Merge branch 'PaddlePaddle:dygraph' into dygraph

doc/doc_ch/algorithm_det_db.md

@@ -32,7 +32,7 @@
 | --- | --- | --- | --- | --- | --- | --- |
 |DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
-|DB++|ResNet50|[configs/det/det_r50_db++_ic15.yml](../../configs/det/det_r50_db++_ic15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
+|DB++|ResNet50|[configs/det/det_r50_db++_icdar15.yml](../../configs/det/det_r50_db++_icdar15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
 
 在TD_TR文本检测公开数据集上，算法复现效果如下：
 

test_tipc/benchmark_train.sh

@@ -139,8 +139,8 @@ else
     device_num=${params_list[4]}
     IFS=";"
 
-    if [ ${precision} = "null" ];then
-        precision="fp32"
+    if [ ${precision} = "fp16" ];then
+        precision="amp"
     fi
 
     fp_items_list=($precision)
@@ -150,10 +150,16 @@ fi
 
 IFS="|"
 for batch_size in ${batch_size_list[*]}; do 
-    for precision in ${fp_items_list[*]}; do
+    for train_precision in ${fp_items_list[*]}; do
         for device_num in ${device_num_list[*]}; do
             # sed batchsize and precision
-            func_sed_params "$FILENAME" "${line_precision}" "$precision"
+            if [ ${train_precision} = "amp" ];then
+                precision="fp16"
+            else
+                precision="fp32"
+            fi
+
+            func_sed_params "$FILENAME" "${line_precision}" "$train_precision"
             func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
             func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
             gpu_id=$(set_gpu_id $device_num)

test_tipc/configs/en_table_structure/train_pact_infer_python.txt

@@ -6,7 +6,7 @@ Global.use_gpu:True|True
 Global.auto_cast:fp32
 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=2
 Global.pretrained_model:./pretrain_models/en_ppocr_mobile_v2.0_table_structure_train/best_accuracy
 train_model_name:latest
 train_infer_img_dir:./ppstructure/docs/table/table.jpg

test_tipc/prepare.sh

@@ -58,7 +58,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf ch_PP-OCRv3_det_distill_train.tar && cd ../
     fi
-    if [ ${model_name} == "en_table_structure" ];then
+    if [ ${model_name} == "en_table_structure" ] || [ ${model_name} == "en_table_structure_PACT" ];then
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf en_ppocr_mobile_v2.0_table_structure_train.tar  && cd ../
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate

test_tipc/test_ptq_inference_python.sh

@@ -139,7 +139,7 @@ if [ ${MODE} = "whole_infer" ]; then
             save_infer_dir="${infer_model}_klquant"
             set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
             set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
-            export_log_path="${LOG_PATH}_export_${Count}.log"
+            export_log_path="${LOG_PATH}/${MODE}_export_${Count}.log"
             export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 "
             echo ${infer_run_exports[Count]} 
             echo $export_cmd

test_tipc/test_train_inference_python.sh

@@ -265,7 +265,7 @@ else
                 if [ ${run_train} = "null" ]; then
                     continue
                 fi
-                set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
+
                 set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
                 set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
                 set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
@@ -287,11 +287,11 @@ else
 
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                 if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
-                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
+                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain}  ${set_batchsize} ${set_train_params1} ${set_amp_config} "
                 elif [ ${#ips} -le 15 ];then  # train with multi-gpu
-                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain}  ${set_batchsize} ${set_train_params1} ${set_amp_config}"
                 else     # train with multi-machine
-                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch}  ${set_batchsize} ${set_train_params1} ${set_amp_config}"
                 fi
                 # run train
                 eval $cmd

tools/infer_det.py

@@ -106,7 +106,7 @@ def main():
                     dt_boxes_list = []
                     for box in boxes:
                         tmp_json = {"transcription": ""}
-                        tmp_json['points'] = list(box)
+                        tmp_json['points'] = np.array(box).tolist()
                         dt_boxes_list.append(tmp_json)
                     det_box_json[k] = dt_boxes_list
                     save_det_path = os.path.dirname(config['Global'][
@@ -118,7 +118,7 @@ def main():
                 # write result
                 for box in boxes:
                     tmp_json = {"transcription": ""}
-                    tmp_json['points'] = list(box)
+                    tmp_json['points'] = np.array(box).tolist()
                     dt_boxes_json.append(tmp_json)
                 save_det_path = os.path.dirname(config['Global'][
                     'save_res_path']) + "/det_results/"