2019-04-17 10:49:11 +08:00
										 
									 
								 
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								//  VulkanROIPooling.cpp
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								//  MNN
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								//  Created by MNN on 2019/01/31.
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								//  Copyright © 2018, Alibaba Group Holding Limited
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											2019-12-27 22:16:57 +08:00
										 
									 
								 
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								#include "backend/vulkan/execution/VulkanROIPooling.hpp"
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								#include "core/Macro.h"
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								namespace MNN {
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								struct GpuParam {
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								    ivec4 inputImgSize;
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								    ivec4 outputImgSize;
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								    float spatialScale;
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								};
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								VulkanROIPooling::VulkanROIPooling(Backend* bn, const float SpatialScale)
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								    : VulkanBasicExecution(bn), mSpatialScale(SpatialScale) {
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								    std::vector<VkDescriptorType> VulkanROIPoolingTypes{
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								        VK_DESCRIPTOR_TYPE_STORAGE_IMAGE,
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								        VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER,
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								        VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER,
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								        VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER,
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								    };
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								    auto extra                = static_cast<VulkanBackend*>(bn);
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								    mVulkanROIPoolingPipeline = extra->getPipeline(
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								        "glsl_roipooling_comp",
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								        /*"glsl_VulkanROIPooling_comp", glsl_roipooling_comp, glsl_roipooling_comp_len,*/ VulkanROIPoolingTypes);
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								    mParamBuffer.reset(
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								        new VulkanBuffer(extra->getMemoryPool(), false, sizeof(GpuParam), nullptr, VK_BUFFER_USAGE_UNIFORM_BUFFER_BIT));
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								    mSampler = extra->getCommonSampler();
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								}
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								VulkanROIPooling::~VulkanROIPooling() {
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								}
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								ErrorCode VulkanROIPooling::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
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								                                     const VulkanCommandPool::Buffer* cmdBuffer) {
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								    auto input            = inputs[0];
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								    auto roi              = inputs[1];
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								    auto output           = outputs[0];
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								    const int channelDiv4 = UP_DIV(input->channel(), 4);
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								    auto VulkanROIPoolingParam = reinterpret_cast<GpuParam*>(mParamBuffer->map());
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								    ::memset(VulkanROIPoolingParam, 0, sizeof(GpuParam));
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								    VulkanROIPoolingParam->inputImgSize[0]  = input->width();
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								    VulkanROIPoolingParam->inputImgSize[1]  = input->height();
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								    VulkanROIPoolingParam->inputImgSize[2]  = channelDiv4;
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								    VulkanROIPoolingParam->inputImgSize[3]  = input->batch();
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								    VulkanROIPoolingParam->outputImgSize[0] = output->width();
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								    VulkanROIPoolingParam->outputImgSize[1] = output->height();
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								    VulkanROIPoolingParam->outputImgSize[2] = channelDiv4;
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								    VulkanROIPoolingParam->outputImgSize[3] = output->batch();
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								    VulkanROIPoolingParam->spatialScale     = mSpatialScale;
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								    mParamBuffer->flush(true, 0, sizeof(GpuParam));
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								    mParamBuffer->unmap();
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								    mDescriptorSet.reset(mVulkanROIPoolingPipeline->createSet());
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								    mDescriptorSet->writeImage(reinterpret_cast<VkImageView>(output->deviceId()), mSampler->get(),
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								                               VK_IMAGE_LAYOUT_GENERAL, 0);
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								    mDescriptorSet->writeImage(reinterpret_cast<VkImageView>(input->deviceId()), mSampler->get(),
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								                               VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 1);
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								    mDescriptorSet->writeImage(reinterpret_cast<VkImageView>(roi->deviceId()), mSampler->get(),
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								                               VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 2);
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								    mDescriptorSet->writeBuffer(mParamBuffer->buffer(), 3, mParamBuffer->size());
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								    mVulkanROIPoolingPipeline->bind(cmdBuffer->get(), mDescriptorSet->get());
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								    vkCmdDispatch(cmdBuffer->get(), UP_DIV(output->width(), 8), UP_DIV(output->height(), 8),
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								                  channelDiv4 * output->batch());
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								    return NO_ERROR;
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								}
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								class VulkanROIPoolingCreator : public VulkanBackend::Creator {
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								public:
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												- dynamic computation graph (beta)
	- add supports (/express)
	- add tests
	- add benchmarks with it (/benchmark/exprModels)
- Python
	- MNN engine and tools were submitted to pip
	- available on Windows/macOS/Linux
- Engine/Converter
	- add supports for each op benchmarking
	- refactor optimizer by separating steps
- CPU
	- add supports for Conv3D, Pool3D, ELU, ReverseSequence
	- fix ArgMax, Permute, Scale, BinaryOp, Slice, SliceTf
- OpenCL
	- add half transform in CPU
	- add broadcast supports for binary
	- optimize Conv2D, Reshape, Eltwise, Gemm, etc.
- OpenGL
	- add sub, real div supports for binary
	- add supports for unary
	- optimize Conv2D, Reshape
- Vulkan
	- add max supports for eltwise
- Metal
	- fix metallib missing problem
- Train/Quantization
	- use express to refactor training codes
											
										 
										
											2019-09-26 21:02:07 +08:00
										 
									 
								 
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								    virtual VulkanBasicExecution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op, Backend* bn) const override {
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								        return new VulkanROIPooling(bn, op->main_as_RoiPooling()->spatialScale());
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								    }
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								};
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								static bool gResistor = []() {
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								    VulkanBackend::addCreator(OpType_ROIPooling, new VulkanROIPoolingCreator);
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								    return true;
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								}();
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								} // namespace MNN
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