Merge remote-tracking branch 'origin/dygraph' into dygraph

test_tipc/test_inference_cpp.sh

@@ -2,38 +2,38 @@
 source test_tipc/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==52, NR==66{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==16{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
 lines=(${dataline})
 
 # parser cpp inference model 
-use_opencv=$(func_parser_value "${lines[1]}")
-cpp_infer_model_dir_list=$(func_parser_value "${lines[2]}")
-cpp_infer_is_quant=$(func_parser_value "${lines[3]}")
+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]}")
 # parser cpp inference 
-inference_cmd=$(func_parser_value "${lines[4]}")
-cpp_use_gpu_key=$(func_parser_key "${lines[5]}")
-cpp_use_gpu_list=$(func_parser_value "${lines[5]}")
-cpp_use_mkldnn_key=$(func_parser_key "${lines[6]}")
-cpp_use_mkldnn_list=$(func_parser_value "${lines[6]}")
-cpp_cpu_threads_key=$(func_parser_key "${lines[7]}")
-cpp_cpu_threads_list=$(func_parser_value "${lines[7]}")
-cpp_batch_size_key=$(func_parser_key "${lines[8]}")
-cpp_batch_size_list=$(func_parser_value "${lines[8]}")
-cpp_use_trt_key=$(func_parser_key "${lines[9]}")
-cpp_use_trt_list=$(func_parser_value "${lines[9]}")
-cpp_precision_key=$(func_parser_key "${lines[10]}")
-cpp_precision_list=$(func_parser_value "${lines[10]}")
-cpp_infer_model_key=$(func_parser_key "${lines[11]}")
-cpp_image_dir_key=$(func_parser_key "${lines[12]}")
-cpp_infer_img_dir=$(func_parser_value "${lines[12]}")
-cpp_infer_key1=$(func_parser_key "${lines[13]}")
-cpp_infer_value1=$(func_parser_value "${lines[13]}")
-cpp_benchmark_key=$(func_parser_key "${lines[14]}")
-cpp_benchmark_value=$(func_parser_value "${lines[14]}")
-
+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]}")
 
 LOG_PATH="./test_tipc/output"
 mkdir -p ${LOG_PATH}

test_tipc/test_inference_jeston.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+source test_tipc/test_train_inference_python.sh
+
+FILENAME=$1
+# MODE be one of ['whole_infer']
+MODE=$2
+
+dataline=$(awk 'NR==1, NR==17{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+
+infer_model_dir_list=$(func_parser_value "${lines[3]}")
+infer_export_list=$(func_parser_value "${lines[4]}")
+infer_is_quant=$(func_parser_value "${lines[5]}")
+# parser inference 
+inference_py=$(func_parser_value "${lines[6]}")
+use_gpu_key=$(func_parser_key "${lines[7]}")
+use_gpu_list=$(func_parser_value "${lines[7]}")
+use_mkldnn_key=$(func_parser_key "${lines[8]}")
+use_mkldnn_list=$(func_parser_value "${lines[8]}")
+cpu_threads_key=$(func_parser_key "${lines[9]}")
+cpu_threads_list=$(func_parser_value "${lines[9]}")
+batch_size_key=$(func_parser_key "${lines[10]}")
+batch_size_list=$(func_parser_value "${lines[10]}")
+use_trt_key=$(func_parser_key "${lines[11]}")
+use_trt_list=$(func_parser_value "${lines[11]}")
+precision_key=$(func_parser_key "${lines[12]}")
+precision_list=$(func_parser_value "${lines[12]}")
+infer_model_key=$(func_parser_key "${lines[13]}")
+image_dir_key=$(func_parser_key "${lines[14]}")
+infer_img_dir=$(func_parser_value "${lines[14]}")
+save_log_key=$(func_parser_key "${lines[15]}")
+benchmark_key=$(func_parser_key "${lines[16]}")
+benchmark_value=$(func_parser_value "${lines[16]}")
+infer_key1=$(func_parser_key "${lines[17]}")
+infer_value1=$(func_parser_value "${lines[17]}")
+
+
+LOG_PATH="./test_tipc/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results_python.log"
+
+
+if [ ${MODE} = "whole_infer" ]; then
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    # set CUDA_VISIBLE_DEVICES
+    eval $env
+    export Count=0
+    IFS="|"
+    infer_run_exports=(${infer_export_list})
+    infer_quant_flag=(${infer_is_quant})
+    for infer_model in ${infer_model_dir_list[*]}; do
+        # run export
+        if [ ${infer_run_exports[Count]} != "null" ];then
+            save_infer_dir=$(dirname $infer_model)
+            set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
+            set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
+            export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
+            echo ${infer_run_exports[Count]} 
+            echo  $export_cmd
+            eval $export_cmd
+            status_export=$?
+            status_check $status_export "${export_cmd}" "${status_log}"
+        else
+            save_infer_dir=${infer_model}
+        fi
+        #run inference
+        is_quant=${infer_quant_flag[Count]}
+        if [ ${MODE} = "klquant_infer" ]; then
+            is_quant="True"
+        fi
+        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+        Count=$(($Count + 1))
+    done
+fi
+

