mirror of
https://github.com/open-mmlab/mmdeploy.git
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73cf3b5feb
* hold async data and wait only at the end of the pipeline * fix use-after-free bugs * fix wording * bypass trivial cases for Pad to avoid ppl.cv's bug * fix pad * fix lint * cleanup * fix DefaultFormatBundle * fix all cpu preprocess impl * suppress log * fix dynamic library build & add comments for SyncOnScopeExit
107 lines
2.8 KiB
C++
107 lines
2.8 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "normalize.h"
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#include "archive/json_archive.h"
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#include "core/registry.h"
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#include "core/tensor.h"
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using namespace std;
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namespace mmdeploy {
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// MMDEPLOY_DEFINE_REGISTRY(NormalizeImpl);
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NormalizeImpl::NormalizeImpl(const Value& args) : TransformImpl(args) {
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if (!args.contains("mean") || !args.contains("std")) {
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MMDEPLOY_ERROR("no 'mean' or 'std' is configured");
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throw std::invalid_argument("no 'mean' or 'std' is configured");
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}
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for (auto& v : args["mean"]) {
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arg_.mean.push_back(v.get<float>());
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}
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for (auto& v : args["std"]) {
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arg_.std.push_back(v.get<float>());
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}
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arg_.to_rgb = args.value("to_rgb", true);
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}
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/**
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input:
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{
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"ori_img": Mat,
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"img": Tensor,
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"attribute": "",
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"img_shape": [int],
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"ori_shape": [int],
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"img_fields": [int]
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}
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output:
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{
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"img": Tensor,
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"attribute": "",
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"img_shape": [int],
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"ori_shape": [int],
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"img_fields": [string],
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"img_norm_cfg": {
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"mean": [float],
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"std": [float],
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"to_rgb": true
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}
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}
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*/
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Result<Value> NormalizeImpl::Process(const Value& input) {
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MMDEPLOY_DEBUG("input: {}", to_json(input).dump(2));
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// copy input data, and update its properties later
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Value output = input;
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auto img_fields = GetImageFields(input);
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for (auto& key : img_fields) {
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Tensor tensor = input[key].get<Tensor>();
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auto desc = tensor.desc();
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assert(desc.data_type == DataType::kINT8 || desc.data_type == DataType::kFLOAT);
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assert(desc.shape.size() == 4 /*n, h, w, c*/);
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assert(desc.shape[3] == arg_.mean.size());
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OUTCOME_TRY(auto dst, NormalizeImage(tensor));
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SetTransformData(output, key, std::move(dst));
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for (auto& v : arg_.mean) {
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output["img_norm_cfg"]["mean"].push_back(v);
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}
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for (auto v : arg_.std) {
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output["img_norm_cfg"]["std"].push_back(v);
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}
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output["img_norm_cfg"]["to_rgb"] = arg_.to_rgb;
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}
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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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Normalize::Normalize(const Value& args, int version) : Transform(args) {
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auto impl_creator = Registry<NormalizeImpl>::Get().GetCreator(specified_platform_, version);
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if (nullptr == impl_creator) {
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MMDEPLOY_ERROR("'Normalize' is not supported on '{}' platform", specified_platform_);
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throw std::domain_error("'Normalize' 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 NormalizeCreator : public Creator<Transform> {
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public:
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const char* GetName() const override { return "Normalize"; }
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int GetVersion() const override { return version_; }
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ReturnType Create(const Value& args) override { return make_unique<Normalize>(args, version_); }
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private:
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int version_{1};
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};
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REGISTER_MODULE(Transform, NormalizeCreator);
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MMDEPLOY_DEFINE_REGISTRY(NormalizeImpl);
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} // namespace mmdeploy
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