inputs, workspace, outputs);
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}
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return true;
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}
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void CumSumCPUKernel::Reshape() {
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dims_[0] = 1;
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dims_[1] = shape_[IntToSize(axis_)];
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dims_[2] = 1;
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for (size_t i = 0; i < IntToSize(axis_); i++) {
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dims_[0] *= shape_[i];
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}
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for (size_t i = IntToSize(axis_) + 1; i < shape_.size(); i++) {
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dims_[2] *= shape_[i];
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}
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stride_ = dims_[1] * dims_[2];
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stride2_ = dims_[2];
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return;
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}
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template <typename T>
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void CumSumCPUKernel::LeftMove(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2, size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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size_t k1 = i / dim2 % dim0;
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size_t k2 = i % dim2;
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size_t offset = k1 * stride + k2;
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for (size_t j = 0; j < dim1; ++j) {
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size_t read_index = j * stride2 + offset;
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if (j == 0) {
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output[read_index] = (T)0;
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} else {
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size_t read_index2 = (j - 1) * stride2 + offset;
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output[read_index] = input[read_index2];
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}
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}
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}
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}
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template <typename T>
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void CumSumCPUKernel::RightMove(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2, size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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size_t k1 = i / dim2 % dim0;
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size_t k2 = i % dim2;
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size_t offset = k1 * stride + k2;
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for (int j = SizeToInt(dim1 - 1); j >= 0; --j) {
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size_t read_index = j * stride2 + offset;
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if (j == SizeToInt(dim1 - 1)) {
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output[read_index] = (T)0;
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} else {
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size_t read_index2 = (j + 1) * stride2 + offset;
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output[read_index] = input[read_index2];
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}
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}
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}
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}
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template <typename T>
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void CumSumCPUKernel::Copy(T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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size_t k1 = i / dim2 % dim0;
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size_t k2 = i % dim2;
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size_t offset = k1 * stride + k2;
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for (size_t j = 0; j < dim1; ++j) {
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size_t read_index = j * stride2 + offset;
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input[read_index] = output[read_index];
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}
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}
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}
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template <typename T>
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void CumSumCPUKernel::CumSumKernelReverse(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2,
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size_t stride, size_t stride2, size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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size_t k1 = i / dim2 % dim0;
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size_t k2 = i % dim2;
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size_t offset = k1 * stride + k2;
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for (int j = SizeToInt(dim1 - 1); j >= 0; --j) {
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size_t read_index = j * stride2 + offset;
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if (j == SizeToInt(dim1 - 1)) {
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output[read_index] = input[read_index];
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} else {
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size_t read_index2 = (j + 1) * stride2 + offset;
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output[read_index] = output[read_index2] + input[read_index];
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}
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}
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}
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}
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template <typename T>
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void CumSumCPUKernel::CumSumKernel(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2, size_t start, size_t end) {
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for (size_t i = start; i < end; i++) {
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size_t k1 = i / dim2 % dim0;
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size_t k2 = i % dim2;
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size_t offset = k1 * stride + k2;
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for (size_t j = 0; j < dim1; ++j) {
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size_t read_index = j * stride2 + offset;
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if (j == 0) {
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output[read_index] = input[read_index];
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} else {
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size_t read_index2 = (j - 1) * stride2 + offset;
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output[read_index] = output[read_index2] + input[read_index];
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}
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}
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}
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}
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template <typename T>
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void CumSumCPUKernel::LaunchCumSum(const T *input, T *output, T *workspace, size_t start, size_t end) {
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start = start / dims_[1];
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end = end / dims_[1];
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if (exclusive_) {
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if (reverse_) {
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RightMove(input, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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Copy(workspace, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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CumSumKernelReverse(workspace, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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} else {
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LeftMove(input, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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Copy(workspace, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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CumSumKernel(workspace, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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}
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} else {
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if (reverse_) {
