fix bugs

dataset/chain_dataset

+little_train
\ No newline at end of file

dataset/little_train/train.txt

+train/ILSVRC2012_val_00000032.JPEG 573
+train/ILSVRC2012_val_00000033.JPEG 150
+train/ILSVRC2012_val_00000034.JPEG 981
+train/ILSVRC2012_val_00000035.JPEG 586
+train/ILSVRC2012_val_00000036.JPEG 887
+train/ILSVRC2012_val_00000037.JPEG 32
+train/ILSVRC2012_val_00000038.JPEG 398
+train/ILSVRC2012_val_00000039.JPEG 777
+train/ILSVRC2012_val_00000040.JPEG 74
+train/ILSVRC2012_val_00000041.JPEG 516
+train/ILSVRC2012_val_00000042.JPEG 756
+train/ILSVRC2012_val_00000043.JPEG 129
+train/ILSVRC2012_val_00000044.JPEG 198
+train/ILSVRC2012_val_00000045.JPEG 256
+train/ILSVRC2012_val_00000046.JPEG 725
+train/ILSVRC2012_val_00000047.JPEG 565
+train/ILSVRC2012_val_00000048.JPEG 167
+train/ILSVRC2012_val_00000049.JPEG 717
+train/ILSVRC2012_val_00000050.JPEG 394
+train/ILSVRC2012_val_00000051.JPEG 92
+train/ILSVRC2012_val_00000052.JPEG 29
+train/ILSVRC2012_val_00000053.JPEG 844
+train/ILSVRC2012_val_00000054.JPEG 591
+train/ILSVRC2012_val_00000055.JPEG 358
+train/ILSVRC2012_val_00000056.JPEG 468
+train/ILSVRC2012_val_00000057.JPEG 259
+train/ILSVRC2012_val_00000058.JPEG 994
+train/ILSVRC2012_val_00000059.JPEG 872
+train/ILSVRC2012_val_00000060.JPEG 588

dataset/little_train/val.txt

+val/ILSVRC2012_val_00000001.JPEG 65
+val/ILSVRC2012_val_00000002.JPEG 970
+val/ILSVRC2012_val_00000003.JPEG 230
+val/ILSVRC2012_val_00000004.JPEG 809
+val/ILSVRC2012_val_00000005.JPEG 516
+val/ILSVRC2012_val_00000006.JPEG 57
+val/ILSVRC2012_val_00000007.JPEG 334
+val/ILSVRC2012_val_00000008.JPEG 415
+val/ILSVRC2012_val_00000009.JPEG 674
+val/ILSVRC2012_val_00000010.JPEG 332
+val/ILSVRC2012_val_00000011.JPEG 109
+val/ILSVRC2012_val_00000012.JPEG 286
+val/ILSVRC2012_val_00000013.JPEG 370
+val/ILSVRC2012_val_00000014.JPEG 757
+val/ILSVRC2012_val_00000015.JPEG 595

deploy/python/predict_cls.py

@@ -42,7 +42,7 @@ class ClsPredictor(Predictor):
             self.postprocess = build_postprocess(config["PostProcess"])
 
         # for whole_chain project to test each repo of paddle
-        self.benchmark = config.get(["benchmark"], False)
+        self.benchmark = config.get("benchmark", False)
         if self.benchmark:
             import auto_log
             import os

deploy/test.sh

+python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=True -o Global.use_tensorrt=False -o Global.use_fp16=False -o Global.inference_model_dir=.././test/output/ResNet50_vd_gpus_0,1/inference -o Global.batch_size=1 -o Global.infer_imgs=.././dataset/chain_dataset/val -o Global.save_log_path=.././test/output/ResNet50_vd_infer_gpu_usetrt_True_precision_False_batchsize_1.log -o benchmark=True

