forked from mindspore-Ecosystem/mindspore
add ops :depthtospace,spacetodepth
Signed-off-by: kanghui <405194527@qq.com>
This commit is contained in:
kanghui
parent
6801ef61e0
commit
cb8e1fa3f1
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/**
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* Copyright 2021 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 "backend/kernel_compiler/cpu/depthtospace_cpu_kernel.h"
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#include <vector>
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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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template <typename T>
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void DepthToSpaceCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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CheckParam(kernel_node);
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input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
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block_size_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "block_size");
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}
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template <typename T>
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bool DepthToSpaceCPUKernel<T>::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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auto input_addr = reinterpret_cast<T *>(inputs[0]->addr);
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auto output_addr = reinterpret_cast<T *>(outputs[0]->addr);
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size_t size = IntToSize(inputs[0]->size / sizeof(T));
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std::vector<size_t> input_shape = input_shape_;
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std::vector<size_t> output_shape = output_shape_;
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size_t block_size = block_size_;
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size_t input_dimension = input_shape.size();
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size_t output_strides[3] = {1, 1, 1};
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for (size_t i = input_dimension - 1; i >= 1; --i) {
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for (size_t j = 0; j < i; ++j) {
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output_strides[j] *= output_shape[i];
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}
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}
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auto task = [&, input_addr, output_addr](size_t start, size_t end) {
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std::vector<size_t> output_pos_array(input_dimension, 0);
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for (size_t i = start; i < end; ++i) {
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size_t tmp_pos = i;
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for (size_t j = 0; j < input_dimension - 1; ++j) {
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output_pos_array[j] = tmp_pos / output_strides[j];
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tmp_pos %= output_strides[j];
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}
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output_pos_array.back() = tmp_pos;
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size_t input_pos = output_pos_array[0];
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input_pos =
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(input_pos * input_shape[1]) +
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(output_pos_array[1] +
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(block_size * (output_pos_array[2] % block_size) + output_pos_array[3] % block_size) * output_shape[1]);
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input_pos = (input_pos * input_shape[2]) + (output_pos_array[2] / block_size);
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input_pos = (input_pos * input_shape[3]) + (output_pos_array[3] / block_size);
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output_addr[i] = input_addr[input_pos];
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}
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};
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CPUKernelUtils::ParallelFor(task, size);
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return true;
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}
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template <typename T>
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void DepthToSpaceCPUKernel<T>::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) << "Input number is " << input_num << ", but DepthToSpaceCPUKerrnel needs 1 input.";
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but DepthToSpaceCPUKernel needs 1 output.";
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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 +1,85 @@
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/**
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* Copyright 2021 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_DEPTHTOSPACE_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_DEPTHTOSPACE_CPU_KERNEL_H_
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#include <memory>
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#include <string>
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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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template <typename T>
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class DepthToSpaceCPUKernel : public CPUKernel {
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public:
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DepthToSpaceCPUKernel() = default;
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~DepthToSpaceCPUKernel() override = default;
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void InitKernel(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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private:
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void CheckParam(const CNodePtr &kernel_node);
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std::vector<size_t> input_shape_;
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std::vector<size_t> output_shape_;
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size_t block_size_;
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};
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MS_REG_CPU_KERNEL_T(
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DepthToSpace, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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DepthToSpaceCPUKernel, float);
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MS_REG_CPU_KERNEL_T(
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DepthToSpace, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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DepthToSpaceCPUKernel, float16);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
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DepthToSpaceCPUKernel, int8_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
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DepthToSpaceCPUKernel, int16_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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DepthToSpaceCPUKernel, int);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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DepthToSpaceCPUKernel, int64_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
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DepthToSpaceCPUKernel, uint8_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
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DepthToSpaceCPUKernel, uint16_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
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DepthToSpaceCPUKernel, uint32_t);
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MS_REG_CPU_KERNEL_T(DepthToSpace,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
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DepthToSpaceCPUKernel, uint64_t);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_DEPTHTOSPACE_CPU_KERNEL_H_
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/**