test_tipc/test_lite.sh → test_tipc/test_lite_arm_cpu_cpp.sh

@@ -3,8 +3,7 @@ source ./common_func.sh
 export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
 
 FILENAME=$1
-dataline=$(awk 'NR==102, NR==111{print}'  $FILENAME)
-echo $dataline
+dataline=$(cat $FILENAME)
 # parser params
 IFS=$'\n'
 lines=(${dataline})
@@ -12,13 +11,14 @@ lines=(${dataline})
 # parser lite inference
 lite_inference_cmd=$(func_parser_value "${lines[1]}")
 lite_model_dir_list=$(func_parser_value "${lines[2]}")
-lite_cpu_threads_list=$(func_parser_value "${lines[3]}")
-lite_batch_size_list=$(func_parser_value "${lines[4]}")
-lite_power_mode_list=$(func_parser_value "${lines[5]}")
-lite_infer_img_dir_list=$(func_parser_value "${lines[6]}")
-lite_config_dir=$(func_parser_value "${lines[7]}")
-lite_rec_dict_dir=$(func_parser_value "${lines[8]}")
-lite_benchmark_value=$(func_parser_value "${lines[9]}")
+runtime_device=$(func_parser_value "${lines[3]}")
+lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
+lite_batch_size_list=$(func_parser_value "${lines[5]}")
+lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
+lite_config_dir=$(func_parser_value "${lines[9]}")
+lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
+lite_benchmark_value=$(func_parser_value "${lines[11]}")
+
 
 LOG_PATH="./output"
 mkdir -p ${LOG_PATH}
@@ -37,23 +37,14 @@ function func_lite(){
     else
         precision="FP32"
     fi
-    is_single_img=$(echo $_img_dir | grep -E ".jpg|.jpeg|.png|.JPEG|.JPG")
-    if [[ "$is_single_img" != "" ]]; then
-        single_img="True"
-    else
-        single_img="False"
-    fi
 
     # lite inference
     for num_threads in ${lite_cpu_threads_list[*]}; do
-        for power_mode in ${lite_power_mode_list[*]}; do
-            for batchsize in ${lite_batch_size_list[*]}; do
-                model_name=$(echo $lite_model | awk -F "/" '{print $NF}')
-                _save_log_path="${_log_path}/lite_${model_name}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}_powermode_${power_mode}_singleimg_${single_img}.log"
-                command="${_script} ${lite_model} ${precision} ${num_threads} ${batchsize} ${power_mode} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
-                eval ${command}
-                status_check $? "${command}" "${status_log}"
-            done
+	for batchsize in ${lite_batch_size_list[*]}; do
+            _save_log_path="${_log_path}/lite_${_lite_model}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}.log"
+            command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize}  ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
+            eval ${command}
+            status_check $? "${command}" "${status_log}"
         done
     done
 }
@@ -64,6 +55,6 @@ IFS="|"
 for lite_model in ${lite_model_dir_list[*]}; do
     #run lite inference
     for img_dir in ${lite_infer_img_dir_list[*]}; do
-        func_lite "${lite_inference_cmd}" "${lite_model}" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
+        func_lite "${lite_inference_cmd}" "${lite_model}_opt.nb" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
     done
 done

test_tipc/test_paddle2onnx.sh

@@ -11,31 +11,33 @@ python=$(func_parser_value "${lines[2]}")
 
