Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleClas into add_en_doc
stephon
commit
12bc3be5be
|
|
@ -7,7 +7,7 @@ function _set_params(){
|
|||
batch_size=${2:-"64"}
|
||||
fp_item=${3:-"fp32"} # fp32|fp16
|
||||
epochs=${4:-"2"} # 可选，如果需要修改代码提前中断
|
||||
model_name=${5:-"model_name"}
|
||||
model_item=${5:-"model_item"}
|
||||
run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
|
||||
|
||||
index=1
|
||||
|
|
@ -23,16 +23,17 @@ function _set_params(){
|
|||
device=${CUDA_VISIBLE_DEVICES//,/ }
|
||||
arr=(${device})
|
||||
num_gpu_devices=${#arr[*]}
|
||||
log_file=${run_log_path}/clas_${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
|
||||
log_file=${run_log_path}/clas_${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
|
||||
model_name=${model_item}_bs${batch_size}_${fp_item} # model_item 用于yml匹配，model_name用于入库
|
||||
}
|
||||
function _train(){
|
||||
echo "Train on ${num_gpu_devices} GPUs"
|
||||
echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
|
||||
|
||||
if [ ${fp_item} = "fp32" ];then
|
||||
model_config=`find ppcls/configs/ImageNet -name ${model_name}.yaml`
|
||||
model_config=`find ppcls/configs/ImageNet -name ${model_item}.yaml`
|
||||
else
|
||||
model_config=`find ppcls/configs/ImageNet -name ${model_name}_fp16.yaml`
|
||||
model_config=`find ppcls/configs/ImageNet -name ${model_item}_fp16.yaml`
|
||||
fi
|
||||
|
||||
train_cmd="-c ${model_config} -o DataLoader.Train.sampler.batch_size=${batch_size} -o Global.epochs=${epochs} -o Global.eval_during_train=False -o Global.print_batch_step=2"
|
||||
|
|
|
|||
|
|
@ -215,9 +215,9 @@ cp ../configs/inference_cls.yaml tools/
|
|||
#### 2.3.2 执行
|
||||
|
||||
```shell
|
||||
./build/clas_system -c inference_cls.yaml
|
||||
./build/clas_system -c tools/inference_cls.yaml
|
||||
# or
|
||||
./build/clas_system -config inference_cls.yaml
|
||||
./build/clas_system -config tools/inference_cls.yaml
|
||||
```
|
||||
|
||||
最终屏幕上会输出结果，如下图所示。
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ namespace Feature {
|
|||
cv::Mat resize_img;
|
||||
std::vector<double> time;
|
||||
|
||||
auto preprocess_start = std::chrono::system_clock::now();
|
||||
auto preprocess_start = std::chrono::steady_clock::now();
|
||||
this->resize_op_.Run(img, resize_img, this->resize_short_,
|
||||
this->resize_size_);
|
||||
|
||||
|
|
@ -70,9 +70,9 @@ namespace Feature {
|
|||
auto input_names = this->predictor_->GetInputNames();
|
||||
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
|
||||
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
|
||||
auto preprocess_end = std::chrono::system_clock::now();
|
||||
auto preprocess_end = std::chrono::steady_clock::now();
|
||||
|
||||
auto infer_start = std::chrono::system_clock::now();
|
||||
auto infer_start = std::chrono::steady_clock::now();
|
||||
input_t->CopyFromCpu(input.data());
|
||||
this->predictor_->Run();
|
||||
|
||||
|
|
@ -84,18 +84,18 @@ namespace Feature {
|
|||
|
||||
out_data.resize(out_num);
|
||||
output_t->CopyToCpu(out_data.data());
|
||||
auto infer_end = std::chrono::system_clock::now();
|
||||
auto infer_end = std::chrono::steady_clock::now();
|
||||
|
||||
auto postprocess_start = std::chrono::system_clock::now();
|
||||
auto postprocess_start = std::chrono::steady_clock::now();
|
||||
if (this->feature_norm)
|
||||
FeatureNorm(out_data);
|
||||
auto postprocess_end = std::chrono::system_clock::now();
|
||||
auto postprocess_end = std::chrono::steady_clock::now();
|
||||
|
||||
std::chrono::duration<float> preprocess_diff =
|
||||
preprocess_end - preprocess_start;
|
||||
time.push_back(double(preprocess_diff.count()));
|
||||
time.push_back(double(preprocess_diff.count()) * 1000);
|
||||
std::chrono::duration<float> inference_diff = infer_end - infer_start;
|
||||
double inference_cost_time = double(inference_diff.count());
|
||||
double inference_cost_time = double(inference_diff.count() * 1000);
|
||||
time.push_back(inference_cost_time);
|
||||
// std::chrono::duration<float> postprocess_diff =
|
||||
// postprocess_end - postprocess_start;
|
||||
|
|
|
|||
|
|
@ -179,9 +179,9 @@ int main(int argc, char **argv) {
|
|||
if (config.config_file["Global"]["visual_det"].IsDefined()) {
|
||||
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
|
||||
}
|
||||
bool run_benchmark = false;
|
||||
bool benchmark = false;
|
||||
if (config.config_file["Global"]["benchmark"].IsDefined()) {
|
||||
run_benchmark = config.config_file["Global"]["benchmark"].as<bool>();
|
||||
benchmark = config.config_file["Global"]["benchmark"].as<bool>();
|
||||
}
|
||||
int max_det_results = 5;
|
||||
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
|
||||
|
|
@ -210,6 +210,8 @@ int main(int argc, char **argv) {
|
|||
// for time log
|
||||
std::vector<double> cls_times = {0, 0, 0};
|
||||
std::vector<double> det_times = {0, 0, 0};
|
||||
std::vector<double> search_times = {0, 0, 0};
|
||||
int instance_num = 0;
|
||||
// for read images
|
||||
std::vector <cv::Mat> batch_imgs;
|
||||