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* Copyright 2021 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 "backend/kernel_compiler/cpu/spacetodepth_cpu_kernel.h"
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#include <vector>
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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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template <typename T>
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void SpaceToDepthCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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CheckParam(kernel_node);
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input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
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block_size_ = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "block_size");
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}
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template <typename T>
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bool SpaceToDepthCPUKernel<T>::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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auto input_addr = reinterpret_cast<T *>(inputs[0]->addr);
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auto output_addr = reinterpret_cast<T *>(outputs[0]->addr);
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size_t size = IntToSize(inputs[0]->size / sizeof(T));
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std::vector<size_t> input_shape = input_shape_;
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std::vector<size_t> output_shape = output_shape_;
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size_t block_size = block_size_;
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size_t input_dimension = input_shape.size();
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size_t input_strides[3] = {1, 1, 1};
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for (size_t i = input_dimension - 1; i >= 1; --i) {
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for (size_t j = 0; j < i; ++j) {
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input_strides[j] *= input_shape[i];
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}
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}
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auto task = [&, input_addr, output_addr](size_t start, size_t end) {
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std::vector<size_t> input_pos_array(input_dimension, 0);
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for (size_t i = start; i < end; ++i) {
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size_t tmp_pos = i;
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for (size_t j = 0; j < input_dimension - 1; ++j) {
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input_pos_array[j] = tmp_pos / input_strides[j];
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tmp_pos %= input_strides[j];
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}
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input_pos_array.back() = tmp_pos;
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size_t output_pos = input_pos_array[0];
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output_pos =
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(output_pos * output_shape[1]) +
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(input_pos_array[1] +
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(block_size * (input_pos_array[2] % block_size) + input_pos_array[3] % block_size) * input_shape[1]);
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output_pos = (output_pos * output_shape[2]) + (input_pos_array[2] / block_size);
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output_pos = (output_pos * output_shape[3]) + (input_pos_array[3] / block_size);
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output_addr[output_pos] = input_addr[i];
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}
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};
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CPUKernelUtils::ParallelFor(task, size);
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return true;
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}
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template <typename T>
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void SpaceToDepthCPUKernel<T>::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) << "Input number is " << input_num << ", but DepthToSpaceCPUKerrnel needs 1 input.";
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but DepthToSpaceCPUKernel needs 1 output.";
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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 +1,84 @@
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/**
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* Copyright 2021 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_SPACETODEPTH_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPACETODEPTH_CPU_KERNEL_H_
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#include <string>
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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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template <typename T>
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class SpaceToDepthCPUKernel : public CPUKernel {
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public:
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SpaceToDepthCPUKernel() = default;
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~SpaceToDepthCPUKernel() override = default;
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void InitKernel(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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private:
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void CheckParam(const CNodePtr &kernel_node);
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std::vector<size_t> input_shape_;
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std::vector<size_t> output_shape_;
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size_t block_size_;
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};
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MS_REG_CPU_KERNEL_T(
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SpaceToDepth, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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SpaceToDepthCPUKernel, float);
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MS_REG_CPU_KERNEL_T(
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SpaceToDepth, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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SpaceToDepthCPUKernel, float16);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
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SpaceToDepthCPUKernel, int8_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
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SpaceToDepthCPUKernel, int16_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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SpaceToDepthCPUKernel, int);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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SpaceToDepthCPUKernel, int64_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
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SpaceToDepthCPUKernel, uint8_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
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SpaceToDepthCPUKernel, uint16_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
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SpaceToDepthCPUKernel, uint32_t);
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MS_REG_CPU_KERNEL_T(SpaceToDepth,
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KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
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SpaceToDepthCPUKernel, uint64_t);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPACETODEPTH_CPU_KERNEL_H_
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@ -0,0 +1,115 @@
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# Copyright 2021 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.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
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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.
