Initial commit

benchmark/PaddleOCR_DBNet/test_tipc/configs/det_res50_db/train_infer_python.txt

+===========================train_params===========================
+model_name:det_res50_db
+python:python
+gpu_list:0|0,1
+trainer.use_gpu:True|True
+amp:null
+trainer.epochs:lite_train_lite_infer=1|whole_train_whole_infer=300
+trainer.output_dir:./output/
+dataset.train.loader.batch_size:lite_train_lite_infer=8|whole_train_lite_infer=8
+trainer.finetune_checkpoint:null
+train_model_name:checkpoint/model_latest.pth
+train_infer_img_dir:imgs/paper/db.jpg
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py --config_file config/icdar2015_resnet50_FPN_DBhead_polyLR.yaml -o trainer.log_iter=1 trainer.enable_eval=False dataset.train.loader.shuffle=false arch.backbone.pretrained=False
+quant_export:null
+fpgm_export:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================infer_params===========================
+trainer.output_dir:./output/
+trainer.resume_checkpoint:
+norm_export:tools/export_model.py --config_file config/icdar2015_resnet50_FPN_DBhead_polyLR.yaml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_r50_vd_db_v2.0_train/best_accuracy
+infer_export:tools/export_model.py --config_file config/icdar2015_resnet50_FPN_DBhead_polyLR.yaml -o
+infer_quant:False
+inference:tools/infer.py
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--batch_size:1
+--use_tensorrt:False
+--precision:fp32
+--model_dir:
+--img_path:imgs/paper/db.jpg
+--save_log_path:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
+===========================to_static_train_benchmark_params===========================
+to_static_train:trainer.to_static=true

benchmark/PaddleOCR_DBNet/test_tipc/prepare.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+FILENAME=$1
+
+# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
+#                 'whole_infer', 'klquant_whole_infer',
+#                 'cpp_infer', 'serving_infer']
+
+MODE=$2
+
+dataline=$(cat ${FILENAME})
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# The training params
+model_name=$(func_parser_value "${lines[1]}")
+
+trainer_list=$(func_parser_value "${lines[14]}")
+
+if [ ${MODE} = "lite_train_lite_infer" ];then
+    python_name_list=$(func_parser_value "${lines[2]}")
+    array=(${python_name_list}) 
+    python_name=${array[0]}
+    ${python_name} -m pip install -r requirement.txt
+    if [[ ${model_name} =~ "det_res50_db" ]];then
+        wget -nc https://paddle-wheel.bj.bcebos.com/benchmark/resnet50-19c8e357.pth -O /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth
+
+        # 下载数据集并解压
+        rm -rf datasets
+        wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/benchmark_train/datasets.tar
+        tar xf datasets.tar
+    fi
+elif [ ${MODE} = "benchmark_train" ];then
+    python_name_list=$(func_parser_value "${lines[2]}")
+    array=(${python_name_list}) 
+    python_name=${array[0]}
+    ${python_name} -m pip install -r requirement.txt
+    if [[ ${model_name} =~ "det_res50_db" ]];then
+        wget -nc https://paddle-wheel.bj.bcebos.com/benchmark/resnet50-19c8e357.pth -O /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth
+
+        # 下载数据集并解压
+        rm -rf datasets
+        wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/benchmark_train/datasets.tar
+        tar xf datasets.tar
+        # expand gt.txt 2 times
+        # cd ./train_data/icdar2015/text_localization
+        # for i in `seq 2`;do cp train_icdar2015_label.txt dup$i.txt;done
+        # cat dup* > train_icdar2015_label.txt && rm -rf dup*
+        # cd ../../../
+    fi
+fi

benchmark/PaddleOCR_DBNet/tools/__init__.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/8 13:14
+# @Author  : zhoujun

