import tqdm import argparse import math # import torchac import sys import os import time import logging from datetime import datetime import torch import torch.nn as nn import torch.optim as optim import torchvision from torch.utils.data import DataLoader from torchvision import transforms from torchvision.models import resnet50 import yaml from pytorch_msssim import ms_ssim from DISTS_pytorch import DISTS from util.lpips import LPIPS from torch.nn import functional as F from torchvision import utils as vutils import matplotlib.pyplot as plt import numpy as np import glob import util.misc as misc import util.lr_sched as lr_sched from torch.utils.tensorboard import SummaryWriter import models_mage_codec import mage.models_mage_codec_rope as models_mage_codec_rope import timm.optim.optim_factory as optim_factory from util.misc import NativeScalerWithGradNormCount as NativeScaler import json import PIL.Image as Image import torch.backends.cudnn as cudnn from pathlib import Path import random import torch.distributed as dist class CalMetrics(nn.Module): """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" def __init__(self): super().__init__() self.mse = nn.MSELoss() def bpp_loss(self, ori, out_net): b, _, h, w = ori.shape num_pixels = b * h * w # bpp = sum( # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) # for likelihoods in out_net["likelihoods"].values() # ) bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) bs_mask_token = out_net['bs_mask_token'] bytes_length = len(bs_mask_token) # 因为每个字节包含8位，所以总位数是字节数 * 8 total_bits = bytes_length * 8 # 计算每像素的位数（bpp） bpp_mask = total_bits / num_pixels return bpp, bpp_mask def psnr(self, rec, ori): mse = torch.mean((rec - ori) ** 2) if(mse == 0): return 100 max_pixel = 1. psnr = 10 * torch.log10(max_pixel / mse) return torch.mean(psnr) def lpips(self, rec, ori): lpips_func = LPIPS().eval().to(device=rec.device) lipis_value = lpips_func(rec, ori) return lipis_value.mean() def dists(self, rec, ori): D = DISTS().cuda() dists_value = D(rec, ori) return dists_value.mean() def cal_total_loss(self, lpips, bpp, out_net): # task_loss = out_net['task_loss'] + 0.1 * lpips task_loss = out_net['task_loss'] total_loss = bpp + out_net['lambda'] * task_loss return total_loss def forward(self, ori, out_net, rec=None): out = {} out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) out["bpp_loss"] = out["bpp"] + out["bpp_mask"] # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) if rec is not None: out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) return out class FeatureHook(): def __init__(self, module): module.register_forward_hook(self.attach) def attach(self, model, input, output): self.feature = output class Clsloss(nn.Module): def __init__(self, device, cls_loss=True) -> None: super().__init__() self.ce = nn.CrossEntropyLoss() self.classifier = resnet50(True) self.classifier.requires_grad_(False) self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss self.classifier.layer1, self.classifier.layer2, self.classifier.layer3, self.classifier.layer4, ]] self.classifier = self.classifier.to(device) for k, p in self.classifier.named_parameters(): p.requires_grad = False self.classifier.eval() self.cls_loss = cls_loss self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) def accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res def forward(self, d, rec, y_true): # output = codec(d), d = original image, y_true = label x_hat = torch.clamp(rec,0,1) pred = self.classifier(self.transform(x_hat)) # transform is normalization cls_loss = self.ce(pred, y_true) accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] if self.perceptual_loss: pred_feat = [i.feature.clone() for i in self.hooks] _ = self.classifier(self.transform(d)) ori_feat = [i.feature.clone() for i in self.hooks] perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) perc_loss = perc_loss.mean() return perc_loss return cls_loss, accu, None class AverageMeter: """Compute running average.""" def __init__(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count class CustomDataParallel(nn.DataParallel): """Custom DataParallel to access the module methods.""" def __getattr__(self, key): try: return super().__getattr__(key) except AttributeError: return getattr(self.module, key) def init(args): base_dir = f'{args.root}/{args.exp_name}/' os.makedirs(base_dir, exist_ok=True) return base_dir def setup_logger(log_dir): log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") root_logger = logging.getLogger() root_logger.setLevel(logging.INFO) log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') log_file_handler.setFormatter(log_formatter) root_logger.addHandler(log_file_handler) log_stream_handler = logging.StreamHandler(sys.stdout) log_stream_handler.setFormatter(log_formatter) root_logger.addHandler(log_stream_handler) logging.info('Logging file is %s' % log_dir) def load_img(p, padding=True, factor=64): x = Image.open(p) x = torch.from_numpy(np.asarray(x)) if len(x.shape) == 2: x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) h, w = x.shape[2:4] if padding: dh = factor * math.ceil(h / factor) - h dw = factor * math.ceil(w / factor) - w x = F.pad(x, (0, dw, 0, dh)) return x, h, w def save_img(img: torch.Tensor, vis_path, input_p, mask=False): img = img.clone().detach() img = img.to(torch.device('cpu')) if os.path.isdir(vis_path) is not True: os.makedirs(vis_path) end = '/' if mask: img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) else: img_name = vis_path + str(input_p[input_p.rfind(end):]) vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) def train_one_epoch(model, data_loader, metrics_criterion, device, optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): ## ======================= set configs ======================= ## model.train(True) metric_logger = misc.