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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>
105 lines
3.0 KiB
C++
105 lines
3.0 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "catch.hpp"
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#include "mmdeploy/core/tensor.h"
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#include "mmdeploy/preprocess/transform/transform.h"
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using namespace mmdeploy;
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using namespace std;
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TEST_CASE("test collect constructor", "[collect]") {
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Device device{"cpu"};
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Stream stream{device};
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Value cfg = {{"context", {{"device", device}, {"stream", stream}}}};
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std::string transform_type{"Collect"};
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auto creator = Registry<Transform>::Get().GetCreator(transform_type, 1);
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REQUIRE(creator != nullptr);
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REQUIRE_THROWS(creator->Create(cfg));
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SECTION("args with 'keys' which is not an array") {
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auto _cfg = cfg;
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_cfg["keys"] = "img";
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REQUIRE_THROWS(creator->Create(_cfg));
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}
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SECTION("args with keys in array") {
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auto _cfg = cfg;
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_cfg["keys"] = {"img"};
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auto module = creator->Create(_cfg);
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REQUIRE(module != nullptr);
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}
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SECTION("args with meta_keys that is not an array") {
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auto _cfg = cfg;
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_cfg["keys"] = {"img"};
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_cfg["meta_keys"] = "ori_img";
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REQUIRE_THROWS(creator->Create(_cfg));
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}
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SECTION("args with meta_keys in array") {
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auto _cfg = cfg;
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_cfg["keys"] = {"img"};
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_cfg["meta_keys"] = {"ori_img"};
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auto module = creator->Create(_cfg);
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REQUIRE(module != nullptr);
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}
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}
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TEST_CASE("test collect", "[collect]") {
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std::string transform_type{"Collect"};
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vector<std::string> keys{"img"};
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vector<std::string> meta_keys{"filename", "ori_filename", "ori_shape", "img_shape",
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"flip", "flip_direction", "img_norm_cfg"};
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Value args;
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Device device{"cpu"};
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Stream stream{device};
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args["context"]["device"] = device;
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args["context"]["stream"] = stream;
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for (auto& key : keys) {
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args["keys"].push_back(key);
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}
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for (auto& meta_key : meta_keys) {
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args["meta_keys"].push_back(meta_key);
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}
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auto creator = Registry<Transform>::Get().GetCreator(transform_type, 1);
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REQUIRE(creator != nullptr);
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auto module = creator->Create(args);
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REQUIRE(module != nullptr);
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Value input;
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SECTION("input is empty") {
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auto ret = module->Process(input);
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REQUIRE(ret.has_error());
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REQUIRE(ret.error() == eInvalidArgument);
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}
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SECTION("input has 'ori_img' and 'attribute'") {
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input["ori_img"] = Tensor{};
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input["attribute"] = "this is a faked image";
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auto ret = module->Process(input);
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REQUIRE(ret.has_error());
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REQUIRE(ret.error() == eInvalidArgument);
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}
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SECTION("array input with correct keys and meta keys") {
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Tensor tensor;
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Value input{{"img", tensor},
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{"filename", "test.jpg"},
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{"ori_filename", "/the/path/of/test.jpg"},
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{"ori_shape", {1000, 1000, 3}},
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{"img_shape", {1, 3, 224, 224}},
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{"flip", "false"},
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{"flip_direction", "horizontal"},
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{"img_norm_cfg",
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{{"mean", {123.675, 116.28, 103.53}},
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{"std", {58.395, 57.12, 57.375}},
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{"to_rgb", true}}}};
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auto ret = module->Process(input);
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REQUIRE(ret.has_value());
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}
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}
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