# 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 # # #