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CumSumKernelReverse(input, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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} else {
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CumSumKernel(input, output, dims_[0], dims_[1], dims_[2], stride_, stride2_, start, end);
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}
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}
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return;
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}
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template <typename T>
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void CumSumCPUKernel::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> &workspace,
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const std::vector<kernel::AddressPtr> &outputs) {
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auto input = reinterpret_cast<T *>(inputs[0]->addr);
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auto ws = reinterpret_cast<T *>(workspace[0]->addr);
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auto output = reinterpret_cast<T *>(outputs[0]->addr);
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// multithreading
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size_t lens = inputs[0]->size > 0 ? static_cast<size_t>(inputs[0]->size / sizeof(T)) : 1;
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auto max_thread_num = std::thread::hardware_concurrency();
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size_t thread_num = lens < 128 * max_thread_num ? std::ceil(lens / 128.0) : max_thread_num;
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MS_LOG(INFO) << "Lens=" << lens << "; use thread_num=" << thread_num << "; max_thread_num: " << max_thread_num;
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std::vector<std::thread> threads;
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threads.reserve(thread_num);
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size_t start = 0;
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size_t once_compute_size = (lens + thread_num - 1) / thread_num;
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if (thread_num < 1 || once_compute_size < 1) {
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MS_LOG(ERROR) << "Invalid value: thread_num " << thread_num << "; once_compute_size " << once_compute_size;
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return;
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}
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while (start < lens) {
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size_t end = (start + once_compute_size) > lens ? lens : (start + once_compute_size);
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threads.emplace_back(std::thread(&CumSumCPUKernel::LaunchCumSum<T>, this, input, output, ws, start, end));
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start += once_compute_size;
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}
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for (size_t i = 0; i < threads.size(); ++i) {
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threads[i].join();
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}
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return;
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}
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void CumSumCPUKernel::CheckParam(const CNodePtr &kernel_node) {
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but CumSumGpuKernel needs 1.";
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}
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}
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,95 @@
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/**
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* Copyright 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 MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_CUMSUM_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_CUMSUM_CPU_KERNEL_H_
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#include <memory>
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#include <vector>
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#include "backend/kernel_compiler/cpu/cpu_kernel.h"
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#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
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namespace mindspore {
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namespace kernel {
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class CumSumCPUKernel : public CPUKernel {
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public:
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CumSumCPUKernel() = default;
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~CumSumCPUKernel() override = default;
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void InitKernel(const CNodePtr &kernel_node) override;
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void InitInputOutputSize(const CNodePtr &kernel_node) override;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) override;
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template <typename T>
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void InitWorkspaceSize();
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template <typename T>
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void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs);
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template <typename T>
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void LaunchCumSum(const T *input_addr, T *output_addr, T *ws_addr, size_t start, size_t end);
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private:
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void CheckParam(const CNodePtr &kernel_node);
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void Reshape();
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template <typename T>
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void LeftMove(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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size_t start, size_t end);
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template <typename T>
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void RightMove(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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size_t start, size_t end);
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template <typename T>
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void Copy(T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2, size_t start,
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size_t end);
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template <typename T>
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void CumSumKernelReverse(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2, size_t start, size_t end);
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template <typename T>
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void CumSumKernel(const T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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size_t start, size_t end);
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std::vector<size_t> shape_;
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std::vector<size_t> dst_shape;
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size_t input_size_0_;
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size_t stride_;
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size_t stride2_;
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size_t dims_[3] = {};
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int exclusive_;
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int reverse_;
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int axis_;
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TypeId dtype_{kTypeUnknown};
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};
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MS_REG_CPU_KERNEL(CumSum, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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CumSumCPUKernel);
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MS_REG_CPU_KERNEL(CumSum, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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CumSumCPUKernel);
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MS_REG_CPU_KERNEL(CumSum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), CumSumCPUKernel);
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MS_REG_CPU_KERNEL(CumSum, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), CumSumCPUKernel);
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MS_REG_CPU_KERNEL(CumSum, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8), CumSumCPUKernel);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_CUMSUM_CPU_KERNEL_H_
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@ -844,7 +844,7 @@ class CumSum(PrimitiveWithInfer):
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Tensor, the shape of the output tensor is consistent with the input tensor's.