test/parames.txt

@@ -6,29 +6,29 @@ model_name_kl:ResNet50_vd|MobileNetV3_large_x1_0
 python:python3.7
 gpu_list:0|0,1|-1
 Global.epoch_num:10
-Global.save_model_dir:./output/
+Global.output_dir:./output/
 Global.pretrained_model:null
 Global.save_inference_dir:null
-
-
-
-
-
-
-
-
+#
+#
+#
+#
+#
+#
+#
+#
 ===========================scripts===========================
-train:tools/train.py -c test_ci/benchmark.yaml
-eval:tools/eval.py -c test_ci/benchmark.yaml
-norm_export:tools/export_model.py -c test_ci/benchmark.yaml
+train:tools/train.py -c test/benchmark.yaml
+eval:tools/eval.py -c test/benchmark.yaml
+norm_export:tools/export_model.py -c test/benchmark.yaml
 inference:python/predict_cls.py -c configs/inference_cls.yaml
-
-
-
-
-
-
-
+#
+#
+#
+#
+#
+#
+#
 ===========================infer_params===========================
 Global.save_log_path:./test/output/
 Global.use_gpu:True|False
@@ -39,13 +39,13 @@ Global.use_tensorrt:True|False
 Global.use_fp16:True|False
 Global.inference_model_dir:./inference
 Global.infer_imgs:./dataset/chain_dataset/val
-
-
-
-
-
-
-
+#
+#
+#
+#
+#
+#
+#
 ===========================pretrained_model===========================
 ResNet50_vd:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams   
 ResNeXt101_vd_64x4d:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams

test/prepare.sh

@@ -9,9 +9,10 @@ IFS=$'\n'
 lines=(${dataline})
 function func_parser_value(){
     strs=$1
-    ifs=":"
+    IFS=":"
     array=(${strs})
-    tmp=${array[1]}
+    IFS="|"
+    tmp="${array[1]}:${array[2]}"
     echo ${tmp}
 }
 ResNet50_vd=$(func_parser_value "${lines[49]}")
@@ -23,8 +24,6 @@ MobileNetV1=$(func_parser_value "${lines[54]}")
 MobileNetV2=$(func_parser_value "${lines[55]}")
 ShuffleNetV2_x1_0=$(func_parser_value "${lines[56]}")
 
-model_name_list=$(func_parser_value "${lines[1]}")
-
 if [ ${MODE} = "lite_train_infer" ] || [ ${MODE} = "whole_infer" ];then
     # pretrain lite train data
     cd dataset
@@ -36,7 +35,7 @@ elif [ ${MODE} = "infer" ];then
     # download data
     cd dataset
     wget -nc http://10.21.226.181:8011/dataset/quanliantiao/infer.tar
-    tar xf little.tar
+    tar xf infer.tar
     ln -s infer chain_dataset
     cd ../
     # download pretrained model

test/test.sh

@@ -2,9 +2,7 @@
 FILENAME=$1
 # MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
 MODE=$2
-
 dataline=$(cat ${FILENAME})
-
 # parser params
 IFS=$'\n'
 lines=(${dataline})
@@ -166,21 +164,21 @@ for gpu in ${gpu_list[*]}; do
         status_check $? "${cmd}" "${status_log}"
 
         # run eval
-        eval_cmd="${python} ${eval_py} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/best_model" 
+        eval_cmd="${python} ${eval_py} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/latest" 
         eval $eval_cmd
         status_check $? "${eval_cmd}" "${status_log}"
 
         # run export model
-        save_infer_path="${save_log}"
-        export_cmd="${python} ${norm_export} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/best_model -o ${save_infer_key}=${save_infer_path}"
+        save_infer_path="${save_log}/inference"
+        export_cmd="${python} ${norm_export} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/latest -o ${save_infer_key}=${save_infer_path}"
         eval $export_cmd
         status_check $? "${export_cmd}" "${status_log}"
 
         #run inference
         eval $env
-        save_infer_path="${save_log}"
+        save_infer_path="${save_log}/inference"
 	cd deploy
-        func_inference "${python}" "${inference_py}" "../${save_log}" "../${LOG_PATH}" "../${infer_img_dir}" model_name
+        func_inference "${python}" "${inference_py}" "../${save_infer_path}" "../${LOG_PATH}" "../${infer_img_dir}" "${model_name}"
         eval "unset CUDA_VISIBLE_DEVICES"
 	cd ..
     done
@@ -203,7 +201,7 @@ else
     #run inference
     cd deploy
     for model_name in ${model_name_list[*]}; do
-        func_inference "${python}" "${inference_py}" "../inference_models/${model_name}" "../${LOG_PATH}" "../${infer_img_dir}"
+        func_inference "${python}" "${inference_py}" "../inference_models/${model_name}" "../${LOG_PATH}" "../${infer_img_dir}" "${model_name}"
     done
     cd ..
 fi