pytorch版本PSEnet训练并部署方式

torch==1.9.1
torchvision==0.10.1
python==3.8.0
cuda==10.2
mmcv==0.2.12
editdistance==0.5.3
Polygon3==3.0.9.1
pyclipper==1.3.0
opencv-python==3.4.2.17
Cython==0.29.24

./compile.sh

import os
from lxml import etree
import numpy as np
import math
src_xml = "ANN"
txt_dir = "gt"
xml_listdir = os.listdir(src_xml)
xml_listpath = [os.path.join(src_xml,xml_listdir1) for xml_listdir1 in xml_listdir]
def xml_out(xml_path):
    gt_lines = []
    ET = etree.parse(xml_path)
    objs = ET.findall("object")
    for ix,obj in enumerate(objs):
        name = obj.find("name").text
        robox = obj.find("robndbox")
        cx = int(float(robox.find("cx").text))
        cy = int(float(robox.find("cy").text))
        w = int(float(robox.find("w").text))
        h = int(float(robox.find("h").text))
        angle = float(robox.find("angle").text)
        # angle = math.degrees(angle1)
        wx1 = cx - int(0.5 * w)
        wy1 = cy - int(0.5 * h)
        wx2 = cx + int(0.5 * w)
        wy2 = cy - int(0.5 * h)
        wx3 = cx - int(0.5 * w)
        wy3 = cy + int(0.5 * h)
        wx4 = cx + int(0.5 * w)
        wy4 = cy + int(0.5 * h)
        x1 = int((wx1 - cx) * np.cos(angle) - (wy1 - cy) * np.sin(angle) + cx)
        y1 = int((wx1 - cx) * np.sin(angle) - (wy1 - cy) * np.cos(angle) + cy)
        x2 = int((wx2 - cx) * np.cos(angle) - (wy2 - cy) * np.sin(angle) + cx)
        y2 = int((wx2 - cx) * np.sin(angle) - (wy2 - cy) * np.cos(angle) + cy)
        x3 = int((wx3 - cx) * np.cos(angle) - (wy3 - cy) * np.sin(angle) + cx)
        y3 = int((wx3 - cx) * np.sin(angle) - (wy3 - cy) * np.cos(angle) + cy)
        x4 = int((wx4 - cx) * np.cos(angle) - (wy4 - cy) * np.sin(angle) + cx)
        y4 = int((wx4 - cx) * np.sin(angle) - (wy4 - cy) * np.cos(angle) + cy)
        lines = str(x1)+","+str(y1)+","+str(x2)+","+str(y2)+","+\
                str(x3)+","+str(y3)+","+str(x4)+","+str(y4)+","+str(name)+"\n"
        gt_lines.append(lines)
        return gt_lines
def main():
    count = 0
    for xml_dir in xml_listdir:
        gt_lines = xml_out(os.path.join(src_xml,xml_dir))
        txt_path = "gt_" + xml_dir[:-4] + ".txt"
        with open(os.path.join(txt_dir,txt_path),"a+") as fd:
            fd.writelines(gt_lines)
        count +=1
        print("Write file %s" % str(count))
if __name__ == "__main__":
    main()

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py config/psenet/psenet_r50_ic15_736.py

import torch
import numpy as np
import argparse
import os
import os.path as osp
import sys
import time
import json
from mmcv import Config
import cv2
from torchvision import transforms
from dataset import build_data_loader
from models import build_model
from models.utils import fuse_module
from utils import ResultFormat, AverageMeter
def prepare_image(image, target_size):
    """Do image preprocessing before prediction on any data.
    :param image:       original image
    :param target_size: target image size
    :return:
                        preprocessed image
    """
    #assert os.path.exists(img), 'file is not exists'
    #img = cv2.imread(img)
    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    # h, w = image.shape[:2]
    # scale = long_size / max(h, w)
    img = cv2.resize(img, target_size)
    # 将图片由(w,h)变为(1,img_channel,h,w)
    tensor = transforms.ToTensor()(img)
    tensor = tensor.unsqueeze_(0)
    tensor = tensor.to(torch.device("cuda:0"))
    return tensor
def report_speed(outputs, speed_meters):
    total_time = 0
    for key in outputs:
        if 'time' in key:
            total_time += outputs[key]
            speed_meters[key].update(outputs[key])
            print('%s: %.4f' % (key, speed_meters[key].avg))
    speed_meters['total_time'].update(total_time)
    print('FPS: %.1f' % (1.0 / speed_meters['total_time'].avg))
def load_model(cfg):
    model = build_model(cfg.model)
    model = model.cuda()
    model.eval()
    checkpoint = "psenet_r50_ic15_1024_finetune/checkpoint_580ep.pth.tar"
    if checkpoint is not None:
        if os.path.isfile(checkpoint):
            print("Loading model and optimizer from checkpoint '{}'".format(checkpoint))
            sys.stdout.flush()
            checkpoint = torch.load(checkpoint)
            d = dict()
            for key, value in checkpoint['state_dict'].items():
                tmp = key[7:]
                d[tmp] = value
            model.load_state_dict(d)
        else:
            print("No checkpoint found at")
            raise
        # fuse conv and bn
    model = fuse_module(model)
    return model
if __name__ == '__main__':
    src_dir = "testimg/"
    save_dir = "test_save/"
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    cfg = Config.fromfile("PSENet/config/psenet/psenet_r50_ic15_1024_finetune.py")
    for d in [cfg, cfg.data.test]:
        d.update(dict(
            report_speed=False
        ))
    if cfg.report_speed:
        speed_meters = dict(
            backbone_time=AverageMeter(500),
            neck_time=AverageMeter(500),
            det_head_time=AverageMeter(500),
            det_pse_time=AverageMeter(500),
            rec_time=AverageMeter(500),
            total_time=AverageMeter(500)
        )
    model = load_model(cfg)
    model.eval()
    count = 0
    for img_name in os.listdir(src_dir):
        img = cv2.imread(src_dir + img_name)
        tensor = prepare_image(img, target_size=(1376, 1024))
        data = dict()
        img_metas = dict()
        data['imgs'] = tensor
        img_metas['org_img_size'] = torch.tensor([[img.shape[0], img.shape[1]]])
        img_metas['img_size'] = torch.tensor([[1376, 1024]])
        data['img_metas'] = img_metas
        data.update(dict(
            cfg=cfg
        ))
        with torch.no_grad():
            outputs = model(**data)
        if cfg.report_speed:
            report_speed(outputs, speed_meters)
        for bboxes in outputs['bboxes']:
            x1 = bboxes[0]
            y1 = bboxes[1]
            x2 = bboxes[4]
            y2 = bboxes[5]
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 3)
        count = count + 1
        cv2.imwrite(save_dir + img_name, img)
        print("img test:", count)

from dataset import build_data_loader
from models import build_model
from models.utils import fuse_module
from utils import ResultFormat, AverageMeter