benchmark/PaddleOCR_DBNet/tools/eval.py

+# -*- coding: utf-8 -*-
+# @Time    : 2018/6/11 15:54
+# @Author  : zhoujun
+import os
+import sys
+import pathlib
+
+__dir__ = pathlib.Path(os.path.abspath(__file__))
+sys.path.append(str(__dir__))
+sys.path.append(str(__dir__.parent.parent))
+
+import argparse
+import time
+import paddle
+from tqdm.auto import tqdm
+
+
+class EVAL:
+    def __init__(self, model_path, gpu_id=0):
+        from models import build_model
+        from data_loader import get_dataloader
+        from post_processing import get_post_processing
+        from utils import get_metric
+
+        self.gpu_id = gpu_id
+        if (
+            self.gpu_id is not None
+            and isinstance(self.gpu_id, int)
+            and paddle.device.is_compiled_with_cuda()
+        ):
+            paddle.device.set_device("gpu:{}".format(self.gpu_id))
+        else:
+            paddle.device.set_device("cpu")
+        checkpoint = paddle.load(model_path)
+        config = checkpoint["config"]
+        config["arch"]["backbone"]["pretrained"] = False
+
+        self.validate_loader = get_dataloader(
+            config["dataset"]["validate"], config["distributed"]
+        )
+
+        self.model = build_model(config["arch"])
+        self.model.set_state_dict(checkpoint["state_dict"])
+
+        self.post_process = get_post_processing(config["post_processing"])
+        self.metric_cls = get_metric(config["metric"])
+
+    def eval(self):
+        self.model.eval()
+        raw_metrics = []
+        total_frame = 0.0
+        total_time = 0.0
+        for i, batch in tqdm(
+            enumerate(self.validate_loader),
+            total=len(self.validate_loader),
+            desc="test model",
+        ):
+            with paddle.no_grad():
+                start = time.time()
+                preds = self.model(batch["img"])
+                boxes, scores = self.post_process(
+                    batch, preds, is_output_polygon=self.metric_cls.is_output_polygon
+                )
+                total_frame += batch["img"].shape[0]
+                total_time += time.time() - start
+                raw_metric = self.metric_cls.validate_measure(batch, (boxes, scores))
+                raw_metrics.append(raw_metric)
+        metrics = self.metric_cls.gather_measure(raw_metrics)
+        print("FPS:{}".format(total_frame / total_time))
+        return {
+            "recall": metrics["recall"].avg,
+            "precision": metrics["precision"].avg,
+            "fmeasure": metrics["fmeasure"].avg,
+        }
+
+
+def init_args():
+    parser = argparse.ArgumentParser(description="DBNet.paddle")
+    parser.add_argument(
+        "--model_path",
+        required=False,
+        default="output/DBNet_resnet18_FPN_DBHead/checkpoint/1.pth",
+        type=str,
+    )
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = init_args()
+    eval = EVAL(args.model_path)
+    result = eval.eval()
+    print(result)

benchmark/PaddleOCR_DBNet/tools/export_model.py

+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "..")))
+
+import argparse
+
+import paddle
+from paddle.jit import to_static
+
+from models import build_model
+from utils import Config, ArgsParser
+
+
+def init_args():
+    parser = ArgsParser()
+    args = parser.parse_args()
+    return args
+
+
+def load_checkpoint(model, checkpoint_path):
+    """
+    load checkpoints
+    :param checkpoint_path: Checkpoint path to be loaded
+    """
+    checkpoint = paddle.load(checkpoint_path)
+    model.set_state_dict(checkpoint["state_dict"])
+    print("load checkpoint from {}".format(checkpoint_path))
+
+
+def main(config):
+    model = build_model(config["arch"])
+    load_checkpoint(model, config["trainer"]["resume_checkpoint"])
+    model.eval()
+
+    save_path = config["trainer"]["output_dir"]
+    save_path = os.path.join(save_path, "inference")
+    infer_shape = [3, -1, -1]
+    model = to_static(
+        model,
+        input_spec=[
+            paddle.static.InputSpec(shape=[None] + infer_shape, dtype="float32")
+        ],
+    )
+
+    paddle.jit.save(model, save_path)
+    print("inference model is saved to {}".format(save_path))
+
+
+if __name__ == "__main__":
+    args = init_args()
+    assert os.path.exists(args.config_file)
+    config = Config(args.config_file)
+    config.merge_dict(args.opt)
+    main(config.cfg)

benchmark/PaddleOCR_DBNet/trainer/__init__.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/8/23 21:58
+# @Author  : zhoujun
+from .trainer import Trainer