mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
36124f6205
* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
53 lines
1.4 KiB
C++
53 lines
1.4 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
|
|
|
|
#include "transform.h"
|
|
|
|
#include "core/registry.h"
|
|
|
|
namespace mmdeploy {
|
|
|
|
TransformImpl::TransformImpl(const Value &args) {
|
|
Device device{"cpu"};
|
|
if (args.contains("context")) {
|
|
device_ = args["context"].value("device", device);
|
|
stream_ = args["context"].value("stream", Stream::GetDefault(device_));
|
|
} else {
|
|
device_ = device;
|
|
stream_ = Stream::GetDefault(device_);
|
|
}
|
|
}
|
|
std::vector<std::string> TransformImpl::GetImageFields(const Value &input) {
|
|
if (input.contains("img_fields")) {
|
|
if (input["img_fields"].is_string()) {
|
|
return {input["img_fields"].get<std::string>()};
|
|
} else if (input["img_fields"].is_array()) {
|
|
std::vector<std::string> img_fields;
|
|
for (auto &v : input["img_fields"]) {
|
|
img_fields.push_back(v.get<std::string>());
|
|
}
|
|
return img_fields;
|
|
}
|
|
} else {
|
|
return {"img"};
|
|
}
|
|
throw_exception(eInvalidArgument);
|
|
}
|
|
|
|
Transform::Transform(const Value &args) {
|
|
Device device{"cpu"};
|
|
if (args.contains("context")) {
|
|
device = args["context"].value("device", device);
|
|
}
|
|
|
|
Platform platform(device.platform_id());
|
|
specified_platform_ = platform.GetPlatformName();
|
|
|
|
if (!(specified_platform_ == "cpu")) {
|
|
// add cpu platform, so that a transform op can fall back to its cpu
|
|
// version if it hasn't implementation on the specific platform
|
|
candidate_platforms_.push_back("cpu");
|
|
}
|
|
}
|
|
|
|
} // namespace mmdeploy
|