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Supported Platforms:
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``Ascend`` ``GPU``
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input = Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float32))
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@ -0,0 +1,271 @@
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# Copyright 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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axis0 = 0
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axis1 = 1
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axis2 = 2
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axis3 = 3
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axis4 = 4
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axis5 = -1
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axis6 = -2
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x0 = np.random.rand(3, 3, 4, 5, 3).astype(np.float32)
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x1 = np.random.rand(2, 3, 4, 5, 3).astype(np.float16)
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x2 = np.random.randint(-10000, 10000, size=(2, 3, 4, 5, 3)).astype(np.int32)
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x3 = np.random.randint(-5, 5, size=(2, 3, 4, 5, 3)).astype(np.int8)
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x4 = np.random.randint(0, 10, size=(2, 3, 4, 5, 3)).astype(np.uint8)
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x5 = np.random.rand(3).astype(np.float32)
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list1 = [x0, x1, x2, x3, x4]
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list2 = [axis0, axis1, axis2, axis3, axis4, axis5, axis6]
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class CumSum(nn.Cell):
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def __init__(self, exclusive=False, reverse=False):
|
||||
super(CumSum, self).__init__()
|
||||
self.cumsum_op = P.CumSum(exclusive, reverse)
|
||||
|
||||
self.x0 = Tensor(x0)
|
||||
self.axis0 = axis0
|
||||
self.x1 = Tensor(x0)
|
||||
self.axis1 = axis1
|
||||
self.x2 = Tensor(x0)
|
||||
self.axis2 = axis2
|
||||
self.x3 = Tensor(x0)
|
||||
self.axis3 = axis3
|
||||
self.x4 = Tensor(x0)
|
||||
self.axis4 = axis4
|
||||
self.x5 = Tensor(x0)
|
||||
self.axis5 = axis5
|
||||
self.x6 = Tensor(x0)
|
||||
self.axis6 = axis6
|
||||
|
||||
self.x7 = Tensor(x1)
|
||||
self.axis7 = axis0
|
||||
self.x8 = Tensor(x1)
|
||||
self.axis8 = axis1
|
||||
self.x9 = Tensor(x1)
|
||||
self.axis9 = axis2
|
||||
self.x10 = Tensor(x1)
|
||||
self.axis10 = axis3
|
||||
self.x11 = Tensor(x1)
|
||||
self.axis11 = axis4
|
||||
self.x12 = Tensor(x1)
|
||||
self.axis12 = axis5
|
||||
self.x13 = Tensor(x1)
|
||||
self.axis13 = axis6
|
||||
|
||||
self.x14 = Tensor(x2)
|
||||
self.axis14 = axis0
|
||||
self.x15 = Tensor(x2)
|
||||
self.axis15 = axis1
|
||||
self.x16 = Tensor(x2)
|
||||
self.axis16 = axis2
|
||||
self.x17 = Tensor(x2)
|
||||
self.axis17 = axis3
|
||||
self.x18 = Tensor(x2)
|
||||
self.axis18 = axis4
|
||||
self.x19 = Tensor(x2)
|
||||
self.axis19 = axis5
|
||||
self.x20 = Tensor(x2)
|
||||
self.axis20 = axis6
|
||||
|
||||
self.x21 = Tensor(x3)
|
||||
self.axis21 = axis0
|
||||
self.x22 = Tensor(x3)
|
||||
self.axis22 = axis1
|
||||
self.x23 = Tensor(x3)
|
||||
self.axis23 = axis2
|
||||
self.x24 = Tensor(x3)
|
||||
self.axis24 = axis3
|
||||
self.x25 = Tensor(x3)
|
||||
self.axis25 = axis4
|
||||
self.x26 = Tensor(x3)
|
||||
self.axis26 = axis5
|
||||
self.x27 = Tensor(x3)
|
||||
self.axis27 = axis6
|
||||
|
||||
self.x28 = Tensor(x4)
|
||||
self.axis28 = axis0
|
||||
self.x29 = Tensor(x4)
|
||||
self.axis29 = axis1
|
||||
self.x30 = Tensor(x4)
|
||||
self.axis30 = axis2
|
||||
self.x31 = Tensor(x4)
|
||||
self.axis31 = axis3
|
||||
self.x32 = Tensor(x4)
|
||||
self.axis32 = axis4
|
||||
self.x33 = Tensor(x4)
|
||||
self.axis33 = axis5
|
||||
self.x34 = Tensor(x4)
|
||||
self.axis34 = axis6
|
||||
|
||||
self.x35 = Tensor(x5)
|
||||
self.axis35 = axis0
|
||||
|
||||
def construct(self):
|
||||