std::vector <std::string> img_paths;
|
||||
|
|
@ -222,7 +224,12 @@ int main(int argc, char **argv) {
|
|||
// for nms
|
||||
std::vector<int> indeices;
|
||||
|
||||
int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
|
||||
int warmup_iter = img_files_list.size() > 5 ? 5 : img_files_list.size();
|
||||
if (benchmark) {
|
||||
img_files_list.insert(img_files_list.begin(), img_files_list.begin(),
|
||||
img_files_list.begin() + warmup_iter);
|
||||
}
|
||||
|
||||
for (int idx = 0; idx < img_files_list.size(); ++idx) {
|
||||
std::string img_path = img_files_list[idx];
|
||||
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
|
||||
|
|
@ -238,12 +245,14 @@ int main(int argc, char **argv) {
|
|||
|
||||
// step1: get all detection results
|
||||
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
|
||||
det_bbox_num, det_times, visual_det, run_benchmark);
|
||||
det_bbox_num, det_times, visual_det, false);
|
||||
|
||||
// select max_det_results bbox
|
||||
if (det_result.size() > max_det_results) {
|
||||
det_result.resize(max_det_results);
|
||||
}
|
||||
instance_num += det_result.size();
|
||||
|
||||
// step2: add the whole image for recognition to improve recall
|
||||
Detection::ObjectResult result_whole_img = {
|
||||
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
|
||||
|
|
@ -262,16 +271,25 @@ int main(int argc, char **argv) {
|
|||
}
|
||||
|
||||
// step4: get search result
|
||||
auto search_start = std::chrono::steady_clock::now();
|
||||
search_result = searcher.Search(features.data(), det_result.size());
|
||||
auto search_end = std::chrono::steady_clock::now();
|
||||
|
||||
// nms for search result
|
||||
for (int i = 0; i < det_result.size(); ++i) {
|
||||
det_result[i].confidence = search_result.D[search_result.return_k * i];
|
||||
}
|
||||
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
|
||||
auto nms_end = std::chrono::steady_clock::now();
|
||||
std::chrono::duration<float> search_diff = search_end - search_start;
|
||||
search_times[1] += double(search_diff.count() * 1000);
|
||||
|
||||
std::chrono::duration<float> nms_diff = nms_end - search_end;
|
||||
search_times[2] += double(nms_diff.count() * 1000);
|
||||
|
||||
// print result
|
||||
PrintResult(img_path, det_result, indeices, searcher, search_result);
|
||||
if (not benchmark or (benchmark and idx >= warmup_iter))
|
||||
PrintResult(img_path, det_result, indeices, searcher, search_result);
|
||||
|
||||
// for postprocess
|
||||
batch_imgs.clear();
|
||||
|
|
@ -281,18 +299,44 @@ int main(int argc, char **argv) {
|
|||
feature.clear();
|
||||
features.clear();
|
||||
indeices.clear();
|
||||
if (benchmark and warmup_iter == idx + 1) {
|
||||
det_times = {0, 0, 0};
|
||||
cls_times = {0, 0, 0};
|
||||
search_times = {0, 0, 0};
|
||||
instance_num = 0;
|
||||
}
|
||||
}
|
||||
|
||||
std::string presion = "fp32";
|
||||
if (benchmark) {
|
||||
std::string presion = "fp32";
|
||||
if (config.config_file["Global"]["use_fp16"].IsDefined() and
|
||||
config.config_file["Global"]["use_fp16"].as<bool>())
|
||||
presion = "fp16";
|
||||
bool use_gpu = config.config_file["Global"]["use_gpu"].as<bool>();
|
||||
bool use_tensorrt = config.config_file["Global"]["use_tensorrt"].as<bool>();
|
||||
bool enable_mkldnn =
|
||||
config.config_file["Global"]["enable_mkldnn"].as<bool>();
|
||||
int cpu_num_threads =
|
||||
config.config_file["Global"]["cpu_num_threads"].as<int>();
|
||||
int batch_size = config.config_file["Global"]["batch_size"].as<int>();
|
||||
std::vector<int> shape =
|
||||
config.config_file["Global"]["image_shape"].as < std::vector < int >> ();
|
||||
std::string det_shape = std::to_string(shape[0]);
|
||||
for (int i = 1; i < shape.size(); ++i)
|
||||
det_shape = det_shape + ", " + std::to_string(shape[i]);
|
||||
|
||||
// if (config.use_fp16)
|
||||
// presion = "fp16";
|
||||
// if (config.benchmark) {
|
||||
// AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
|
||||
// config.use_mkldnn, config.cpu_threads, 1,
|
||||
// "1, 3, 224, 224", presion, cls_times,
|
||||
// img_files_list.size());
|
||||
// autolog.report();
|
||||
// }
|
||||
AutoLogger autolog_det("Det", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, det_shape, presion,
|
||||
det_times, img_files_list.size() - warmup_iter);
|
||||
autolog_det.report();
|
||||
AutoLogger autolog_rec("Rec", use_gpu, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "3, 224, 224", presion,
|
||||
cls_times, instance_num);
|
||||
autolog_rec.report();
|
||||
AutoLogger autolog_search("Search", false, use_tensorrt, enable_mkldnn,
|
||||
cpu_num_threads, batch_size, "dynamic", presion,
|
||||
search_times, instance_num);
|
||||
autolog_search.report();
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,20 +14,21 @@ def parse_args():
|
|||
|
||||
def main():
|
||||
args = parse_args()
|
||||
with open(args.config)as fd:
|
||||
config = yaml.load(fd)
|
||||
with open(args.config) as fd:
|
||||
config = yaml.load(fd.read(), yaml.FullLoader)
|
||||
index_dir = ""