|
||||
# See the License for the specific language governing permissions and
|
||||
# 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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import mindspore.ops.operations.array_ops as P
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class DepthToSpaceNet(nn.Cell):
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def __init__(self, nptype, block_size=2, input_shape=(1, 12, 1, 1)):
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super(DepthToSpaceNet, self).__init__()
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self.DepthToSpace = P.DepthToSpace(block_size)
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input_size = 1
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for i in input_shape:
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input_size = input_size*i
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data_np = np.arange(input_size).reshape(input_shape).astype(nptype)
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self.x1 = Parameter(initializer(Tensor(data_np), input_shape), name='x1')
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@ms_function
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def construct(self):
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y1 = self.DepthToSpace(self.x1)
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return y1
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def DepthToSpace(nptype, block_size=2, input_shape=(1, 12, 1, 1)):
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input_size = 1
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for i in input_shape:
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input_size = input_size*i
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expect = np.array([[[[0, 3],
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[6, 9]],
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[[1, 4],
|
||||
[7, 10]],
|
||||
[[2, 5],
|
||||
[8, 11]]]]).astype(nptype)
|
||||
dts = DepthToSpaceNet(nptype, block_size, input_shape)
|
||||
output = dts()
|
||||
assert (output.asnumpy() == expect).all()
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_float32():
|
||||
DepthToSpace(np.float32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_float16():
|
||||
DepthToSpace(np.float16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_int32():
|
||||
DepthToSpace(np.int32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_int64():
|
||||
DepthToSpace(np.int64)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_int8():
|
||||
DepthToSpace(np.int8)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_int16():
|
||||
DepthToSpace(np.int16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_uint8():
|
||||
DepthToSpace(np.uint8)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_uint16():
|
||||
DepthToSpace(np.uint16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_uint32():
|
||||
DepthToSpace(np.uint32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_depthtospace_graph_uint64():
|
||||
DepthToSpace(np.uint64)
|
||||
|
|
@ -0,0 +1,109 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
import numpy as np
|
||||
import pytest
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
import mindspore.ops.operations.array_ops as P
|
||||
from mindspore import Tensor
|
||||
from mindspore.common.api import ms_function
|
||||
from mindspore.common.initializer import initializer
|
||||
from mindspore.common.parameter import Parameter
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
||||
class SpaceToDepthNet(nn.Cell):
|
||||
def __init__(self, nptype):
|
||||
super(SpaceToDepthNet, self).__init__()
|
||||
self.SpaceToDepth = P.SpaceToDepth(2)
|
||||
|
||||
data_np = np.array([[[[0, 3],
|
||||
[6, 9]],
|
||||
[[1, 4],
|
||||
[7, 10]],
|
||||
[[2, 5],
|
||||
[8, 11]]]]).astype(nptype)
|
||||
self.data_np = data_np
|
||||
self.x = Parameter(initializer(Tensor(self.data_np), (1, 3, 2, 2)), name='x')
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
return self.SpaceToDepth(self.x)
|
||||
|
||||
|
||||
def SpaceToDepth(nptype):
|
||||
expect = np.arange(12).reshape((1, 12, 1, 1)).astype(nptype)
|
||||
std = SpaceToDepthNet(nptype)
|
||||
output = std()
|
||||
assert (output.asnumpy() == expect).all()
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_float32():
|
||||
SpaceToDepth(np.float32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_float16():
|
||||
SpaceToDepth(np.float16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_int32():
|
||||
SpaceToDepth(np.int32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_int64():
|
||||
SpaceToDepth(np.int64)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_int8():
|
||||
SpaceToDepth(np.int8)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_int16():
|
||||
SpaceToDepth(np.int16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_uint8():
|
||||
SpaceToDepth(np.uint8)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_uint16():
|
||||
SpaceToDepth(np.uint16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_uint32():
|
||||
SpaceToDepth(np.uint32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_spacetodepth_graph_uint64():
|
||||
SpaceToDepth(np.uint64)
|
||||
Loading…
Reference in New Issue