fix rec pact hang

deploy/slim/quantization/export_model.py

@@ -127,6 +127,7 @@ def main():
     arch_config = config["Architecture"]
     if arch_config["algorithm"] in ["Distillation", ]:  # distillation model
         for idx, name in enumerate(model.model_name_list):
+            model.model_list[idx].eval()
             sub_model_save_path = os.path.join(save_path, name, "inference")
             export_single_model(quanter, model.model_list[idx], infer_shape,
                                 sub_model_save_path, logger)

ppocr/modeling/heads/rec_ctc_head.py

@@ -80,8 +80,9 @@ class CTCHead(nn.Layer):
             result = (x, predicts)
         else:
             result = predicts
-
+        print("self.training: ", self.training)
         if not self.training:
+            print("self.training, ", self.training)
             predicts = F.softmax(predicts, axis=2)
             result = predicts
 

test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt

 ===========================train_params===========================
-model_name:PPOCRv2_ocr_rec_pact
+model_name:ch_PPOCRv2_rec_PACT
 python:python3.7
-gpu_list:0|0,1
+gpu_list:6
 Global.use_gpu:True|True
 Global.auto_cast:fp32
-Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=6|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
-Global.pretrained_model:null
+Global.pretrained_model:pretrain_models/ch_PP-OCRv2_rec_train/best_accuracy
 train_model_name:latest
 train_infer_img_dir:./inference/rec_inference
 null:null

test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt

 ===========================train_params===========================
 model_name:det_mv3_east_v2.0
 python:python3.7
-gpu_list:0
+gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:fp32
 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
-Global.pretrained_model:null
+Global.pretrained_model:./pretrain_models/det_mv3_east_v2.0_train/best_accuracy
 train_model_name:latest
 train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null

test_tipc/prepare.sh

@@ -64,6 +64,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget  -nc -P  ./pretrain_models/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf ch_ppocr_server_v2.0_det_train.tar  && cd ../
     fi
+    if [ ${model_name} == "ch_PPOCRv2_rec" ] || [ ${model_name} == "ch_PPOCRv2_rec_PACT" ]; then
+        wget  -nc -P  ./pretrain_models/  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_PP-OCRv2_rec_train.tar && cd ../
+    fi
     if [ ${model_name} == "det_r18_db_v2_0" ]; then
         wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams  --no-check-certificate
     fi
@@ -91,6 +95,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../
     fi
+    if [ ${model_name} == "det_mv3_east_v2.0" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf det_mv3_east_v2.0_train.tar && cd ../
+    fi
 
 elif [ ${MODE} = "whole_train_whole_infer" ];then
     wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate

tools/infer/utility.py

@@ -312,12 +312,28 @@ def create_predictor(args, mode, logger):
         input_names = predictor.get_input_names()
         for name in input_names:
             input_tensor = predictor.get_input_handle(name)
-        output_names = predictor.get_output_names()
-        output_tensors = []
+        output_tensors = get_output_tensors(args, mode, predictor)
+        #output_names = predictor.get_output_names()
+        #output_tensors = []
+        #print("output names", output_names)
+        #for output_name in output_names:
+        #    output_tensor = predictor.get_output_handle(output_name)
+        #    output_tensors.append(output_tensor)
+        return predictor, input_tensor, output_tensors, config
+
+
+def get_output_tensors(args, mode, predictor):
+    output_names = predictor.get_output_names()
+    output_tensors = []
+    if mode == "rec" and args.rec_algorithm == "CRNN":
+        output_name = 'softmax_0.tmp_0'
+        if output_name in output_names:
+            return [predictor.get_output_handle(output_name)]
+    else:
         for output_name in output_names:
             output_tensor = predictor.get_output_handle(output_name)
             output_tensors.append(output_tensor)
-        return predictor, input_tensor, output_tensors, config
+    return output_tensors
 
 
 def get_infer_gpuid():