|
||||
try:
|
||||
index_dir = config["IndexProcess"]["index_dir"]
|
||||
except Exception as e:
|
||||
print("The IndexProcess.index_dir in config_file dose not exist")
|
||||
print("The IndexProcess.index_dir in config_file dose not exist")
|
||||
exit(1)
|
||||
id_map_path = os.path.join(index_dir, "id_map.pkl")
|
||||
assert os.path.exists(id_map_path), "The id_map file dose not exist: {}".format(id_map_path)
|
||||
assert os.path.exists(
|
||||
id_map_path), "The id_map file dose not exist: {}".format(id_map_path)
|
||||
|
||||
with open(id_map_path, "rb")as fd:
|
||||
with open(id_map_path, "rb") as fd:
|
||||
ids = pickle.load(fd)
|
||||
with open(os.path.join(index_dir, "id_map.txt"), "w")as fd:
|
||||
with open(os.path.join(index_dir, "id_map.txt"), "w") as fd:
|
||||
for k, v in ids.items():
|
||||
v = v.split("\t")[1]
|
||||
fd.write(str(k) + " " + v + "\n")
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 113 KiB After Width: | Height: | Size: 58 KiB |
|
|
@ -1,61 +1,46 @@
|
|||
## 识别效果展示
|
||||
- 商品识别
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/channelhandle_5.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/cliniqueblush_1.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/daoxiangcunjinzhubing_10.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/gannidress_10.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/gbyingerche_15.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/lafiolewine_03.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/taochunqiu_8.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_product/weiduomeinaiyougege_10.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277277-7b29f596-35f6-4f00-8d2b-0ef0be57a090.jpg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277291-f7d2b2a1-5790-4f5b-a0e6-f5c52d04a69a.jpg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277300-8ce0d5ce-e0ca-46ea-bb9a-74df0df66ae3.jpg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277308-14a097bd-2bcd-41ce-a9e6-5e9cd0bd8b08.jpg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277311-208ae574-a708-46e2-a41e-c639322913b1.jpg" width = "400" /> </div>
|
||||
|
||||
[更多效果图](../../images/recognition/more_demo_images/product.md)
|
||||
|
||||
|
||||
- 动漫人物识别
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_cartoon/labixiaoxin-005.jpeg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_cartoon/liuchuanfeng-010.jpeg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_cartoon/zhangchulan-007.jpeg" width = "400" />
|
||||
</div>
|
||||
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069108-ad54ae1d-610d-4cfa-9cd6-8ee8d280d61d.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069100-7539d292-1bd8-4655-8a6d-d1f2238bd618.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069080-a821e0b7-8a10-4946-bf05-ff093cc16064.jpeg" width = "400" /> </div>
|
||||
|
||||
[更多效果图](../../images/recognition/more_demo_images/cartoon.md)
|
||||
|
||||
|
||||
- logo识别
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_logo/cctv_4.jpg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_logo/mangguo_8.jpeg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_logo/zhongshiyou-007.jpg" width = "400" />
|
||||
</div>
|
||||
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096687-5b562e2d-0653-4be6-861d-1936a4440df2.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096701-4a4b2bd9-85f2-4d55-be4b-be6ab5e0fb81.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096706-ef4ad024-7284-4cb3-975a-779fd06b96f5.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096713-48e629aa-c637-4603-b005-18570fa94d6d.jpeg" width = "400" /> </div>
|
||||
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096752-1f98c937-5d83-4c29-b495-01971b5fb258.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096767-e8f701eb-d0e8-4304-b031-e2bff8c199f3.jpeg" width = "400" /> </div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[更多效果图](../../images/recognition/more_demo_images/logo.md)
|
||||
|
||||
|
||||
- 车辆识别
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_vehicle/audia5-115.jpeg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_vehicle/bentian-yage-101.jpeg" width = "400" />
|
||||
</div>
|
||||
<div align="center">
|
||||
<img src="../../images/recognition/more_demo_images/output_vehicle/bmw-m340-107.jpeg" width = "400" />
|
||||
</div>
|
||||
|
||||
[更多效果图](../../images/recognition/more_demo_images)
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243899-c60f0a51-db9b-438a-9f2d-0d2893c200bb.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243905-7eeb938d-d88f-4540-a667-06e08dcf1f55.jpeg" width = "400" /> </div>
|
||||
<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243911-735a6ec0-a624-4965-b3cd-2b9f52fa8d65.jpeg" width = "400" /> </div>
|
||||
|
||||
[更多效果图](../../images/recognition/more_demo_images/vehicle.md)
|
||||
|
|
|
|||
|
|
@ -11,10 +11,10 @@
|
|||
- [4. 模型训练](#4)
|
||||
- [4.1 使用 CPU 进行模型训练](#4.1)
|
||||
- [4.1.1 不使用预训练模型进行训练](#4.1.1)
|
||||
- [4.1.2 不使用预训练模型进行训练](#4.1.2)