 
 # parser params
-dataline=$(awk 'NR==111, NR==123{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==12{print}'  $FILENAME)
 IFS=$'\n'
 lines=(${dataline})
 
 # parser paddle2onnx
-padlle2onnx_cmd=$(func_parser_value "${lines[1]}")
-infer_model_dir_key=$(func_parser_key "${lines[2]}")
-infer_model_dir_value=$(func_parser_value "${lines[2]}")
-model_filename_key=$(func_parser_key "${lines[3]}")
-model_filename_value=$(func_parser_value "${lines[3]}")
-params_filename_key=$(func_parser_key "${lines[4]}")
-params_filename_value=$(func_parser_value "${lines[4]}")
-save_file_key=$(func_parser_key "${lines[5]}")
-save_file_value=$(func_parser_value "${lines[5]}")
-opset_version_key=$(func_parser_key "${lines[6]}")
-opset_version_value=$(func_parser_value "${lines[6]}")
-enable_onnx_checker_key=$(func_parser_key "${lines[7]}")
-enable_onnx_checker_value=$(func_parser_value "${lines[7]}")
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+padlle2onnx_cmd=$(func_parser_value "${lines[3]}")
+infer_model_dir_key=$(func_parser_key "${lines[4]}")
+infer_model_dir_value=$(func_parser_value "${lines[4]}")
+model_filename_key=$(func_parser_key "${lines[5]}")
+model_filename_value=$(func_parser_value "${lines[5]}")
+params_filename_key=$(func_parser_key "${lines[6]}")
+params_filename_value=$(func_parser_value "${lines[6]}")
+save_file_key=$(func_parser_key "${lines[7]}")
+save_file_value=$(func_parser_value "${lines[7]}")
+opset_version_key=$(func_parser_key "${lines[8]}")
+opset_version_value=$(func_parser_value "${lines[8]}")
+enable_onnx_checker_key=$(func_parser_key "${lines[9]}")
+enable_onnx_checker_value=$(func_parser_value "${lines[9]}")
 # parser onnx inference 
-inference_py=$(func_parser_value "${lines[8]}")
-use_gpu_key=$(func_parser_key "${lines[9]}")
-use_gpu_value=$(func_parser_value "${lines[9]}")
-det_model_key=$(func_parser_key "${lines[10]}")
-image_dir_key=$(func_parser_key "${lines[11]}")
-image_dir_value=$(func_parser_value "${lines[11]}")
+inference_py=$(func_parser_value "${lines[10]}")
+use_gpu_key=$(func_parser_key "${lines[11]}")
+use_gpu_value=$(func_parser_value "${lines[11]}")
+det_model_key=$(func_parser_key "${lines[12]}")
+image_dir_key=$(func_parser_key "${lines[13]}")
+image_dir_value=$(func_parser_value "${lines[13]}")
 
 
 LOG_PATH="./test_tipc/output"

test_tipc/test_serving.sh

@@ -2,7 +2,7 @@
 source test_tipc/common_func.sh
 
 FILENAME=$1
-dataline=$(awk 'NR==67, NR==84{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==18{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'

test_tipc/test_train_inference_python.sh

@@ -244,7 +244,7 @@ else
     export Count=0
     USE_GPU_KEY=(${train_use_gpu_value})
     for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
+        train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         ips=""
         if [ ${gpu} = "-1" ];then
@@ -302,11 +302,20 @@ else
                 set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
                 set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
                 set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
-                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
-                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-                
+                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
+                if [ ${#ips} -le 26 ];then
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+                    nodes=1
+                else
+                    IFS=","
+                    ips_array=(${ips})
+                    IFS="|"
+                    nodes=${#ips_array[@]}
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
+                fi
+
                 # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
+                if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
                     set_pretrain="${load_norm_train_model}"
                 fi
 