return (self.cumsum_op(self.x0, self.axis0),
|
||||
self.cumsum_op(self.x1, self.axis1),
|
||||
self.cumsum_op(self.x2, self.axis2),
|
||||
self.cumsum_op(self.x3, self.axis3),
|
||||
self.cumsum_op(self.x4, self.axis4),
|
||||
self.cumsum_op(self.x5, self.axis5),
|
||||
self.cumsum_op(self.x6, self.axis6),
|
||||
self.cumsum_op(self.x7, self.axis7),
|
||||
self.cumsum_op(self.x8, self.axis8),
|
||||
self.cumsum_op(self.x9, self.axis9),
|
||||
self.cumsum_op(self.x10, self.axis10),
|
||||
self.cumsum_op(self.x11, self.axis11),
|
||||
self.cumsum_op(self.x12, self.axis12),
|
||||
self.cumsum_op(self.x13, self.axis13),
|
||||
self.cumsum_op(self.x14, self.axis14),
|
||||
self.cumsum_op(self.x15, self.axis15),
|
||||
self.cumsum_op(self.x16, self.axis16),
|
||||
self.cumsum_op(self.x17, self.axis17),
|
||||
self.cumsum_op(self.x18, self.axis18),
|
||||
self.cumsum_op(self.x19, self.axis19),
|
||||
self.cumsum_op(self.x20, self.axis20),
|
||||
self.cumsum_op(self.x21, self.axis21),
|
||||
self.cumsum_op(self.x22, self.axis22),
|
||||
self.cumsum_op(self.x23, self.axis23),
|
||||
self.cumsum_op(self.x24, self.axis24),
|
||||
self.cumsum_op(self.x25, self.axis25),
|
||||
self.cumsum_op(self.x26, self.axis26),
|
||||
self.cumsum_op(self.x27, self.axis27),
|
||||
self.cumsum_op(self.x28, self.axis28),
|
||||
self.cumsum_op(self.x29, self.axis29),
|
||||
self.cumsum_op(self.x30, self.axis30),
|
||||
self.cumsum_op(self.x31, self.axis31),
|
||||
self.cumsum_op(self.x32, self.axis32),
|
||||
self.cumsum_op(self.x33, self.axis33),
|
||||
self.cumsum_op(self.x34, self.axis34),
|
||||
self.cumsum_op(self.x35, self.axis35))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.env_onecard
|
||||
def test_cumsum():
|
||||
cumsum = CumSum()
|
||||
output = cumsum()
|
||||
|
||||
k = 0
|
||||
|
||||
for i in list1:
|
||||
for j in list2:
|
||||
expect = np.cumsum(i, axis=j)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
k += 1
|
||||
|
||||
expect = np.cumsum(x5, axis=axis0)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
|
||||
|
||||
def test_cumsum2():
|
||||
cumsum = CumSum(exclusive=False, reverse=True)
|
||||
output = cumsum()
|
||||
|
||||
k = 0
|
||||
|
||||
for i in list1:
|
||||
for j in list2:
|
||||
result1 = np.flip(i, axis=j)
|
||||
result2 = np.cumsum(result1, axis=j)
|
||||
expect = np.flip(result2, axis=j)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
k += 1
|
||||
|
||||
result1 = np.flip(x5, axis=axis0)
|
||||
result2 = np.cumsum(result1, axis=axis0)
|
||||
expect = np.flip(result2, axis=axis0)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
|
||||
|
||||
def test_cumsum3():
|
||||
cumsum = CumSum(exclusive=True, reverse=False)
|
||||
output = cumsum()
|
||||
|
||||
k = 0
|
||||
|
||||
for i in list1:
|
||||
for j in list2:
|
||||
result1 = np.insert(i, 0, [0], axis=j)
|
||||
result2 = np.delete(result1, -1, axis=j)
|
||||
expect = np.cumsum(result2, axis=j)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
k += 1
|
||||
|
||||
result1 = np.insert(x5, 0, [0], axis=axis0)
|
||||
result2 = np.delete(result1, -1, axis=axis0)
|
||||
expect = np.cumsum(result2, axis=axis0)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
|
||||
|
||||
def test_cumsum4():
|
||||
cumsum = CumSum(exclusive=True, reverse=True)
|
||||
output = cumsum()
|
||||
|
||||
k = 0
|
||||
|
||||
for i in list1:
|
||||
for j in list2:
|
||||
result1 = np.flip(i, axis=j)
|
||||
result2 = np.insert(result1, 0, [0], axis=j)
|
||||
result3 = np.delete(result2, -1, axis=j)
|
||||
result4 = np.cumsum(result3, axis=j)
|
||||
expect = np.flip(result4, axis=j)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
k += 1
|
||||
|
||||
result1 = np.flip(x5, axis=axis0)
|
||||
result2 = np.insert(result1, 0, [0], axis=axis0)
|
||||
result3 = np.delete(result2, -1, axis=axis0)
|
||||
result4 = np.cumsum(result3, axis=axis0)
|
||||
expect = np.flip(result4, axis=axis0)
|
||||
diff = abs(output[k].asnumpy() - expect)
|
||||
error = np.ones(shape=expect.shape) * 1.0e-5
|
||||
assert np.all(diff < error)
|
||||
assert output[k].shape == expect.shape
|
||||
Loading…
Reference in New Issue