|
||||
- [4.1.2 使用预训练模型进行训练](#4.1.2)
|
||||
- [4.2 使用 GPU 进行模型训练](#4.2)
|
||||
- [4.2.1 不使用预训练模型进行训练](#4.2.1)
|
||||
- [4.2.2 不使用预训练模型进行训练](#4.2.2)
|
||||
- [4.2.2 使用预训练模型进行训练](#4.2.2)
|
||||
- [5. 模型预测](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
|
|
|
|||
|
|
@ -152,7 +152,7 @@ python3 -m paddle.distributed.launch \
|
|||
|
||||
* **注意**
|
||||
|
||||
* 其他数据增广的配置文件可以参考 `ppcls/configs/DataAugment` 中的配置文件。
|
||||
* 其他数据增广的配置文件可以参考 `ppcls/configs/ImageNet/DataAugment/` 中的配置文件。
|
||||
* 训练 CIFAR100 的迭代轮数较少，因此进行训练时，验证集的精度指标可能会有 1% 左右的波动。
|
||||
|
||||
<a name="4"></a>
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,7 +16,8 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x0_25
|
||||
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x0_35
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x0_5
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x0_75
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x1_5
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,7 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x2_0
|
||||
|
||||
class_num: 1000
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@ Global:
|
|||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
class_num: 1000
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
|
|
@ -17,6 +16,7 @@ Global:
|
|||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x2_5
|
||||
class_num: 1000
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
|
|
|
|||
|
|
@ -373,7 +373,7 @@ class Engine(object):
|
|||
"inference")
|
||||
if self.quanter:
|
||||
self.quanter.save_quantized_model(
|
||||
model,
|
||||
model.base_model,
|
||||
save_path,
|
||||
input_spec=[
|
||||
paddle.static.InputSpec(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,19 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPShiTu
|
||||
cpp_infer_type:shitu
|
||||
feature_inference_model_dir:./feature_inference/
|
||||
det_inference_model_dir:./det_inference
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
|
||||
det_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp_shitu/build/pp_shitu -c inference_drink.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:True|False
|
||||
cpu_threads:1|6
|
||||
batch_size:1
|
||||
use_tensorrt:False|True
|
||||
precision:fp32|fp16
|
||||
data_dir:./dataset/drink_dataset_v1.0
|
||||
benchmark:True
|
||||
generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
|
||||
transform_index_cmd:python3 deploy/cpp_shitu/tools/transform_id_map.py -c inference_drink.yaml
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:ResNet50_vd
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./cls_inference/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:True|False
|
||||
cpu_threads:1|6
|
||||
batch_size:1
|
||||
use_tensorrt:False|True
|
||||
precision:fp32|fp16
|
||||
image_dir:./dataset/ILSVRC2012/val
|
||||
benchmark:True
|
||||
generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 49 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 63 KiB |
|
|
@ -0,0 +1,86 @@
|
|||
# C++预测功能测试
|
||||
|
||||
C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测库的模型推理功能。
|
||||
|
||||
## 1. 测试结论汇总
|
||||
|
||||
基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
|
||||
|
||||
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
|
||||
| ---- | ---- | ---- | :----: | :----: | :----: |
|
||||
| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
|
||||
| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
|
||||
| 量化模型 | GPU | 1/6 | int8 | - | - |
|
||||
| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
|
||||
|
||||
## 2. 测试流程
|
||||
运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
|
||||
|
||||
### 2.1 功能测试
|
||||
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
|
||||
|
||||
```shell
|
||||
bash test_tipc/prepare.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
|
||||
|
||||
# 用法1:
|
||||
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
|
||||
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
|
||||
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 1
|
||||
```
|
||||
|
||||
运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：
|
||||
|
||||
```shell
|
||||
test_tipc/output/
|
||||
|- results_cpp.log # 运行指令状态的日志
|
||||
|- cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log # CPU上不开启Mkldnn，线程数设置为1，测试batch_size=1条件下的预测运行日志
|
||||
|- cls_cpp_infer_cpu_usemkldnn_False_threads_6_precision_fp32_batchsize_1.log # CPU上不开启Mkldnn，线程数设置为6，测试batch_size=1条件下的预测运行日志
|
||||
|- cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log # GPU上不开启TensorRT，测试batch_size=1的fp32精度预测日志
|
||||
|- cls_cpp_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log # GPU上开启TensorRT，测试batch_size=1的fp16精度预测日志
|
||||
......
|
||||
```
|
||||
其中results_cpp.log中包含了每条指令的运行状态，如果运行成功会输出：
|
||||
|
||||
```
|
||||
Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
|
||||
......
|
||||
```
|
||||
如果运行失败，会输出：
|
||||
```
|
||||
Run failed with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
|
||||
......
