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https://github.com/open-mmlab/mmdeploy.git
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6b01a2e649
* add collect_impl.cpp to cuda device * add dummy compute node wich device elena * add compiler & dynamic library loader * add code to compile with gen code(elena) * move folder * fix lint * add tracer module * add license * update type id * add fuse kernel registry * remove compilier & dynamic_library * update fuse kernel interface * Add elena-mmdeploy project in 3rd-party * Fix README.md * fix cmake file * Support cuda device and clang format all file * Add cudaStreamSynchronize for cudafree * fix cudaStreamSynchronize * rename to __tracer__ * remove unused code * update kernel * update extract elena script * update gitignore * fix ci * Change the crop_size to crop_h and crop_w in arglist * update Tracer * remove cond * avoid allocate memory * add build.sh for elena * remove code * update test * Support bilinear resize with float input * Rename elena-mmdeploy to delete * Introduce public submodule * use get_ref * update elena * update tools * update tools * update fuse transform docs * add fuse transform doc link to get_started * fix shape in crop * remove fuse_transform_ == true check * remove fuse_transform_ member * remove elena_int.h * doesn't dump transform_static.json * update tracer * update CVFusion to remove compile warning * remove mmcv version > 1.5.1 dep * fix tests * update docs * add elena use option * remove submodule of CVFusion * update doc * use auto * use throw_exception(eEntryNotFound); * update Co-authored-by: cx <cx@ubuntu20.04> Co-authored-by: miraclezqc <969226879@qq.com>
64 lines
2.0 KiB
C++
64 lines
2.0 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "image2tensor.h"
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#include <cassert>
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#include "mmdeploy/archive/json_archive.h"
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#include "mmdeploy/core/tensor.h"
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#include "mmdeploy/preprocess/transform/tracer.h"
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namespace mmdeploy {
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ImageToTensorImpl::ImageToTensorImpl(const Value& args) : TransformImpl(args) {
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for (auto& key : args["keys"]) {
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arg_.keys.push_back(key.get<std::string>());
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}
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}
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Result<Value> ImageToTensorImpl::Process(const Value& input) {
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MMDEPLOY_DEBUG("input: {}", to_json(input).dump(2));
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Value output = input;
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for (auto& key : arg_.keys) {
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assert(input.contains(key));
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Tensor src_tensor = input[key].get<Tensor>();
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auto& shape = src_tensor.desc().shape;
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assert(shape.size() == 4);
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assert(shape[3] == 1 || shape[3] == 3);
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OUTCOME_TRY(auto dst, HWC2CHW(src_tensor));
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SetTransformData(output, key, std::move(dst));
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if (output.contains("__tracer__")) {
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output["__tracer__"].get_ref<Tracer&>().ImageToTensor(src_tensor.data_type());
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}
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} // for key
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MMDEPLOY_DEBUG("output: {}", to_json(output).dump(2));
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return output;
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}
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ImageToTensor::ImageToTensor(const Value& args, int version) : Transform(args) {
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auto impl_creator = Registry<ImageToTensorImpl>::Get().GetCreator(specified_platform_, version);
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if (nullptr == impl_creator) {
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MMDEPLOY_ERROR("'ImageToTensor' is not supported on '{}' platform", specified_platform_);
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throw std::domain_error("'ImageToTensor' is not supported on specified platform");
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}
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impl_ = impl_creator->Create(args);
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}
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class ImageToTensorCreator : public Creator<Transform> {
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public:
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const char* GetName() const override { return "ImageToTensor"; }
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int GetVersion() const override { return version_; }
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ReturnType Create(const Value& args) override {
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return std::make_unique<ImageToTensor>(args, version_);
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}
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private:
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int version_{1};
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};
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REGISTER_MODULE(Transform, ImageToTensorCreator);
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MMDEPLOY_DEFINE_REGISTRY(ImageToTensorImpl);
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} // namespace mmdeploy
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