|
- # import shutil
- # from collections import OrderedDict # collections模块中的OrderedDict类
- # import glob
- # import os
- # import cv2
- # import numpy as np
- # import torch
- # import sys # 引入某一模块的方法
- # sys.path.append("../") # 为了导入上级目录的d2lzh_pytorch.py，添加一个新路径
- # import time
- #
- #
- # class Saver(object):
- #
- # def __init__(self, args):
- # self.args = args
- # self.directory = os.path.join('runs', args.dataset, args.checkname) # 路径拼接：runs\pascal\deeplab-resnet
- # self.runs = sorted(glob.glob(os.path.join(self.directory, 'experiment_*'))) # 搜索并排序（默认升序）
- # run_id = int(self.runs[-1].split('_')[-1]) + 1 if self.runs else 0 # split() 通过指定分隔符对字符串进行切片,run_id=？，从0开始加1
- #
- # # self.experiment_dir = os.path.join(self.directory, 'experiment_{}'.format(str(run_id))) # runs\pascal\deeplab-resnet\experiment_？
- # self.experiment_dir = args.output_dir # 自行设置的输出目录
- # if not os.path.exists(self.experiment_dir):
- # os.makedirs(self.experiment_dir) # 生成该路径下的目录
- #
- # # def save_checkpoint(self, state, is_best, filename='checkpoint.pth'):
- # # """Saves checkpoint to disk"""
- # # filename = os.path.join(self.experiment_dir, filename) # runs\pascal\deeplab-resnet\experiment_？\checkpoint.pth.tar
- # # torch.save(state, filename) # 生成checkpoint.pth
- # # if is_best:
- # # best_pred = state['best_pred']
- # # epoch = state['epoch']
- # # str_ = ("%15.5g;" * 2) % (epoch, best_pred)
- # # with open(os.path.join(self.experiment_dir, 'best_pred.txt'), 'a') as f:
- # # f.write(str_+'\n')
- # # # if self.runs:
- # # # previous_miou = [0.0]
- # # # for run in self.runs:
- # # # run_id = run.split('_')[-1]
- # # # path = os.path.join(self.directory, 'experiment_{}'.format(str(run_id)), 'best_pred.txt')
- # # # if os.path.exists(path):
- # # # with open(path, 'r') as f:
- # # # miou = float(f.readline())
- # # # previous_miou.append(miou)
- # # # else:
- # # # continue
- # # # max_miou = max(previous_miou)
- # # # if best_pred > max_miou:
- # # # shutil.copyfile(filename, os.path.join(self.directory, 'model_best.pth'))#全局最佳模型
- # # # else:
- # # # shutil.copyfile(filename, os.path.join(self.directory, 'model_best.pth'))
- #
- # def save_val_result(self, epoch, Acc, Acc_class, mIoU, class_IoU, FWIoU, recall, precision, f1):
- # str_ = ("%15.5g;" * 13) % (epoch, Acc, Acc_class, mIoU, FWIoU, class_IoU[0], class_IoU[1], recall[0], recall[1], precision[0], precision[1], f1[0], f1[1]) # txt保存指标
- # with open(os.path.join(self.experiment_dir, 'val_result.txt'), 'a') as f: # 这句话自带文件关闭功能，所以和那些先open再write再close的方式来说，更加pythontic！
- # f.write(str_ + '\n')
- #
- # # def save_experiment_config(self, num_pictures):
- # # logfile = os.path.join(self.experiment_dir, 'parameters.txt') # runs\pascal\deeplab-resnet\experiment_？\parameters.txt
- # # log_file = open(logfile, 'w')
- # # p = OrderedDict() # 创建实例对象
- # # # 字典能够将信息关联起来，但它们不记录键值对的顺序。OrederedDict实例的行为与字典相同，区别在于记录了添加的键值对的顺序。
- # # p['datset'] = self.args.dataset
- # # p['backbone'] = self.args.backbone
- # # p['out_stride'] = self.args.out_stride
- # # p['lr'] = self.args.lr
- # # p['lr_scheduler'] = self.args.lr_scheduler
- # # p['loss_type'] = self.args.loss_type
- # # p['epoch'] = self.args.epochs
- # # p['base_size'] = self.args.base_size
- # # p['crop_size'] = self.args.crop_size
- # # p['batch_size'] = self.args.batch_size
- # # p['num_pictures'] = num_pictures
- # #
- # # for key, val in p.items():