|
||||
```
|
||||
可以很方便的根据results_cpp.log中的内容判定哪一个指令运行错误。
|
||||
|
||||
|
||||
### 2.2 精度测试
|
||||
|
||||
使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
|
||||
- 提取日志中的预测坐标；
|
||||
- 从本地文件中提取保存好的坐标结果；
|
||||
- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
|
||||
|
||||
#### 使用方式
|
||||
运行命令：
|
||||
```shell
|
||||
python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cls_cpp_*.txt --log_file=./test_tipc/output/cls_cpp_*.log --atol=1e-3 --rtol=1e-3
|
||||
```
|
||||
|
||||
参数介绍：
|
||||
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
|
||||
- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入
|
||||
- atol: 设置的绝对误差
|
||||
- rtol: 设置的相对误差
|
||||
|
||||
#### 运行结果
|
||||
|
||||
正常运行效果如下图：
|
||||
<img src="compare_cpp_right.png" width="1000">
|
||||
|
||||
出现不一致结果时的运行输出：
|
||||
<img src="compare_cpp_wrong.png" width="1000">
|
||||
|
||||
|
||||
## 3. 更多教程
|
||||
|
||||
本文档为功能测试用，更详细的c++预测使用教程请参考：[服务器端C++预测](../../docs/zh_CN/inference_deployment/)
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
import os
|
||||
import yaml
|
||||
import argparse
|
||||
|
||||
|
||||
def str2bool(v):
|
||||
if v.lower() == 'true':
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--type', required=True, choices=["cls", "shitu"])
|
||||
parser.add_argument('--batch_size', type=int, default=1)
|
||||
parser.add_argument('--mkldnn', type=str2bool, default=True)
|
||||
parser.add_argument('--gpu', type=str2bool, default=False)
|
||||
parser.add_argument('--cpu_thread', type=int, default=1)
|
||||
parser.add_argument('--tensorrt', type=str2bool, default=False)
|
||||
parser.add_argument('--precision', type=str, choices=["fp32", "fp16"])
|
||||
parser.add_argument('--benchmark', type=str2bool, default=True)
|
||||
parser.add_argument('--gpu_id', type=int, default=0)
|
||||
parser.add_argument(
|
||||
'--cls_yaml_path',
|
||||
type=str,
|
||||
default="deploy/configs/inference_cls.yaml")
|
||||
parser.add_argument(
|
||||
'--shitu_yaml_path',
|
||||
type=str,
|
||||
default="deploy/configs/inference_drink.yaml")
|
||||
parser.add_argument('--data_dir', type=str, required=True)
|
||||
parser.add_argument('--save_path', type=str, default='./')
|
||||
parser.add_argument('--cls_model_dir', type=str)
|
||||
parser.add_argument('--det_model_dir', type=str)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
if args.type == "cls":
|
||||
save_path = os.path.join(args.save_path,
|
||||
os.path.basename(args.cls_yaml_path))
|
||||
fd = open(args.cls_yaml_path)
|
||||
else:
|
||||
save_path = os.path.join(args.save_path,
|
||||
os.path.basename(args.shitu_yaml_path))
|
||||
fd = open(args.shitu_yaml_path)
|
||||
config = yaml.load(fd, yaml.FullLoader)
|
||||
fd.close()
|
||||
|
||||
config["Global"]["batch_size"] = args.batch_size
|
||||
config["Global"]["use_gpu"] = args.gpu
|
||||
config["Global"]["enable_mkldnn"] = args.mkldnn
|
||||
config["Global"]["benchmark"] = args.benchmark
|
||||
config["Global"]["use_tensorrt"] = args.tensorrt
|
||||
config["Global"]["use_fp16"] = True if args.precision == "fp16" else False
|
||||
config["Global"]["gpu_id"] = args.gpu_id
|
||||
if args.type == "cls":
|
||||
config["Global"]["infer_imgs"] = args.data_dir
|
||||
assert args.cls_model_dir
|
||||
config["Global"]["inference_model_dir"] = args.cls_model_dir
|
||||
else:
|
||||
config["Global"]["infer_imgs"] = os.path.join(args.data_dir,
|
||||
"test_images")
|
||||
config["IndexProcess"]["index_dir"] = os.path.join(args.data_dir,
|
||||
"index")
|
||||
assert args.cls_model_dir
|
||||
assert args.det_model_dir
|
||||
config["Global"]["det_inference_model_dir"] = args.det_model_dir
|
||||
config["Global"]["rec_inference_model_dir"] = args.cls_model_dir
|
||||
|
||||
with open(save_path, 'w') as fd:
|
||||
yaml.dump(config, fd)
|
||||
print("Generate new yaml done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -33,6 +33,59 @@ function func_parser_value(){
|
|||
fi
|
||||
}
|
||||
|
||||
function func_get_url_file_name(){
|
||||
strs=$1
|
||||
IFS="/"
|
||||
array=(${strs})
|
||||
tmp=${array[${#array[@]}-1]}
|
||||
echo ${tmp}
|
||||
}
|
||||
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
|
||||
if [ ${MODE} = "cpp_infer" ];then
|
||||
if [[ $FILENAME == *infer_cpp_linux_gpu_cpu.txt ]];then
|
||||
cpp_type=$(func_parser_value "${lines[2]}")
|
||||
cls_inference_model_dir=$(func_parser_value "${lines[3]}")
|
||||
det_inference_model_dir=$(func_parser_value "${lines[4]}")
|
||||
cls_inference_url=$(func_parser_value "${lines[5]}")
|
||||
det_inference_url=$(func_parser_value "${lines[6]}")
|
||||
|
||||
if [[ $cpp_type == "cls" ]];then
|
||||
eval "wget -nc $cls_inference_url"
|
||||
tar xf "${model_name}_inference.tar"
|
||||
eval "mv inference $cls_inference_model_dir"
|
||||
cd dataset
|
||||
rm -rf ILSVRC2012
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
|
||||
tar xf whole_chain_infer.tar
|
||||
ln -s whole_chain_infer ILSVRC2012
|
||||
cd ..
