# -------------------------------------------------------- # DIT: SELF-SUPERVISED PRE-TRAINING FOR DOCUMENT IMAGE TRANSFORMER # Based on Beit # --------------------------------------------------------' import argparse import datetime import numpy as np import time import torch import torch.backends.cudnn as cudnn import json import os from pathlib import Path from timm.data.mixup import Mixup from timm.models import create_model from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy from timm.utils import ModelEma from optim_factory import create_optimizer, get_parameter_groups, LayerDecayValueAssigner import webdataset as wds from datasets import build_dataset from engine_for_finetuning import train_one_epoch, evaluate from utils import NativeScalerWithGradNormCount as NativeScaler import utils from scipy import interpolate def get_args(): parser = argparse.ArgumentParser('BEiT fine-tuning and evaluation script for image classification', add_help=False) parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--epochs', default=30, type=int) parser.add_argument('--update_freq', default=1, type=int) parser.add_argument('--save_ckpt_freq', default=5, type=int) parser.add_argument('--eval_freq', default=5, type=int) # Model parameters parser.add_argument('--model', default='deit_base_patch16_224', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--rel_pos_bias', action='store_true') parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias') parser.set_defaults(rel_pos_bias=True) parser.add_argument('--abs_pos_emb', action='store_true') parser.add_argument('--qkv_bias', action='store_true') parser.add_argument('--layer_scale_init_value', default=0.1, type=float, help="0.1 for base, 1e-5 for large. set 0 to disable layer scale") parser.add_argument('--input_size', default=224, type=int, help='images input size') parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', help='Dropout rate (default: 0.)') parser.add_argument('--attn_drop_rate', type=float, default=0.0, metavar='PCT', help='Attention dropout rate (default: 0.)') parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT', help='Drop path rate (default: 0.1)') parser.add_argument('--disable_eval_during_finetuning', action='store_true', default=False) parser.add_argument('--model_ema', action='store_true', default=False) parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='') parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='') # Optimizer parameters parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER', help='Optimizer (default: "adamw"') parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON', help='Optimizer Epsilon (default: 1e-8)') parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA', help='Optimizer Betas (default: None, use opt default)') parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM', help='Clip gradient norm (default: None, no clipping)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--weight_decay', type=float, default=0.05, help='weight decay (default: 0.05)') parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the weight decay. We use a cosine schedule for WD and using a larger decay by the end of training improves performance for ViTs.""") parser.add_argument('--lr', type=float, default=5e-4, metavar='LR', help='learning rate (default: 5e-4)') parser.add_argument('--layer_decay', type=float, default=0.9) parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR', help='warmup learning rate (default: 1e-6)') parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR', help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N', help='epochs to warmup LR, if scheduler supports') parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N', help='num of steps to warmup LR, will overload warmup_epochs if set > 0') # Augmentation parameters parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT', help='Color jitter factor (default: 0.4)') parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'), parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)') parser.add_argument('--train_interpolation', type=str, default='bicubic', help='Training interpolation (random, bilinear, bicubic default: "bicubic")') # Evaluation parameters parser.add_argument('--crop_pct', type=float, default=None) # * Random Erase params parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT', help='Random erase prob (default: 0.25)') parser.add_argument('--remode', type=str, default='pixel', help='Random erase mode (default: "pixel")') parser.add_argument('--recount', type=int, default=1, help='Random erase count (default: 1)') parser.add_argument('--resplit', action='store_true', default=False, help='Do not random erase first (clean) augmentation split') # * Mixup params parser.add_argument('--mixup', type=float, default=0, help='mixup alpha, mixup enabled if > 0.') parser.add_argument('--cutmix', type=float, default=0, help='cutmix alpha, cutmix enabled if > 0.') parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None, help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)') parser.add_argument('--mixup_prob', type=float, default=1.0, help='Probability of performing mixup or cutmix when either/both is