@@ -325,7 +334,7 @@ else
 
                 set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
                 # save norm trained models to set pretrain for pact training and fpgm training 
-                if [ ${trainer} = ${trainer_norm} ]; then
+                if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1]; then
                     load_norm_train_model=${set_eval_pretrain}
                 fi
                 # run eval 

tools/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -60,7 +60,7 @@ def main():
     else:
         model_type = None
 
-    best_model_dict = load_dygraph_params(config, model, logger, None)
+    best_model_dict = load_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_center.py

@@ -27,7 +27,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -57,7 +57,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    best_model_dict = load_dygraph_params(config, model, logger, None)
+    best_model_dict = load_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_model.py

@@ -26,7 +26,7 @@ from paddle.jit import to_static
 
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.logging import get_logger
 from tools.program import load_config, merge_config, ArgsParser
 
@@ -107,7 +107,7 @@ def main():
         else:  # base rec model
             config["Architecture"]["Head"]["out_channels"] = char_num
     model = build_model(config["Architecture"])
-    init_model(config, model)
+    load_model(config, model)
     model.eval()
 
     save_path = config["Global"]["save_inference_dir"]

tools/infer/utility.py

@@ -17,7 +17,7 @@ import os
 import sys
 import cv2
 import numpy as np
-import json
+import paddle
 from PIL import Image, ImageDraw, ImageFont
 import math
 from paddle import inference
@@ -205,7 +205,7 @@ def create_predictor(args, mode, logger):
                     "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
                 }
                 max_input_shape = {
-                    "x": [1, 3, 2000, 2000],
+                    "x": [1, 3, 1280, 1280],
                     "conv2d_92.tmp_0": [1, 120, 400, 400],
                     "conv2d_91.tmp_0": [1, 24, 200, 200],
                     "conv2d_59.tmp_0": [1, 96, 400, 400],
@@ -255,16 +255,16 @@ def create_predictor(args, mode, logger):
                 opt_input_shape.update(opt_pact_shape)
             elif mode == "rec":
                 min_input_shape = {"x": [1, 3, 32, 10]}
-                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
                 opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
             elif mode == "cls":
                 min_input_shape = {"x": [1, 3, 48, 10]}
-                max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
                 opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
             else:
                 min_input_shape = {"x": [1, 3, 10, 10]}
-                max_input_shape = {"x": [1, 3, 1000, 1000]}
-                opt_input_shape = {"x": [1, 3, 500, 500]}
+                max_input_shape = {"x": [1, 3, 512, 512]}
+                opt_input_shape = {"x": [1, 3, 256, 256]}
             config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
                                               opt_input_shape)
 
@@ -601,5 +601,12 @@ def get_rotate_crop_image(img, points):
     return dst_img
 
 
+def check_gpu(use_gpu):
+    if use_gpu and not paddle.is_compiled_with_cuda():
+
+        use_gpu = False
+    return use_gpu
+
+
 if __name__ == '__main__':
     pass

tools/infer_cls.py

@@ -32,7 +32,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -59,7 +59,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    _ = load_dygraph_params(config, model, logger, None)
+    load_model(config, model)
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
 

tools/infer_e2e.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -68,7 +68,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],

tools/infer_rec.py

@@ -33,7 +33,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -58,7 +58,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -75,9 +75,7 @@ def main():
                     'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
                 ]
             elif config['Architecture']['algorithm'] == "SAR":
-                op[op_name]['keep_keys'] = [
-                    'image', 'valid_ratio'
-                ]
+                op[op_name]['keep_keys'] = ['image', 'valid_ratio']
             else:
                 op[op_name]['keep_keys'] = ['image']
         transforms.append(op)

tools/infer_table.py

@@ -34,11 +34,12 @@ from paddle.jit import to_static
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 import cv2
 
+
 def main(config, device, logger, vdl_writer):
     global_config = config['Global']
 
@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
     main(config, device, logger, vdl_writer)
-

tools/train.py

@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 import tools.program as program
 
 dist.get_world_size()
@@ -97,7 +97,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
+    pre_best_model_dict = load_model(config, model, optimizer)
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(