|
||||
elif [[ $cpp_type == "shitu" ]];then
|
||||
eval "wget -nc $cls_inference_url"
|
||||
tar_name=$(func_get_url_file_name "$cls_inference_url")
|
||||
model_dir=${tar_name%.*}
|
||||
eval "tar xf ${tar_name}"
|
||||
eval "mv ${model_dir} ${cls_inference_model_dir}"
|
||||
|
||||
eval "wget -nc $det_inference_url"
|
||||
tar_name=$(func_get_url_file_name "$det_inference_url")
|
||||
model_dir=${tar_name%.*}
|
||||
eval "tar xf ${tar_name}"
|
||||
eval "mv ${model_dir} ${det_inference_model_dir}"
|
||||
cd dataset
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
|
||||
tar -xf drink_dataset_v1.0.tar
|
||||
else
|
||||
echo "Wrong cpp type in config file in line 3. only support cls, shitu"
|
||||
fi
|
||||
exit 0
|
||||
else
|
||||
echo "use wrong config file"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
model_url_value=$(func_parser_value "${lines[35]}")
|
||||
model_url_key=$(func_parser_key "${lines[35]}")
|
||||
|
|
@ -114,63 +167,3 @@ if [ ${MODE} = "serving_infer" ];then
|
|||
cd ./deploy/paddleserving
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
|
||||
fi
|
||||
|
||||
if [ ${MODE} = "cpp_infer" ];then
|
||||
cd deploy/cpp
|
||||
echo "################### build opencv ###################"
|
||||
rm -rf 3.4.7.tar.gz opencv-3.4.7/
|
||||
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
|
||||
tar -xf 3.4.7.tar.gz
|
||||
install_path=$(pwd)/opencv-3.4.7/opencv3
|
||||
cd opencv-3.4.7/
|
||||
|
||||
rm -rf build
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
-DCMAKE_INSTALL_PREFIX=${install_path} \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DBUILD_SHARED_LIBS=OFF \
|
||||
-DWITH_IPP=OFF \
|
||||
-DBUILD_IPP_IW=OFF \
|
||||
-DWITH_LAPACK=OFF \
|
||||
-DWITH_EIGEN=OFF \
|
||||
-DCMAKE_INSTALL_LIBDIR=lib64 \
|
||||
-DWITH_ZLIB=ON \
|
||||
-DBUILD_ZLIB=ON \
|
||||
-DWITH_JPEG=ON \
|
||||
-DBUILD_JPEG=ON \
|
||||
-DWITH_PNG=ON \
|
||||
-DBUILD_PNG=ON \
|
||||
-DWITH_TIFF=ON \
|
||||
-DBUILD_TIFF=ON
|
||||
make -j
|
||||
make install
|
||||
cd ../../
|
||||
echo "################### build opencv finished ###################"
|
||||
|
||||
echo "################### build PaddleClas demo ####################"
|
||||
OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
|
||||
LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
|
||||
CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
|
||||
CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
|
||||
|
||||
BUILD_DIR=build
|
||||
rm -rf ${BUILD_DIR}
|
||||
mkdir ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
cmake .. \
|
||||
-DPADDLE_LIB=${LIB_DIR} \
|
||||
-DWITH_MKL=ON \
|
||||
-DDEMO_NAME=clas_system \
|
||||
-DWITH_GPU=OFF \
|
||||
-DWITH_STATIC_LIB=OFF \
|
||||
-DWITH_TENSORRT=OFF \
|
||||
-DTENSORRT_DIR=${TENSORRT_DIR} \
|
||||
-DOPENCV_DIR=${OPENCV_DIR} \
|
||||
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
|
||||
-DCUDA_LIB=${CUDA_LIB_DIR} \
|
||||
|
||||
make -j
|
||||
echo "################### build PaddleClas demo finished ###################"
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -2,6 +2,10 @@
|
|||
source test_tipc/common_func.sh
|
||||
|
||||
FILENAME=$1
|
||||
GPUID=$2
|
||||
if [[ ! $GPUID ]];then
|
||||
GPUID=0
|
||||
fi
|
||||
dataline=$(awk 'NR==1, NR==16{print}' $FILENAME)
|
||||
|
||||
# parser params
|
||||
|
|
@ -10,37 +14,29 @@ lines=(${dataline})
|
|||
|
||||
# parser cpp inference model
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
use_opencv=$(func_parser_value "${lines[2]}")
|
||||
cpp_infer_model_dir_list=$(func_parser_value "${lines[3]}")
|
||||
cpp_infer_is_quant=$(func_parser_value "${lines[4]}")
|
||||
cpp_infer_type=$(func_parser_value "${lines[2]}")
|
||||
cpp_infer_model_dir=$(func_parser_value "${lines[3]}")
|
||||
cpp_det_infer_model_dir=$(func_parser_value "${lines[4]}")
|
||||
cpp_infer_is_quant=$(func_parser_value "${lines[7]}")
|
||||
# parser cpp inference
|
||||
inference_cmd=$(func_parser_value "${lines[5]}")
|
||||
cpp_use_gpu_key=$(func_parser_key "${lines[6]}")
|
||||
cpp_use_gpu_list=$(func_parser_value "${lines[6]}")
|
||||
cpp_use_mkldnn_key=$(func_parser_key "${lines[7]}")
|
||||
cpp_use_mkldnn_list=$(func_parser_value "${lines[7]}")
|
||||
cpp_cpu_threads_key=$(func_parser_key "${lines[8]}")
|
||||
cpp_cpu_threads_list=$(func_parser_value "${lines[8]}")
|
||||
cpp_batch_size_key=$(func_parser_key "${lines[9]}")
|
||||
cpp_batch_size_list=$(func_parser_value "${lines[9]}")
|
||||
cpp_use_trt_key=$(func_parser_key "${lines[10]}")
|
||||
cpp_use_trt_list=$(func_parser_value "${lines[10]}")
|
||||
cpp_precision_key=$(func_parser_key "${lines[11]}")
|
||||
cpp_precision_list=$(func_parser_value "${lines[11]}")
|
||||
cpp_infer_model_key=$(func_parser_key "${lines[12]}")
|
||||
cpp_image_dir_key=$(func_parser_key "${lines[13]}")
|
||||
cpp_infer_img_dir=$(func_parser_value "${lines[13]}")
|
||||
cpp_infer_key1=$(func_parser_key "${lines[14]}")
|
||||
cpp_infer_value1=$(func_parser_value "${lines[14]}")
|
||||
cpp_benchmark_key=$(func_parser_key "${lines[15]}")
|
||||
cpp_benchmark_value=$(func_parser_value "${lines[15]}")
|
||||
inference_cmd=$(func_parser_value "${lines[8]}")
|
||||
cpp_use_gpu_list=$(func_parser_value "${lines[9]}")
|
||||
cpp_use_mkldnn_list=$(func_parser_value "${lines[10]}")
|
||||
cpp_cpu_threads_list=$(func_parser_value "${lines[11]}")
|
||||
cpp_batch_size_list=$(func_parser_value "${lines[12]}")
|
||||
cpp_use_trt_list=$(func_parser_value "${lines[13]}")
|
||||
cpp_precision_list=$(func_parser_value "${lines[14]}")
|
||||
cpp_image_dir_value=$(func_parser_value "${lines[15]}")
|
||||
cpp_benchmark_value=$(func_parser_value "${lines[16]}")
|
||||
generate_yaml_cmd=$(func_parser_value "${lines[17]}")
|
||||
transform_index_cmd=$(func_parser_value "${lines[18]}")
|
||||
|
||||