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 20 accum_iter = args.accum_iter optimizer.zero_grad() if log_writer is not None: print('log_dir: {}'.format(log_writer.log_dir)) vis_path = os.path.join("./MIM_vbr/", stage) os.makedirs(vis_path, exist_ok=True) # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): samples = samples.to(device, non_blocking=True) # samples = original image # we use a per iteration (instead of per epoch) lr scheduler if data_iter_step % accum_iter == 0: lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) with torch.cuda.amp.autocast(): out_net = model(samples, is_training=True, manual_mask_rate=None) rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) rec = rec.to(device) out_criterion = metrics_criterion(samples, out_net, rec) loss_value = out_criterion['total_loss'].item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) out_criterion['total_loss'] /= accum_iter loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), update_grad=(data_iter_step + 1) % accum_iter == 0) if (data_iter_step + 1) % accum_iter == 0: optimizer.zero_grad() torch.cuda.synchronize() metric_logger.update(loss=loss_value) lr = optimizer.param_groups[0]["lr"] metric_logger.update(lr=lr) metric_logger.update(bpp=out_criterion['bpp_loss']) metric_logger.update(bpp_mask=out_criterion['bpp_mask']) metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 metric_logger.update(lmbda=out_net['lambda']) metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 metric_logger.update(dists=out_criterion['dists'].item()) loss_value_reduce = misc.all_reduce_mean(loss_value) if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: """ We use epoch_1000x as the x-axis in tensorboard. This calibrates different curves when batch size changes. """ epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) log_writer.add_scalar('lr', lr, epoch_1000x) ## ======================= update progress bar & visualization ======================= ## if data_iter_step % 1000 == 0: with torch.no_grad(): real_fake_images = torch.cat((samples, rec), dim=0) vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): model.eval() bpp_loss = AverageMeter() bpp_mask = AverageMeter() psnr = AverageMeter() msssim = AverageMeter() lpips = AverageMeter() dists = AverageMeter() test_loss = AverageMeter() vis_path = os.path.join("./MIM_vbr/", stage) os.makedirs(vis_path, exist_ok=True) if stage == 'test': test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) os.makedirs(test_vis_path, exist_ok=True) with torch.no_grad(): # tqdm_meter = tqdm.tqdm(eval_path, leave=False) tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) for i, (d, l) in tqdm_meter: d = d.to(device) # l = l.to(device) # for input_p in eval_path: # x, hx, wx = load_img(input_p, padding=True, factor=64) # x = x.to(device) out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) # x = x[:, :, :hx, :wx] # rec = rec[:, :, :hx, :wx] rec = rec.to(device) out_criterion = metrics_criterion(d, out_net, rec) bpp_loss.update(out_criterion["bpp_loss"]) bpp_mask.update(out_criterion["bpp_mask"]) psnr.update(out_criterion['psnr']) msssim.update(out_criterion['msssim']) lpips.update(out_criterion['lpips']) dists.update(out_criterion['dists']) test_loss.update(out_criterion['total_loss']) ## ======================= update progress bar & visualization ======================= ## if stage == 'val': if i % 5 == 0: with torch.no_grad(): real_fake_images = torch.cat((d, rec), dim=0) vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) if stage == 'test': with torch.no_grad(): vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" # tqdm_meter.set_postfix_str(txt) model.train() # 假设其它变量和环境已经正确设置 if torch.distributed.is_initialized(): rank = dist.get_rank() else: rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 if rank == 0: log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" logging.info(log_txt) return test_loss.avg def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): model.eval() bpp_loss = AverageMeter() bpp_mask = AverageMeter() psnr = AverageMeter() msssim = AverageMeter() lpips = AverageMeter() dists = AverageMeter() accuracy = AverageMeter() test_loss = AverageMeter() if stage == 'test': # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) os.makedirs(test_vis_path, exist_ok=True) with torch.no_grad(): # tqdm_meter = tqdm.tqdm(eval_path, leave=False) tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) for i, (d, l) in tqdm_meter: d = d.to(device) # l = l.to(device) # for input_p in eval_path: # x, hx, wx = load_img(input_p, padding=True, factor=64) # x = x.to(device) # _, _, hx, wx = d.shape out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) # x = x[:, :, :hx, :wx] # rec = rec[:, :, :hx, :wx] rec = rec.to(device) # out_criterion = metrics_criterion(d, out_net, rec) # _, accu, _ = cls_criterion(d, rec, l) # bpp_loss.update(out_criterion["bpp_loss"]) # bpp_mask.update(out_criterion["bpp_mask"]) # psnr.update(out_criterion['psnr']) # msssim.update(out_criterion['msssim']) # lpips.update(out_criterion['lpips']) # dists.update(out_criterion['dists']) # test_loss.update(out_criterion['total_loss']) # accuracy.update(accu) ## ======================= update progress bar & visualization ======================= ## if stage == 'test': with torch.no_grad(): vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" # tqdm_meter.set_postfix_str(txt) model.train() # 假设其它变量和环境已经正确设置 if torch.distributed.is_initialized(): rank = dist.get_rank() else: rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 if rank == 0: log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" logging.info(log_txt) return test_loss.avg def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): torch.save(state, base_dir+filename) if is_best: torch.save(state, base_dir+"checkpoint_best.pth.tar") # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") def parse_args(argv): parser = argparse.ArgumentParser(description="Example training script.") parser.add_argument( "-c", "--config", default="config/vpt_default.yaml", help="Path to config file", ) parser.add_argument( '--name', default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), type=str, help='Result dir name', ) parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)') given_configs, remaining = parser.parse_known_args(argv) # distributed training parameters parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local-rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') with open(given_configs.config) as file: yaml_data= yaml.safe_load(file) parser.set_defaults(**yaml_data) parser.add_argument( "-T", "--TEST", # action='store_true', default=False, help='Testing' ) args = parser.parse_args(remaining) return args def load_eval_ps(eval_path): eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) return eval_ps def main(argv): args = parse_args(argv) base_dir = init(args) # create the base dir for saving the results if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) args.log_dir = args.output_dir misc.init_distributed_mode(args) print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) print("{}".format(args).replace(', ', ',\n')) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + misc.get_rank() torch.manual_seed(seed) torch.cuda.manual_seed(seed) np.random.seed(seed) random.seed(seed) cudnn.benchmark = True setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') msg = f'======================= {args.name} =======================' logging.info(msg) for k in args.__dict__: logging.info(k + ':' + str(args.__dict__[k])) logging.info('=' * len(msg)) ## ======================= prepare dataset ======================= ## transform_train = transforms.Compose([ transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) transform_test = transforms.Compose( # crop to 256x256 [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] ) transform_test_pos = transforms.Compose( # crop to 256x256 [transforms.ToTensor()] ) if args.dataset=='imagenet': train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) test_dataset_pos = torchvision.datasets.ImageFolder(os.path.join('/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets', "COCO"), transform=transform_test_pos) val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" # if args.distributed: if True: num_tasks = misc.get_world_size() global_rank = misc.get_rank() sampler_test = torch.utils.data.DistributedSampler( test_dataset_pos, num_replicas=num_tasks, rank=global_rank, shuffle=True ) sampler_val = torch.utils.data.DistributedSampler( val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True ) else: sampler_train = torch.utils.data.RandomSampler(train_dataset) if global_rank == 0 and args.log_dir is not None: os.makedirs(args.log_dir, exist_ok=True) log_writer = SummaryWriter(log_dir=args.log_dir) else: log_writer = None val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) test_dataloader = DataLoader(test_dataset_pos, sampler=sampler_test, batch_size=1, num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) ## ======================= prepare model ======================= ## vqgan_ckpt_path = '/home/v-ruoyufeng/v-ruoyufeng/qyp/mage_copy/ckpt_pretrained/models--Qiyp--mage/snapshots/b0692a453d4725bd80c37c2362549a46b4ff5c33/vqgan_jax_strongaug.ckpt' # model = models_mage_codec.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, # vqgan_ckpt_path=vqgan_ckpt_path) model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, vqgan_ckpt_path=vqgan_ckpt_path) model.to(device) model_without_ddp = model print("Model = %s" % str(model_without_ddp)) eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() if args.lr is None: # only base_lr is specified args.lr = args.blr * eff_batch_size / 256 print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) print("actual lr: %.2e" % args.lr) print("accumulate grad iterations: %d" % args.accum_iter) print("effective batch size: %d" % eff_batch_size) if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) model_without_ddp = model.module # following timm: set wd as 0 for bias and norm layers param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) print(optimizer) loss_scaler = NativeScaler() # resume from a checkpoint misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, strict=False) metrics_criterion = CalMetrics() cls_criterion = Clsloss(device, True) ## ======================= Start Training ======================= ## last_epoch = args.start_epoch # if args.TEST: # test only if True: best_loss = float("inf") tqrange = tqdm.trange(last_epoch, args.epochs) # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') for manual_mask_ratio in [0.3]: loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') return ## ======================= pre validation ======================= ## print("############## pre validation ##############") best_loss = float("inf") tqrange = tqdm.trange(last_epoch, args.epochs) val_mask_ratio = 0.75 test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') ## ======================= start training ======================= ## print(f"############## Start training for {args.epochs} epochs ##############") start_time = time.time() for epoch in tqrange: current_dataset = small_train_datasets[epoch % len(small_train_datasets)] sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) data_loader_train = DataLoader( current_dataset, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True, ) if args.distributed: data_loader_train.sampler.set_epoch(epoch) train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') is_best = test_loss < best_loss best_loss = min(test_loss, best_loss) if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): misc.save_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch) if is_best: misc.save_model_last( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) # misc.save_model_last( # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, # loss_scaler=loss_scaler, epoch=epoch) log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch,} if args.output_dir and misc.is_main_process(): if log_writer is not None: log_writer.flush() with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: f.write(json.dumps(log_stats) + "\n") total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('Training time {}'.format(total_time_str)) if __name__ == "__main__": main(sys.argv[1:])