print('Start training...') start_step = (start_epoch - 1) * len(train_dataloader) global_step = start_step total_steps = opt.epochs * len(train_dataloader) start = time.time() ##################### # 定义scheduler ##################### scheduler = model.scheduler ###################### # Summary_writer ###################### writer = create_summary_writer(log_root) start_time = time.time() ###################### # Train loop ###################### try: eval_result = '' for epoch in range(start_epoch, opt.epochs + 1): for iteration, data in enumerate(train_dataloader): global_step += 1 rate = (global_step - start_step) / (time.time() - start) remaining = (total_steps - global_step) / rate img, label = data['input'], data[
import numpy as np from options import opt # from dataloader import paired_dataset from mscv.summary import create_summary_writer, write_image from mscv.image import tensor2im from dataloader.dataloaders import train_dataloader, val_dataloader import cv2 import misc_utils as utils import random """ source domain 是clear的 """ writer = create_summary_writer('logs/preview') """ 这个改成需要预览的数据集 """ previewed = train_dataloader # train_dataloader, val_dataloader from PIL import Image, ImageDraw, ImageFont names = { "0": "其他垃圾/一次性快餐盒", "1": "其他垃圾/污损塑料", "2": "其他垃圾/烟蒂", "3": "其他垃圾/牙签", "4": "其他垃圾/破碎花盆及碟碗", "5": "其他垃圾/竹筷", "6": "厨余垃圾/剩饭剩菜",