enabled') parser.add_argument('--mixup_switch_prob', type=float, default=0.5, help='Probability of switching to cutmix when both mixup and cutmix enabled') parser.add_argument('--mixup_mode', type=str, default='batch', help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') # * Finetuning params parser.add_argument('--finetune', default='', help='finetune from checkpoint') parser.add_argument('--model_key', default='model|module', type=str) parser.add_argument('--model_prefix', default='', type=str) parser.add_argument('--init_scale', default=0.001, type=float) parser.add_argument('--use_mean_pooling', action='store_true') parser.set_defaults(use_mean_pooling=True) parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling') parser.add_argument('--disable_weight_decay_on_rel_pos_bias', action='store_true', default=False) # Dataset parameters parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str, help='dataset path') parser.add_argument('--eval_data_path', default=None, type=str, help='dataset path for evaluation') parser.add_argument('--nb_classes', default=0, type=int, help='number of the classification types') parser.add_argument('--imagenet_default_mean_and_std', default=False, action='store_true') parser.add_argument('--data_set', default='IMNET', choices=['CIFAR', 'IMNET', 'image_folder', "rvlcdip", "rvlcdip_wds"], type=str, help='ImageNet dataset path') parser.add_argument('--output_dir', default='', help='path where to save, empty for no saving') parser.add_argument('--log_dir', default=None, help='path where to tensorboard log') parser.add_argument('--device', default='cuda', help='device to use for training / testing') parser.add_argument('--seed', default=0, type=int) parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('--auto_resume', action='store_true') parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume') parser.set_defaults(auto_resume=True) parser.add_argument('--save_ckpt', action='store_true') parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt') parser.set_defaults(save_ckpt=True) parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch') parser.add_argument('--eval', action='store_true', help='Perform evaluation only') parser.add_argument('--dist_eval', action='store_true', default=False, help='Enabling distributed evaluation') parser.add_argument('--num_workers', default=10, type=int) parser.add_argument('--pin_mem', action='store_true', help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem') parser.set_defaults(pin_mem=True) # 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') parser.add_argument('--enable_deepspeed', action='store_true', default=False) parser.add_argument('--zero_stage', default=0, type=int, help='ZeRO optimizer stage (default: 0)') known_args, _ = parser.parse_known_args() if known_args.enable_deepspeed: try: import deepspeed from deepspeed import DeepSpeedConfig parser = deepspeed.add_config_arguments(parser) ds_init = deepspeed.initialize except: print("Please 'pip install deepspeed==0.4.0'") exit(0) else: ds_init = None return parser.parse_args(), ds_init def main(args, ds_init): utils.init_distributed_mode(args) if ds_init is not None: utils.create_ds_config(args) print(args) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + utils.get_rank() torch.manual_seed(seed) np.random.seed(seed) # random.seed(seed) cudnn.benchmark = True dataset_train, args.nb_classes = build_dataset(is_train=True, args=args) if args.disable_eval_during_finetuning: dataset_val = None else: dataset_val, _ = build_dataset(is_train=False, args=args) if True: # args.distributed: num_tasks = utils.get_world_size() global_rank = utils.get_rank() if not isinstance(dataset_train, torch.utils.data.IterableDataset): sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_train)) if args.dist_eval: if len(dataset_val) % num_tasks != 0: print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. ' 'This will slightly alter validation results as extra duplicate entries are added to achieve ' 'equal num of samples per-process.') sampler_val = torch.utils.data.DistributedSampler( dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False) else: sampler_val = torch.utils.data.SequentialSampler(dataset_val) else: sampler_train = torch.utils.data.RandomSampler(dataset_train) sampler_val = torch.utils.data.SequentialSampler(dataset_val) if 'AMLT_OUTPUT_DIR' in os.environ: args.log_dir = os.environ['AMLT_OUTPUT_DIR'] print(f'update log_dir to {args.log_dir}') if global_rank == 0 and args.log_dir is not None: os.makedirs(args.log_dir, exist_ok=True) log_writer = utils.TensorboardLogger(log_dir=args.log_dir) else: log_writer = None dataset_size_train = len(dataset_train) if isinstance(dataset_train, torch.utils.data.IterableDataset): dataset_train = dataset_train.batched(args.batch_size, partial=False) data_loader_train = wds.WebLoader( dataset_train, num_workers=args.num_workers, batch_size=None, shuffle=False, ) data_loader_train = data_loader_train.ddp_equalize(dataset_size_train // args.batch_size, with_length=True) else: data_loader_train = torch.utils.data.DataLoader( dataset_train, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True, ) if dataset_val is not None: dataset_size_val = len(dataset_val) if not isinstance(dataset_val, torch.utils.data.IterableDataset): data_loader_val = torch.utils.data.DataLoader( dataset_val, sampler=sampler_val, batch_size=int(1.5 * args.batch_size), num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False ) else: dataset_val = dataset_val.batched(args.batch_size, partial=False) data_loader_val = wds.WebLoader( dataset_val, num_workers=args.num_workers, batch_size=None, shuffle=False, ) data_loader_val = data_loader_val.ddp_equalize(dataset_size_val // args.batch_size, with_length=True) else: data_loader_val = None mixup_fn = None mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None if mixup_active: print("Mixup is activated!") mixup_fn = Mixup( mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode, label_smoothing=args.smoothing, num_classes=args.nb_classes) if "beit" not in args.model: model = create_model(args.model, pretrained=False, num_classes=args.nb_classes, distilled=False) else: model = create_model( args.model, pretrained=False, num_classes=args.nb_classes, drop_rate=args.drop, drop_path_rate=args.drop_path, attn_drop_rate=args.attn_drop_rate, drop_block_rate=None, use_mean_pooling=args.use_mean_pooling, init_scale=args.init_scale, use_rel_pos_bias=args.rel_pos_bias, use_abs_pos_emb=args.abs_pos_emb, init_values=args.layer_scale_init_value, ) patch_size = model.patch_embed.patch_size print("Patch size = %s" % str(patch_size)) args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1]) args.patch_size = patch_size if args.finetune: if args.finetune.startswith('https'): checkpoint = torch.hub.load_state_dict_from_url( args.finetune, map_location='cpu', check_hash=False) else: checkpoint = torch.load(args.finetune, map_location='cpu') print("Load ckpt from %s" % args.finetune) checkpoint_model = None for model_key in args.model_key.split('|'): if model_key in checkpoint: checkpoint_model = checkpoint[model_key] print("Load state_dict by model_key = %s" % model_key) break if checkpoint_model is None: checkpoint_model = checkpoint state_dict = model.state_dict() for k in ['head.weight', 'head.bias']: if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape: print(f"Removing key {k} from pretrained checkpoint") del checkpoint_model[k] if getattr(model, "use_rel_pos_bias", False) and "rel_pos_bias.relative_position_bias_table" in checkpoint_model: print("Expand the shared relative position embedding to each transformer block. ") num_layers = model.get_num_layers() rel_pos_bias = checkpoint_model["rel_pos_bias.relative_position_bias_table"] for i in range(num_layers): checkpoint_model["blocks.%d.attn.relative_position_bias_table" % i] = rel_pos_bias.clone() checkpoint_model.pop("rel_pos_bias.relative_position_bias_table") all_keys = list(checkpoint_model.keys()) for key in all_keys: if "relative_position_index" in key: checkpoint_model.pop(key) if "relative_position_bias_table" in key: rel_pos_bias = checkpoint_model[key] src_num_pos, num_attn_heads = rel_pos_bias.size() dst_num_pos, _ = model.state_dict()[key].size() dst_patch_shape = model.patch_embed.patch_shape if dst_patch_shape[0] != dst_patch_shape[1]: raise NotImplementedError() num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) src_size = int((src_num_pos - num_extra_tokens) ** 0.5) dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) if src_size != dst_size: print("Position interpolate for %s from %dx%d to %dx%d" % ( key, src_size, src_size, dst_size, dst_size)) extra_tokens = rel_pos_bias[-num_extra_tokens:, :] rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] def geometric_progression(a, r, n): return a * (1.0 - r ** n) / (1.0 - r) left, right = 1.01, 1.5 while right - left > 1e-6: q = (left + right) / 2.0 gp = geometric_progression(1, q, src_size // 2) if gp > dst_size // 2: right = q else: left = q # if q > 1.090307: # q = 1.090307 dis = [] cur = 1 for i in range(src_size // 2): dis.append(cur) cur += q ** (i + 1) r_ids = [-_ for _ in reversed(dis)] x = r_ids + [0] + dis y = r_ids + [0] + dis t = dst_size // 2.0 dx = np.arange(-t, t + 0.1, 1.0) dy = np.arange(-t, t + 0.1, 1.0) print("Original positions = %s" % str(x)) print("Target positions = %s" % str(dx)) all_rel_pos_bias = [] for i in range(num_attn_heads): z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() f = interpolate.interp2d(x, y, z, kind='cubic') all_rel_pos_bias.append( torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) checkpoint_model[key] = new_rel_pos_bias # interpolate position embedding if 'pos_embed' in checkpoint_model: pos_embed_checkpoint = checkpoint_model['pos_embed'] embedding_size = pos_embed_checkpoint.shape[-1] num_patches = model.patch_embed.num_patches num_extra_tokens = model.pos_embed.shape[-2] - num_patches # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches ** 0.5) # class_token and dist_token are kept unchanged if orig_size != new_size: print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed utils.load_state_dict(model, checkpoint_model, prefix=args.model_prefix) # model.load_state_dict(checkpoint_model, strict=False) model.to(device) model_ema = None if args.model_ema: # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper model_ema = ModelEma( model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else '', resume='') print("Using EMA with decay = %.8f" % args.model_ema_decay) model_without_ddp = model n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print("Model = %s" % str(model_without_ddp)) print('number of params:', n_parameters) total_batch_size = args.batch_size * args.update_freq * utils.get_world_size() num_training_steps_per_epoch = dataset_size_train // total_batch_size print("LR = %.8f" % args.lr) print("Batch size = %d" % total_batch_size) print("Update frequent = %d" % args.update_freq) print("Number of training examples = %d" % dataset_size_train) print("Number of training training per epoch = %d" % num_training_steps_per_epoch) # num_layers = model_without_ddp.get_num_layers() num_layers = len(model_without_ddp.blocks) if args.layer_decay < 1.0: assigner = LayerDecayValueAssigner(list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2))) else: assigner = None if assigner is not None: print("Assigned values = %s" % str(assigner.values)) skip_weight_decay_list = model.no_weight_decay() if args.disable_weight_decay_on_rel_pos_bias: for i in range(num_layers): skip_weight_decay_list.add("blocks.%d.attn.relative_position_bias_table" % i) if args.distributed: torch.distributed.barrier() if args.enable_deepspeed: loss_scaler = None optimizer_params = get_parameter_groups( model, args.weight_decay, skip_weight_decay_list, assigner.get_layer_id if assigner is not None else None, assigner.get_scale if assigner is not None else None) model, optimizer, _, _ = ds_init( args=args, model=model, model_parameters=optimizer_params, dist_init_required=not args.distributed, ) print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps()) assert model.gradient_accumulation_steps() == args.update_freq else: if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) model_without_ddp = model.module optimizer = create_optimizer( args, model_without_ddp, skip_list=skip_weight_decay_list, get_num_layer=assigner.get_layer_id if assigner is not None else None, get_layer_scale=assigner.get_scale if assigner is not None else None) loss_scaler = NativeScaler() print("Use step level LR scheduler!") lr_schedule_values = utils.cosine_scheduler( args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch, warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps, ) if args.weight_decay_end is None: args.weight_decay_end = args.weight_decay wd_schedule_values = utils.cosine_scheduler( args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch) print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values))) if mixup_fn is not None: # smoothing is handled with mixup label transform criterion = SoftTargetCrossEntropy() elif args.smoothing > 0.: criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing) else: criterion = torch.nn.CrossEntropyLoss() print("criterion = %s" % str(criterion)) utils.auto_load_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema) if args.eval: test_stats = evaluate(data_loader_val, model, device) print(f"Accuracy of the network on the {dataset_size_val} test images: {test_stats['acc1']:.1f}%") exit(0) print(f"Start training for {args.epochs} epochs") start_time = time.time() max_accuracy = 0.0 for epoch in range(args.start_epoch, args.epochs): if args.distributed: sampler = getattr(data_loader_train, "sampler", None) if sampler is not None and hasattr(sampler, "set_epoch"): sampler.set_epoch(epoch) if log_writer is not None: log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq) train_stats = train_one_epoch( model, criterion, data_loader_train, optimizer, device, epoch, loss_scaler, args.clip_grad, model_ema, mixup_fn, log_writer=log_writer, start_steps=epoch * num_training_steps_per_epoch, lr_schedule_values=lr_schedule_values, wd_schedule_values=wd_schedule_values, num_training_steps_per_epoch=num_training_steps_per_epoch, update_freq=args.update_freq, ) if args.output_dir and args.save_ckpt: if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs: utils.save_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema) if data_loader_val is not None and ((epoch + 1) % args.eval_freq == 0 or epoch + 1 == args.epochs): test_stats = evaluate(data_loader_val, model, device) print(f"Accuracy of the network on the {dataset_size_val} test images: {test_stats['acc1']:.1f}%") if max_accuracy < test_stats["acc1"]: max_accuracy = test_stats["acc1"] if args.output_dir and args.save_ckpt: utils.save_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch="best", model_ema=model_ema) print(f'Max accuracy: {max_accuracy:.2f}%') if log_writer is not None: log_writer.update(test_acc1=test_stats['acc1'], head="perf", step=epoch) log_writer.update(test_acc5=test_stats['acc5'], head="perf", step=epoch) log_writer.update(test_loss=test_stats['loss'], head="perf", step=epoch) log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, **{f'test_{k}': v for k, v in test_stats.items()}, 'epoch': epoch, 'n_parameters': n_parameters} else: log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, # **{f'test_{k}': v for k, v in test_stats.items()}, 'epoch': epoch, 'n_parameters': n_parameters} if args.output_dir and utils.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__': opts, ds_init = get_args() if opts.output_dir: Path(opts.output_dir).mkdir(parents=True, exist_ok=True) main(opts, ds_init)