LOG_PATH="./test_tipc/output"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results_cpp.log"
|
||||
# generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py"
|
||||
|
||||
|
||||
function func_cpp_inference(){
|
||||
function func_shitu_cpp_inference(){
|
||||
IFS='|'
|
||||
_script=$1
|
||||
_model_dir=$2
|
||||
|
|
@ -48,6 +44,7 @@ function func_cpp_inference(){
|
|||
_img_dir=$4
|
||||
_flag_quant=$5
|
||||
# inference
|
||||
|
||||
for use_gpu in ${cpp_use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
|
||||
for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
|
||||
|
|
@ -60,17 +57,14 @@ function func_cpp_inference(){
|
|||
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
|
||||
precison="int8"
|
||||
fi
|
||||
_save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
|
||||
set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
|
||||
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
|
||||
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
_save_log_path="${_log_path}/shitu_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
|
||||
command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
|
||||
eval $command
|
||||
eval $transform_index_cmd
|
||||
command="${_script} 2>&1|tee ${_save_log_path}"
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "${status_log}"
|
||||
done
|
||||
done
|
||||
|
|
@ -88,20 +82,75 @@ function func_cpp_inference(){
|
|||
continue
|
||||
fi
|
||||
for batch_size in ${cpp_batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
|
||||
set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
|
||||
set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
|
||||
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
|
||||
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
_save_log_path="${_log_path}/shitu_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
|
||||
eval $command
|
||||
eval $transform_index_cmd
|
||||
command="${_script} 2>&1|tee ${_save_log_path}"
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${_script}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
else
|
||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
function func_cls_cpp_inference(){
|
||||
IFS='|'
|
||||
_script=$1
|
||||
_model_dir=$2
|
||||
_log_path=$3
|
||||
_img_dir=$4
|
||||
_flag_quant=$5
|
||||
# inference
|
||||
|
||||
for use_gpu in ${cpp_use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
|
||||
for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
|
||||
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
|
||||
continue
|
||||
fi
|
||||
for threads in ${cpp_cpu_threads_list[*]}; do
|
||||
for batch_size in ${cpp_batch_size_list[*]}; do
|
||||
precision="fp32"
|
||||
if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
|
||||
precison="int8"
|
||||
fi
|
||||
_save_log_path="${_log_path}/cls_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
|
||||
command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
|
||||
eval $command
|
||||
command1="${_script} 2>&1|tee ${_save_log_path}"
|
||||
eval ${command1}
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${command1}" "${status_log}"
|
||||
done
|
||||
done
|
||||
done
|
||||
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
|
||||
for use_trt in ${cpp_use_trt_list[*]}; do
|
||||
for precision in ${cpp_precision_list[*]}; do
|
||||
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
|
||||
continue
|
||||
fi
|
||||
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
|
||||
continue
|
||||
fi
|
||||
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
|
||||
continue
|
||||
fi
|
||||
for batch_size in ${cpp_batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/cls_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
|
||||
eval $command
|
||||
command="${_script} 2>&1|tee ${_save_log_path}"
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "${status_log}"
|
||||
|
||||
done
|
||||
done
|
||||
done
|
||||
|
|
@ -112,24 +161,56 @@ function func_cpp_inference(){
|
|||
}
|
||||
|
||||
|
||||
cd deploy/cpp_infer
|
||||
if [ ${use_opencv} = "True" ]; then
|
||||
if [ -d "opencv-3.4.7/opencv3/" ] && [ $(md5sum opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ];then
|
||||
echo "################### build opencv skipped ###################"
|
||||
else
|
||||
echo "################### build opencv ###################"
|
||||
rm -rf opencv-3.4.7.tar.gz opencv-3.4.7/
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
|
||||
tar -xf opencv-3.4.7.tar.gz
|
||||
if [[ $cpp_infer_type == "cls" ]]; then
|
||||
cd deploy/cpp
|
||||
elif [[ $cpp_infer_type == "shitu" ]]; then
|
||||
cd deploy/cpp_shitu
|
||||
else
|
||||
echo "Only support cls and shitu"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
cd opencv-3.4.7/
|
||||
install_path=$(pwd)/opencv3
|
||||
if [[ $cpp_infer_type == "shitu" ]]; then
|
||||
echo "################### update cmake ###################"
|
||||
wget -nc https://github.com/Kitware/CMake/releases/download/v3.22.0/cmake-3.22.0.tar.gz
|
||||
tar xf cmake-3.22.0.tar.gz
|
||||
cd ./cmake-3.22.0
|
||||
export root_path=$PWD
|
||||
export install_path=${root_path}/cmake
|
||||
eval "./bootstrap --prefix=${install_path}"
|
||||
make -j
|
||||
make install
|
||||
export PATH=${install_path}/bin:$PATH
|
||||
cd ..
|
||||
echo "################### update cmake done ###################"
|
||||
|
||||
rm -rf build
|
||||
mkdir build
|
||||
cd build
|
||||
echo "################### build faiss ###################"
|
||||
apt-get install -y libopenblas-dev
|
||||
git clone https://github.com/facebookresearch/faiss.git
|
||||
cd faiss
|
||||
export faiss_install_path=$PWD/faiss_install
|
||||
eval "cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}"
|
||||
make -C build -j faiss
|
||||
make -C build install
|
||||
cd ..