- # # log_file.write(key + ':' + str(val) + '\n')
- # # log_file.close()
- #
- # # def predict_save_images(self, model, args, epoch, label_info, test_loader, pathName):
- # def predict_save_images(self, model, args, epoch, label_info, test_loader):
- # # print('调用成功了')
- # # if not args.dataset=='potsdam':
- # # csv_path = os.path.join('path', args.dataset, 'class_dict.csv')
- #
- # # else:
- # # csv_path = os.path.join('path/ISPRS', args.dataset, 'class_dict.csv')
- # # label_info = get_label_info(csv_path)
- # # print(test_loader)
- # # Time_model = []
- # # Time_test = []
- # # time00 = time.time()
- # # cnt_list = []
- # # for (sample, name, WH) in test_loader:
- # for (sample, name, WH) in test_loader: #封装在CbySegmentation.__getitem__里，需调整
- # bs = len(name) #name里是图名，WH是高宽
- # # cnt_list.append(bs)
- # # begin1 = time.time()
- # # image = sample[0] #取第一个tensor是原图，第二个是mask
- # image = sample #取第一个tensor是原图，第二个是mask 这里要送进去四维的，batch为1。将mask去掉了
- # # print('sample_shape',sample.shape)
- # # print('image_shape',image.shape)
- # # print('sample',sample)
- # # print('name',name)
- # # print('WH',WH)
- # model.eval()
- # if args.cuda:
- # image = image.cuda()
- # # begin2 = time.time()
- # with torch.no_grad():
- # predict = model(image)
- # # end2 = time.time()
- # # time_model = end2 - begin2
- # # print('batchTime:%.3f ms, each:%.3f , bs:%d ' % (time_model*1000.0, time_model*1000.0/(bs * 1.0), bs))
- #
- # # predict=torch.squeeze(predict)
- #
- # predict = predict.data.cpu().numpy()
- # predict = np.argmax(predict, axis=1)
- #
- # label_values = [label_info[key] for key in label_info]
- # colour_codes = np.array(label_values)
- #
- # predict = colour_codes[predict.astype(int)]
- #
- # # crop_size恢复到原图尺寸
- # for ii in range(bs):
- # # print('line120:',WH)
- # w, h = WH[0][ii], WH[1][ii]
- # # w,h=WH
- # predict_one = cv2.resize(predict[ii], (int(w), int(h)), interpolation=cv2.INTER_NEAREST)
- # # save_path = os.path.join(self.experiment_dir, pathName)
- # save_path = self.experiment_dir
- # if not os.path.exists(save_path):
- # os.makedirs(save_path) # 生成该路径下的目录
- # # save_path = os.path.join(save_path, '%d_' % epoch+name[ii])
- # save_path = os.path.join(save_path, name[ii])
- # # print('save_path',save_path)
- # # print('epoch',epoch)
- # # print('name[ii]',name[ii])
- #
- # cv2.imwrite(save_path, cv2.cvtColor(np.uint8(predict_one), cv2.COLOR_RGB2BGR)) # 保存图片
- # # end1 = time.time()
- # # time_test = end1 - begin1
- #
- # # print('time test: batchTime:%.3f ms, one Time:%.3f, bs:%d '%(time_test*1000.0,time_test*1000.0/(bs*1.0),bs))
- # # Time_model.append(time_model)
- # # Time_test.append(time_test)
- # # time11 = time.time()
- #
- # # Max_model = max(Time_model) # 原始
- # # Min_model = min(Time_model) # 原始
- # # Max_test = max(Time_test) # 原始
- # # Min_test = min(Time_test) # 原始
- # #
- # # cnt_sample = sum(cnt_list)
- # # ave_model = np.mean(Time_model)
- # # ave_test = np.mean(Time_test)
- # # print()
- # # print('each model: ave:%.3f ms bs:%d' % (sum(Time_model)*1000.0/cnt_sample, cnt_list[0]))
- # # print('bacthc inference:max:%.3f ms ,min:%3f ms,ave:%3f ms bs:%d ' % (Max_model*1000.0, Min_model*1000.0, ave_model*1000.0, cnt_list[1])) # 原始
- # # print('All task total time:%.3f s' % (time11-time00))
- # #
- # # print('ave_mo del:max:%.3f ms ,min:%.3f ms,ave:%.3f ms'%(Max_test*1000.0,Min_test*1000.0,ave_test*1000.0)) # 原始
- # return sample,predict_one
- #
- #
- #
|
