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",