|
||||
fi
|
||||
|
||||
cmake .. \
|
||||
if [ -d "opencv-3.4.7/opencv3/" ] && [ $(md5sum opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ];then
|
||||
echo "################### build opencv skipped ###################"
|
||||
else
|
||||
echo "################### build opencv ###################"
|
||||
rm -rf opencv-3.4.7.tar.gz opencv-3.4.7/
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
|
||||
tar -xf opencv-3.4.7.tar.gz
|
||||
|
||||
cd opencv-3.4.7/
|
||||
install_path=$(pwd)/opencv3
|
||||
|
||||
rm -rf build
|
||||
mkdir build
|
||||
cd build
|
||||
|
||||
cmake .. \
|
||||
-DCMAKE_INSTALL_PREFIX=${install_path} \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DBUILD_SHARED_LIBS=OFF \
|
||||
|
|
@ -147,20 +228,15 @@ if [ ${use_opencv} = "True" ]; then
|
|||
-DWITH_TIFF=ON \
|
||||
-DBUILD_TIFF=ON
|
||||
|
||||
make -j
|
||||
make install
|
||||
cd ../
|
||||
echo "################### build opencv finished ###################"
|
||||
fi
|
||||
make -j
|
||||
make install
|
||||
cd ../../
|
||||
echo "################### build opencv finished ###################"
|
||||
fi
|
||||
|
||||
|
||||
echo "################### build PaddleOCR demo ####################"
|
||||
if [ ${use_opencv} = "True" ]; then
|
||||
OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
|
||||
else
|
||||
OPENCV_DIR=''
|
||||
fi
|
||||
echo "################### build PaddleClas demo ####################"
|
||||
OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
|
||||
# LIB_DIR=/work/project/project/test/paddle_inference/
|
||||
LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
|
||||
CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
|
||||
CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
|
||||
|
|
@ -169,40 +245,59 @@ BUILD_DIR=build
|
|||
rm -rf ${BUILD_DIR}
|
||||
mkdir ${BUILD_DIR}
|
||||
cd ${BUILD_DIR}
|
||||
cmake .. \
|
||||
-DPADDLE_LIB=${LIB_DIR} \
|
||||
-DWITH_MKL=ON \
|
||||
-DWITH_GPU=OFF \
|
||||
-DWITH_STATIC_LIB=OFF \
|
||||
-DWITH_TENSORRT=OFF \
|
||||
-DOPENCV_DIR=${OPENCV_DIR} \
|
||||
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
|
||||
-DCUDA_LIB=${CUDA_LIB_DIR} \
|
||||
-DTENSORRT_DIR=${TENSORRT_DIR} \
|
||||
|
||||
if [[ $cpp_infer_type == cls ]]; then
|
||||
cmake .. \
|
||||
-DPADDLE_LIB=${LIB_DIR} \
|
||||
-DWITH_MKL=ON \
|
||||
-DWITH_GPU=ON \
|
||||
-DWITH_STATIC_LIB=OFF \
|
||||
-DWITH_TENSORRT=OFF \
|
||||
-DOPENCV_DIR=${OPENCV_DIR} \
|
||||
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
|
||||
-DCUDA_LIB=${CUDA_LIB_DIR} \
|
||||
-DTENSORRT_DIR=${TENSORRT_DIR}
|
||||
else
|
||||
cmake ..\
|
||||
-DPADDLE_LIB=${LIB_DIR} \
|
||||
-DWITH_MKL=ON \
|
||||
-DWITH_GPU=ON \
|
||||
-DWITH_STATIC_LIB=OFF \
|
||||
-DWITH_TENSORRT=OFF \
|
||||
-DOPENCV_DIR=${OPENCV_DIR} \
|
||||
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
|
||||
-DCUDA_LIB=${CUDA_LIB_DIR} \
|
||||
-DTENSORRT_DIR=${TENSORRT_DIR} \
|
||||
-DFAISS_DIR=${faiss_install_path} \
|
||||
-DFAISS_WITH_MKL=OFF
|
||||
fi
|
||||
make -j
|
||||
cd ../../../
|
||||
echo "################### build PaddleOCR demo finished ###################"
|
||||
# cd ../../
|
||||
echo "################### build PaddleClas demo finished ###################"
|
||||
|
||||
|
||||
# set cuda device
|
||||
GPUID=$2
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
set CUDA_VISIBLE_DEVICES
|
||||
eval $env
|
||||
# GPUID=$2
|
||||
# if [ ${#GPUID} -le 0 ];then
|
||||
# env="export CUDA_VISIBLE_DEVICES=0"
|
||||
# else
|
||||
# env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
# fi
|
||||
# set CUDA_VISIBLE_DEVICES
|
||||
# eval $env
|
||||
|
||||
|
||||
echo "################### run test ###################"
|
||||
export Count=0
|
||||
IFS="|"
|
||||
infer_quant_flag=(${cpp_infer_is_quant})
|
||||
for infer_model in ${cpp_infer_model_dir_list[*]}; do
|
||||
for infer_model in ${cpp_infer_model_dir[*]}; do
|
||||
#run inference
|
||||
is_quant=${infer_quant_flag[Count]}
|
||||
func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
|
||||
if [[ $cpp_infer_type == "cls" ]]; then
|
||||
func_cls_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
|
||||
else
|
||||
func_shitu_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
|
||||
fi
|
||||
Count=$(($Count + 1))
|
||||
done
|
||||
|
|
|
|||
Loading…
Reference in New Issue